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Banking and Finance Dissertation Topics – Selected for Business Students

Published by Owen Ingram at January 2nd, 2023 , Revised On August 16, 2023

Looking for an interesting banking and finance research idea for your dissertation? Your search for the best finance and banking dissertation topics ends right here because, a t ResearchProspect, we help students choose the most authentic and relevant topic for their dissertation projects.

Bank taxes, financial management, financial trading, credit management, market analysis for private investors, economic research methods, the economics of money and banking, international trade and multinational business, the wellbeing of people and society, principles and practices of banking, management and cost accounting, governance and ethics in banking, investment banking, introductory econometrics, and capital investment management are among the many topics covered in banking and finance.

Without further ado, here is our selection of the besting banking and finance thesis topics and ideas.

List of Banking and Finance Dissertation Topics

A Comprehensive Analysis of the Economic Crisis as It Relates to Banking and Finance
A Critical Review of Standard Deviation in Business
The Political and Economic Risks Involving National Bank Transactions
A Study of Corporate Developments in European Countries Regarding Banking and Finance
Security Measures Implemented in Financial Institutions Around the World
Banking and Finance Approaches from Around the World
An in-depth study of the World Trade Organization’s role in banking and finance
A Study of the Relationship Between Corporate Strategy and Capital Structures
Contrasting global, multinational banks with regional businesses
Preventing Repetitive Economic Collapse in National and Global Finances
The Motivations for Becoming International Expats All Over the World
The Difference Between Islamic Banking and Other Religious Denominations in Banking and Financial Habits
How Can Small-Scale Industries Survive the Global Banking Demands?
A Study of the Economic Crisis’s Impact on Banking and Finance
The Impact of the International Stock Exchange on Domestic Bank Transactions
A 2025 Projected Report on World Trade and Banking Statistics
How Can We Address the Issue of the Government’s Financial Deficit in Banking?
A Comparison of Contemporary and Classic Business Models and Companies’ Banking and Financial Habits
Which of the following should be the principal area of money investment that has arrived at the bank in the form of deposits?
How to strike a balance between investing money in various plans to generate a profit and managing depositor trust
What are banks’ responsibilities to their depositors, and how may such liabilities be managed without jeopardising depositor trust?
How the new banking financing laws enacted by governments throughout the world are better protecting depositors’ rights?
What is the terminology related to banking finance, which oversees the investment of deposited funds as well as the banks’ responsibilities to depositors?
Explain the most recent developments in research related to the topic of banking finance
How research in the banking finance industry assists governments and banking authorities in properly managing their finances?
What is the most recent credit rating software that assists in determining the rewards and dangers of investing bank funds in the stock market?
How banking finance assists the world’s top banks in managing consumer expectations and profit?
The negative impact of a manager’s poor management of a bank’s banking financing
Is it feasible to conduct a banking firm without the assistance of banking finance management?
What are the most significant aspects of banking financing that allow businesses to develop without constraints?

The importance of banking finance cannot be overstated. These are only a few of the most extensive subjects on which you may write a banking and finance dissertation. Remember that if you want to succeed in your studies, you must be able to offer reliable numbers and facts on the history and current state of banking and finance throughout the world. Otherwise, you will very certainly be unable to justify your study effectively. We hope you can take some inspiration and ideas from the above banking and finance dissertation topics .

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Research Topics & Ideas: Finance

120+ Finance Research Topic Ideas To Fast-Track Your Project

If you’re just starting out exploring potential research topics for your finance-related dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research topic ideation process by providing a hearty list of finance-centric research topics and ideas.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . To develop a suitable education-related research topic, you’ll need to identify a clear and convincing research gap , and a viable plan of action to fill that gap.

If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, if you’d like hands-on help, consider our 1-on-1 coaching service .

Overview: Finance Research Topics

Corporate finance topics
Investment banking topics
Private equity & VC
Asset management
Hedge funds
Financial planning & advisory
Quantitative finance
Treasury management
Financial technology (FinTech)
Commercial banking
International finance

Corporate Finance

These research topic ideas explore a breadth of issues ranging from the examination of capital structure to the exploration of financial strategies in mergers and acquisitions.

Evaluating the impact of capital structure on firm performance across different industries
Assessing the effectiveness of financial management practices in emerging markets
A comparative analysis of the cost of capital and financial structure in multinational corporations across different regulatory environments
Examining how integrating sustainability and CSR initiatives affect a corporation’s financial performance and brand reputation
Analysing how rigorous financial analysis informs strategic decisions and contributes to corporate growth
Examining the relationship between corporate governance structures and financial performance
A comparative analysis of financing strategies among mergers and acquisitions
Evaluating the importance of financial transparency and its impact on investor relations and trust
Investigating the role of financial flexibility in strategic investment decisions during economic downturns
Investigating how different dividend policies affect shareholder value and the firm’s financial performance

Investment Banking

The list below presents a series of research topics exploring the multifaceted dimensions of investment banking, with a particular focus on its evolution following the 2008 financial crisis.

Analysing the evolution and impact of regulatory frameworks in investment banking post-2008 financial crisis
Investigating the challenges and opportunities associated with cross-border M&As facilitated by investment banks.
Evaluating the role of investment banks in facilitating mergers and acquisitions in emerging markets
Analysing the transformation brought about by digital technologies in the delivery of investment banking services and its effects on efficiency and client satisfaction.
Evaluating the role of investment banks in promoting sustainable finance and the integration of Environmental, Social, and Governance (ESG) criteria in investment decisions.
Assessing the impact of technology on the efficiency and effectiveness of investment banking services
Examining the effectiveness of investment banks in pricing and marketing IPOs, and the subsequent performance of these IPOs in the stock market.
A comparative analysis of different risk management strategies employed by investment banks
Examining the relationship between investment banking fees and corporate performance
A comparative analysis of competitive strategies employed by leading investment banks and their impact on market share and profitability

Private Equity & Venture Capital (VC)

These research topic ideas are centred on venture capital and private equity investments, with a focus on their impact on technological startups, emerging technologies, and broader economic ecosystems.

Investigating the determinants of successful venture capital investments in tech startups
Analysing the trends and outcomes of venture capital funding in emerging technologies such as artificial intelligence, blockchain, or clean energy
Assessing the performance and return on investment of different exit strategies employed by venture capital firms
Assessing the impact of private equity investments on the financial performance of SMEs
Analysing the role of venture capital in fostering innovation and entrepreneurship
Evaluating the exit strategies of private equity firms: A comparative analysis
Exploring the ethical considerations in private equity and venture capital financing
Investigating how private equity ownership influences operational efficiency and overall business performance
Evaluating the effectiveness of corporate governance structures in companies backed by private equity investments
Examining how the regulatory environment in different regions affects the operations, investments and performance of private equity and venture capital firms

Research Topic Kickstarter - Need Help Finding A Research Topic?

Asset Management

This list includes a range of research topic ideas focused on asset management, probing into the effectiveness of various strategies, the integration of technology, and the alignment with ethical principles among other key dimensions.

Analysing the effectiveness of different asset allocation strategies in diverse economic environments
Analysing the methodologies and effectiveness of performance attribution in asset management firms
Assessing the impact of environmental, social, and governance (ESG) criteria on fund performance
Examining the role of robo-advisors in modern asset management
Evaluating how advancements in technology are reshaping portfolio management strategies within asset management firms
Evaluating the performance persistence of mutual funds and hedge funds
Investigating the long-term performance of portfolios managed with ethical or socially responsible investing principles
Investigating the behavioural biases in individual and institutional investment decisions
Examining the asset allocation strategies employed by pension funds and their impact on long-term fund performance
Assessing the operational efficiency of asset management firms and its correlation with fund performance

Hedge Funds

Here we explore research topics related to hedge fund operations and strategies, including their implications on corporate governance, financial market stability, and regulatory compliance among other critical facets.

Assessing the impact of hedge fund activism on corporate governance and financial performance
Analysing the effectiveness and implications of market-neutral strategies employed by hedge funds
Investigating how different fee structures impact the performance and investor attraction to hedge funds
Evaluating the contribution of hedge funds to financial market liquidity and the implications for market stability
Analysing the risk-return profile of hedge fund strategies during financial crises
Evaluating the influence of regulatory changes on hedge fund operations and performance
Examining the level of transparency and disclosure practices in the hedge fund industry and its impact on investor trust and regulatory compliance
Assessing the contribution of hedge funds to systemic risk in financial markets, and the effectiveness of regulatory measures in mitigating such risks
Examining the role of hedge funds in financial market stability
Investigating the determinants of hedge fund success: A comparative analysis

Financial Planning and Advisory

This list explores various research topic ideas related to financial planning, focusing on the effects of financial literacy, the adoption of digital tools, taxation policies, and the role of financial advisors.

Evaluating the impact of financial literacy on individual financial planning effectiveness
Analysing how different taxation policies influence financial planning strategies among individuals and businesses
Evaluating the effectiveness and user adoption of digital tools in modern financial planning practices
Investigating the adequacy of long-term financial planning strategies in ensuring retirement security
Assessing the role of financial education in shaping financial planning behaviour among different demographic groups
Examining the impact of psychological biases on financial planning and decision-making, and strategies to mitigate these biases
Assessing the behavioural factors influencing financial planning decisions
Examining the role of financial advisors in managing retirement savings
A comparative analysis of traditional versus robo-advisory in financial planning
Investigating the ethics of financial advisory practices

Free Webinar: How To Find A Dissertation Research Topic

The following list delves into research topics within the insurance sector, touching on the technological transformations, regulatory shifts, and evolving consumer behaviours among other pivotal aspects.

Analysing the impact of technology adoption on insurance pricing and risk management
Analysing the influence of Insurtech innovations on the competitive dynamics and consumer choices in insurance markets
Investigating the factors affecting consumer behaviour in insurance product selection and the role of digital channels in influencing decisions
Assessing the effect of regulatory changes on insurance product offerings
Examining the determinants of insurance penetration in emerging markets
Evaluating the operational efficiency of claims management processes in insurance companies and its impact on customer satisfaction
Examining the evolution and effectiveness of risk assessment models used in insurance underwriting and their impact on pricing and coverage
Evaluating the role of insurance in financial stability and economic development
Investigating the impact of climate change on insurance models and products
Exploring the challenges and opportunities in underwriting cyber insurance in the face of evolving cyber threats and regulations

Quantitative Finance

These topic ideas span the development of asset pricing models, evaluation of machine learning algorithms, and the exploration of ethical implications among other pivotal areas.

Developing and testing new quantitative models for asset pricing
Analysing the effectiveness and limitations of machine learning algorithms in predicting financial market movements
Assessing the effectiveness of various risk management techniques in quantitative finance
Evaluating the advancements in portfolio optimisation techniques and their impact on risk-adjusted returns
Evaluating the impact of high-frequency trading on market efficiency and stability
Investigating the influence of algorithmic trading strategies on market efficiency and liquidity
Examining the risk parity approach in asset allocation and its effectiveness in different market conditions
Examining the application of machine learning and artificial intelligence in quantitative financial analysis
Investigating the ethical implications of quantitative financial innovations
Assessing the profitability and market impact of statistical arbitrage strategies considering different market microstructures

Treasury Management

The following topic ideas explore treasury management, focusing on modernisation through technological advancements, the impact on firm liquidity, and the intertwined relationship with corporate governance among other crucial areas.

Analysing the impact of treasury management practices on firm liquidity and profitability
Analysing the role of automation in enhancing operational efficiency and strategic decision-making in treasury management
Evaluating the effectiveness of various cash management strategies in multinational corporations
Investigating the potential of blockchain technology in streamlining treasury operations and enhancing transparency
Examining the role of treasury management in mitigating financial risks
Evaluating the accuracy and effectiveness of various cash flow forecasting techniques employed in treasury management
Assessing the impact of technological advancements on treasury management operations
Examining the effectiveness of different foreign exchange risk management strategies employed by treasury managers in multinational corporations
Assessing the impact of regulatory compliance requirements on the operational and strategic aspects of treasury management
Investigating the relationship between treasury management and corporate governance

Financial Technology (FinTech)

The following research topic ideas explore the transformative potential of blockchain, the rise of open banking, and the burgeoning landscape of peer-to-peer lending among other focal areas.

Evaluating the impact of blockchain technology on financial services
Investigating the implications of open banking on consumer data privacy and financial services competition
Assessing the role of FinTech in financial inclusion in emerging markets
Analysing the role of peer-to-peer lending platforms in promoting financial inclusion and their impact on traditional banking systems
Examining the cybersecurity challenges faced by FinTech firms and the regulatory measures to ensure data protection and financial stability
Examining the regulatory challenges and opportunities in the FinTech ecosystem
Assessing the impact of artificial intelligence on the delivery of financial services, customer experience, and operational efficiency within FinTech firms
Analysing the adoption and impact of cryptocurrencies on traditional financial systems
Investigating the determinants of success for FinTech startups

Commercial Banking

These topic ideas span commercial banking, encompassing digital transformation, support for small and medium-sized enterprises (SMEs), and the evolving regulatory and competitive landscape among other key themes.

Assessing the impact of digital transformation on commercial banking services and competitiveness
Analysing the impact of digital transformation on customer experience and operational efficiency in commercial banking
Evaluating the role of commercial banks in supporting small and medium-sized enterprises (SMEs)
Investigating the effectiveness of credit risk management practices and their impact on bank profitability and financial stability
Examining the relationship between commercial banking practices and financial stability
Evaluating the implications of open banking frameworks on the competitive landscape and service innovation in commercial banking
Assessing how regulatory changes affect lending practices and risk appetite of commercial banks
Examining how commercial banks are adapting their strategies in response to competition from FinTech firms and changing consumer preferences
Analysing the impact of regulatory compliance on commercial banking operations
Investigating the determinants of customer satisfaction and loyalty in commercial banking

International Finance

The folowing research topic ideas are centred around international finance and global economic dynamics, delving into aspects like exchange rate fluctuations, international financial regulations, and the role of international financial institutions among other pivotal areas.

Analysing the determinants of exchange rate fluctuations and their impact on international trade
Analysing the influence of global trade agreements on international financial flows and foreign direct investments
Evaluating the effectiveness of international portfolio diversification strategies in mitigating risks and enhancing returns
Evaluating the role of international financial institutions in global financial stability
Investigating the role and implications of offshore financial centres on international financial stability and regulatory harmonisation
Examining the impact of global financial crises on emerging market economies
Examining the challenges and regulatory frameworks associated with cross-border banking operations
Assessing the effectiveness of international financial regulations
Investigating the challenges and opportunities of cross-border mergers and acquisitions

Choosing A Research Topic

These finance-related research topic ideas are starting points to guide your thinking. They are intentionally very broad and open-ended. By engaging with the currently literature in your field of interest, you’ll be able to narrow down your focus to a specific research gap .

When choosing a topic , you’ll need to take into account its originality, relevance, feasibility, and the resources you have at your disposal. Make sure to align your interest and expertise in the subject with your university program’s specific requirements. Always consult your academic advisor to ensure that your chosen topic not only meets the academic criteria but also provides a valuable contribution to the field.

If you need a helping hand, feel free to check out our private coaching service here.

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thank you for suggest those topic, I want to ask you about the subjects related to the fintech, can i measure it and how?

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Dissertation Topic in Finance

Updated on
Jan 11, 2024

Dissertation Topics in Finance- MBA, Banking, Accounting Projects-04 (1)

Also known as the study of investments, Finance is a combination of two interrelated subjects – how money is handled and the process of obtaining money. One of the reasons why postgraduate students struggle with their Finance dissertation topics is that they do not spend enough time planning it. It is important for students to be extremely careful while writing a finance dissertation as it contributes a lot to their respective degrees. This blog provides you with the best topics, a dissertation structure, and more.

This Blog Includes:

What is a finance dissertation, why finance dissertation topics are important, tips to find excellent dissertation topics on finance, writing tips for finance dissertation, how to plan your work on a finance dissertation, how to structure a finance dissertation, finance dissertation general topics , topics related to india, mba dissertation topics, banking dissertation topics , accounting dissertation topics, research project example, final consideration and conclusion.

Finance dissertations, as the name implies, are pieces of writing that study a certain finance topic chosen by the student. The subjects covered include anything from the stock market to banking and risk management to healthcare finance. This dissertation gives the student academic self-assurance and personal happiness in the subject of finance. Finance writing necessitates substantial research in order to produce a compelling report.

The majority of students have no idea why finance dissertation themes are so crucial. However, put yourself in the shoes of your lecturer. You’ve already read hundreds of theses. The majority of them covered the same ground — issues that you’re already tired of hearing about. Then there’s a topic with a distinct, intriguing theme. Something that piques your interest and entices you to read more. Wouldn’t you give those pupils some extra credit? You’d do it! This is why there are so many fantastic finance dissertation topics. You can get extra points for your efforts. The topic of your paper might mean the difference between a good and a terrific grade.

It’s difficult to come up with anything unique and interesting. There are, nevertheless, ways to come up with interesting ideas. Here are a few pointers on how to locate them:

Read a fantastic finance dissertation and find for areas where further study is needed.
Go to the library and read a couple theses to get some ideas.
Inquire with a writing agency about some ideas from one of their professional dissertation writers.
In writing forums and blogs, ask for assistance. If you ask gently, people will give you some excellent suggestions.
Look for ideas on the internet, but don’t use them exactly as they are. Make them distinctive by changing them.
Talk to other students who are working on their dissertations and find out what other ideas they had before settling on the present topic.
Narrow down your topic : Your financial topic should be narrowed down to a certain niche. It should concentrate on a single area, such as microfinance, microfinance, or online banking.
Verify your facts: Finance is a topic that requires a great deal of logical analysis of statistical data. As a result, double-check facts and statistics using credible sources before using them in your paper.
Write concisely: You should condense a financial paper into a tight, succinct work, unlike other papers with extended narrative narratives. At this length, the adage of ‘short is sweet’ theoretically applies.
Arrange your data neatly: A report that is crammed with numbers and graphs may turn off a reader at first glance. Know how and when to utilise your data for a great financial thesis.
Write simply: Avoid using jargon that might be confusing to a non-technical reader. When technical terminology are required, utilise accessible examples to convey them. In a finance dissertation, simplicity is king. So make good use of it.

Dissertation submission is very important to obtain a PG Degree. You are supposed to submit the work by the end of your study course, so by the last year of your degree, you may have got enough ideas and problems dealing with finance. While starting with a finance dissertation topic you should always remember that the purpose of a Finance Dissertation is to demonstrate your research ability, how you analyze specific data and come up with a conclusion. Mentioned below is a step to step guide for you to start working with:

Step 1 : Choose a relevant and interesting topic for your research

Step 2 : Discuss and receive feedback from your supervisor

Step 3 : Finalise the research methods to prove the significance of the selected topic

Step 4 : Gather the required data from relevant sources

Step 5 : Conduct the research and analyse the acquired results

Step 6 : Work on the outline of your dissertation

Step 7 : Make a draft and proofread it. Discuss with your advisors if any changes are to be made

Step 8 : Make the required corrections.

Step 9 : Draft the final dissertation

Also Read: Check out the Top Course in Finance

There are so many different ways you can structure your dissertation. But the most common and universally accepted way is as follows:

Introduction
Literature review
Methodology
Analysis of the data and Significance/Implications of the acquired results

Finance Dissertation Topics

Finance is an extensive field, you can explore a lot of areas related to finance to choose a dissertation topic. Here we’ve mentioned the best finance dissertation topics to make it easier for you:

Mentioned below are some of the topics related to the recent issues in the world:

The negative impact of microfinance in developing countries.
The effects of population growth on economic growth in China
Cryptocurrency: Are we ready to digitalise the monetary world?
Analyzing the financial statements of VISA and MasterCard
Why do banks oppose digital currency?
Risks and benefits associated with digital money transferring technology

Best MBA Dissertation Topics

Be careful while choosing an MBA Dissertation Topic as it involves more intense study. Make sure the topic you’ve chosen remains within your field of study. We’ve listed some of the best topics you can choose for an MBA Dissertation:

Management skills an entrepreneur need
The place of communication for effective management in the workplace
How technology took over management
The impact of good leadership in an organization
How does a strong social media presence affect a company’s marketing strategies?
Human resource management in non-profit organizations
The importance of employee motivation programs on productivity
Management’s socio-cultural background and how it influences leadership relationships
How do employment benefits impact employee and company’s productivity?
Business team performance in multinational corporations

Best Finance Universities in the USA

Study on Future Options in Markets in India
Gold as an Investable Commodity in India
Study on Impact Of Corruption On FDI Inflows In India
The Impact Of The Money Supply On Economic Growth In India
Capital Structure Of The Business Enterprises In Delhi NCR
GST And Its Effect on MNC Manufacturing Companies
Analysis of the Insurance Industry in India
Analysis of HDFC Bank Finance
Comparative analysis of HDFC Bank with ICICI bank
Comparison of Market Share in Public Sector Banks VS Private Sector Banks
The impact of online banking on the world.
Risk factors and security issues that are inherent in online banking.
Fraud and identity theft is accomplished via internet banking.
Advantages and disadvantages of internet banking for consumers.
Risk management in investment banking
The rise of growing banking sectors in developing nations.
Issues surrounding banking in China’s growing economy.
The impact of the Federal Reserve on the United States and global economy
Banking and asset-liability in management.
The strategies to use online banking technology to attract customers.

