semi strong market hypothesis

Efficient Market Hypothesis (EMH)

Written by True Tamplin, BSc, CEPF®

Reviewed by subject matter experts.

Updated on July 12, 2023

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Table of contents, efficient market hypothesis (emh) overview.

The Efficient Market Hypothesis (EMH) is a theory that suggests financial markets are efficient and incorporate all available information into asset prices.

According to the EMH, it is impossible to consistently outperform the market by employing strategies such as technical analysis or fundamental analysis.

The hypothesis argues that since all relevant information is already reflected in stock prices, it is not possible to gain an advantage and generate abnormal returns through stock picking or market timing.

The EMH comes in three forms: weak, semi-strong, and strong, each representing different levels of market efficiency.

While the EMH has faced criticisms and challenges, it remains a prominent theory in finance that has significant implications for investors and market participants.

Types of Efficient Market Hypothesis

The Efficient Market Hypothesis can be categorized into the following:

Weak Form EMH

The weak form of EMH posits that all past market prices and data are fully reflected in current stock prices.

Therefore, technical analysis methods, which rely on historical data, are deemed useless as they cannot provide investors with a competitive edge. However, this form doesn't deny the potential value of fundamental analysis.

Semi-strong Form EMH

The semi-strong form of EMH extends beyond historical prices and suggests that all publicly available information is instantly priced into the market.

This includes financial statements, news releases, economic indicators, and other public disclosures. Therefore, neither technical analysis nor fundamental analysis can yield superior returns consistently.

Strong Form EMH

The most extreme version of EMH, the strong form, asserts that all information, both public and private, is fully reflected in stock prices.

Even insiders with privileged information cannot consistently achieve higher-than-average market returns. This form, however, is widely criticized as it conflicts with securities regulations that prohibit insider trading .

Assumptions of the Efficient Market Hypothesis

Three fundamental assumptions underpin the Efficient Market Hypothesis.

All Investors Have Access to All Publicly Available Information

This assumption holds that the dissemination of information is perfect and instantaneous. All market participants receive all relevant news and data about a security or market simultaneously, and no investor has privileged access to information.

All Investors Have a Rational Expectation

In EMH, it is assumed that investors collectively have a rational expectation about future market movements. This means that they will act in a way that maximizes their profits based on available information, and their collective actions will cause securities' prices to adjust appropriately.

Investors React Instantly to New Information

In an efficient market, investors instantaneously incorporate new information into their investment decisions. This immediate response to news and data leads to swift adjustments in securities' prices, rendering it impossible to "beat the market."

Implications of the Efficient Market Hypothesis

The EMH has several implications across different areas of finance.

Implications for Individual Investors

For individual investors, EMH suggests that "beating the market" consistently is virtually impossible. Instead, investors are advised to invest in a well-diversified portfolio that mirrors the market, such as index funds.

Implications for Portfolio Managers

For portfolio managers , EMH implies that active management strategies are unlikely to outperform passive strategies consistently. It discourages the pursuit of " undervalued " stocks or timing the market.

Implications for Corporate Finance

In corporate finance, EMH implies that a company's stock is always fairly priced, meaning it should be indifferent between issuing debt and equity . It also suggests that stock splits , dividends , and other financial decisions have no impact on a company's value.

Implications for Government Regulation

For regulators , EMH supports policies that promote transparency and information dissemination. It also justifies the prohibition of insider trading.

Criticisms and Controversies Surrounding the Efficient Market Hypothesis

Despite its widespread acceptance, the EMH has attracted significant criticism and controversy.

Behavioral Finance and the Challenge to EMH

Behavioral finance argues against the notion of investor rationality assumed by EMH. It suggests that cognitive biases often lead to irrational decisions, resulting in mispriced securities.

Examples include overconfidence, anchoring, loss aversion, and herd mentality, all of which can lead to market anomalies.

Market Anomalies and Inefficiencies

EMH struggles to explain various market anomalies and inefficiencies. For instance, the "January effect," where stocks tend to perform better in January, contradicts the EMH.