All you need to know about a Banking Course

Case study of the impact of industry and public knowledge on the market share index’s fluctuation
Significance of auditing for large corporations
Examining India’s country’s tax scheme
What to consider when investing in financial markets?
From an accounting perspective, risk-taking in companies and its effects
Evaluate the differences and similarities between external and internal auditors
Can taxation be considered a human rights policy? Analyse the problem
What are the consequences of India’s current tax structure on individuals with a lower income?

Accounting courses

We’ve included a Finance Dissertation Research Example with reference to a Finance Dissertation Structure:

The Indian Economic Crisis of 1991 – The title of your Finance Dissertation must focus on your research objective.
Abstract – The 1991 Indian economic crisis was…………….. imports and other external factors. The abstract part must include a summary of the research problem or objective of the research, the research design and a summary of the results.
Introduction – The introduction must reflect your research on the Indian Economic Crisis of 1991 in a way that the audience already gets to know what the research is going to include.

3.1 Background (background of the study)

3.2 Problem Statement (significance of the problem in context)

3.3 Purpose/Research Questions (What caused the Crisis, how was the crisis revived etc.)

Review of Literature – The Review of Literature Section must include a theoretical rationale of the problem, the importance of the study, and the significance of the results.
Methodology – The Methodology Section must include the description of the subjects, research methods used in the data collection and any limitations issues involved.
Significance/Implications (Results of the Discussion)

*Please note that the above-mentioned structure is only for your reference to get an idea of writing a Finance Dissertation.

Choosing the right topic for your Finance dissertation to plan the work, all the above-mentioned aspects must be given equal importance. This blog has included the best dissertation topic in finance in MBA, accounting, and banking you can choose while writing a dissertation.

Finance research papers and dissertations should be prepared in a way that answers the core question while also being relevant to the remainder of the study. For example, if the dissertation’s major question is “what is the link between foreign exchange rates and the interest rates of a specific country,” the dissertation should provide suitable illustrations to help illustrate the topic. It should also go through the major and minor concerns that are relevant to this topic. Furthermore, utilise proper language to ensure that the article is readily understood by readers. The overall purpose of the project is to produce a well-written, well-researched, and well-supported dissertation.

It takes around 2 years to complete an MBA in India while 1 year to complete a full-time MBA in other countries.

A finance dissertation must be 100-300 pages long.

It takes around 5 years to obtain a Doctorate in Finance.

Hopefully, this blog assisted you in finding out your finance dissertation topics and structure for your course. If you require any assistance regarding your application process while enrolling for your further studies, our experts at Leverage Edu are just one click away. Call us anytime at 1800 572 000 for a free counselling session!

Damanpreet Kaur Vohra

Daman is an author with profound expertise in writing engaging and informative content focused on EdTech and Study Abroad. With a keen understanding of these domains, Daman excels at creating complex concepts into accessible, reader-friendly material. With a proven track record of insightful articles, Daman stands as a reliable source for providing content for EdTech and Study Abroad.

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200 Finance Dissertation Topics: Quick Ideas For Students

Finance dissertation topics are on-demand in the 21st century. But why is this so? It may perplex you how everyone is up and down looking for interesting, quality finance topics. However, the answer is simple: because fascinating finance dissertation topics can earn students bonus points.

We will delve into that in just a second. Your finance topic dictates the difficulty of the assignment you are going to handle. Landing on the right topic means that you will not have to toil as much as when you pick a highly complex topic. Does it make sense?

Let’s explore the nitty-gritty of finance dissertation papers before we get into mentioning the top-rated finance research topics list.

What Is A Finance Dissertation?

As the name goes, finance dissertation is a kind of writing that investigates a particular finance topic selected by the student. The topics range from the stock market, banking, and risk management to healthcare finance topics.

This dissertation provides the student with a degree of academic self-confidence and personal satisfaction in the finance field. Finance writing requires extensive research to create a persuasive paper in the end.

Writing Tips For Finance Dissertations

Are you uncertain concerning what you need to do to compose a top-notch finance dissertation? Worry no more! Our professional writers have put together some essential suggestions to kick you off. In the next few minutes, you will be in a position to create a perfect finance dissertation painstakingly:

Narrow down your topic : Trim down your finance topic to a specific niche. It should focus on one region; either micro-finance, macro-finance, or internet banking.
Verify your facts : Finance is a field that includes a lot of statistical data to be followed logically. Therefore, verify facts and figures with reliable sources before opting to use them in your paper.
Write concisely : Unlike other papers with long narrative tales, you should encapsulate a finance paper into a tight, concise paper. The rule of ‘short is sweet’ technically applies here at great length.
Arrange your data neatly : A paper that is stuffed with numerals and charts all over may turn down a reader at first sight. For an impressive finance thesis, know-how and when to use your data.
Write simply : Avoid jargon that may confuse an ordinary reader. Where a need is for technical terms to be used, illustrate them with relatable examples. Simplicity is gold in a finance dissertation. So, use it well.

With these tips and tricks, you are all set to start writing your finance paper. We now advance to another crucial part that will make sure your finance paper is refined and at per with your institution’s academic standards.

General Structure of a Finance Dissertation

It is crucial to consult your supervisor regarding your dissertation’s research methodology, structure, style, and reasonable length. Depending on the guidance of your supervisor, the structure may vary. Nonetheless, as a general guide, ensure the following sections are part and parcel of your dissertation:

Introduction: State the problem that you intend to address in your dissertation. It also includes a definition of key terms, the relevance of the topic and a summary of hypotheses.
Theoretical and empirical literature, hypotheses development and contribution: It provides the theoretical framework of your study. The hypotheses are based on the literature review.
Data and methodology: State the model (i.e. dependent and key independent variables) that you want to use the drawing on theoretical framework or economic argument that you may employ for your analysis. Define all control variables and describe the data used to test the hypothesis.
Empirical results: Describe the results and mention whether they are consistent with the hypotheses and relate them with the existing evidence in the literature. You will also describe the statistical and practical/economic significance of your findings.
Summary and conclusion: Summarize your research and state the general conclusion with relevant implications.

It is important to have all the dataset you want to use readily available before finalizing the topic. The dataset is essential for testing your hypotheses.

There are thousands of research topics for finance students available all over the internet and academic books. You only have to browse and lookup for the latest research or refer to past readings or course lectures.

Even though this exercise may look simple enough on the surface, it takes a lot of time to consider what makes for interesting finance topics adequately. Not all ideas you find will achieve the academic requirements that your supervisor expects from you.

Here is a list of freshly mint topics to use for numerous finance situations:

Impressive Healthcare Finance Topics

Healthcare involves more than just treating patients and administering injections. There are finance aspects that also come into play, including:

Strategies for marketplace achievement in turbulent times: Medical staff marketing
Effects of the employer executive compensation and benefits plan after the Tax Reform Act of 1986
Improving profitability through accelerating philanthropic giving to healthcare systems
Acceleration and effective information strategies for cash management in hospitals
Finding the system’s solution to health care cost accounting
How hospitals spend money from charitable organizations and donor funding
Models of enhancing cost accounting efforts by improving existing information sources
Strategies of increasing cash flow with a patient accounting review
A systematic review of productivity, cost accounting, and information systems
A study of the cost accounting strategies under the prospective payment system
How to manage bad debt and charity care accounts in hospitals
Achieving more value from managed care efforts in healthcare systems
Strategies of achieving economies of scale through shared ancillary and support services
Profitable ways of financing the acquisition of a health care enterprise
Effects of mergers and acquisitions on private hospitals
Measuring nursing costs with patient acuity data in hospitals
Affordable treatment and care for long-term and terminal diseases
Survey of the organization and structure of a hospital’s administration concerning financing
Impact of culture and globalization on healthcare financing
Discuss the necessity for universal health coverage in the United States

Finance Management Project Topics

If you are a finance management enthusiast, this section will impress you the most:

The impact of corrupt bank managers on its sustainability
How banks finance small and medium-scale enterprises
Loan granting and its recovery problems on commercial banks
An evaluation of credit management in the banking industry
The role of microfinance banks in the alleviation of poverty in the US
Comparative evaluation strategies in mergers and acquisitions
How to plan and invest in the insurance sector and tax planning
Impact of shareholders on decision-making processes on banks
How diversity in banks affects management and leadership practices
Credit management techniques that work for small scale enterprises
Appraisal on the impact of effective credit management on the profitability of commercial banks
The impact of quantitative tools of monetary policy on the performance of deposit of commercial banks
Financial management practices in the insurance industry and risk management
The role of the capital market in economic development
Problems facing financial institutions to the growth of small scale business in the USA
Why training and development of human resources is a critical factor in bank operations
The impact of universal banking financial system on the credibility
Security threats to effective management in banks
The effect of fiscal and monetary policy in controlling unemployment
The effects of financial leverage on company performance

Topics in Mathematics With Applications in Finance

Mathematics and finance correlate in several ways in that they borrow concepts from each other. Here are some of the mathematics concepts that apply to finance paper topics:

Linear algebra
Probability theory
Stochastic processes
Regression analysis
Value at risk models
Time series analysis
Volatility modelling
Regularized pricing and risk models
Commodity models
Portfolio theory
Factor modelling
Stochastic differential equations
Ross recovery theorem
Option, price, and probability duality
Black-Scholes formula, Risk-neutral valuation
Introduction to counterparty credit risk
HJM model for interest rates and credit
Quanto credit hedging
Calculus in finance and its application

International Finance Topics

International finance research topics deal with a range of monetary exchanges between two or more nations. Below is a list of international research topics in finance for you to browse through and pick a relevant one:

A study of the most important concepts in international finance
How internal auditing enhances good corporate governance practice in an organization
Factors that affect the capital structure of Go Public manufacturing companies
A financial engineering perspective on the causes of large price changes
Corporate governance and board of directors responsibilities
An exploratory study on the management of support services in international organizations
An accounting perspective of the need for theorizing corporation
Impact of coronavirus on international trade relations
Is business ethics attainable in the global market arena
How exchange rates affect international trading
The role of currency derivatives in shaping the global market
How to improve international capital structure
How to forecast exchange rates
Ways of measuring exposure to exchange rates fluctuations
How to hedge exposure to exchange rates fluctuations globally
How foreign direct investment puts individual countries at risk
How to stabilize international capital markets
A study of shadow banking in the global environment
A comparative analysis of Western markets and African markets
Exploring the monetary funding opportunities by the International Monetary Fund

Corporate Finance Research Topics

These 20 topics have the potential to help you write an amazing corporate finance paper, provided you have the will to work hard on your paper:

Short- and long-term investment needs for working capital trends
Identifying proper capital structure models for a company
How capital structure and an organization’s funding of its operations relate
Corporate finance decision making in unstable stock markets
The effect of firm size on financial decision making incorporates
Compare and contrast the different internationally recognized corporate financial reporting standards
Evaluate the emerging concept integrated reporting in corporate finance
Managing transparency in corporate financial decisions
How technological connectivity has helped in integrated financial management
How different investment models contribute to the success of a corporate
The essence of valuation of cash flows in financial and non-financial corporates
Identify the prevalent financial innovations in the USA
Ways in which governance influences corporate financial activities
Impact of taxes on dividend policies in developed nations
How corporate strategies related to corporate finance
Implications of the global economic crisis in the backdrop of corporate finance concepts
How information technology impact corporate relations among companies
Evaluate the effectiveness of corporate financing tools and techniques
How do FDI strategies compare in Europe and Asia?
The role of transparency and liquidity in alternative corporate investments

Finance Debate Topics

These finance debate topics are formulated in keeping with emerging financial issues globally:

Is China’s economy on the verge of ousting that of the US?
Does the dynamic nature of the global market affect the financial alienations of countries?
Is Foreign Direct Investment in retail sector good for the US?
Is it possible to maintain stable oil prices in the world?
Are multinational corporations good for the global economy?
Does the country of origin matter in selling a product?
Are financial companies misusing ethics in marketing?
Why should consumer always be king in marketing messages?
Does commercialization serve in the best interest of the consumer?
Why should companies bother having a mission statement?
Why should hospitals receive tax subsidies and levies on drugs?
Is television the best medium for advertisement?
Is the guarantor principle security or a myth?
Compare and contrast market trends in capitalism versus Marxism states
Does the name of a business have an impact on its development record?
Is it the responsibility of the government to finance small-scale business enterprises?
Does budgeting truly serve its purpose in a company?
Why should agricultural imports be banned?
Is advertising a waste of company resources?
Why privatization will lead to less corruption in companies

Finance Topics For Presentation

Is your group or individual finance presentation giving you sleepless nights just because you do not have a topic? Worry no more!

The role of diplomatic ties in enhancing financial relations between countries
Should banks use force when recovering loans from long-term defaulters?
Why mortgages are becoming difficult to repay among the middle class
Ways of improving the skilled workforce in developing
How technology creates income disparities among social classes
The role of rational thinking in making financial decisions
How much capital is necessary for a start-up?
Are investments in betting firms good for young people?
How co-operatives are important in promoting communism in a society
Why should countries stop receiving foreign aids and depend on themselves?
Compare and contrast the performance of private sectors over public sectors
How frequent should reforms be conducted in companies?
How globalization affects nationalism
Theories of financial development that is still applicable today
Should business people head the finance ministry of countries?
The impact of the transport sector on revenue and tax collection
The impact of space exploration on the country’s economy
How regional blocs are impacting developing nations
Factors contributing to the growth of online scams
What is the impact of trade unions in promoting businesses?

Finance Research Topics For MBA

Here is our best list of top-rated MBA financial topics to write about in 2023, which will generate more passion for a debate:

Evaluate the effect of the Global crisis to use the line of credit in maintaining cash flow
Discuss options for investment in the shipping industry in the US
Financial risk management in the maritime industry: A case study of the blue economy
Analyze the various financial risk indicators
Financial laws that prevent volatility in the financial market
How the global recession has impacted domestic banking industries
Discuss IMF’s initiatives in tackling internal inefficiency of new projects
How the WTO is essential in the global financial market
The link between corporate and capital structures
Why is it important to have an individual investment?
How to handle credit crisis in financial marketing
Financial planning for salaried employee and strategies for tax savings
A study on Cost And Costing Models in Companies
A critical study on investment patterns and preferences of retail investors
Risk portfolio and perception management of equity investors
Is there room for improvement in electronic payment systems?
Risks and opportunities of investments versus savings
Impact of investor awareness towards commodities in the market
Is taxation a selling tool for life insurance
Impact of earnings per share

Public Finance Topics

These interesting finance topics may augur well with university students majoring in public finance:

Financial assistance for businesses and workers during Coronavirus lockdowns
Debt sustainability in developing countries
How we can use public money to leverage private funds
Analyze the use of public funds in developed versus developing countries
The reliability of sovereign credit ratings for investors in government securities
Propose a method of analysis on the cost-benefit ratio of any government project
The role of entities in charge of financial intermediation
The reciprocity and impact of tariff barriers
Impact of the exempted goods prices on the trade deficit
Investor penalties and its impact in the form of taxes and penalties
Public government projects that use private funds
Ways of measuring the cost of sustainability
Maintaining economic growth to avoid a strong recession
The impact of the declining income and consumption rates
Effects of quarantine and forced suspension of economic activity
Innovative means of limiting the scale of pandemic development
The growing scale of the public debt of the public finance system
A critical analysis of the epidemiological safety instruments used in countries
The growing debt crisis of the state finance system
How to permanently improve and increase the scale of anti-crisis socio-economic policy planning

Business Finance Topics

You can address the following business finance research papers topics for your next assignment:

How organizations are raising and managing funds
Analyze the planning, analysis, and control operations and responsibilities of the financial manager
Why business managers should take advantage of the federal stimulus package
Economical ways of negotiating for lower monthly bills
Evaluate the best retirement plans for entrepreneurs
Tax reform changes needed to spearhead businesses to the next level
How politicians can help small businesses make it to the top
Setting up life insurance policies from which you can sidestep the banks and loan yourself money
Why every business manager should know about profit and loss statements, revenue by customers and more.
Advantages of creating multiple corporations to business entrepreneurs
Why good liquidity is a vital weapon in the face of a crisis
Reasons why many people are declaring bankruptcy during the coronavirus pandemic
Why you should closely examine the numbers before making any financial decisions
Benefits of corporations to small scale business ventures
How to start a business without money at hand
Strategies for improving your company’s online presence
Discuss the challenge of debt versus equity for small-scale businesses
The impact of financial decisions on the profitability and the risk of a firm’s operations
Striking a balance between risk and profitability
Why taking the ratio of current assets to current liabilities is important to any business

You can use any of the hot topics mentioned above for your finance dissertation paper or opt for our thesis writing services. We have competitive finance dissertation writing experts ready to tackle your paper to the core.

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Finance Theses and Dissertations

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Home » Blog » Dissertation » Topics » Finance » Banking and Finance » Banking and Finance Dissertation Topics (28 Examples) For Research

Banking and Finance Dissertation Topics (28 Examples) For Research

Mark May 26, 2020 Jun 5, 2020 Banking and Finance , Finance No Comments

Are you searching for banking and finance dissertation topics? We understand that selecting a dissertation topic is one of the biggest challenges. So, we offer a wide range of banking and finance dissertation topics and project topics on banking and finance. You can also visit our site for corporate finance dissertation topics and other business […]

Are you searching for banking and finance dissertation topics? We understand that selecting a dissertation topic is one of the biggest challenges. So, we offer a wide range of project topics on banking and finance.

Our team of writers can provide quality work on your selected banking and finance research topics. Once you select from the research topics on banking and finance, we will provide an outline, which can provide guidance on how the study should be carried out .

If you have come to this post after searching for corporate finance or finance topics, following are the seperate posts made on these topics.

Finance Research Topics
Corporate Finance Research Topics

Banking and finance dissertation topics

Role of micro-loans in the modern financial industry.

Online currencies like Bitcoin brought changes in the concept of fiat currencies.

Identifying the forces causing American retail banking centres to change.

Analysing the treatment of off-balance sheet activities.

Examining the role of internet banking in society.

Evaluating how the modern economy prevents a run on the banks from happening.

To find out whether the technology can replace the role of retail banking centre.

Relationship between housing loans and the 2008 recession.

Impact of foreign direct investment on the emerging economies.

Identifying the best capital structure for a retail bank.

To study the effect of mergers and acquisition on employee’s morale and performance in the case of banks.

Evaluating the credit management and issues of bad debts in commercial banks in the UAE.

To what extent the electronic banking has affected customer satisfaction.

Portfolio management and its impact on the profitability level of banks.

Impact of interest rate on loan repayment in microfinance banks.

An appraisal of operational problems facing micro-finance banks in delta state.

Studying the impact of risk management on the profitability of banks.

Evaluation of bank lending and credit management.

Role of automated teller machine on customer satisfaction and retention.

Examining the impact of bank consolidation on operational efficiency.

Competitive strategies and changes in the banking industry.

Development of rural banking in the case of developed countries.

The effect of electronic payment systems on the behaviour and satisfaction level of customers.

How does the organisational structure affect the commercial banks and their performance?

How can banks use ratio analysis as a bank lending tool?

Evaluating the relationship between e-banking and cybercrime.

Studying the importance of credit management in the banking industry.

Problems related to loan granting and recovery.

Topic With Mini-Proposal (Paid Service)

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Key literature resources identification.
Suitable methodology with identification of raw sample size, and data collection method
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Department of Finance

Quicklinks und sprachwechsel, main navigation.

The preparation for the application as well as the writing of the Bachelor or Master's thesis at the Department of Finance entails different steps, whereby the application process is described in detail here.

You find your own topic, write a Research Proposal and submit it via DF Thesis Market.
Choose one of the provided Topic Proposals and apply for the corresponding topic on the DF Thesis Market including a letter of motivation.
Supervisor: If your Research Proposal has been accepted or your application for an existing topic has been successful, you will be contacted by the person who will supervise you in your thesis. Based on your Research Proposal inputs, your supervisor will formulate the thesis assignment.
Thesis assignment on OLAT: The task assignment includes your official thesis assignment for the written thesis. It can be collected on OLAT from the moment you have been informed by your supervisor. Once you collect the thesis assignment on OLAT, the time limit of six months starts.
Time limit: Start working on your thesis early and discuss problems with your supervisor. Nevertheless, remember that a Bachelor or Master’s thesis is to be written independently.
Submission: You must submit your thesis via OLAT i.e. you upload your thesis and any additional documents/attachments as a ZIP-file to OLAT.
Grading of task: You receive your grade within a month after submission.

The following Video in German shows the recording of a presentation (PDF, 276 KB) in which the students were informed. It goes more into detail about the individual steps.

The prerequisite for writing a Bachelor and Master's thesis at the Department of Finance is relevant prior knowledge in the corresponding subject area. In particular, the relevant lectures must have been attended and passed. Provided Topic Proposals may contain further requirements, which you will find in the respective proposal.

It is also important that the rules and instructions of the Dean's Office ( study and graduation ) are generally to be noted. The responsibility regarding compliance with these regulations lies with you.

Application

In order to write a thesis at the Department of Finance, a digital application via the DF Thesis Market is required. You will need the following documents for the application:

Curriculum Vitae (as PDF)
Transcript of records (as PDF, current export from the Module Booking)
Research Proposal (if you propose a topic of your own)

For the application via the Department of Finance Thesis Market, you also need your UZH login credentials (shortname and password).

There are two ways to apply for a thesis at the Department of Finance:

Option A: Own topic

Elaborate your own suggested topic and write a Research Proposal. On two to three pages, the Research Proposal summarises your motivation, the objectives, the planned procedure and the expected results. The following documents serve as a guide:

Instructions for writing a Research Proposal (PDF, 114 KB)
Example of a Research Proposal (PDF, 205 KB)

If you would like to write an empirical paper, check before submitting your application whether the data you need can be found in the available databases . It is advisable to check with a concrete example whether the data quality is sufficient (e.g., availability of time series).

It is important that you assign your Research Proposal to the correct research area so that it can be made available to appropriate supervisors. The research areas and fields of interest listed in the table below can serve as a decision-making aid. The links of the Professors lead to the Bachelor and Master's theses that they supervised so far. They can give an intuition on typical topics for writing a thesis.

Based on your Research Proposal, the final thesis assignment will be issued. However, the Department of Finance reserves the right to ask for improvements to the proposal, to make changes, to provide a different topic or to reject the application.

Option B: Provided topic

Apply for a topic provided by a supervisor via DF Thesis Market . Look at the topics on the marketplace, choose one and apply. Note that in addition to a CV and transcript of records, a short letter of motivation is also required for the application. Describe how the provided topic matches your skills and interests, and how the topic fits into your course of study.

Useful documents

As guidance to help you estimate the length of a thesis, we provide you with two sample theses:

Bachelor’s thesis: The different theories of the 2010 Flash Crash with main focus on high-frequency trading (PDF, 1 MB)
Master’s thesis: Strategic Allocation to Return Factors (PDF, 1 MB)

Additionally you can find a template for LaTeX (ZIP, 414 KB) .

Further notes

The Department of Finance strongly recommends that you start finding a topic for your Bachelor or Master's thesis at an early stage. The application must be submitted at least one month before the desired starting month. For example if you want to start writing your thesis at the beginning of May, you must apply by the end of March.

The matching process usually takes about a month, sometimes longer (especially for your own proposals), since all supervisors supervise several theses, and your own proposal must match the interests of your supervisor in terms of content. We will inform you as soon as the matching process is completed, or if we need further information or adjustments from you. If you want/need to start as soon as possible, you also have the option to apply to one of the posted proposals.

The DF endeavours to offer all applicants the opportunity to write a thesis at the Department of Finance, but we cannot guarantee a specific topic or a specific supervisor. Temporary bottlenecks may occur in the supervision. If you have any questions or problems in connection with your application, please contact the study coordinator and Managing Director of the DF, Dr. Benjamin Wilding, at [email protected] .

Research interest

Bereichs-navigation, unterseiten von theses.

DF Thesis Market
FAQ for Students

Open access
Published: 08 June 2020

Deep learning in finance and banking: A literature review and classification

Jian Huang 1 ,
Junyi Chai ORCID: orcid.org/0000-0003-1560-845X 2 &
Stella Cho 2

Frontiers of Business Research in China volume 14 , Article number: 13 ( 2020 ) Cite this article

62k Accesses

86 Citations

69 Altmetric

Metrics details

Deep learning has been widely applied in computer vision, natural language processing, and audio-visual recognition. The overwhelming success of deep learning as a data processing technique has sparked the interest of the research community. Given the proliferation of Fintech in recent years, the use of deep learning in finance and banking services has become prevalent. However, a detailed survey of the applications of deep learning in finance and banking is lacking in the existing literature. This study surveys and analyzes the literature on the application of deep learning models in the key finance and banking domains to provide a systematic evaluation of the model preprocessing, input data, and model evaluation. Finally, we discuss three aspects that could affect the outcomes of financial deep learning models. This study provides academics and practitioners with insight and direction on the state-of-the-art of the application of deep learning models in finance and banking.

Introduction

Deep learning (DL) is an advanced technique of machine learning (ML) based on artificial neural network (NN) algorithms. As a promising branch of artificial intelligence, DL has attracted great attention in recent years. Compared with conventional ML techniques such as support vector machine (SVM) and k-nearest neighbors (kNN), DL possesses advantages of the unsupervised feature learning, a strong capability of generalization, and a robust training power for big data. Currently, DL has been applied comprehensively in classification and prediction tasks, computer visions, image processing, and audio-visual recognition (Chai and Li 2019 ). Although DL was developed in the field of computer science, its applications have penetrated diversified fields such as medicine, neuroscience, physics and astronomy, finance and banking (F&B), and operations management (Chai et al. 2013 ; Chai and Ngai 2020 ). The existing literature lacks a good overview of DL applications in F&B fields. This study attempts to bridge this gap.

While DL is the focus of computer vision (e.g., Elad and Aharon 2006 ; Guo et al. 2016 ) and natural language processing (e.g., Collobert et al. 2011 ) in the mainstream, DL applications in F&B are developing rapidly. Shravan and Vadlamani (2016) investigated the tools of text mining for F&B domains. They examined the representative ML algorithms, including SVM, kNN, genetic algorithm (GA), and AdaBoost. Butaru et al. ( 2016 ) compared performances of DL algorithms, including random forests, decision trees, and regularized logistic regression. They found that random forests gained the highest classification accuracy in the delinquency status.

Cavalcante et al. ( 2016 ) summarized the literature published from 2009 to 2015. They analyzed DL models, including multi-layer perceptron (MLP) (a fast library for approximate nearest neighbors), Chebyshev functional link artificial NN, and adaptive weighting NN. Although the study constructed a prediction framework in financial trading, some notable DL techniques such as long short-term memory (LSTM) and reinforcement learning (RL) models are neglect. Thus, the framework cannot ascertain the optimal model in a specific condition.

The reviews of the existing literature are either incomplete or outdated. However, our study provides a comprehensive and state-of-the-art review that could capture the relationships between typical DL models and various F&B domains. We identified critical conditions to limit our collection of articles. We employed academic databases in Science Direct, Springer-Link Journal, IEEE Xplore, Emerald, JSTOR, ProQuest Database, EBSCOhost Research Databases, Academic Search Premier, World Scientific Net, and Google Scholar to search for articles. We used two groups of keywords for our search. One group is related to the DL, including “deep learning,” “neural network,” “convolutional neural networks” (CNN), “recurrent neural network” (RNN), “LSTM,” and “RL.” The other group is related to finance, including “finance,” “market risk,” “stock risk,” “credit risk,” “stock market,” and “banking.” It is important to conduct cross searches between computer-science-related and finance-related literature. Our survey exclusively focuses on the financial application of DL models rather than other DL models like SVM, kNN, or random forest. The time range of our review was set between 2014 and 2018. In this stage, we collected more than 150 articles after cross-searching. We carefully reviewd each article and considered whether it is worthy of entering our pool of articles for review. We removed the articles if they are not from reputable journals or top professional conferences. Moreover, articles were discarded if the details of financial DL models presented were not clarified. Thus, 40 articles were selected for this review eventually.

This study contributes to the literature in the following ways. First, we systematically review the state-of-the-art applications of DL in F&B fields. Second, we summarize multiple DL models regarding specified F&B domains and identify the optimal DL model of various application scenarios. Our analyses rely on the data processing methods of DL models, including preprocessing, input data, and evaluation rules. Third, our review attempts to bridge the technological and application levels of DL and F&B, respectively. We recognize the features of various DL models and highlight their feasibility toward different F&B domains. The penetration of DL into F&B is an emerging trend. Researchers and financial analysts should know the feasibilities of particular DL models toward a specified financial domain. They usually face difficulties due to the lack of connections between core financial domains and numerous DL models. This study will fill this literature gap and guide financial analysts.

The rest of this paper is organized as follows. Section 2 provides a background of DL techniques. Section 3 introduces our research framework and methodology. Section 4 analyzes the established DL models. Section 5 analyzes key methods of data processing, including data preprocessing and data inputs. Section 6 captures appeared criteria for evaluating the performance of DL models. Section 7 provides a general comparison of DL models against identified F&B domains. Section 8 discusses the influencing factors in the performance of financial DL models. Section 9 concludes and outlines the scope for promising future studies.

Background of deep learning

Regarding DL, the term “deep” presents the multiple layers that exist in the network. The history of DL can be traced back to stochastic gradient descent in 1952, which is employed for an optimization problem. The bottleneck of DL at that time was the limit of computer hardware, as it was very time-consuming for computers to process the data. Today, DL is booming with the developments of graphics processing units (GPUs), dataset storage and processing, distributed systems, and software such as Tensor Flow. This section briefly reviews the basic concept of DL, including NN and deep neural network (DNN). All of these models have greatly contributed to the applications in F&B.

The basic structure of NN can be illustrated as Y = F ( X T w + c ) regarding the independent (input) variables X , the weight terms w , and the constant terms c . Y is the dependent variable and X is formed as an n × m matrix for the number of training sample n and the number of input variables m . To apply this structure in finance, Y can be considered as the price of next term, the credit risk level of clients, or the return rate of a portfolio. F is an activation function that is unique and different from regression models. F is usually formulated as sigmoid functions and tanh functions. Other functions can also be used, including ReLU functions, identity functions, binary step functions, ArcTan functions, ArcSinh functions, ISRU functions, ISRLU functions, and SQNL functions. If we combine several perceptrons in each layer and add a hidden layer from Z 1 to Z 4 in the middle, we term a single layer as a neural network, where the input layers are the X s , and the output layers are the Y s . In finance, Y can be considered as the stock price. Moreover, multiple Y s are also applicable; for instance, fund managers often care about future prices and fluctuations. Figure 1 illustrates the basic structure.

The structure of NN

Based on the basic structure of NN shown in Fig. 1 , traditional networks include DNN, backpropagation (BP), MLP, and feedforward neural network (FNN). Using these models can ignore the order of data and the significance of time. As shown in Fig. 2 , RNN has a new NN structure that can address the issues of long-term dependence and the order between input variables. As financial data in time series are very common, uncovering hidden correlations is critical in the real world. RNN can be better at solving this problem, as compared to other moving average (MA) methods that have been frequently adopted before. A detailed structure of RNN for a sequence over time is shown in Part B of the Appendix (see Fig. 7 in Appendix ).

The abstract structure of RNN

Although RNN can resolve the issue of time-series order, the issue of long-term dependencies remains. It is difficult to find the optimal weight for long-term data. LSTM, as a type of RNN, added a gated cell to overcome long-term dependencies by combining different activation functions (e.g., sigmoid or tanh). Given that LSTM is frequently used for forecasting in the finance literature, we extract LSTM from RNN models and name other structures of standard RNN as RNN(O).

As we focus on the application rather than theoretical DL aspect, this study will not consider other popular DL algorithms, including CNN and RL, as well as Latent variable models such as variational autoencoders and generative adversarial network. Table 6 in Appendix shows a legend note to explain the abbreviations used in this paper. We summarize the relationship between commonly used DL models in Fig. 3 .

Relationships of reviewed DL models for F&B domains

Research framework and methodology

Our research framework is illustrated in Fig. 4 . We combine qualitative and quantitative analyses of the articles in this study. Based on our review, we recognize and identify seven core F&B domains, as shown in Fig. 5 . To connect the DL side and the F&B side, we present our review on the application of the DL model in seven F&B domains in Section 4. It is crucial to analyze the feasibility of a DL model toward particular domains. To do so, we provide summarizations in three key aspects, including data preprocessing, data inputs, and evaluation rules, according to our collection of articles. Finally, we determine optimal DL models regarding the identified domains. We further discuss two common issues in using DL models for F&B: overfitting and sustainability.

The research framework of this study

The identified domains of F&B for DL applications

Figure 5 shows that the application domains can be divided into two major areas: (1) banking and credit risk and (2) financial market investment. The former contains two domains: credit risk prediction and macroeconomic prediction. The latter contains financial prediction, trading, and portfolio management. Prediction tasks are crucial, as emphasized by Cavalcante et al. ( 2016 ). We study this domain from three aspects of prediction, including exchange rate, stock market, and oil price. We illustrate this structure of application domains in F&B.

Figure 6 shows a statistic in the listed F&B domains. We illustrate the domains of financial applications on the X-axis and count the number of articles on the Y-axis. Note that a reviewed article could cover more than one domain in this figure; thus, the sum of the counts (45) is larger than the size of our review pool (40 articles). As shown in Fig. 6 , stock marketing prediction and trading dominate the listed domains, followed by exchange rate prediction. Moreover, we found two articles on banking credit risk and two articles on portfolio management. Price prediction and macroeconomic prediction are two potential topics that deserve more studies.

A count of articles over seven identified F&B domains

Application of DL models in F&B domains

Based on our review, six types of DL models are reported. They are FNN, CNN, RNN, RL, deep belief networks (DBN), and restricted Boltzmann machine (RBM). Regarding FNN, several papers use the alternative terms of backpropagation artificial neural network (ANN), FNN, MLP, and DNN. They have an identical structure. Regarding RNN, one of its well-known models in the time-series analysis is called LSTM. Nearly half of the reviewed articles apply FNN as the primary DL technique. Nine articles apply LSTM, followed by eight articles for RL, and six articles for RNN. Minor ones that are applied in F&B include CNN, DBM, and RBM. We count the number of articles that use various DL models in seven F&B domains, as shown in Table 1 . FNN is the principal model used in exchange rate, price, and macroeconomic predictions, as well as banking default risk and credit. LSTM and FNN are two kinds of popular models for stock market prediction. Differently, RL and FNN are frequently used regarding stock trading. FNN, RL, and simple RNN can be conducted in portfolio management. FNN is the primary model in macroeconomic and banking risk prediction. CNN, LSTM, and RL are emerging research approaches in banking risk prediction. The detailed statistics that contain specific articles can be found in Table 5 in Appendix .

Exchange rate prediction

Shen et al. ( 2015 ) construct an improved DBN model by including RBM and find that their model outperforms the random walk algorithm, auto-regressive-moving-average (ARMA), and FNN with fewer errors. Zheng et al. ( 2017 ) examine the performance of DBN and find that the DBN model estimates the exchange rate better than FNN model does. They find that a small number of layer nodes engender a more significant effect on DBN.

Several scholars believe that a hybrid model should have better performance. Ravi et al. ( 2017 ) contribute a hybrid model by using MLP (FNN), chaos theory, and multi-objective evolutionary algorithms. Their Chaos+MLP + NSGA-II model Footnote 1 has a mean squared error (MSE) with 2.16E-08 that is very low. Several articles point out that only a complicated neural network like CNN can gain higher accuracy. For example, Galeshchuk and Mukherjee ( 2017 ) conduct experiments and claim that a single hidden layer NN or SVM performs worse than a simple model like moving average (MA). However, they find that CNN could achieve higher classification accuracy in predicting the direction of the change of exchange rate because of successive layers of DNN.

Stock market prediction

In stock market prediction, some studies suggest that market news may influence the stock price and DL model, such as using a magic filter to extract useful information for price prediction. Matsubara et al. ( 2018 ) extract information from the news and propose a deep neural generative model to predict the movement of the stock price. This model combines DNN and a generative model. It suggests that this hybrid approach outperforms SVM and MLP.

Minh et al. ( 2017 ) develop a novel framework with two streams combining the gated recurrent unit network and the Stock2vec. It employs a word embedding and sentiment training system on financial news and the Harvard IV-4 dataset. They use the historical price and news-based signals from the model to predict the S&P500 and VN-index price directions. Their model shows that the two-stream gated recurrent unit is better than the gated recurrent unit or the LSTM. Jiang et al. ( 2018 ) establish a recurrent NN that extracts the interaction between the inner-domain and cross-domain of financial information. They prove that their model outperforms the simple RNN and MLP in the currency and stock market. Krausa and Feuerriegel ( 2017 ) propose that they can transform financial disclosure into a decision through the DL model. After training and testing, they point out that LSTM works better than the RNN and conventional ML methods such as ridge regression, Lasso, elastic net, random forest, SVR, AdaBoost, and gradient boosting. They further pre-train words embeddings with transfer learning (Krausa and Feuerriegel 2017 ). They conclude that better performance comes from LSTM with word embeddings. In the sentiment analysis, Sohangir et al. ( 2018 ) compares LSTM, doc2vec, and CNN to evaluate the stock opinions on the StockTwits. They conclude that CNN is the optimal model to predict the sentiment of authors. This result may be further applied to predict the stock market trend.

Data preprocessing is conducted to input data into the NN. Researchers may apply numeric unsupervised methods of feature extraction, including principal component analysis, autoencoder, RBM, and kNN. These methods can reduce the computational complexity and prevent overfitting. After the input of high-frequency transaction data, Chen et al. ( 2018b ) establish a DL model with an autoencoder and an RBM. They compare their model with backpropagation FNN, extreme learning machine, and radial basis FNN. They claim that their model can better predict the Chinese stock market. Chong et al. ( 2017 ) apply the principal component analysis (PCA) and RBM with high-frequency data of the South Korean market. They find that their model can explain the residual of the autoregressive model. The DL model can thus extract additional information and improve prediction performance. More so, Singh and Srivastava ( 2017 ) describe a model involving 2-directional and 2-dimensional (2D 2 ) PCA and DNN. Their model outperforms 2D 2 with radial basis FNN and RNN.

For time-series data, sometimes it is difficult to judge the weight of long-term and short-term data. The LSTM model is just for resolving this problem in financial prediction. The literature has attempted to prove that LSTM models are applicable and outperform conventional FNN models. Yan and Ouyang ( 2017 ) apply LSTM to challenge the MLP, SVM, and kNN in predicting a static and dynamic trend. After a wavelet decomposition and a reconstruction of the financial time series, their model can be used to predict a long-term dynamic trend. Baek and Kim ( 2018 ) apply LSTM not only in predicting the price of S&P500 and KOSPI200 but also in preventing overfitting. Kim and Won ( 2018 ) apply LSTM in the prediction of stock price volatility. They propose a hybrid model that combines LSTM with three generalized autoregressive conditional heteroscedasticity (GARCH)-type models. Hernandez and Abad ( 2018 ) argue that RBM is inappropriate for dynamic data modeling in the time-series analysis because it cannot retain memory. They apply a modified RBM model called p -RBM that can retain the memory of p past states. This model is used in predicting market directions of the NASDAQ-100 index. Compared with vector autoregression (VAR) and LSTM, notwithstanding, they find that LSTM is better because it can uncover the hidden structure within the non-linear data while VAR and p -RBM cannot capture the non-linearity in data.

CNN was established to predict the price with a complicated structure. Making the best use of historical price, Dingli and Fournier ( 2017 ) develop a new CNN model. This model can predict next month’s price. Their results cannot surpass other comparable models, such as logistic regression (LR) and SVM. Tadaaki ( 2018 ) applies the financial ratio and converts them into a “grayscale image” in the CNN model. The results reveal that CNN is more efficient than decision trees (DT), SVM, linear discriminant analysis, MLP, and AdaBoost. To predict the stock direction, Gunduz et al. ( 2017 ) establish a CNN model with a so-called specially ordered feature set whose classifier outperforms either CNN or LR.

Stock trading

Many studies adopt the conventional FNN model and try to set up a profitable trading system. Sezer et al. ( 2017 ) combine GA with MLP. Chen et al. ( 2017 ) adopt a double-layer NN and discover that its accuracy is better than ARMA-GARCH and single-layer NN. Hsu et al. ( 2018 ) equip the Black-Scholes model and a three-layer fully-connected feedforward network to estimate the bid-ask spread of option price. They argue that this novel model is better than the conventional Black-Scholes model with lower RMSE. Krauss et al. ( 2017 ) apply DNN, gradient-boosted-trees, and random forests in statistical arbitrage. They argue that their returns outperform the market index S&P500.

Several studies report that RNN and its derivate models are potential. Deng et al. ( 2017 ) extend the fuzzy learning into the RNN model. After comparing their model to different DL models like CNN, RNN, and LSTM, they claim that their model is the optimal one. Fischer and Krauss ( 2017 ) and Bao et al. ( 2017 ) argue that LSTM can create an optimal trading system. Fischer and Krauss ( 2017 ) claim that their model has a daily return of 0.46 and a sharp ratio of 5.8 prior to the transaction cost. Given the transaction cost, however, LSTM’s profitability fluctuated around zero after 2010. Bao et al. ( 2017 ) advance Fischer and Krauss’s ( 2017 ) work and propose a novel DL model (i.e., WSAEs-LSTM model). It uses wavelet transforms to eliminate noise, stacked autoencoders (SAEs) to predict stock price, and LSTM to predict the close price. The result shows that their model outperforms other models such as WLSTM, Footnote 2 LSTM, and RNN in predictive accuracy and profitability.