Similarly, the "momentum effect" suggests that stocks that have performed well recently tend to continue performing well, which also challenges EMH.

Financial Crises and the Question of Market Efficiency

The Global Financial Crisis of 2008 raised serious questions about market efficiency. The catastrophic market failure suggested that markets might not always price securities accurately, casting doubt on the validity of EMH.

Empirical Evidence of the Efficient Market Hypothesis

Empirical evidence on the EMH is mixed, with some studies supporting the hypothesis and others refuting it.

Evidence Supporting EMH

Several studies have found that professional fund managers, on average, do not outperform the market after accounting for fees and expenses.

This finding supports the semi-strong form of EMH. Similarly, numerous studies have shown that stock prices tend to follow a random walk, supporting the weak form of EMH.

Evidence Against EMH

Conversely, other studies have documented persistent market anomalies that contradict EMH.

The previously mentioned January and momentum effects are examples of such anomalies. Moreover, the occurrence of financial bubbles and crashes provides strong evidence against the strong form of EMH.

Efficient Market Hypothesis in Modern Finance

Despite criticisms, the EMH continues to shape modern finance in profound ways.

EMH and the Rise of Passive Investing

The EMH has been a driving force behind the rise of passive investing. If markets are efficient and all information is already priced into securities, then active management cannot consistently outperform the market.

As a result, many investors have turned to passive strategies, such as index funds and ETFs .

Impact of Technology on Market Efficiency

Advances in technology have significantly improved the speed and efficiency of information dissemination, arguably making markets more efficient. High-frequency trading and algorithmic trading are now commonplace, further reducing the possibility of beating the market.

Future of EMH in Light of Evolving Financial Markets

While the debate over market efficiency continues, the growing influence of machine learning and artificial intelligence in finance could further challenge the EMH.

These technologies have the potential to identify and exploit subtle patterns and relationships that human investors might miss, potentially leading to market inefficiencies.

The Efficient Market Hypothesis is a crucial financial theory positing that all available information is reflected in market prices, making it impossible to consistently outperform the market. It manifests in three forms, each with distinct implications.

The weak form asserts that all historical market information is accounted for in current prices, suggesting technical analysis is futile.

The semi-strong form extends this to all publicly available information, rendering both technical and fundamental analysis ineffective.

The strongest form includes even insider information, making all efforts to beat the market futile. EMH's implications are profound, affecting individual investors, portfolio managers, corporate finance decisions, and government regulations.

Despite criticisms and evidence of market inefficiencies, EMH remains a cornerstone of modern finance, shaping investment strategies and financial policies.

Efficient Market Hypothesis (EMH) FAQs

What is the efficient market hypothesis (emh), and why is it important.

The Efficient Market Hypothesis (EMH) is a theory suggesting that financial markets are perfectly efficient, meaning that all securities are fairly priced as their prices reflect all available public information. It's important because it forms the basis for many investment strategies and regulatory policies.

What are the three forms of the Efficient Market Hypothesis (EMH)?

The three forms of the EMH are the weak form, semi-strong form, and strong form. The weak form suggests that all past market prices are reflected in current prices. The semi-strong form posits that all publicly available information is instantly priced into the market. The strong form asserts that all information, both public and private, is fully reflected in stock prices.

How does the Efficient Market Hypothesis (EMH) impact individual investors and portfolio managers?

According to the EMH, consistently outperforming the market is virtually impossible because all available information is already factored into the prices of securities. Therefore, it suggests that individual investors and portfolio managers should focus on creating well-diversified portfolios that mirror the market rather than trying to beat the market.

What are some criticisms of the Efficient Market Hypothesis (EMH)?

Criticisms of the EMH often come from behavioral finance, which argues that cognitive biases can lead investors to make irrational decisions, resulting in mispriced securities. Additionally, the EMH has difficulty explaining certain market anomalies, such as the "January effect" or the "momentum effect." The occurrence of financial crises also raises questions about the validity of EMH.