RL is popular recently despite its complexity. We find that five studies apply this model. Chen et al. ( 2018a ) propose an agent-based RL system to mimic 80% professional trading strategies. Feuerriegel and Prendinger ( 2016 ) convert the news sentiment into the signal in the trading system, although their daily returns and abnormal returns are nearly zero. Chakraborty ( 2019 ) cast the general financial market fluctuation into a stochastic control problem and explore the power of two RL models, including Q-learning Footnote 3 and state-action-reward-state-action (SARSA) algorithm. Both models can enhance profitability (e.g., 9.76% for Q-learning and 8.52% for SARSA). They outperform the buy-and-hold strategy. Footnote 4 Zhang and Maringer ( 2015 ) conduct a hybrid model called GA, with recurrent RL. GA is used to select an optimal combination of technical indicators, fundamental indicators, and volatility indicators. The out-of-sample trading performance is improved due to a significantly positive Sharpe ratio. Martinez-Miranda et al. ( 2016 ) create a new topic of trading. It uses a market manipulation scanner model rather than a trading system. They use RL to model spoofing-and-pinging trading. This study reveals that their model just works on the bull market. Jeong and Kim ( 2018 ) propose a model called deep Q-network that is constructed by RL, DNN, and transfer learning. They use transfer learning to solve the overfitting issue incurred as a result of insufficient data. They argue that the profit yields in this system increase by four times the amount in S&P500, five times in KOSPI, six times in EuroStoxx50, and 12 times in HIS.

Banking default risk and credit

Most articles in this domain focus on FNN applications. Rönnqvist and Sarlin ( 2017 ) propose a model for detecting relevant discussions in texting and extracting natural language descriptions of events. They convert the news into a signal of the bank-distress report. In their back-test, their model reflects the distressing financial event of the 2007–2008 period.

Zhu et al. ( 2018 ) propose a hybrid CNN model with a feature selection algorithm. Their model outperforms LR and random forest in consumer credit scoring. Wang et al. ( 2019 ) consider that online operation data can be used to predict consumer credit scores. They thus convert each kind of event into a word and apply the Event2vec model to transform the word into a vector in the LSTM network. The probability of default yields higher accuracy than other models. Jurgovsky et al. ( 2018 ) employs the LSTM to detect credit card fraud and find that LSTM can enhance detection accuracy.

Han et al. ( 2018 ) report a method that adopts RL to assess the credit risk. They claim that high-dimensional partial differential equations (PDEs) can be reformulated by using backward stochastic differential equations. NN approximates the gradient of the unknown solution. This model can be applied to F&B risk evaluation after considering all elements such as participating agents, assets, and resources, simultaneously.

Portfolio management

Song et al. ( 2017 ) establish a model after combining ListNet and RankNet to make a portfolio. They take a long position for the top 25% stocks and hold the short position for the bottom 25% stocks weekly. The ListNetlong-short model is the optimal one, which can achieve a return of 9.56%. Almahdi and Yang ( 2017 ) establish a better portfolio with a combination of RNN and RL. The result shows that the proposed trading system respond to transaction cost effects efficiently and outperform hedge fund benchmarks consistently.

Macroeconomic prediction

Sevim et al. ( 2014 ) develops a model with a back-propagation learning algorithm to predict the financial crises up to a year before it happened. This model contains three-layer perceptrons (i.e., MLP) and can achieve an accuracy rate of approximately 95%, which is superior to DT and LR. Chatzis et al. ( 2018 ) examine multiple models such as classification tree, SVM, random forests, DNN, and extreme gradient boosting to predict the market crisis. The results show that crises encourage persistence. Furthermore, using DNN increases the classification accuracy that makes global warning systems more efficient.

Price prediction

For price prediction, Sehgal and Pandey ( 2015 ) review ANN, SVM, wavelet, GA, and hybrid systems. They separate the time-series models into stochastic models, AI-based models, and regression models to predict oil prices. They reveal that researchers prevalently use MLP for price prediction.

Data preprocessing and data input

Data preprocessing.

Data preprocessing is conducted to denoise before data training of DL. This section summarizes the methods of data preprocessing. Multiple preprocessing techniques discussed in Part 4 include the principal component analysis (Chong et al. 2017 ), SVM (Gunduz et al. 2017 ), autoencoder, and RBM (Chen et al. 2018b ). There are several additional techniques of feature selection as follows.

Relief: The relief algorithm (Zhu et al. 2018 ) is a simple approach to weigh the importance of the feature. Based on NN algorithms, relief repeats the process for n times and divides each final weight vector by n . Thus, the weight vectors are the relevance vectors, and features are selected if their relevance is larger than the threshold τ .

Wavelet transforms: Wavelet transforms are used to fix the noise feature of the financial time series before feeding into a DL network. It is a widely used technique for filtering and mining single-dimensional signals (Bao et al. 2017 ).

Chi-square: Chi-square selection is commonly used in ML to measure the dependence between a feature and a class label. The representative usage is by Gunduz et al. ( 2017 ).

Random forest: Random forest algorithm is a two-stage process that contains random feature selection and bagging. The representative usage is by Fischer and Krauss ( 2017 ).

Data inputs

Data inputs are an important criterion for judging whether a DL model is feasible for particular F&B domains. This section summarizes the method of data inputs that have been adopted in the literature. Based on our review, five types of input data in the F&B domain can be presented. Table 2 provides a detailed summary of the input variable in F&B domains.

History price: The daily exchange rate can be considered as history price. The price can be the high, low, open, and close price of the stock. Related articles include Bao et al. ( 2017 ), Chen et al. ( 2017 ), Singh and Srivastava ( 2017 ), and Yan and Ouyang ( 2017 ).

Technical index: Technical indexes include MA, exponential MA, MA convergence divergence, and relative strength index. Related articles include Bao et al. ( 2017 ), Chen et al. ( 2017 ), Gunduz et al. ( 2017 ), Sezer et al. ( 2017 ), Singh and Srivastava ( 2017 ), and Yan and Ouyang ( 2017 ).

Financial news: Financial news covers financial message, sentiment shock score, and sentiment trend score. Related articles include Feuerriegel and Prendinger ( 2016 ), Krausa and Feuerriegel ( 2017 ), Minh et al. ( 2017 ), and Song et al. ( 2017 ).

Financial report data: Financial report data can account for items in the financial balance sheet or the financial report data (e.g., return on equity, return on assets, price to earnings ratio, and debt to equity ratio). Zhang and Maringer ( 2015 ) is a representative study on the subject.

Macroeconomic data: This kind of data includes macroeconomic variables. It may affect elements of the financial market, such as exchange rate, interest rate, overnight interest rate, and gross foreign exchange reserves of the central bank. Representative articles include Bao et al. ( 2017 ), Kim and Won ( 2018 ), and Sevim et al. ( 2014 ).

Stochastic data: Chakraborty ( 2019 ) provides a representative implementation.

Evaluation rules

It is critical to judge whether an adopted DL model works well in a particular financial domain. We, thus, need to consider evaluation systems of criteria for gauging the performance of a DL model. This section summarizes the evaluation rules of F&B-oriented DL models. Based on our review, three evaluation rules dominate: the error term, the accuracy index, and the financial index. Table 3 provides a detailed summary. The evaluation rules can be boiled down to the following categories.

Error term: Suppose Y t + i and F t + i are the real data and the prediction data, respectively, where m is the total number. The following is a summary of the functional formula commonly employed for evaluating DL models.

Mean Absolute Error (MAE): $ {\sum}_{i=1}^m\frac{\left|{Y}_{t+i}-{F}_{t+i}\right|}{m} $ ;

Mean Absolute Percent Error (MAPE): $ \frac{100}{m}{\sum}_{i=1}^m\frac{\left|{Y}_{t+i}-{F}_{t+i}\right|}{Y_{t+i}} $ ;

Mean Squared Error (MSE): $ {\sum}_{i=1}^m\frac{{\left({Y}_{t+i}-{F}_{t+i}\right)}^2}{m} $ ;

Root Mean Squared Error (RMSE): $ \sqrt{\sum_{i=1}^m\frac{{\left({Y}_{t+i}-{F}_{t+i}\right)}^2}{m}} $ ;

Normalized Mean Square Error (NMSE): $ \frac{1}{m}\frac{\sum {\left({Y}_{t+i}-{F}_{t+i}\right)}^2}{\mathit{\operatorname{var}}\left({Y}_{t+i}\right)} $ .

Accuracy index: According to Matsubara et al. ( 2018 ), we use TP, TN, FP, and FN to represent the number of true positives, true negatives, false positives, and false negatives, respectively, in a confusion matrix for classification evaluation. Based on our review, we summarize the accuracy indexes as follows.

Directional Predictive Accuracy (DPA): $ \frac{1}{N}{\sum}_{t=1}^N{D}_t $ , if ( Y t + 1 − Y t ) × ( F t + 1 − Y t ) ≥ 0, D t = 1, otherwise, D t = 0;

Actual Correlation Coefficient (ACC): $ \frac{TP+ TN}{TP+ FP+ FN+ TN} $ ;

Matthews Correlation Coefficient (MCC): $ \frac{TP\times TN- FP\times FN}{\sqrt{\left( TP+ FP\right)\left( TP+ FN\right)\left( TN+ FP\right)\left( TN+ FN\right)}} $ .

Financial index: Financial indexes involve total return, Sharp ratio, abnormal return, annualized return, annualized number of transaction, percentage of success, average profit percent per transaction, average transaction length, maximum profit percentage in the transaction, maximum loss percentage in the transaction, maximum capital, and minimum capital.

For the prediction by regressing the numeric dependent variables (e.g., exchange rate prediction or stock market prediction), evaluation rules are mostly error terms. For the prediction by classification in the category data (e.g., direction prediction on oil price), the accuracy indexes are widely conducted. For stock trading and portfolio management, financial indexes are the final evaluation rules.

General comparisons of DL models

This study identifies the most efficient DL model in each identified F&B domain. Table 4 illustrates our comparisons of the error terms in the pool of reviewed articles. Note that “A > B” means that the performance of model A is better than that of model B. “A + B” indicates the hybridization of multiple DL models.

At this point, we have summarized three methods of data processing in DL models against seven specified F&B domains, including data preprocessing, data inputs, and evaluation rules. Apart from the technical level of DL, we find the following:

NN has advantages in handling cross-sectional data;

RNN and LSTM are more feasible in handling time series data;

CNN has advantages in handling the data with multicollinearity.

Apart from application domains, we can induce the following viewpoints. Cross-sectional data usually appear in exchange rate prediction, price prediction, and macroeconomic prediction, for which NN could be the most feasible model. Time series data usually appear in stock market prediction, for which LSTM and RNN are the best options. Regarding stock trading, a feasible DL model requires the capabilities of decision and self-learning, for which RL can be the best. Moreover, CNN is more suitable for the multivariable environment of any F&B domains. As shown in the statistics of the Appendix , the frequency of using corresponding DL models corresponds to our analysis above. Selecting proper DL models according to the particular needs of financial analysis is usually challenging and crucial. This study provides several recommendations.

We summarize emerging DL models in F&B domains. Nevertheless, can these models refuse the efficient market hypothesis (EMH)? Footnote 5 According to the EMH, the financial market has its own discipline. There is no long-term technical tool that could outperform an efficient market. If so, using DL models may not be practical in long-term trading as it requires further experimental tests. However, why do most of the reviewed articles argue that their DL models of trading outperform the market returns? This argument has challenged the EMH. A possible explanation is that many DL algorithms are still challenging to apply in the real-world market. The DL models may raise trading opportunities to gain abnormal returns in the short-term. In the long run, however, many algorithms may lose their superiority, whereas EMH still works as more traders recognize the arbitrage gap offered by these DL models.

This section discusses three aspects that could affect the outcomes of DL models in finance.

Training and validation of data processing

The size of the training set.

The optimal way to improve the performance of models is by enhancing the size of the training data. Bootstrap can be used for data resampling, and generative adversarial network (GAN) can extend the data features. However, both can recognize numerical parts of features. Sometimes, the sample set is not diverse enough; thus, it loses its representativeness. Expanding the data size could make the model more unstable. The current literature reported diversified sizes of training sets. The requirements of data size in the training stage could vary by different F&B tasks.

The number of input factors

Input variables are independent variables. Based on our review, multi-factor models normally perform better than single-factor models in the case that the additional input factors are effective. In the time-series data model, long-term data have less prediction errors than that for a short period. The number of input factors depends on the employment of the DL structure and the specific environment of F&B tasks.

The quality of data

Several methods can be used to improve the data quality, including data cleaning (e.g., dealing with missing data), data normalization (e.g., taking the logarithm, calculating the changes of variables, and calculating the t -value of variables), feature selection (e.g., Chi-square test), and dimensionality reduction (e.g., PCA). Financial DL models require that the input variables should be interpretable in economics. When inputting the data, researchers should clarify the effective variables and noise. Several financial features, such as technical indexes, are likely to be created and added into the model.

Selection on structures of DL models

DL model selection should depend on problem domains and cases in finance. NN is suitable for processing cross-sectional data. LSTM and other RNNs are optimal choices for time-series data in prediction tasks. CNN can settle the multicollinearity issue through data compression. Latent variable models like GAN can be better for dimension reduction and clustering. RL is applicable in the cases with judgments like portfolio management and trading. The return levels and outcomes on RL can be affected significantly by environment (observation) definitions, situation probability transfer matrix, and actions.

The setting of objective functions and the convexity of evaluation rules

Objective function selection affects training processes and expected outcomes. For predictions on stock price, low MAE merely reflects the effectiveness of applied models in training; however, it may fail in predicting future directions. Therefore, it is vital for additional evaluation rules for F&B. Moreover, it can be more convenient to resolve the objective functions if they are convex.

The influence of overfitting (underfitting)

Overfitting (underfitting) commonly happens in using DL models, which is clearly unfavorable. A generated model performs perfectly in one case but usually cannot replicate good performance with the same model and identical coefficients. To solve this problem, we have to trade off the bias against variances. Bias posits that researchers prefer to keep it small to illustrate the superiority of their models. Generally, a deeper (i.e., more layered) NN model or neurons can reduce errors. However, it is more time-consuming and could reduce the feasibility of applied DL models.

One solution is to establish validation sets and testing sets for deciding the numbers of layers and neurons. After setting optimal coefficients in the validation set (Chong et al. 2017 ; Sevim et al. 2014 ), the result in the testing sets reveals the level of errors that could mitigate the effect of overfitting. One can input more samples of financial data to check the stability of the model’s performance. This method is known as the early stopping. It stops training more layers in the network once the testing result has achieved an optimal level.

Moreover, regularization is another approach to conquer the overfitting. Chong et al. ( 2017 ) introduces a constant term for the objective function and eventually reduces the variates of the result. Dropout is also a simple method to address overfitting. It reduces the dimensions and layers of the network (Minh et al. 2017 ; Wang et al. 2019 ). Finally, the data cleaning process (Baek and Kim 2018 ; Bao et al. 2017 ), to an extent, could mitigate the impact of overfitting.

Financial models

The sustainability of the model.

According to our reviews, the literature focus on evaluating the performance of historical data. However, crucial problems remain. Given that prediction is always complicated, the problem of how to justify the robustness of the used DL models in the future remains. More so, whether a DL model could survive in dynamic environments must be considered.

The following solutions could be considered. First, one can divide the data into two groups according to the time range; performance can subsequently be checked (e.g., using the data for the first 3 years to predict the performance of the fourth year). Second, the feature selection can be used in the data preprocessing, which could improve the sustainability of models in the long run. Third, stochastic data can be generated for each input variable by fixing them with a confidence interval, after which a simulation to examine the robustness of all possible future situations is conducted.

The popularity of the model

Whether a DL model is effective for trading is subject to the popularity of the model in the financial market. If traders in the same market conduct an identical model with limited information, they may run identical results and adopt the same trading strategy accordingly. Thus, they may lose money because their strategy could sell at a lower price after buying at a higher.

Conclusion and future works

Concluding remarks.

This paper provides a comprehensive survey of the literature on the application of DL in F&B. We carefully review 40 articles refined from a collection of 150 articles published between 2014 and 2018. The review and refinement are based on a scientific selection of academic databases. This paper first recognizes seven core F&B domains and establish the relationships between the domains and their frequently-used DL models. We review the details of each article under our framework. Importantly, we analyze the optimal models toward particular domains and make recommendations according to the feasibility of various DL models. Thus, we summarize three important aspects, including data preprocessing, data inputs, and evaluation rules. We further analyze the unfavorable impacts of overfitting and sustainability when applying DL models and provide several possible solutions. This study contributes to the literature by presenting a valuable accumulation of knowledge on related studies and providing useful recommendations for financial analysts and researchers.

Future works

Future studies can be conducted from the DL technical and F&B application perspectives. Regarding the perspective of DL techniques, training DL model for F&B is usually time-consuming. However, effective training could greatly enhance accuracy by reducing errors. Most of the functions can be simulated with considerable weights in complicated networks. First, one of the future works should focus on data preprocessing, such as data cleaning, to reduce the negative effect of data noise in the subsequent stage of data training. Second, further studies on how to construct layers of networks in the DL model are required, particularly when considering a reduction of the unfavorable effects of overfitting and underfitting. According to our review, the comparisons between the discussed DL models do not hinge on an identical source of input data, which renders these comparisons useless. Third, more testing regarding F&B-oriented DL models would be beneficial.

In addition to the penetration of DL techniques in F&B fields, more structures of DL models should be explored. From the perspective of F&B applications, the following problems need further research to investigate desirable solutions. In the case of financial planning, can a DL algorithm transfer asset recommendations to clients according to risk preferences? In the case of corporate finance, how can a DL algorithm benefit capital structure management and, thus, maximize the values of corporations? How can managers utilize DL technical tools to gauge the investment environment and financial data? How can they use such tools to optimize cash balances and cash inflow and outflow? Until recently, DL models like RL and generative adversarial networks are rarely used. More investigations on constructing DL structures for F&B regarding preferences would be beneficial. Finally, the developments of professional F&B software and system platforms that implement DL techniques are highly desirable.

Availability of data and materials

Not applicable.

In the model, NSGA stands for non-dominated sorting genetic algorithm.

A combination of Wavelet transforms (WT) and long-short term memory (LSTM) is called WLSTM in Bao et al. ( 2017 ).

Q-learning is a model-free reinforcement learning algorithm.

Buy-and-hold is a passive investment strategy in which an investor buys stocks (or ETFs) and holds them for a long period regardless of fluctuations in the market.

EMH was developed from a Ph.D. dissertation by economist Eugene Fama in the 1960s. It says that at any given time, stock prices reflect all available information and trade at exactly their fair value at all times. It is impossible to consistently choose stocks that will beat the returns of the overall stock market. Therefore, this hypothesis implies that the pursuit of market-beating performance is more about chance than it is about researching and selecting the right stocks.

Almahdi, S., & Yang, S. Y. (2017). An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown. Expert Systems with Applications, 87 , 267–279.

Article Google Scholar

Baek, Y., & Kim, H. Y. (2018). ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module. Expert Systems with Applications, 113 , 457–480.

Bao, W., Yue, J., & Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short-term memory. PLoS One, 12 (7), e0180944.

Butaru, F., Chen, Q., Clark, B., Das, S., Lo, A. W., & Siddique, A. (2016). Risk and risk management in the credit card industry. Journal of Banking & Finance, 72 , 218–239.

Cavalcante, R. C., Brasileiro, R. C., Souza, V. L. F., Nobrega, J. P., & Oliveira, A. L. I. (2016). Computational intelligence and financial markets: A survey and future directions. Expert System with Application, 55 , 194–211.

Chai, J. Y., & Li, A. M. (2019). Deep learning in natural language processing: A state-of-the-art survey. In The proceeding of the 2019 international conference on machine learning and cybernetics (pp. 535–540). Japan: Kobe.

Google Scholar

Chai, J. Y., Liu, J. N. K., & Ngai, E. W. T. (2013). Application of decision-making techniques in supplier selection: A systematic review of literature. Expert Systems with Applications, 40 (10), 3872–3885.

Chai, J. Y., & Ngai, E. W. T. (2020). Decision-making techniques in supplier selection: Recent accomplishments and what lies ahead. Expert Systems with Applications, 140 , 112903. https://doi.org/10.1016/j.eswa.2019.112903 .

Chakraborty, S. (2019). Deep reinforcement learning in financial markets Retrieved from https://arxiv.org/pdf/1907.04373.pdf . Accessed 04 Apr 2020.

Chatzis, S. P., Siakoulis, V., Petropoulos, A., Stavroulakis, E., & Vlachogiannakis, E. (2018). Forecasting stock market crisis events using deep and statistical machine learning techniques. Expert Systems with Applications, 112 , 353–371.

Chen, C. T., Chen, A. P., & Huang, S. H. (2018a). Cloning strategies from trading records using agent-based reinforcement learning algorithm. In The proceeding of IEEE international conference on agents (pp. 34–37).

Chen, H., Xiao, K., Sun, J., & Wu, S. (2017). A double-layer neural network framework for high-frequency forecasting. ACM Transactions on Management Information Systems, 7 (4), 11.

Chen, L., Qiao, Z., Wang, M., Wang, C., Du, R., & Stanley, H. E. (2018b). Which artificial intelligence algorithm better predicts the Chinese stock market? IEEE Access, 6 , 48625–48633.

Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83 , 187–205.

Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12 , 2493–2537.

Deng, Y., Bao, F., Kong, Y., Ren, Z., & Dai, Q. (2017). Deep direct reinforcement learning for financial signal representation and trading. IEEE Transactions on Neural Networks and Learning Systems, 28 (3), 653–664.

Dingli, A., & Fournier, K. S. (2017). Financial time series forecasting—A machine learning approach. International Journal of Machine Learning and Computing, 4 , 11–27.

Elad, M., & Aharon, M. (2006). Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 15 (12), 3736–3745.

Feuerriegel, S., & Prendinger, H. (2016). News-based trading strategies. Decision Support Systems, 90 , 65–74.

Fischer, T., & Krauss, C. (2017). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270 (2), 654–669.

Galeshchuk, S., & Mukherjee, S. (2017). Deep networks for predicting the direction of change in foreign exchange rates. Intelligent Systems in Accounting, Finance and Maangement, 24 (4), 100–110.

Gunduz, H., Yaslan, Y., & Cataltepe, Z. (2017). Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations. Knowledge-Based Systems, 137 , 138–148.

Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187 , 27–48.

Han, J., Jentzen, A., & Weinan, E. (2018). Solving high-dimensional partial differential equations using deep learning. The proceedings of the National Academy of Sciences of the United States of America (PNAS) ; 8505–10).

Hernandez, J., & Abad, A. G. (2018). Learning from multivariate discrete sequential data using a restricted Boltzmann machine model. In The proceeding of IEEE 1st Colombian conference on applications in computational intelligence (ColCACI) (pp. 1–6).

Hsu, P. Y., Chou, C., Huang, S. H., & Chen, A. P. (2018). A market making quotation strategy based on dual deep learning agents for option pricing and bid-ask spread estimation. The proceeding of IEEE international conference on agents (pp. 99–104).

Jeong, G., & Kim, H. Y. (2018). Improving financial trading decisions using deep Q-learning: Predicting the number of shares, action strategies and transfer learning. Expert Systems with Applications, 117 , 125–138.

Jiang, X., Pan, S., Jiang, J., & Long, G. (2018). Cross-domain deep learning approach for multiple financial market predictions. The proceeding of international joint conference on neural networks (pp. 1–8).

Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P. E., Guelton, L. H., & Caelen, O. (2018). Sequence classification for credit-card fraud detection. Expert Systems with Applications, 100 , 234–245.

Kim, H. Y., & Won, C. H. (2018). Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models. Expert Systems with Applications, 103 , 25–37.

Krausa, M., & Feuerriegel, S. (2017). Decision support from financial disclosures with deep neural networks and transfer learning Retrieved from https://arxiv.org/pdf/1710.03954.pdf Accessed 04 Apr 2020.

Book Google Scholar

Krauss, C., Do, X. A., & Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P500. European Journal of Operational Research, 259 (2), 689–702.

Martinez-Miranda, E., McBurney, P., & Howard, M. J. W. (2016). Learning unfair trading: A market manipulation analysis from the reinforcement learning perspective. In The proceeding of 2016 IEEE conference on evolving and adaptive intelligent systems (EAIS) (pp. 103–109).

Chapter Google Scholar

Matsubara, T., Akita, R., & Uehara, K. (2018). Stock price prediction by deep neural generative model of news articles. IEICE Transactions on Information and Systems, 4 , 901–908.

Minh, D. L., Sadeghi-Niaraki, A., Huy, H. D., Min, K., & Moon, H. (2017). Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network. IEEE Access, 6 , 55392–55404.

Ravi, V., Pradeepkumar, D., & Deb, K. (2017). Financial time series prediction using hybrids of chaos theory, multi-layer perceptron and multi-objective evolutionary algorithms. Swarm and Evolutionary Computation, 36 , 136–149.

Rönnqvist, S., & Sarlin, P. (2017). Bank distress in the news describing events through deep learning. Neurocomputing, 264 (15), 57–70.

Sehgal, N., & Pandey, K. K. (2015). Artificial intelligence methods for oil price forecasting: A review and evaluation. Energy System, 6 , 479–506.

Sevim, C., Oztekin, A., Bali, O., Gumus, S., & Guresen, E. (2014). Developing an early warning system to predict currency crises. European Journal of Operational Research, 237 (3), 1095–1104.

Sezer, O. B., Ozbayoglu, M., & Gogdu, E. (2017). A deep neural-network-based stock trading system based on evolutionary optimized technical analysis parameters. Procedia Computer Science, 114 , 473–480.

Shen, F., Chao, J., & Zhao, J. (2015). Forecasting exchange rate using deep belief networks and conjugate gradient method. Neurocomputing, 167 , 243–253.

Singh, R., & Srivastava, S. (2017). Stock prediction using deep learning. Multimedia Tools Application, 76 , 18569–18584.

Sohangir, S., Wang, D., Pomeranets, A., & Khoshgoftaar, T. M. (2018). Big data: Deep learning for financial sentiment analysis. Journal of Big Data, 5 (3), 1–25.

Song, Q., Liu, A., & Yang, S. Y. (2017). Stock portfolio selection using learning-to-rank algorithms with news sentiment. Neurocomputing, 264 , 20–28.

Tadaaki, H. (2018). Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert Systems with Applications, 117 , 287–299.

Wang, C., Han, D., Liu, Q., & Luo, S. (2019). A deep learning approach for credit scoring of peer-to-peer lending using attention mechanism LSTM. IEEE Access, 7 , 2161–2167.

Yan, H., & Ouyang, H. (2017). Financial time series prediction based on deep learning. Wireless Personal Communications, 102 , 683–700.

Zhang, J., & Maringer, D. (2015). Using a genetic algorithm to improve recurrent reinforcement learning for equity trading. Computational Economics, 47 , 551–567.

Zheng, J., Fu, X., & Zhang, G. (2017). Research on exchange rate forecasting based on a deep belief network. Neural Computing and Application, 31 , 573–582.

Zhu, B., Yang, W., Wang, H., & Yuan, Y. (2018). A hybrid deep learning model for consumer credit scoring. In The proceeding of international conference on artificial intelligence and big data (pp. 205–208).

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Acknowledgments

The constructive comments of the editor and three anonymous reviewers on an earlier version of this paper are greatly appreciated. The authors are indebted to seminar participants at 2019 China Accounting and Financial Innovation Form at Zhuhai for insightful discussions. The corresponding author thanks the financial supports from BNU-HKBU United International College Research Grant under Grant R202026.

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JH carried out the collections and analyses of the literature, participated in the design of this study and preliminarily drafted the manuscript. JC initiated the idea and research project, identified the research gap and motivations, carried out the collections and analyses of the literature, participated in the design of this study, helped to draft the manuscript and proofread the manuscript. SC participated in the design of the study and the analysis of the literature, helped to draft the manuscript and proofread the manuscript. The authors read and approved the final manuscript.

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Part A. Summary of publications in DL and F&B domains

Part b. detailed structure of standard rnn.

The abstract structure of RNN for a sequence cross over time can be extended, as shown in Fig. 7 in Appendix , which presents the inputs as X , the outputs as Y , the weights as w , and the Tanh functions.

The detailed structure of RNN

Part C. List of abbreviations

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50 Best Finance Dissertation Topics For Research Students 2024

Finance Dissertation Made Easier!

Embarking on your dissertation adventure? Look no further! Choosing the right finance dissertation topics is like laying the foundation for your research journey in Finance, and we're here to light up your path. In this blog, we're diving deep into why dissertation topics in finance matter so much. We've got some golden writing tips to share with you! We're also unveiling the secret recipe for structuring a stellar finance dissertation and exploring intriguing topics across various finance sub-fields. Whether you're captivated by cryptocurrency, risk management strategies, or exploring the wonders of Internet banking, microfinance, retail and commercial banking - our buffet of Finance dissertation topics will surely set your research spirit on fire!

What is a Finance Dissertation?

Finance dissertations are academic papers that delve into specific finance topics chosen by students, covering areas such as stock markets, banking, risk management, and healthcare finance. These dissertations require extensive research to create a compelling report and contribute to the student's confidence and satisfaction in the field of Finance. Now, let's understand why these dissertations are so important and why choosing the right Finance dissertation topics is crucial!

Why Are Finance Dissertation Topics Important?

Choosing the dissertation topics for Finance students is essential as it will influence the course of your research. It determines the direction and scope of your study. You must make sure that the Finance dissertation topics you choose are relevant to your field of interest, or you may end up finding it more challenging to write. Here are a few reasons why finance thesis topics are important:

1. Relevance

Opting for relevant finance thesis topics ensures that your research contributes to the existing body of knowledge and addresses contemporary issues in the field of Finance. Choosing a dissertation topic in Finance that is relevant to the industry can make a meaningful impact and advance understanding in your chosen area.

2. Personal Interest

Selecting Finance dissertation topics that align with your interests and career goals is vital. When genuinely passionate about your research area, you are more likely to stay motivated during the dissertation process. Your interest will drive you to explore the subject thoroughly and produce high-quality work.

3. Future Opportunities

Well-chosen Finance dissertation topics can open doors to various future opportunities. It can enhance your employability by showcasing your expertise in a specific finance area. It may lead to potential research collaborations and invitations to conferences in your field of interest.

4. Academic Supervision

Your choice of topics for dissertation in Finance also influences the availability of academic supervisors with expertise in your chosen area. Selecting a well-defined research area increases the likelihood of finding a supervisor to guide you effectively throughout the dissertation. Their knowledge and guidance will greatly contribute to the success of your research.

Writing Tips for Finance Dissertation

A lot of planning, formatting, and structuring goes into writing a dissertation. It starts with deciding on topics for a dissertation in Finance and conducting tons of research, deciding on methods, and so on. However, you can navigate the process more effectively with proper planning and organisation. Below are some tips to assist you along the way, and here is a blog on the 10 tips on writing a dissertation that can give you more information, should you need it!

1. Select a Manageable Topic

Choosing Finance research topics within the given timeframe and resources is important. Select a research area that interests you and aligns with your career goals. It will help you stay inspired throughout the dissertation process.

2. Conduct a Thorough Literature Review

A comprehensive literature review forms the backbone of your research. After choosing the Finance dissertation topics, dive deep into academic papers, books, and industry reports, gaining a solid understanding of your chosen area to identify research gaps and establish the significance of your study.

3. Define Clear Research Objectives

Clearly define your dissertation's research questions and objectives. It will provide a clear direction for your research and guide your data collection, analysis, and overall structure. Ensure your objectives are specific, measurable, achievable, relevant, and time-bound (SMART).

4. Collect and Analyse Data

Depending on your research methodology and your Finance dissertation topics, collect and analyze relevant data to support your findings. It may involve conducting surveys, interviews, experiments, and analyzing existing datasets. Choose appropriate statistical techniques and qualitative methods to derive meaningful insights from your data.

5. Structure and Organization

Pay attention to the structure and organization of your dissertation. Follow a logical progression of chapters and sections, ensuring that each chapter contributes to the overall coherence of your study. Use headings, subheadings, and clear signposts to guide the reader through your work.

6. Proofread and Edit

Once you have completed the writing process, take the time to proofread and edit your dissertation carefully. Check for clarity, coherence, and proper grammar. Ensure that your arguments are well-supported, and eliminate any inconsistencies or repetitions. Pay attention to formatting, citation styles, and consistency in referencing throughout your dissertation.

Don't let student accommodation hassles derail your finance research.

Finance Dissertation Topics

Now that you know what a finance dissertation is and why they are important, it's time to have a look at some of the best Finance dissertation topics. For your convenience, we have segregated these topics into categories, including cryptocurrency, risk management, internet banking, and so many more. So, let's dive right in and explore the best Finance dissertation topics:

Dissertation topics in Finance related to Cryptocurrency

1. The Impact of Regulatory Frameworks on the Volatility and Liquidity of Cryptocurrencies.

2. Exploring the Factors Influencing Cryptocurrency Adoption: A Comparative Study.

3. Assessing the Efficiency and Market Integration of Cryptocurrency Exchanges.

4. An Analysis of the Relationship between Cryptocurrency Prices and Macroeconomic Factors.

5. The Role of Initial Coin Offerings (ICOs) in Financing Startups: Opportunities and Challenges.

Dissertation topics in Finance related to Risk Management

1. The Effectiveness of Different Risk Management Strategies in Mitigating Financial Risks in Banking Institutions.

2. The Role of Derivatives in Hedging Financial Risks: A Comparative Study.

3. Analyzing the Impact of Risk Management Practices on Firm Performance: A Case Study of a Specific Industry.

4. The Use of Stress Testing in Evaluating Systemic Risk: Lessons from the Global Financial Crisis.

5. Assessing the Relationship between Corporate Governance and Risk Management in Financial Institutions.

Dissertation topics in Finance related to Internet Banking

1. Customer Adoption of Internet Banking: An Empirical Study on Factors Influencing Usage.

Enhancing Security in Internet Banking: Exploring Biometric Authentication Technologies.

2. The Impact of Mobile Banking Applications on Customer Engagement and Satisfaction.

3. Evaluating the Efficiency and Effectiveness of Internet Banking Services in Emerging Markets.

4. The Role of Social Media in Shaping Customer Perception and Adoption of Internet Banking.

Dissertation topics in Finance related to Microfinance

1. The Impact of Microfinance on Poverty Alleviation: A Comparative Study of Different Models.

2. Exploring the Role of Microfinance in Empowering Women Entrepreneurs.

3. Assessing the Financial Sustainability of Microfinance Institutions in Developing Countries.

4. The Effectiveness of Microfinance in Promoting Rural Development: Evidence from a Specific Region.

5. Analyzing the Relationship between Microfinance and Entrepreneurial Success: A Longitudinal Study.

Dissertation topics in Finance related to Retail and Commercial Banking

1. The Impact of Digital Transformation on Retail and Commercial Banking: A Case Study of a Specific Bank.

2. Customer Satisfaction and Loyalty in Retail Banking: An Analysis of Service Quality Dimensions.

3. Analyzing the Relationship between Bank Branch Expansion and Financial Performance.

4. The Role of Fintech Startups in Disrupting Retail and Commercial Banking: Opportunities and Challenges.

5. Assessing the Impact of Mergers and Acquisitions on the Performance of Retail and Commercial Banks.

Dissertation topics in Finance related to Alternative Investment

1. The Performance and Risk Characteristics of Hedge Funds: A Comparative Analysis.

2. Exploring the Role of Private Equity in Financing and Growing Small and Medium-Sized Enterprises.

3. Analyzing the Relationship between Real Estate Investments and Portfolio Diversification.

4. The Potential of Impact Investing: Evaluating the Social and Financial Returns.

5. Assessing the Risk-Return Tradeoff in Cryptocurrency Investments: A Comparative Study.

Dissertation topics in Finance related to International Affairs

1. The Impact of Exchange Rate Volatility on International Trade: A Case Study of a Specific Industry.

2. Analyzing the Effectiveness of Capital Controls in Managing Financial Crises: Comparative Study of Different Countries.

3. The Role of International Financial Institutions in Promoting Economic Development in Developing Countries.

4. Evaluating the Implications of Trade Wars on Global Financial Markets.

5. Assessing the Role of Central Banks in Managing Financial Stability in a Globalized Economy.

Dissertation topics in Finance related to Sustainable Finance

1. The impact of sustainable investing on financial performance.

2. The role of green bonds in financing climate change mitigation and adaptation.

3. The development of carbon markets.

4. The use of environmental, social, and governance (ESG) factors in investment decision-making.

5. The challenges and opportunities of sustainable Finance in emerging markets.

Dissertation topics in Finance related to Investment Banking

1. The valuation of distressed assets.

2. The pricing of derivatives.

3. The risk management of financial institutions.

4. The regulation of investment banks.

5. The impact of technology on the investment banking industry.

Dissertation topics in Finance related to Actuarial Science

1. The development of new actuarial models for pricing insurance products.

2. The use of big data in actuarial analysis.

3. The impact of climate change on insurance risk.

4. The design of pension plans that are sustainable in the long term.

5. The use of actuarial science to manage risk in other industries, such as healthcare and Finance.

Tips To Find Good Finance Dissertation Topics

Embarking on a financial dissertation journey requires careful consideration of various factors. Your choice of topic in finance research topics is pivotal, as it sets the stage for the entire research process. Finding a good financial dissertation topic is essential to blend your interests with the current trends in the financial landscape. We suggest the following tips that can help you pick the perfect dissertation topic:

1. Identify your interests and strengths

2. Check for current relevance

3. Feedback from your superiors

4. Finalise the research methods

5. Gather the data

6. Work on the outline of your dissertation

7. Make a draft and proofread it

In this blog, we have discussed the importance of finance thesis topics and provided valuable writing tips and tips for finding the right topic, too. We have also presented a list of topics within various subfields of Finance. With this, we hope you have great ideas for finance dissertations. Good luck with your finance research journey!

Frequently Asked Questions

How do i research for my dissertation project topics in finance, what is the best topic for dissertation topics for mba finance, what is the hardest finance topic, how do i choose the right topic for my dissertation in finance, where can i find a dissertation topic in finance.

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Master of Science in Finance and Banking

Getting ready to graduate.

In order to complete the Master of Science Degree, students are required to present and defend a written thesis in front of an Examination Board.

To be admitted to the graduation session, students must earn the minimum number of 96 credits and fulfill all financial obligations toward Tor Vergata University of Rome. Students must also register for the graduation session through the Delphi online platform, submit the required documents, pay the required fee, and submit a final thesis, which is worth 24 credits.

Learn more about:

Winter Graduation Session - March 28, 2024, 10:00 am, Sala del Consiglio - Graduation Committee
List of Past Theses Titles
Guidelines on graduation application and thesis upload on Delphi

Graduation Sessions 2023/2024 A.Y.:

Winter Session: March 28, 2024
Summer Session: July 4, 2024
Fall Session: October 11, 2024

Writing a thesis

Students should keep in mind that, depending on the type of thesis they write, they should make sure to dedicate sufficient time for the research, analysis, and writing of the thesis.

The thesis supervisor should be chosen from the professors with whom the student has sat at least one exam and whose area of research is pertinent to the student’s possible thesis topic. Students must send a motivated request to the professor (well in advance), to decide the topic together and establish a timetable for the collection of data and the writing of each chapter. Students must send regular updates to their thesis supervisor, to keep him/her informed of their progress. Note that your thesis should be between 15,000 and 25,000 words. Each supervisor will have additional specific indications for students to follow.

Students are kindly requested to indicate their expected graduation session to the MSc in Finance and Banking Program Office ( [email protected] ) as soon as they have confirmation from their thesis supervisor that their work is proceeding according to schedule, or at least 60 days before graduation.

Students should use a specific thesis format and document their research. Each supervisor will have additional specific indications for students to follow. Editorial Standard

Documents to submit

Students should submit the following to the Students Administrative Office located on the ground floor of Building B of the School of Economics at least 30 days before the date of thesis defence:

The thesis application form from the Delphi online platform
The exam booklet

Roughly ten days before the graduation session, students will be required to upload their thesis on the Delphi online platform and the Supervisor will accept or decline the submitted thesis. Students will be required to send a copy of their thesis also to the MSc in Finance and Banking Program Office ( [email protected] ) for the Turnitin Anti-plagiarism Check .

One week before the graduation session, candidates will receive an email from the MSc in Finance and Banking Program Office with a request for the PowerPoint or pdf presentation that will be used on the day of graduation.

The graduating student should provide the Supervisor and Co-Supervisor with a printed and bound copy of the thesis before the thesis defense.

Details confirming the candidates and the Graduation Committee will be published a few days before the graduation date.

Cancellation: Students who decide to forgo submitting their dissertation should delete their application in Delphi and make a formal request by email to the Students Administrative Office ( [email protected] ) and in cc the MSc in Finance and Banking Program Office ( [email protected] ) with enclosed also a copy of your ID card/passport at least 15 days before the scheduled thesis presentation date.

Thesis defence and evaluation

The thesis is evaluated on contents, presentation and defense. The graduation session is composed by the oral presentation and the defense which includes answering questions and discussing issues raised by the members of an Examination Board.

The Examination Board is composed by at least 7 members, and it is appointed by the Dean of the School of Economics 20 days before the graduation session.

After the defense, the Commission determines the student's final grade. The evaluation is expressed out of 110. Students will be awarded the MSc in Economics if they obtain a mark of at least 66/110. The grade starting point is the exams' weighted mean transposed in 110, which is increased by the following points:

a maximum of 5 points are assigned, taking into consideration the quality of the thesis contents, presentation and defense
instead, a maximum of 8 points are assigned, taking into consideration the recommendation letter provided by the supervisor for a prestigious thesis. The recommendation letter from the supervisor must be addressed to the President of the Examination Board, and must underline the specific reasons of the positive contribution in the thesis
Special Distinction: to graduates that obtain the maximum grade, 110/110, the Examination Board can unanimously award honors ("lode") upon explanation

Degree Certificates

Degree certificate.