How does the Efficient Market Hypothesis (EMH) influence modern finance and its future?

Despite criticisms, the EMH has profoundly shaped modern finance. It has driven the rise of passive investing and influenced the development of many financial regulations. With advances in technology, the speed and efficiency of information dissemination have increased, arguably making markets more efficient. Looking forward, the growing influence of artificial intelligence and machine learning could further challenge the EMH.

About the Author

True Tamplin, BSc, CEPF®

True Tamplin is a published author, public speaker, CEO of UpDigital, and founder of Finance Strategists.

True is a Certified Educator in Personal Finance (CEPF®), author of The Handy Financial Ratios Guide , a member of the Society for Advancing Business Editing and Writing, contributes to his financial education site, Finance Strategists, and has spoken to various financial communities such as the CFA Institute, as well as university students like his Alma mater, Biola University , where he received a bachelor of science in business and data analytics.

To learn more about True, visit his personal website or view his author profiles on Amazon , Nasdaq and Forbes .

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11.5 Efficient Markets

Learning outcomes.

By the end of this section, you will be able to:

• Understand what is meant by the term efficient markets .
• Understand the term operational efficiency when referring to markets.
• Understand the term informational efficiency when referring to markets.
• Distinguish between strong, semi-strong, and weak levels of efficiency in markets.

Efficient Markets

For the public, the real concern when buying and selling of stock through the stock market is the question, “How do I know if I’m getting the best available price for my transaction?” We might ask an even broader question: Do these markets provide the best prices and the quickest possible execution of a trade? In other words, we want to know whether markets are efficient. By efficient markets , we mean markets in which costs are minimal and prices are current and fair to all traders. To answer our questions, we will look at two forms of efficiency: operational efficiency and informational efficiency.

Operational Efficiency

Operational efficiency concerns the speed and accuracy of processing a buy or sell order at the best available price. Through the years, the competitive nature of the market has promoted operational efficiency.

In the past, the NYSE (New York Stock Exchange) used a designated-order turnaround computer system known as SuperDOT to manage orders. SuperDOT was designed to match buyers and sellers and execute trades with confirmation to both parties in a matter of seconds, giving both buyers and sellers the best available prices. SuperDOT was replaced by a system known as the Super Display Book (SDBK) in 2009 and subsequently replaced by the Universal Trading Platform in 2012.

NASDAQ used a process referred to as the small-order execution system (SOES) to process orders. The practice for registered dealers had been for SOES to publicly display all limit orders (orders awaiting execution at specified price), the best dealer quotes, and the best customer limit order sizes. The SOES system has now been largely phased out with the emergence of all-electronic trading that increased transaction speed at ever higher trading volumes.

Public access to the best available prices promotes operational efficiency. This speed in matching buyers and sellers at the best available price is strong evidence that the stock markets are operationally efficient.

Informational Efficiency

A second measure of efficiency is informational efficiency, or how quickly a source reflects comprehensive information in the available trading prices. A price is efficient if the market has used all available information to set it, which implies that stocks always trade at their fair value (see Figure 11.12 ). If an investor does not receive the most current information, the prices are “stale”; therefore, they are at a trading disadvantage.

Forms of Market Efficiency

Financial economists have devised three forms of market efficiency from an information perspective: weak form, semi-strong form, and strong form. These three forms constitute the efficient market hypothesis. Believers in these three forms of efficient markets maintain, in varying degrees, that it is pointless to search for undervalued stocks, sell stocks at inflated prices, or predict market trends.

In weak form efficient markets, current prices reflect the stock’s price history and trading volume. It is useless to chart historical stock prices to predict future stock prices such that you can identify mispriced stocks and routinely outperform the market. In other words, technical analysis cannot beat the market. The market itself is the best technical analyst out there.

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Home > Finance > Semi-Strong Form Efficiency: Definition And Market Hypothesis

Semi-Strong Form Efficiency: Definition And Market Hypothesis

Published: January 26, 2024

Learn about semi-strong form efficiency in finance and understand its definition and market hypothesis. Discover how it impacts investment decisions.

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Semi-Strong Form Efficiency: Definition and Market Hypothesis Explained

Welcome to our finance blog post where we delve into the fascinating world of market efficiency. In particular, we are going to explore the concept of Semi-Strong Form Efficiency, a fundamental theory in finance. Have you ever wondered whether the stock market truly reflects all available information? What impact do public announcements or news events have on stock prices? We will uncover the answers to these questions and more in this article.

Key Takeaways:

• Semi-Strong Form Efficiency suggests that stock prices already incorporate all publicly available information.
• Efficient market hypothesis states that it is impossible to consistently achieve above-average market returns using only publicly available information.

What is Semi-Strong Form Efficiency?

Semi-Strong Form Efficiency is a concept that forms a significant part of the Efficient Market Hypothesis. It posits that stock prices accurately reflect all publicly available information. This means that analyzing historical market data or relying on recent news events will not provide an edge in generating consistent and above-average returns.

The theory of Semi-Strong Form Efficiency suggests that stocks adjust so quickly and accurately to new information that it becomes virtually impossible for investors to outperform the market based solely on publicly available information. Investors who attempt to beat the market by analyzing news events, company announcements, or financial statements are unlikely to consistently outperform the overall market in the long run.

To better understand this concept, let’s consider an example. Suppose a company releases its quarterly earnings report, which beats market expectations. In an environment of Semi-Strong Form Efficiency, this positive news will be quickly incorporated into the stock price. By the time the information becomes widely available, the stock price will already reflect the positive market sentiment, making it difficult for investors to profit solely from this news.

So, how does Semi-Strong Form Efficiency fit into the broader Efficient Market Hypothesis?

The Efficient Market Hypothesis (EMH) is a theory that states financial markets are efficient and that it is impossible to consistently achieve above-average market returns using only publicly available information. EMH classifies market efficiency into three forms: weak, semi-strong, and strong.

Semi-Strong Form Efficiency lies in the middle of these three forms. It posits that not only are stock prices influenced by past market data (weak form), but they also reflect all publicly available information (semi-strong form). In its strongest form, market efficiency theory suggests that stock prices also incorporate private or insider information that is not available to the public.

The Implications of Semi-Strong Form Efficiency

The theory of Semi-Strong Form Efficiency has several implications for investors:

• Efficient Market Hypothesis Challenges Active Management: As the Efficient Market Hypothesis suggests that investors cannot consistently outperform the market based on publicly available information, proponents argue that active stock picking and market timing are unlikely to lead to superior returns. This challenges the idea that professional fund managers or individual investors can beat the market consistently.
• Focus on Other Investment Strategies: In light of Semi-Strong Form Efficiency, many investors turn to other strategies that do not rely solely on publicly available information. These strategies include passive investing (such as index fund investing) and alternative investment vehicles like private equity or hedge funds that may have access to additional information sources.
• Importance of Fundamental Analysis: Although Semi-Strong Form Efficiency suggests that analyzing publicly available information may not consistently yield above-average returns, it does not render fundamental analysis useless. Understanding a company’s financials, industry trends, and competitive advantages can still provide valuable insights for long-term investment decision making and risk management.

In conclusion, Semi-Strong Form Efficiency is a critical concept within the field of finance. By acknowledging that stock prices efficiently reflect all publicly available information, investors can make more informed decisions and shape their investment strategies accordingly. While it challenges the ability to consistently outperform the market using publicly available data, it highlights the importance of alternative investment strategies and a comprehensive understanding of fundamental analysis.

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Market Efficiency

There are generally two theories to assist pricing. The Efficient Market Hypothesis (EFM) and the Behavioural Finance Theory. Understanding the limitations of each of the theories is critical. Read the three concepts on this page to have a comprehensive understanding of EFM. What are the limitations of the EMH?

Implications and Limitations of the Efficient Market Hypothesis

Weak, semi-strong, and strong.