The degree certificate is only issued by the Students Administrative Office of the School of Economics. To obtain a copy of this document, students are required to provide two €16 duty stamps, available at any tobacco shop. The certificate is available in English or Italian. Request Form

Diploma Supplement

Once a student has graduated, the Students Administrative Office of the School of Economics can issue the Diploma Supplement containing a complete list of exams with marks. The document is available in English or Italian and it is free-of-charge. Request Form

Degree Parchment

The Degree parchment is not immediately available after graduation. Diplomas are ready for pick up for graduates from the July 2022 session and before . Graduates should keep in mind that the MSc in Finance & Banking Program Office does not have information pertaining to when the parchments are issued. Graduates interested in picking up their diploma (or designating someone else to do so) should contact the Students’ Office directly at [email protected] by attaching a valid copy of their ID and including the following information: full name, date of birth, student registration number, name of their academic program and graduation date.

In order to obtain the Degree Certificates please refer to the Students Administrative Office at the Ground Floor of Building B (you can go there personally or delegate someone else). Office hours: Monday and Friday 9:00am-12:00pm, Wednesday 9:00am-12:00pm and 2:00-4:00pm
Otherwise, you can download the certificates from Delphi

Finance, Risk Management & Banking: Theses and Dissertations

Student information letter
Referencing and Plagiarism
Library Technology Support
Journals & Articles
e-Journals and how to use the e-Journal Finder
Theses and Dissertations
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Internet sites
Statistics, Data & Indicators
Research Methodolgy
Reference Management Tools
Publishers Information
Alerting Service
Company Information
Legislation
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Free Apps for Research This link opens in a new window
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Quick Links

openDOAR:: Directory of Open Access Repositories

Open Access Theses and Dissertations

Dissertations are useful for identifying research opportunities.

Very useful for generating reading lists.

Research Proposals

Unisa Study guide Research Proposal: CEMS
PhD Proposal by Gerhard Grebe Mr. Gerhard Grebe gave me his gracious permission to upload this presentation as an example to the structure of a proposal.
Research Proposal Presentation

South African

UnisaETD: electronic theses and dissertations This link opens in a new window Full text theses and dissertations in the Unisa Institutional Repository.

This resource contains full text of completed Unisa theses and dissertations

Unisa Finance/Banking Theses/Dissertations

The following theses and dissertations have been done at Unisa in the subject of Fiance, Banking and Risk Management:

International

ProQuest Dissertations & Theses: Global This link opens in a new window This resource contains abstracts and full text of international theses and dissertations.
WorldCat Dissertations and Theses This link opens in a new window This resource contains abstracts of international theses and dissertations

Open Access Theses

PQDT Open: Open Access Theses and Dissertations
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Josephine Mercado

Nzanzu Luvako

The conviction that microfinance contributes to the reduction of poverty has attracted various investors in the sector. With the continuous growth of investments in microfinance, if the social motive is the most plausible, one would expect countries with high rates of poverty and financial exclusion to be the most attractive for investments in this sector. Aiming to understand whether the distribution of investments in different regions is led by the attractiveness of these regions in terms of risk and return or rather in terms of social impact, we use aggregate data on funds invested in the sector through Microfinance Investment Vehicles (MIVs) to study the spread of investments in microfinance around the world. It comes from the results that investments are attracted by financial performance of MFIs in the different regions, considering the return on assets and the return on equity. Further, the expenses over the assets ratio which influences negatively the variation of the investments destination suggests that regions with high cots MFIs don't receive much funds from investors. On the other side, investments seem to be oriented towards regions in which there are already institutions offering financial services, but regions with less access to financial services don't look to be the destination of investments. Is search for profit the most important driver for investments in microfinance? The results seem to go in that direction, but further research with a wider and deeper database would bring more light. However, the outcomes allow us to confirm that even if it is believed to be a poverty fighting tool, not all the investments in the microfinance sector aim the contribution to poverty reduction.

Hans Dieter Seibel

Our knowledge about microfinance in developing countries has been greatly enriched in recent years by the experience of numerous institutions. Different sound technologies and practices of financial services to all segments of the population have emerged; there is no single best practice or optimal approach that could be simply replicated. People and institutions have to find out what suits them best. Through trial and error, they gain the experience which may then be cast into lasting innovations. Five case studies are presented, each with its own lessons concerning viability, sustainability, and outreach: two from Indonesia, two from Nepal, and one from India. In addition, lessons are drawn from the recent financial crisis in Indonesia concerning the importance of a triad of framework conditions: prudential deregulation, macroeconomic stability, and adequate bank supervision. The data are largely based on the author?s field research and consultancy work. --

Simon Grima

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38 Banking and Finance Dissertation Topics Ideas

Banking and Finance Dissertation Topics: Dissertation topics in banking and finance focus on how monetary exchanges and programs function in the banking sector, both nationally and internationally. This is also a very famous academic discipline throughout the world. Banking and Finance explore the dynamic, fast-paced world of money, shares, credit, and investments. Finance is an essential […]

Banking and Finance explore the dynamic, fast-paced world of money, shares, credit, and investments . Finance is an essential part of our economy as it provides liquidity in terms of money or assets required for individuals and businesses to invest in the future.

If you are looking for banking and finance dissertation topics, your search end here. Below is the list of best-selected banking and finance dissertation topics. Also, you can check our related posts for accounting & finance dissertation topics and financial accounting dissertation topics .

Best Banking and Finance Dissertation Topics ideas for college students

Banking is the business of protecting money for others . Banks lend this money, generating interest that creates profits for the bank and its customers. A bank is a financial institution licensed to accept deposits and make loans.

Research topics in banking and finance have been collected together and presented in the form of an extensive list as below:

Implementing blockchain applications in the field of banking and finance: a descriptive approach.
Banking and finance post-COVID-19 pandemic: a review of the literature.
Studying the effects of monetary policy on banking and finance: a systematic analysis.
Islamic banking and finance: a quantitative study.
Effects of BREXIT on banking and finance contracts: a descriptive approach.
Financial fragility in the domain of banking and finance: a review of the literature.
Development and governance of Islamic banking and finance in X country.
Evaluating risk assessment in the area of banking and finance in X country.
Islamic banking and finance in present-day financial crisis: focus on current and future issues.
Sustainability in Islamic banking and finance: a systematic analysis.
Correlational analysis of financial stability, bank competition, and fire sales.
Development of a hypothetical model for internalization of banking and finance institutions.
The role played by information technology in the field of banking and finance: a systematic review.
Historical analysis of banking and finance in the UK: a quantitative study.
Credit risk versus financial risk in Islamic banking and finance.
Comparative analysis of banking and finance in UK and Asia.
The role played by intuitive decision-making in the field of banking and finance.
Working on mergers and acquisitions in the field of banking and finance.
Investigating illicit cyber activity in the field of banking and finance.
Interest-free banking and finance in the developing countries of the world.
Importance of compensation and risk incentives in the field of banking and finance.
Quality control in banking and finance: an exploratory study.
How e-commerce can be applied in the field of banking and finance.
Studying the big data and analytics as a customer loyalty tool in banking and finance: a descriptive study.
Data mining and banking and finance: a systematic analysis.
Studying the effects of enterprise risk management on the performance of firms in X country: focus on banking and finance sector.
Operations of Islamic banking and finance in the West: a systematic analysis.
Ethics in banking and finance programs: a review of the literature.
Relationship between risk and reputational capital in the banking and finance sector.
Highlighting the social and ethical issues in the banking and finance sector.
The current dilemma of financial instability in the structure of the banking and finance sector.
Comparative analysis of islands and small states within the banking and finance sector.
Studying the operational efficiency in the banking and finance sector: a review of the literature.
Social banking and social finance: a descriptive approach.
Finance employment: focus on UK banks.

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Information for mathematics students, application deadlines, formal requirements, theses archive.

Mathematics and Business Mathematics students can apply any time to be supervised in writing their Bachelor or Master Thesis by IFB via mail to IFB-Theses . Please enclose your CV and latest transcript of records.

Prerequisites

Successful participation in an advanced seminar qualifies for the acceptance of a Bachelor thesis (6 ECTS).
Students who have taken an advanced seminar in the field of finance (chairs of Elsas/Glaser/Richter/Riordan) will be accepted preferentially.
The requirements of the respective examination regulations apply.

Application

a letter of motivation in which you briefly, but as precisely as possible, outline your areas of interest. Please refrain from general statements as "I don't know any special field of interest". You can orientate yourself on the contents of your studies (lectures or advanced seminar) or be inspired by the theses already completed .
an exposé with a short outline (a few sentences) of your own proposed topic. Therein, address the question(s) to be investigated, the methods (data) and the relevant literature. This also applies to theses written in cooperation with a company.
Please send your complete application by email to the IFB: Theses .
We will inform you regarding acceptance or rejection of your application no later than 2 weeks after receipt of your application. In case of acceptance we will assign you a topic and a supervisor.
The thesis at the IFB can generally be started any time though the registration of theses usually starts at the beginning of the month. Please note the application deadlines .
The deadline is determined by the corresponding completion time (8 weeks for Bachelor's theses and 22 weeks for Master's theses).
Before the thesis is registered at the ISC, each student prepares an exposé (max. 2 pages). This is generally done after agreement on a topic. The exposé must be handed in to the respective supervisor at the latest one week after agreement on the topic. In consultation with the supervisor the draft is then revised and extended if necessary.
Upon agreement on the final topic, the thesis is registered at the examination office.
Theses can be written in English or German (English is preferred).
Please follow our formal requirements - if you have any questions, please consult your supervisor.
A thesis colloquium is held on the first Wednesday of each month. The colloquium takes place after about half of the processing time. Students present the status of their work for a maximum of 30 minutes, the presentation is held in English or German. During the colloquium, structure of the work and methodological approach are explained in particular. The subsequent discussion helps to identify problems in dealing with the topic and possible alternative solutions. The colloquium is rounded off by a short insight into the current state of work and possible open questions. Aim of the colloquium is to give constructive feedback - it is attended by the scientific staff and head of the institute.
Thesis submission is effected according to the ISC guidelines. Please inform your supervisor about the planned submission date.
For theses with a programming component, the student is required to submit data and codes to generate the results. The student is also responsible for ensuring that results can be replicated and reproduced by the supervisor.
Your supervisor will inform you as soon as the thesis has been evaluated.

Compliance with these guidelines (PDF, 136 KB) is generally mandatory and is intended to provide you with the necessary guidance to write a formally correct paper.

The Impact of Private Equity Ownership on Post-IPO Performance, Master Thesis
The impact of CSR on M&A uncertainty and premiums, Master Thesis
Are returns to value strategies predictable by their respective value spreads, Master Thesis
Can Twitter help predict firm-level earnings and stock returns, Master Thesis
On the pricing of European Dual Class Stocks, Master Thesis
The effect of corporate hybrid bonds issuance announcements on stock prices, Master Thesis
Performance of S&P 500 Index Additions, Bachelor Thesis
Hedge funds and their performance during the COVID-19 pandemic, Bachelor Thesis
Cryptocurrency price prediction using an LSTM and an ARMA model, Master Thesis
A literature review on bitcoin futures, Bachelor Thesis
The Effect of European Short Position Disclosure Requirement on Market Efficiency, Master Thesis
Earnings Forecasts by Analysts - Accuracy and Timing, Master Thesis
Text mining of corporate disclosures for stock index forecasting, Master Thesis
An empirical study of S&P 500 short term options, Bachelor Thesis
Examining Deterministic Volatility Functions on Short-Term Options, Master Thesis
Deep Learning and Option Pricing, Bachelor Thesis
An empirical analysis of financial advisors and M&A returns, Master Thesis
International Diversification and Market Capitalization, Bachelor Thesis
Identifying Financial Distress in Company Filings, Master Thesis
Investigating Capital Market Reactions to Corporate Events Using Machine Learning, Master Thesis
An Analysis of the Relationship between Short Interest and Stock Performance, Bachelor Thesis
Predicting Hourly Price Movements Of Bitcoin Based On Reddit Sentiment And Price Information Using Machine Learning Algorithms, Bachelor Thesis
An Empirical Analysis of Corporate Social Responsibility and Access to Finance, Bachelor Thesis
The Impact of Activist Short Sellers on the Returns of Target Companies, Bachelor Thesis
Analysis of the influence of Covid-19 on the Ad Hoc Publicity, Master Thesis
Was the retail investement portion significant in the initialization of the short squeeze of GameStop?, Bachelor Thesis
Estimating market betas for Germany, Master Thesis
Text analysis of Reddit WallStreetBets posts, Bachelor Thesis
An empirical analysis of options and implied volatility, Bachelor Thesis
The Impact of M&A Announcements on Short Interest in the German Market - An Event Analysis Approach, Bachelor Thesis
Anomaly Detection in Finance, Master Thesis
A Machine Learning Approach to Risk Factors Applying the Fama-French-Carhart Model: A Case Study on the German Stock Market, Master Thesis
The Heston-Nandi GARCH option valuation model for the DAX30 index options market, Master Thesis
The Effect of Rumors on the Stock Price of potential Target Companies, Bachelor Thesis
Analyse von Investorenreaktionen auf themenübergreifende Finanzpublikationen mittels Anwendung der latenten Dirichlet-Allokation, Master Thesis
News-based Volatility Forecasting using Machine Learning, Master Thesis
Static- and Delta Hedging of Options, Master Thesis
Topics in Ad-Hoc-News, Bachelor Thesis
An Empirical Analysis of OTC Stocks, Master Thesis
Employee satisfaction and equity returnsunder alternative statistical tests, Master Thesis
Idiosyncratic Risk Innovations and the Idiosyncratic Risk-Return Relation in the German Equity Market, Master Thesis
Financial and Legal Advisors and the Performance of M&A Transactions, Bachelor Thesis
Media Coverage of German Listed Companies, Bachelor Thesis
The effects of regulatory uncertainty on merger and acqusition activity, Bachelor Thesis
The Effect of Finntech M&A on Acquirer Stock Performance in the Financial Sector, Bachelor Thesis
Bank Risk Dynamics and Distance to Default, a Simulation based Analysis, Bachelor Thesis
Pricing VSTOXX Futures, Master Thesis
An empirical analysis of temperature shocks and the cost of equity capital, Bachelor Thesis
The Usage of Factor Models to Explain Cross-Sectional Stock Returns, Bachelor Thesis
An empirical analysis of the effects of corporate takeovers on the abnormal return to acquirer competitors, Bachelor Thesis
The release of the subsidiaries into independence: The choice between Sin-offs and Carve-outs, Bachelor Thesis
Value Drivers and Deal Characteristics in Private Equity Transaction Versus Strategic Acquisitions, Master Thesis
The Influence of Ad-hoc Disclosures on Stock Returns, Master Thesis
Isolating the disaster risk premium with equity options in the German market, Bachelor Thesis
Insider-forecasted operating synergies' impact on M&A performance, Bachelor Thesis
An Empirical Analysis of Callable Contingent Convertibles, Master Thesis
Static Replication of Window Double Barrier Options, Bachelor Thesis
The informativeness of textual tone in M&A conference calls and its effect on stock returns, Master Thesis
Comparison of Earnings Surprise Measures, Master Thesis
Intangible Assets and Mergers and Acquisitions, Master Thesis
The Effects of Expected Volatility on Stock Returns in European Equity Markets, Master Thesis
The Success of Acquiring vs. Developing Innovation in Research Intensive Industries, Master Thesis
Robustness of the Distance-to-Default Measure - A Simulation-based Analysis, Bachelor Thesis
Corporate default risk: predictive power of rule-based classifier models compared to traditional bankruptcy prediction models, Bachelor Thesis
Analyzing the bid-ask quotes for a FX broker in responds to volatility, Master Thesis
What are the Most Important Topics for Buy- and Sell-side Analysts? An Investigation of M&A Conference Calls Using Textual Analysis, Bachelor Thesis
Relationship between Exchange Rate Risk and Stock Prices, Bachelor Thesis
Hedging Performance of Volatility Products under Different Stochastic Volatility Models, Master Thesis
Empirical stylized facts of the intra-daily foreign exchange markets, Bachelor Thesis
Similarity between Firms in Domestic vs. Cross-border M&A Activities: A Text-based Approach, Master Thesis
Impact of Migration on Cross-border Merger & Acqusition Activities, Master Thesis
Stock Returns and the Impact of Anaylst Recommendations, Bachelor Thesis
Portfolio Optimization with Simulation and Backtesting of the Value-at-Risk measure, Bachelor Thesis
How Does Language in Corporate Public Disclosures Reflect Actual Firm Performance and Influence Stock Market Reactions?, Master Thesis
Investor Attention and the Cross-section of Stock Return, Master Thesis
Can Ex-Ante Observable Signals for Investor Sentiment or Company Quality Predict the Long-Term Performance of Initial Public Offerings?
An Empirical Study of the US Market for New Equity Issues, Master Thesis
Sentimental Distress: The Analysis of the Tone of 8-K Reports in a Financial Distress Context, Master Thesis
Expected Option Returns for DAX 30 Options, Bachelor Thesis
Delta-Hedged Gains and the Market Volatility Risk Premium: Evidence from the German Market, Master Thesis
The Announcement Effect of Mergers & Acquisitions on Markets and Model-Implied Credit Default Swaps, Master Thesis
Selecting Characteristics for Parametric Portfolio Selection, Master Thesis
Relationships Between Implied Volatility Indexes and Stock Index Returns, Bachelor Thesis
The Information Content of Implied Volatility based on DAX 30 Options, Bachelor Thesis
Merger and Acquisition Announcement Returns and Synergy Expectations, Master Thesis
Does competition between investment banks matter for their clients' M&A performance?, Master Thesis
Monte Carlo Methods for Pricing Asian Options, Bachelor Thesis
Deterministic Implied Volatility Functions: Empirical Tests for Dax Index Options, Bachelor Thesis
Modeling Term Structure Using Macroeconomics Factors, Master Thesis
Estimating the Link Between Default Risk and Stock Returns Using the Implied Cost of Capital, Master Thesis
Problems of Inference in Single-Firm Event Studies, Bachelor Thesis
Competition: Theoretical Concepts, Measurement Methods and their Application in the Banking Sector, Bachelor Thesis
Factor-Model-Based Priors for the Black-Litterman Model
Portfolio Optimization under a VAR Model of Return, Bachelor Thesis
An empirical investigation of valuation premia in IPOs versus acquisitions, Bachelor Thesis
Entry in the Banking Industry: An Empirical Analysis of the Effect of Mergers and Acquisitions, Bachelor Thesis
Banking competition, venture capitalists and their influence on entrepreneurial activity, Master Thesis
The effect of institutional ownership on antitakeover defense and the success of hostile takeover bids, Bachelor Thesis
M&A Transactions and the Role of Related CEO Characteristics, Bachelor Thesis
Determinants of recovery rates implied by CDS spreads, Master Thesis
The influence of the source of capital on corporate financing and investment behavior during the financial crisis 2007-2009, Master Thesis
Der Einfluss von Wettbewerb und Innovationsgrad von Industrien auf die Höhe von Fusionsprämien, Bachelor Thesis
About the Role of Credit Default Swaps in the Emergence of the 2008 Financial Crisis, Bachelor Thesis
Assessing Credit Risk Using Option-Implied Information, Master Thesis
Pricing of Callable Bonds, Bachelor Thesis
Can Michaud resampling techniques improve mean-variance portfolio optimization?, Bachelor Thesis
Porfolio Optimization and Ambiguity Aversion, Master Thesis
Portfolio Optimization Using Implied Covariance Matrix Estimates, Bachelor Thesis
Institutional cross-holdings and their effect on acquisiton decisions, Bachelor Thesis
The Limits of Arbitrage: A Literature Review, Bachelor Thesis
The Role of Correlation Between Market and Credit Risk for Certificate Pricing, Bachelor Thesis
The Role of Credit Default Swaps in the Emergence of the 2008 Financial Crisis, Bachelor Thesis
Peer influence on corporate financial policy: An application to cash flow sensitivities, Master Thesis
Implications of recent changes in banks' market risk framework: empirical examination of different methods for calculation of Value-at-Risk and Expected Shortfall, Master Thesis
The relationship between Corporate Governance and Credit Risk - A Literature Review, Bachelor Thesis,
Optimal Delta Hedging: Evidence from DAX Index Options, Bachelor Thesis
A literature review on institutional investors and competition, Bachelor Thesis
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We appreciate that you are interested in writing a thesis at the Institute of Banking and Finance. The following sections provide information on potential areas for both Bachelor and Master theses. When conducting your thesis, you will have to critically review the relevant literature and to carry out your own quantitative analysis. This requires applying software for statistical analysis (R, Matlab, or Stata). To prepare you, we offer online courses in scientific writing and an introduction to R. We are looking forward to supervising your thesis!

Bachelor theses

Master theses, general information on final theses, contact for general questions about theses, registration.

After you have been assigned to the Institute of Banking and Finance through the central allocation procedure of the Faculty of Economics and Management, you can apply for one of the topics listed below. If you have any questions, please contact Brian von Knoblauch .

Please note: Bachelor theses at our institute are always related to empirical research questions. We there strongly (!) recommend to conduct a seminar thesis at our institute and to take finance related classes.

An information session that covers organizational aspects and introduces available topics will be held on Tuesday, February 13, (Warning: Changed Date!) 2024, from 2:30pm - 4:00pm via Cisco WebEx . To join the session (via browser or app), please click here . Further information is available via this link (in German).

To choose preferences and your preferred starting date, please click here: Application form

Please also note that - to register your thesis - it is mandatory to complete our introductions to Scientific Writing and R .

Bachelor theses not related to the central allocation prodecure (industrial engineers or second attempts) can be registered throughout the whole year. Please note that we can only offer a limited number of Wi-Ing places at our institute in the upcoming summer semester 2023. Currently (as of 01.02.2024) four places are still open.

As soon as you have received your topic, you will have 2 weeks to prepare a proposal (please take into account time to revise the proposal!). On 2-3 pages, the proposal should cover the following elements:

Problem setting and objective of the thesis
Methodology and theoretical and/or conceptual approaches
Necessary data and sources for data acquisition
Expected knowledge gains for research and/or practice
Basic literature (from international, peer-reviewed journals)

After the proposal has been accepted by your supervisor, your bachelor thesis will be registered immediately.

Bachelor theses in Behavioral Finance

Theoretical part of the task:

Explain the "noise trader theory" according to De Long et al (1990).
Define the term "investor sentiment" and outline approaches to measure sentiment.

Empirical part of the task:

Investigate the impact of investor sentiment on stock market returns or anomalies.
Test the robustness of your results with respect to combinations of selected control variables. Are you results robust to subperiods?

Basic literature:

Baker, M. and Wurgler, J. (2006): Investor Sentiment and the Cross-Section of Stock Returns. The Journal of Finance, 61(4), 1645–1680.
Baker, M. and Wurgler, J. (2007): Investor Sentiment in the Stock Market. Journal of Economic Perspectives, 21(2), 129–152.
De Long, J.B., Shleifer, A., Summers, L.H., and Waldmann, R.J. (1990): Noise Trader Risk in Financial Markets. Journal of Political Economy, 98(4), 703–738.
Fisher, K.L. and Statman, M. (2000): Investor Sentiment and Stock Returns. Financial Analysts Journal, 56(2), 16–23.
Frazzini, A. and Pedersen, L.H. (2014): Betting against beta. Journal of Financial Economics, 111(1), 1-25.
Jegadeesh, N. and Titman, S. (1993): Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
Lee, W.Y., Jiang, C.X., and Indro, D.C. (2002): Stock market volatility, excess returns, and the role of investor sentiment. Journal of Banking & Finance, 26(12), 2277–2299.
Lee, C.M.C., Shleifer, A., and Thaler, R.H. (1991): Investor Sentiment and the Closed-End Fund Puzzle. The Journal of Finance, 46(1), 75–109.
Lemmon, M. and Portniaguina, E. (2006): Consumer Confidence and Asset Prices: Some Empirical Evidence. The Review of Financial Studies , 19(4), 1499–1529.
Stambaugh, R.F., Yu, J., and Yuan, Y. (2012): The short of it: Investor sentiment and anomalies. Journal of Financial Economics , 104(2), 288-302.
Kenneth French Data Library
Refinitiv Datastream
Describe the term "investor sentiment" and explain ways to measure it. In particular, address methods for text-based measurement of investor sentiment.
Provide a review of relevant literature examining the relationship between text-based sentiment measures and stock returns.
Calculate a text-based sentiment measure and explain its step-by-step derivation from raw text to final measure.
Perform a descriptive analysis of the sentiment measure.
Analysieren den Zusammenhang zwischen Ihrem hergeleiteten Stimmungsmaß und Aktienrenditen anhand von Regressionsmodellen.
Analyze the relationship between your inferred sentiment measure and stock returns using regression models.
McDonald, B. and Loughran, T. (2011): When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks. The Journal of Finance, 66(1), 35-65.
Smales, L. A. (2017): The importance of fear: investor sentiment and stock market returns. Applied Economics , 49(34), 3395-3421.
Stambaugh, R.F., Yu, J., and Yuan, Y. (2012): The short of it: Investor sentiment and anomalies. Journal of Financial Economics, Special Issue on Investor Sentiment, 104(2), 288-302.
Tetlock, P.C. (2007): Giving Content to Investor Sentiment: The Role of Media in the Stock Market. The Journal of Finance, 62(3), 1139-1168.
Refinitiv Workspace
Loughran-McDonald Master Dictionary
New York Times News Article
Separate the empirical evidence of investor participation from the assumptions of classical portfolio theory. Motivate and explain determinants of participation.
Formulate a probit model in accordance with relevant models from the literature. Introduce the probit regression.
Among other things, you will deal with estimation using the maximum likelihood method.

Empirical part of the task:

Check the developed model by means of a panel data set.
Explicitly refer to the definitions you used to create variables and describe the data set.
Perform the estimation of the probit model and interpret your results.
Grinblatt, M., Keloharju, M., and Linnainmaa, J. (2011): IQ and stock market participation. The Journal of Finance, 66 (6), 2121-2164.
Kaustia, M. and Torstila, S. (2011): Stock market aversion? Political preferences and stock market participation. Journal of Financial Economics, 100(1), 98-112.
Van Rooij, M., Lusardi, A., and Alessie, R. (2011): Financial literacy and stock market participation. Journal of Financial Economics, 101(2), 449-472.
Brooks, C. (2019): Introductory Econometrics for Finance. Fourth edition. Cambridge, United Kingdom; New York, NY: Cambridge University Press.
Polkovnichenko, V. (2005): Household Portfolio Diversification: A Case for Rank-Dependent Preferences. The Review of Financial Studies, 18(4), 1467–1502.
Malmendier, U. and Nagel, S. (2019): Depression Babies: Do Macroeconomic Experiences Affect Risk Taking?. The Quarterly Journal of Economics, 126(1), 373–416.

Data:

Explain the difference between normative and descriptive decision theories.
Introduce and explain selected static and dynamic portfolio insurance strategies.
Explain Cumulative Prospect Theory (CPT) and its role for the evaluation of portfolio insurance strategies.
Conduct a simulation study comparing different selected portfolio insurance strategies in regard to their CPT value and the corresponding expected utility (EUT). Do the decisions of CPT investors differ from an EUT investor?
Interpret your results in regards to the sensitivity of your results to the different CPT parameters. Is any parameter more important than others?
Tversky, A. and Kahneman, D. (1992): Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty , 5(4), 297-323.
Dichtl, H. and Drobetz, W. (2011): Portfolio insurance and prospect theory investors: Popularity and optimal design of capital protected financial products. Journal of Banking and Finance , 35(7), 1683-1697.
Dierkes, M., Erner, C., and Zeisberger, S. (2010): Investment horizon and the attractiveness of investment strategies: a behavioral approach. Journal of Banking and Finance, 34, 1032-1046.
Explain the Cumulative Prospect Theory (CPT) as a descriptive decision theory and outline differences from normative decision theories.
Explain how individual stocks can be evaluated as "prospects" under the CPT.
Present the model-theoretical prediction for stock returns of companies depending on their CPT value.

Quantitative part of the task:

Calculate the CPT values of all companies in a relevant sample of a stock market (e.g., US market).
Analyze the performance of companies depending on their CPT values using portfolio construction and Fama-MacBeth regressions.
Evaluate with your performance analysis whether factor models (e.g., CAPM, Fama-French Three-Factor Model) can explain these returns.
Tversky, A. and Kahneman, D., (1992 ), Advances in prospect theory: Cumulative representation of uncertainty, Journal of Risk and Uncertainty , 5(4), 297-323. Cambridge, United Kingdom.
Barberis, N., Abhiroop, M. and Baolian, W., (2016 ), Prospect theory and stock returns: An empirical test, The review of financial studies , 29(11), 3068-3107. Cambridge, United Kingdom.
Bali, T.G., Engle, R. F. and Murray, S., (2016 ), Empirical asset pricing: The cross section of stock returns, John Wiley & Sons, Cambridge, United Kingdom.

Bachelor theses in Asset Management

Define sustainability criteria (e.g. ESG) and explain the Morningstar-Sustainability-Ranking .
Give an overview of the relevant literature of performance measurements and explain common descriptive and risk-adjusted performance measurements.
Calculate and compare performance measurements for different categories of sustainability funds and a market benchmark.
Identify and interpret differences between the categories.
Bauer, R., Koedijk, K., and Rotten, R. (2005): International evidence on ethical mutual fund performance and investment style. Journal of Banking & Finance, 29(7), 1751-1767.
Brooks, C. (2019): Introductory Econometrics for Finance. Fourth edition. Cambridge, United Kingdom ; New York, NY: Cambridge University Press.
Schroeder, M. (2006): Is there a Difference? The Performance Characteristics of SRI Equity Indices. Journal of Business Finance & Accounting, 34(1-2), 331-348.
Database of Richard Stehle
Morningstar

Bachelor theses in Risk Management

Introduce in general terms the role of volatility in financial markets.
Explain the concept of Realized Volatility and provide an overview of traditional econometric forecasting models, such as Corsi's (2008) heterogenous autoregressive (HAR) model.
Explain selected machine learning methods and their estimation procedures in the context of Realized Volatility predictions.
Evaluate the predictive performance of selected machine learning methods based on a chosen data set, such as daily Realized Volatility of the S&P 500.
Compare your results with those of selected traditional econometric models and discuss your findings.

Basic literature (selection):

Corsi, F. (2008): A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174–196.
Bucci, A. (2020): Realized Volatility Forecasting with Neural Networks. Journal of Financial Econometrics, 18(3), 502–531.
Christensen, K., Siggaard, M., and Veliyev, B. (2022): A Machine Learning Approach to Volatility Forecasting. Journal of Financial Econometrics.
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013): An introduction to statistical learning: with applications in R. 2nd Edition, Springer.

Data Resources:

Oxford Realized Library
Provide an overview of the relevant literature on the forecasting of credit defaults of companies and individuals.Pay special attention to so-called P2P loans.
Identify relevant characteristics of private debtors that potentially affect the risk of credit default.
Explain the logit regression and address the marginal effects and the ROC procedure.
Set up a logit model to estimate the probability of default of personal loans.
Analyse the Lending Club data set and present the characteristics of the loans granted there.
Do you estimate the logit model set up on the basis of the data, can defaults be forecast?
Emekter, R., Tu, Y., Jirasakuldech, B., and Lu, M. (2015): Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending. Applied Economics, 47(1), 54-70.
Hull, J. (2018): Risk management and financial institutions. Hoboken, New Jersey: Wiley & Sons.
Brooks, C. (2014): Introductory econometrics for finance. Cambridge: Cambridge University Press.
Lending Club Privatkredite, via kaggle.com

Bachelor theses in Asset Pricing

Describe the momentum anomaly and explain how to construct the momentum strategy.
Note both advantages and disadvantages of the momentum strategy. In particular, focus on momentum crashes.
Outline the risk management strategies of Barroso and Santa-Clara (2015) and Dierkes and Krupski (2022).
Estimate the momentum strategy for the U.S. market over the period from 1926 to 2022.
Implement the risk management strategies of Barosso and Santa-Clara (2015) and Dierkes and Krupski (2022).
Outline both advantages and disadvantages of each strategy.
Barroso, P. and Santa-Clara, P. (2015): Momentum has its moments. Journal of Financial Economics, 116(1), 111–120.
Cooper, M.J., Gutierrez, R.C., and Hameed, A. (2004): Market States and Momentum. The Journal of Finance, 59(3), 1345–1365.
Dierkes, M. and Krupski, J. (2022): Isolating momentum crashes. Journal of Empirical Finance, 66, 1-22.
Daniel, K. and Moskowitz, T.J. (2016): Momentum crashes. Journal of Financial Economics, 122(1), 221–247.
Jegadeesh, N. and Titman, S. (1993): Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65–91.
Kenneth French's database
Derive the Capital Asset Pricing Model (CAPM) and explain why the use of additional factors can be a useful extension.
Outline the three-factor model of Fama and French (1993).
Explain the value and the size effect on which the three-factor model is built.
Calulate the risk factors yourself using monthly price data.
Analyze to which extend multi-factor models can increase the explanability of return data.
Explicitly conduct a performance test against the CAPM.
What influence do the factors of value and size have on returns? Do they match your expectations?
Fama, E. F. and French, K. R. (1993): Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3–56.
Fama, E. F. and French, K. R. (1992): The cross-section of expected stock returns. Journal of Finance, 47(2), 427–465.
Fama, E. F. and French, K. R. (2015): A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1–22.
Empirical research shows a strong negative relationship between returns and idiosyncratic volatility.
Derive why in neoclassical finance theory idiosyncratic volatility should not affect returns.
Introduce the so-called idiosyncratic volatility puzzle and provide an overview of relevant related literature. Explain possible solutions to the puzzle.
Calculate idiosyncratic volatilities for a cross-section of stocks.
Evaluate pricing effects of idiosyncratic volatility using portfolio formation and investigate whether they are significant.
Ang, A., Hodrick, R. J., Xing, Y., and Zhang, X. (2006): The cross‐section of volatility and expected returns. Journal of Finance, 61(1), 259-299.
Ang, A., Hodrick, R. J., Xing, Y., and Zhang, X. (2009): High idiosyncratic volatility and low returns: International and further US evidence. Journal of Financial Economics, 91(1), 1-23.
Bali, T. G. and Cakici, N. (2008): Idiosyncratic volatility and the cross section of expected returns. Journal of Financial and Quantitative Analysis, 43(01), 29-58.
Short-Term Reversal is one of the most distinctive anomalies in asset pricing. Explain the (short-term) reversal effect and show why this effect counteracts the weak form of the efficient market hypothesis.
Introduce to the relevant literatur.
Provide an overview of the different explanatory approaches.
Conduct an empirical analysis of the short term reversal effect using linear regression and portfolio formation.
Investigate whether the short term reversal effect can be explained by capital market models (e.g. CAPM, Fama-French three factor model).
Jegadeesh, N. (1990): Evidence of predictable behavior of security returns. Journal of Finance, 45(3), 881-898.
Jegadeesh, N. and Titman, S. (1995): Short-horizon return reversals and the bid-ask spread. Journal of Financial Intermediation, 4(2), 116-132.
Campbell, J. Y., Grossman, S. J., and Wang, J. (1993): Trading volume and serial correlation in stock returns. Quarterly Journal of Economics, 108, 905–939.
Kelly, B., Moskowitz, T., and Pruitt, S. (2021): Understanding Momentum and Reversal. Journal of Financial Economics, 140(3), 726-743.
CRSP US Stock Databases
Introduce the topic of economic uncertainty and distinguish this concept from other concepts relevant to finance such as risk and investor sentiment.
Introduce the literature on uncertainty measurement and explain the different methodological approaches. In this context, explain in detail the derivation of two selected measures.
Explain why economic uncertainty can have a theoretical impact on real and financial economics. In this context, present empirical literature that examines the relationship between uncertainty and financial markets.
Perform a descriptive analysis of the selected uncertainty measures.
Analyze the relationship between the selected uncertainty measures and stock returns using regression models.
Bloom, N. (2014): Fluctuations in Uncertainty. Journal of Economic Perspectives, 28(2), 153-176.
Brogaard, J., and Detzel, A. (2015): The Asset-Pricing Implications of Government Economic Policy Uncertainty. Management Science, 61(1), 3-18.
Jurado, K., Ludvigson, S. C., and Serena, N. (2015): Measuring Uncertainty. American Economic Review, 105(3), 1177-1216.
Knight, F.H. (1921): Risk, Uncertainty and Profit. Houghton Mifflin Company, Boston , 682-690.
Datenbank von Sydney Ludvigson
EPU Datenbank

Bachelor theses in Corporate Finance

Standard methods for calculating the cost of capital use realized returns as an approximation for expected future returns. Implicit cost of capital offer an alternative in which the estimator for the cost of capital is derived implicitly and ex ante from a valuation model.
Give an introduction into the valuation of companies.
Derive the cost of capital model according to Ohlson and Juettner-Nauroth (2005).
The cost of capital model above requires forecasts of earnings. Explain how earnings can be estimated via regression using the model of Hou et al. (2012). Additionally, address advantages and disadvantages for using estimates from analysts as alternative.
Conduct an empirical analysis of implicit capital costs at firm and market level for the German (European) stock market.
Compare the implied cost of capital estimates when using analyst forecasts and when using earnings forecasts by the model of Hou et al. (2012), respectively.
Hou, K., Van Dijk, M. A., and Zhang, Y. (2012): The implied cost of capital: A new approach. Journal of Accounting and Economics, 53(3), 504–526.
Ohlson, J. A. and Juettner-Nauroth, B. E. (2005): Expected eps and eps growth as determinants of value. Review of accounting studies, 10(2), 349–365.
CDAX/STOXX Europe 600 (from Refinitiv Workspace)
I/B/E/S Estimates

Application for master theses is possible throughout the year, i.e. there are no fixed deadlines. However, you should contact us at least 4 weeks before the desired registration date to find a topic and prepare a proposal.

Please contact Brian von Knoblauch by e-mail and include the following information:

Choose two preferences from the topics listed below.
Outline your motivation.
When is your master thesis supposed to start?
An up-to-date overview of your grades.

Subsequently, you will receive an e-mail from your supervisor (depending on the topic) to arrange an appointment. In this meeting, we will define the research question of your thesis and outline what should be included in your proposal.

As soon as you have received your topic, you will have roughly 3 weeks to prepare a proposal (please take into account time to revise the proposal!). On 2-3 pages, the proposal should cover the following elements:

After the proposal has been accepted by your supervisor, your master thesis will be registered immediately.

Brief description of the area

Investor sentiment is an important element of Behavioral Finance. Hence, there are numerous studies to analyze the impact of investor sentiment on stock markets. In addition to sentiment measures, recent studies particularly focus on the effects of sentiment on individual and aggregated stock returns. However, both are not conclusively clarified areas of research.

Possible topics (among others) are

Measuring investor sentiment: alternatives to the Baket and Wurgler (2006) sentiment Index
Investor sentiment and stock returns
Investor sentiment and the risk-return trade-off
Effects of investor sentiment on capital market anomalies

Basic literature

De Long, B.J., Shleifer, A., Summers, L.H., and Waldman, R.J. (1990): Noise Trader Risk in Financial Markets. Journal of Political Economy, 98(4), 703–738.
Baker, M. and Wurgler, J. (2006): Investor sentiment and the cross-section of stock returns. The Journal of Finance, 61(1), 1645–1680.
Kozak, S., Nagel, S., and Shrihari, S. (2018): Interpreting Factor Models. The Journal of Finance, 73(3), 1183–1223.
Yu, J. and Yuan, Y. (2011): Investor sentiment and the mean–variance relation. Journal of Financial Economics, 100(2), 367–381.
Stambaugh, R.F., Yu, J., and Yuan, Y. (2012): The short of it: Investor sentiment and anomalies. Journal of Financial Economics, Special Issue on Investor Sentiment, 104(2), 288–302.

Preferences are a behavioral approach to explain the observed deviations of individual investors' behavior from the predictions of neoclassical theory. As of now, the most important theories for decision making under risk are the (Cumulative) Prospect Theory and the Salience theory.

Portfolio insurance strategies under Cumulative Prospect Theory and Salience Theory
The salience effect on the stock market
Expected returns under Cumulative Prospect Theory
Skewness preferences and security prices
Bordalo, P., Gennaioli, N., and Shleifer, A. (2012): Salience theory of choice under risk. The Quarterly Journal of Economics, 127(3), 1243-1285.
Tversky, A. and Kahneman, D. (1992): Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty, 5(4), 297-323.
Dichtl, H. and Dobritz, W. (2011): Portfolio insurance and prospect theory investors: Popularity and optimal design of capital protected financial products. Journal of Banking & Finance, 35(7), 1683-1697.
Cosemans, M. and Frehen, R. (2017): Salience Theory and Stock Prices: Empirical Evidence. Working Paper.
Barberis, N. and Huang, M. (2008): Stocks as Lotteries: The Implications of Probability Weighting for Security Prices. American Economic Review, 95(5), 2066-2100.
Barberis, N., Mukherjee, A., and Wang, B. (2016): Prospect Theory and Stock Returns: An Empirical Test. Review of Financial Studies, 29(11), 3068-3107.

Kurzbeschreibung des Themenbereichs

Sustainability is progressively gaining prominence in investment considerations. Beyond purely financial factors, the inquiry emerges as to the impact of the environmental, social, and governance (ESG) dimensions on both corporations and investors, and how a company's ESG performance influences its returns.