The efficient-market hypothesis emerged as a prominent theory in the mid-1960's. Paul Samuelson had begun to circulate Bachelier's work among economists. In 1964 Bachelier's dissertation along with the empirical studies mentioned above were published in an anthology edited by Paul Cootner. In 1965 Eugene Fama published his dissertation arguing for the random walk hypothesis, and Samuelson published a proof for a version of the efficient-market hypothesis. In 1970 Fama published a review of both the theory and the evidence for the hypothesis. The paper extended and refined the theory, included the definitions for three forms of financial market efficiency: weak, semi-strong, and strong.

It has been argued that the stock market is "micro efficient," but not "macro inefficient. " The main proponent of this view was Samuelson, who asserted that the EMH is much better suited for individual stocks than it is for the aggregate stock market. Research based on regression and scatter diagrams has strongly supported Samuelson's dictum.

• Cost of Capital
• Marginal Cost of Capital
• Required Rate of Return
• Risk Free Rate
• Inflation Premium
• Default Premium
• Liquidity Premium
• Maturity Premium
• Cost of Equity
• Cost of New Equity
• Cost of Preferred Stock
• Flotation Costs
• Cost of Debt
• Sustainable Growth Rate
• Internal Growth Rate
• After-Tax Cost of Debt
• Net Asset Value
• Pure Play Method
• Unlevered Beta
• Strong Form Market Efficiency
• Semi-strong Form Market Efficiency
• Weak Form Market Efficiency
• Theoretical Ex-rights Price
• Yield to Maturity (YTM)
• Current Yield

Semi-strong form of market efficiency exists where security prices already reflect all publicly available information and it is not possible to earn excess return.

Semi-strong form of market efficiency lies between the two other forms of market efficiency, namely the weak form and strong form . A semi-strong form encompasses a weak-form which means that if a market is semi-strong efficient, it is also weak-form efficient.

When a market is semi-strong form efficient, neither technical analysis, which is based on past pattern of return, nor fundamental analysis, which incorporates current information, can help predict future price movements. However, non-public information can be used to earn above average return.

Semi-strong form of efficiency is typically tested by studying how prices and volumes respond to specific events. If price reflect new information quickly, markets are semi-strong form efficient. Such events may include special dividends, stock splits , lawsuits, mergers and acquisitions, tax changes, etc. Evidence suggests that developed markets might be semi-strong efficient while developing markets are not.

Alex held 100 shares of Cure Inc. which he had purchased on 1 January 20X3 for $25 per share. Cure Inc. is a company engaged in research and development of new antibiotics against resistant microbes. Alex is not an active investor so he does not checks the stock performance daily. On 14 January 20X2 (Sunday), he came across an article shared by his friend on Facebook. The article was published on 11 January 20X2 (Friday). According to the article, Cure Inc. has failed in a project worth a net present value of $20 million. Total outstanding shares of Cure Inc. are 5 million. Alex sold off his holding for $2,050 (at $20.5 per share) in the opening hours of 15 January 20X2 (Monday). He was glad that he minimized his loss but towards the end of 15 January 20X2, the company's stock price had even climbed to $21. He is wondering what happened.

The market seems to be semi-strong form efficient. The market had adjusted itself to the public information on Friday (11 January 20X2) as soon as the market came to know about it. Alex should not have used this public information to project a decline on Monday. The drop in price is almost equal to the net present value per share no longer available ($20 million divided by 5 million).

by Obaidullah Jan, ACA, CFA and last modified on Jul 5, 2019

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Semi-strong Form Efficiency - Explained

What is Semi-strong Form Efficiency?

Written by Jason Gordon

Updated at April 23rd, 2024

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What is Semi-Strong Form Efficiency?

Semi-strong form efficiency is a concept that suggests that the release of public news of a particular stock increases its existing stock prices. This concept is a part of the Efficient Market Hypothesis (EMH).

How Does Semi-Strong Form Efficiency Work?

Semi-strong form efficiency suggests that prices change to equilibrium levels, which are as a result of public market information on any security or equity. This theory analyses how the price of stocks increase and decrease with the presence of publicly available information. The semi-strong form efficiency theory, however, has one weakness; it is unable to explain the conditions affecting security prices on material nonpublic information (MNPI). The semi-strong form efficiency is easily the most applicable of all EMH hypotheses, as it deters the belief that technical and fundamental analysis can be used to achieve excess gains by investors. This concept goes on to suggest the use of MNPI as the only channel that could land investors big bucks and profits if they're in search of portfolios that yield more than average. The EMH theory is based solely on a 1960s Ph.D. dissertation by American Economist Eugene Fama, and it states that the prices of securities (stocks and other financial markets) at any given period in a cash market is dependent on the amount of information publicly available on that security. The EMH seems to draw most of its points from already existing researches, thus granting it some credibility up till date. This theory draws on the logic of the Random Walk theory (a theory that states that price changes are random and do not depend on any factor) to suggest that the ability to outperform a market security whose price is a reflection of its available market information is merely a matter of chance and not developed skills. In simple terms, one can compare trying to beat a market with public information to gambling. When applied to stock prices, it suggests that the market information of yesterday would in no way affect the price of stocks today, as there is new information today that takes up that role. It further states that beginners and advanced investors would be able to compete in the market if price changes were not predictable and if market information does not affect security rates. The EMH takes on three forms; the weak form efficiency, the semi-strong form efficiency, and the strong form efficiency.

Detailed Explanation of Efficient Market Hypothesis

As we stated earlier, the EMH has three forms on which it bases all its theory. The weak form EMH states that the movement of stock prices is solely dependent on the information available at that moment and non-other. In other words, the information of yesterday does not affect the security prices of today in any way. It also claims that technical analysis has no input in gathering excess returns, as history doesnt repeat itself in a random walk. The second form, which is the semi-strong form, has been defined above. The strong form of EMH, however, states that security prices are as a result of different information factors. It suggests that undisclosed private information has the same power in determining stock prices as publicly available information. It bases this argument on the fact that huge earnings in the financial market are not consistent because of the information which is not available to the public. If security prices were solely dependent on available information, then advanced investors will never record a loss. Just like all market price determinants, the EMH is not accurate at all times. This can be seen from the 2008 Financial Crisis, where investors questioned its credibility for lack of reality. They stated that if all forms of EMH had held as claimed, that the housing bubble and other crashes which came after it wouldnt have been possible. The EMH was unable to explain high volatility and market rationality. The later was observed in the way that investors were investing largely into the subprime mortgage sector even after reaching its peak (resistance point). This irrationality could not be explained by either form of EMH, even when investors where after high returns, which is a major goal of the efficiency theories. Different controversies raised around this model, as market analysts claimed that an efficient market would have modified asset prices to be on par with rational levels. 

Important Details

• The semi-strong form efficiency EMH hypotheses state that the price of a stock is dependent on its publicly available material information.
• It discredits the use of technical and fundamental analysis in predicting stock prices, arguing that the only true reflection of stock prices is dependent on material nonpublic information (MNPI).

An Illustration of Semi-Strong Form of Efficient Market Hypotheses

Let us assume that stock CSX is trading at $30 per unit, a day before it is required to release its annual financial report. Now, a rumor, at the later hours of the day, came in stating that the company has managed to have a great year with high financial returns. This rumor made the price of CSX increase to $40 per unit. However, the next day, after the financial report is made available, it is seen that the company has indeed suffered a financial decline, and this pushes the price back to $25. Now, the rumor which made the price jump to $40 is the publicly-available information, while the actual news which made the price fall back to $25 is the material nonpublic information (MNPI). If investors were to have an idea of the MNPI before its release, they would have profited a lot than they would. Also, investors that bought more stock shares at above $30 due to the rumor will suffer a loss after the MNPI is released publicly.

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St. Petersburg Paradox and Bernoulu’s Hypothesis (with diagram)

Daniel Bernoulli evinced great interest in the problem known as St. Petersburg paradox and tried to resolve this. St. Petersburg paradox refers to the problem why most people are unwilling to participate in a fair game or bet.