Themenbeispiele

Construction and analysis of an ESG pricing factor
Estimation of the ex-ante Greenium by Implied Cost of Capital
Measurement of "Climate Change" and Analysis of the Risk Premium of Climate Change Betas or Climate Change Risks
Analysis of the Impact of Weather and Pollution on Stock Returns

Basisliteratur

Pástor, Ľ., Stambaugh, R., and Taylor, L.A. (2021): Sustainable investing in equilibrium. Journal of Financial Economics, 142(2), 550-571.
Pástor, Ľ., Stambaugh, R. F., and Taylor, L. A. (2022): Dissecting green returns. Journal of Financial Economics, 146(2), 403-424.
Ardia, D., Bluteau, K., Boudt, K., and Inghelbrecht, K. (2023): Climate change concerns and the performance of green vs. brown stocks. Management Science .
Sautner, Z., Van Lent, L., Vilkov, G. and Zhang, R. (2023): Firm-Level Climate Change Exposure. The Journal of Finance, 78(3), 1449-1498.
Sautner, Z., Van Lent, L., Vilkov, G. and Zhang, R. (2023): Pricing Climate Change Exposure. Management Science.
Loughran, T. and Schultz, P. (2004): Weather, Stock Returns, and the Impact of Localized Trading Behavior. Journal of Financial and Quantitative Analysis, 39(2), 343-364.
Ding, X., Guo, M., and Yang, T. (2021): Air pollution, local bias, and stock returns. Finance Research Letters, 39, 1-6.
Hirshleifer, D. and Shumway, T. (2003): Good Day Sunshine: Stock Returns and the Weather. The Journal of Finance, 58(3), 1009-1032.

The literature provides numerous empirical studies that contradict the predictions of neoclassical theory. In addition to proving the existence and robustness of anomalies across markets and market regimes, examining different approaches to explain the anomalies are of particular interest and can be investigated in the context of your master thesis.

Out-of-sample tests of selected anomalies (e.g. momentum, idiosyncratic volatility, betting-against-beta, max effect)
Anomalies and multi-factor models
Interaction of anomalies (e.g. skewness and momentum)
Risk management strategies and anomalies
Ang, A., Hodrick, R.J., Xing, Y., and Zhang, X. (2006): The cross‐section of volatility and expected returns. Journal of Finance, 61(1), 259-299.
Jegadeesh, N. and Titman, S. (1993): Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65–91.
Frazzini, A. and Pedersen, L.H. (2014): Betting against beta. Journal of Financial Economics, 111(1), 1–25.
Bali, T.G., Cakici, N., and Whitelaw, R.F. (2011): Maxing out: Stocks as lotteries and the cross-section of expected returns. Journal of Financial Economics, 99(2), 427-446.
Hou, K., Mo, H., Xue C., and Zhang, L. (2019): Which Factors?. Review of Finance, 23(1), 1-35.
Barroso, P., Detzel, A.L., and Maio, P.F (2020): Managing the Risk of the Low-Risk anomaly. Working Paper.
Kelly, B. T., Pruitt, S., and Su, Y. (2019). Characteristics are covariances: A unified model of risk and return. Journal of Financial Economics , 134(3): 501–524.

Although machine learning algorithms are becoming increasingly important, they have rarely been used in empirical capital market research. Thus, the comparison of new and established methods provides numerous research questions.

Empirical asset pricing and machine learning
Multi factor models and artificial neural networks
Hastie, T., Tibshirani, R., and Friedman, J. (2017): The Elements of Statistical Learning 2nd Edition. Springer Verlag.
Gu, S., Kelly, B., and Xiu, D. (2020): Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.
Gu, S., Kelly, B., and Xiu, D. (2021): Autoencoder asset pricing models. Journal of Econometrics, 222(1): 429–450.
Gareth, J., Witten, D., Hastie, T., and Tibshirani, R. (2017): An Introductoin to Statistical Learning: With Applicatoins in R. Springer Verlag, New York.
Hou, K. and Lee, J. (2018): Nonlinear CAPM Beta. Working Paper.
Dimson, E. (1979): Risk measurement when shares are subject to infrequent trading. Journal of Financial Economics, 7(2), 167-226.

Market prices of derivatives and, in particular, options provide rich information about market participants' expectations about the future. The elicitation of these expectations is possible via well-known option pricing models, such as Black & Scholes (1973), or numerous model-free approaches.

Estimation of risk-neutral moments from option prices
Option-implied risk preferences
Market indicators of volatility and skewness: VIX and SKEW
Risk premia for variance and skewness
Option pricing and estimation of the volatility surface using neural networks
Bakshi, G., Kapadia, N., and Madan, D. (2003): Stock Return Characteristics, Skew Laws, and the Differential Pricing of Individual Equity Options. Review of Financial Studies, 16(1), 101–143.
Breeden, D.T. and Litzenberger, R.H. (1978): Prices of State-contingent Claims Implicit in Option Prices. Journal of Business, 51(4), 621-651.
Jackwert, J. (2000): Recovering Risk Aversion from Option Prices and Realized Returns. The Review of Financial Studies, 13(2), 433-451.
Liu, Z. and Faff, R. (2017): Hitting SKEW for SIX. Economic Modelling, (64), 449-464.
Bollerslev, T., Tauchen, G., and Zhou, H. (2009): Expected Stock Returns and Variance Risk Premia. The Review of Financial Studies, 22(11), 4463-4492.
Carr, P. and Wu, L. (2009): Variance risk premiums. Review of Financial Studies, 22(3), 1311-1341.

Portfolio selection is one of the classic areas of research in finance. Results not only depend on investor preferences, but also on the data generating process and the investment horizon. While neoclassical models explore the optimal portfolio choice, it is equally important to apply behavioral analyses in order to understand why many people do not engange in the stock market and how investors make portfolio choices.

The optimal portfolio choice under ambiguity
The optimal portfolio choice with a long investment horizon and predictability
The influence of estimation risk on the optimal portfolio selection
Portfolio selection under behavioral decision theories
Participation in the stock market

Basisc literature

Garlappi, L., Uppal, R., and Wang, T. (2007): Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach. The Review of Financial Studies, 20(1), 41-81.

DeMiguel, V., Garlappi, L., and Uppal, R. (2009): Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy?. The Review of Financial Studies, 22(5), 1915–1953.

Barberis, N. (2000): Investing for the Long Run when Returns Are Predictable. The Journal of Finance, 55, 225-264.

Chapman, D.A. and Polkovnichenko, V. (2009): First‐Order Risk Aversion, Heterogeneity, and Asset Market Outcomes. The Journal of Finance, 64, 1863-1887.

Grinblatt, M., Keloharju, M., and Linnainmaa, J. (2011): IQ and stock market participation. The Journal of Finance, 66 (6), 2121-2164.
Kaustia, M. and Torstila, S. (2011): Stock market aversion? Political preferences and stock market participation. Journal of Financial Economics, 100(1), 98-112.
Van Rooij, M., Lusardi, A., and Alessie, R. (2011): Financial literacy and stock market participation. Journal of Financial Economics, 101(2), 449-472.
Brooks, C. (2019): Introductory Econometrics for Finance. Fourth edition. Cambridge, United Kingdom ; New York, NY, Cambridge University Press.
Malmendier, U. and Nagel, S. (2011): Depression Babies: Do Macroeconomic Experiences Affect Risk Taking?. The Quarterly Journal of Economics, 126(1), 373–416.

Although Modigliani and Miller (1958) document that - when assuming a perfect market - capital structure is irrelevant, there are numerous studies to show that this result does not hold empirically. More recent studies, such as Baker and Wurgler (2002), show that financing decisions (and thus capital structure), in particular, depend on market timing.

Empirical validation of theories on IPO underpricing
Long-term performance of IPOs
Market timing of financing decisions
Forecast of earnings and implied cost of capital

Ritter, J. R. (1991): The long‐run performance of initial public offerings. The Journal of Finance, 46 (1), 3-27.

Loughran, T. and Ritter, J. R. (2002): Why don’t issuers get upset about leaving money on the table in IPOs?. The Review of Financial Studies, 15(2), 413-444.
Ritter, J. R. and Welch, I. (2002): A review of IPO activity, pricing, and allocations. The Journal of Finance, 57(4), 1795-1828.
Green, T. C. and Hwang, B. H. (2012): Initial public offerings as lotteries: Skewness preference and first-day returns. Management Science , 58(2), 432-444.
Laeven, L. and Levine, R. (2007): Is there a diversification discount in financial conglomerates?. Journal of Financial Economics, 85(2), 331-367.
Baker, M. and Wurgler, J. (2002): Market timing and capital structure. The Journal of Finance, 57(1), 1-32.
Hou, K., Van Dijk, M. A., and Zhang, Y. (2012): The implied cost of capital: A new approach. Journal of Accounting and Economics, 53(3), 504–526.

On the following pages you will find more information about the scientific work at the Institute for Banking and Finance. Please note the formal information and the dates for the introduction to scientific work.

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Banks Made Big Climate Promises. A New Study Doubts They Work.

Using European Central Bank lending data, researchers said there was not evidence that voluntary commitments were effective in reducing emissions.

Fumes coming out of a factory in the distance, with houses in the foreground.

By Eshe Nelson

Reporting from London

Two and half years ago, bankers and investors attended the United Nations climate summit in Glasgow, an annual event normally dominated by activists and policymakers. It was considered a milestone as the financial sector agreed to put its might into tackling climate change.

Hundreds of banks, insurers and asset managers vowed to plow $130 trillion in capital into reducing carbon emissions and financing the energy transition as they introduced the Glasgow Financial Alliance for Net Zero . But a recent study, published by the European Central Bank, disputed the effectiveness of those promises.

“Our results cast doubt on the efficacy of voluntary climate commitments for reducing financed emissions, whether through divestment or engagement,” wrote economists from the central bank, the Massachusetts Institute of Technology and Columbia Business School who analyzed lending by European banks that had signed on to the Net-Zero Banking Alliance, the banking group of the Glasgow initiative.

The researchers found that since 2018 the banks had reduced lending 20 percent to sectors they had targeted in their climate goals, such as oil and gas and transport. That seems like progress, but the researchers argued it was not sufficient because the decline was the same for banks that had not made the same commitment.

“It’s not OK for the net-zero bank to act exactly like the non-net-zero bank, because we need that to scale up financing,” said Parinitha Sastry, an assistant professor of finance at Columbia Business School and one of the paper’s authors. “We want there to be a behavioral change.”

Expectations for banks from policymakers and climate activists are high. Every year trillions of dollars need to be invested in clean energy if the world is to reach net-zero carbon emissions by 2050, according to the International Energy Agency. Most of that cost will need to be financed privately, and banks are the key facilitators in those deals.

Many banks clamored to make net-zero pledges around the summit in Glasgow, known as COP26. But as pressure builds to lower emissions, climate activists are concerned about waning commitments from banks because of mounting political pressure, demand for cheap energy and shifting geopolitical alliances.

The researchers used data from the European Central Bank on lending from more than 300 European banks. Of those, about 10 percent had joined the Net-Zero Banking Alliance. They tended to be larger and lend more to high-carbon sectors like mining, particularly outside the eurozone.

The economists found that banks in the alliance did not change the interest rates on loans to firms with high emissions and that the companies that received the loans were not more likely to set decarbonization targets. In fact, all banks acted the same regardless of the methods available to them to reduce emissions, including divesting from high emitters, increasing investment to green activities and engaging with firms to cut their own emissions, Ms. Sastry said.

“It’s hard to really say from this evidence that the net-zero commitments are leading to changes in behavior by banks,” she said.

The Net-Zero Banking Alliance, which is backed by the United Nations, is among the strictest of the voluntary climate groups that banks can join. Members have committed to setting emissions targets for 2030, with interim targets for 2050, as well as promises to publish their emissions data annually.

In response to the report, the alliance said it was too early to judge their effectiveness. Members have only just begun to deliver transition plans and other progress reports, Sarah Kemmitt, the secretariat lead for the alliance, said in a statement.

“We believe it is premature to draw conclusions on whether the commitments N.Z.B.A. members banks choose to make have resulted in reductions in their financed emissions,” she said.

The banking group and similar financial coalitions have been confronting a series of challenges, especially in the face of growing backlash against green and other socially responsible initiatives in the United States. The Net-Zero Banking Alliance has been accused of watering down the commitments to appease Wall Street banks, its largest members. The alliance for insurers lost about half its members last year, and Climate Action 100+, a group for investors, suffered departures of prominent members this year.

But for some, the groups are not stringent enough.

GLS, a German bank, pulled out as a founding member of the Net-Zero Banking Alliance last year after a report by European nonprofit groups said the largest banks in the alliance had funneled $270 billion into fossil fuel expansions since they joined.

“What sense does it make to be in an alliance like that?” said Antje Tönnis, a spokeswoman for GLS. “Plus, it is a fair bit of work. Reporting is involved but doesn’t have any consequences.”

Another founding member, Triodos Bank in the Netherlands, said it hoped to strengthen the commitments.

The alliance's “updated guidelines are not strict enough and provide banks with too much leeway,” Jacco Minnaar, the bank’s chief commercial officer, said in a statement. But he acknowledged that they had improved. “We are convinced we will have the most impact within this global commitment,” he said.

Eshe Nelson is a reporter based in London, covering economics and business news for The New York Times. More about Eshe Nelson

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Reporting by Manya Saini and Niket Nishant in Bengaluru and Nupur Anand in New York; Editing by Lananh Nguyen and Stephen Coates

Our Standards: The Thomson Reuters Trust Principles. New Tab , opens new tab

Thomson Reuters

Manya Saini reports on prominent publicly listed U.S. financial firms including Wall Street’s biggest banks, card companies, asset managers and fintechs. Also covers late-stage venture capital funding, initial public offerings on U.S. exchanges alongside news and regulatory developments in the cryptocurrency industry. Her work usually appears in the finance, markets, business and future of money sections of the website.

Nupur Anand is a U.S. banking correspondent at Reuters in New York. She focuses on JPMorgan Chase, Wells Fargo and regional banks. Anand covered banking and finance in India for more than a decade, chronicling the collapse of major lenders and turmoil at digital banks and cryptocurrencies. She has a degree in English literature from Delhi University and a postgraduate diploma in journalism from the Indian Institute of Journalism & New Media in Bangalore. Anand is also an award-winning fiction writer.

A shareholder push to change the statutes of Swiss insurer Baloise has gathered more support as proxy adviser Institutional Shareholder Services (ISS) threw its weight behind the plan after its peer Glass Lewis also backed the initiative.

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Bank of Japan Governor Kazuo Ueda attends a press conference in Tokyo

BOJ's Ueda rules out responding to weak yen with rate hike

Bank of Japan Governor Kazuo Ueda said the central bank would not directly respond to currency moves in setting monetary policy, brushing aside market speculation that the yen's sharp falls could force it to raise interest rates.

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China's economy is unravelling at a pace previously thought unimaginable. Where does that leave Australia?

Analysis China's economy is unravelling at a pace previously thought unimaginable. Where does that leave Australia?

People cross at an intersection with traffic lights and cars. In the distance are high rise buildings and cranes.

Nothing quite focuses the mind like a military build-up on your back door.

A little over a week ago, the Australian government announced an $11 billion investment in new ships as part of a plan to double the size of our navy , a move directly linked to the vast increase in China's military power in recent years.

After years of defence white papers, the AUKUS agreement and growing regional tensions centred around Taiwan, the sudden urge to rebuild our defences comes as no real surprise.

But the irony is that the country openly perceived as our biggest military threat and the source of ongoing regional instability also happens to be our most important trading partner, a contradiction that has yet to be reconciled.

And while attention remains focused on military hardware, a far more immediate threat to our security is emanating from the Middle Kingdom.

China's economy is unravelling at a pace previously thought unimaginable.

Its property market rout has turned into an implosion, its stock markets are at their lowest levels in five years and international investors are abandoning the country.

While the rest of the developed world has been fighting off the worst bout of inflation in almost half a century, China's economy has turned deflationary. Consumer prices are falling, a problem that, if not arrested, can turn into an ugly, negative feedback loop.

None of this has come as a shock, as it was Beijing that put the economy into a downturn before the pandemic.

Rather, it has been the inability or unwillingness of authorities to counter the problems and take remedial action to soften the economic blow that has stunned observers and investors.

Last October, the Reserve Bank of Australia put together a paper outlining the potential threats to global growth , and particularly Australia, from the ongoing meltdown in China's property market.

Since then, the situation has further deteriorated, raising fears that the country's banking and financial system may be impacted.

An electronic board on the side of a pedestrian bridge with red stock market figures. High rise buildings are in the background.

Time is running out

Like Australia, property is the key source of household wealth in China. But that wealth is rapidly being eroded by ongoing collapses in real estate and stock markets.

In the past two years, dwelling prices have fallen about 16 per cent, with the downturn accelerating in December.

At least 62 of the 70 cities surveyed racked up price drops in December with sales, as measured by floor space, down 23 per cent on year-earlier levels, the fastest since 2015 .

According to investment bank Goldman Sachs, the market has yet to reach a low.

In research for clients, its analysts compared China's property meltdown to America's real estate collapse in 2008 which ignited the global financial crisis.

A construction site with a peeling sigh outside.

"Once started, a housing downcycle tends to last for years and policy efforts to prevent negative spillovers – foreclosures and financial stresses in the US case and developer defaults and local government spending cuts in China's case – are crucial," it said.

A major problem is the millions of uncompleted apartments that have been paid for by investors that overhang the market on which building has stalled. That overhang, and the lack of new sales, has crippled local government authorities that rely upon real estate sales, saddling them with around $US13 trillion ($19.87 trillion) in debts.

Last week, the government again cut interest rates to inject cash back into developers' coffers. But the action has done little to instil any confidence, particularly since the collapse of China Evergrande, once one of the world's biggest developers, and the debt defaults of Country Garden.

Stocks on the slide too

Across Europe, the United States and even Japan, stock markets have been punching through new records in recent months.

China has been notching up big movements as well, but in the wrong direction. Since 2021, almost $US7 trillion has flowed out of Chinese stocks in Shenzhen, Shanghai and Hong Kong with about 40 per cent of its value evaporating.

Global investment funds, including big Australian super funds, have been left nursing serious losses, especially those who plunged into the market last year in anticipation of a post-COVID boom, and many have opted to retreat.

In fact, direct foreign investment last year dropped to its lowest level in more than three decades.

This graph tells the story. Japan, Wall Street and Indian markets have been on a tear since early last year, while China's three main markets have been diving.

A graph of six lines. Three are trending upwards, three are trending downwards. The three down are China's stock indices.

Even Vanguard, the global funds manager that helped pioneer passive Exchange Traded Funds, has withdrawn from China.

The exodus has certainly worked to Japan's advantage. The Tokyo market last week finally overcame a 34-year hoodoo to scale the record it last hit in 1989 as investors have relocated their Asian portfolios, partly to take advantage of investor-friendly changes.

For China's 220 million stock market investors, the plunge has been disastrous, adding to the financial pressures wrought by slumping real estate values.

Little wonder then that Chinese consumers are socking cash away, rather than spending it. Excess savings picked up again in the December quarter as consumer prices went backwards.

A man stands in front of an electronic stock board at the Shanghai stock market. The board is lit up with red stocks.

How did it come to this?

In many respects, the economic unravelling has been an own goal.

Even before COVID and the stringent lockdown that kept the country isolated, Beijing went to war with its biggest and most powerful businesses.

Even before it took on technology barons, including Alibaba founder Jack Ma and fired a rocket into the private education sector, it had taken aim at real estate moguls.

Declaring that "houses are for living in, not speculation", President Xi Jinping attempted to take the heat out of an overheated housing market. It certainly worked.

Strict debt rules were put in place – known as the three red lines – that all but crippled developers.

Four workers drain water from a flooded construction site as a person walks past with high rise buildings in the distance.

Beijing has since wound back the rhetoric on property investment but has failed to take any meaningful steps to stop the rout.

That's led to concerns about China's financial system and particularly its shadow banking system, a largely unregulated area that has lured in large numbers of local punters.

At least one major shadow group, Zhongrong International Trust, has defaulted on loan products, raising concerns about the potential fallout on the wider financial system.

"The shadow banking sector remains a source of financial fragility in China as it is opaque, undercapitalised and has interlinkages with the wider financial system, especially banks," the RBA noted in its recent study .

One possible fix is for Beijing to take control of the beleaguered property developers and it recently ordered state-owned institutions to inject funds into the stock markets. It's a solution that may accord with its "common prosperity" mantra. So far, however, its moves have been timid and tentative.

Where does that leave us?

The biggest economic danger lies more in the impact on our export industries, particularly iron ore, should China's economy continue to deteriorate.

Unlike the US, which created a global shock when its banks teetered in the wake of its property and stock market crisis, China's banking system is not as interlinked with the rest of the world.

"The main effects of financial stress in China on Australia would likely be felt through slowing global economic activity, lower global commodity prices and reduced Chinese imports of Australian goods and services," the RBA noted.

What isn't certain is just how Mr Xi will play the politics. History is littered with the tales of militaristic leaders who, in a bid to contain a domestic problem, lash out beyond their borders.

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