For example, offer of participating in a gamble in which a person has even chance (that is, 50-50 odds) of winning or losing Rs. 1000 is a fair game.

To put in mathematical terms, a gamble whose expected value is zero, or more generally, the game in which the fee for the right to play is equal to its expected value is a fair one. Thus, according to St. Petersburg in an uncertain game a most individuals will not make a fair bet or, in other words, will not play the fair game.

Daniel Bernoulli provided a convincing explanation of the said behaviour of rational individual. According to him, a rational individual will take decisions under risky and uncertain situations on the basis of expected utility rather than expected monetary value.

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He further contended that marginal utility of money to the individual declines as he has more of it. Since the individual behaves on the basis of expected utility from the extra money if he wins a game and the marginal utility of money to him declines as he has extra money, most individuals will not ‘play the game’, that is, will not make a bet. It is in this way that Bernoulli resolved ‘St. Petersburg paradox’.

A graphic illustration will make clear Bernoulli’s solution to the paradox. Consider Figure 17.1 in which on the X-axis, the quantity of money (thousands of rupees) and on the Y-axis, marginal utility of money (rupees) to an individual are measured. Suppose an individual has 20 thousands of rupees with him and can make a bet at even odd (i.e., 50-50 chance) of winning or losing rupees one thousand.

If he wins the bet, money with him will rise to 21 thousand (20 + 1) rupees. If as a result of an increase in money with him, his expected marginal utility of money declines, then the expected marginal utility of extra one thousand rupees to him which is depicted by the rectangle CDFE is less than the extra marginal utility of the previous one thousand (i.e., 20th thousand) rupees which is measured by the rectangle ABDC.

In other words, the gain in utility in case of his winning the bet is less than the loss of utility in case of his losing the bet, though the gain and loss is the same in terms of monetary amount (i.e., Rs. one thousand). Thus, given the diminishing marginal utility of money the expected gain in utility is less than the expected loss of utility from one thousand rupees involved in the bet, a rational individual will therefore not make a bet with 50-50 odds.

In case he wins the bet, his monetary gain will be Rs. 1500 which will raise his money income to Rs. 21,500 and gain in his total utility will be given by the black-shaded area and if he loses the bet, his income falls by Rs. 1000 to Rs. 19,000 and as a result he suffers a loss in total utility equal to the red-shaded area.

It will be observed from Figure 17.2 that despite a smaller loss in money terms, the loss in terms of total utility is greater than the gain in total utility despite a greater increase in money in case he wins the bet. This happened due to the rapid decline in marginal utility of money as individual’s money increases.

It may be pointed out that in our discussion aboveabout the individual’s betting it is assumed that individual derives no pleasure from gambling, that is, he does not enjoy gambling for its own sake. This is another way of saying that the individual behaves rationally in the sense that he will behave on the basis of expected gains and losses of utility from winning and losing money through gambling.

Although Bernoulli’s hypothesis that individual decision to participate in a gamble or not, depends on his expected utility rather than expected money value of the game is of crucial significance in any discussion of individual’s behaviour under risky and uncertain situations. So long as there is no upper bound on the utility function, the prize in a gamble can be appropriately adjusted so that the paradox is regenerated. Further Bernoulli’s main point that an individual considers expected utility from the extra money rather than monetary value of the gain itself has found wide acceptance among economists.

However, a major drawback of Bernoulli’s expected utility hypothesis is that it assumes cardinally measurable utility which economists today find it difficult to believe. J. Von Neumann and O. Morgenstern adopted an entirely new approach to assigning numerical values to the utilities obtained from extra money by the individuals behaving in risky or uncertain situations, such as in case of gambling and insurance and they based their method of constructing utility index (which is envied at in a different way from the cardinal measurement of utility by neoclassical economists) on the expected utility hypothesis of Bernoulli. They showed that we can analyse the choice by an individual under risky and uncertain situation on the basis of N – M utility index.

Neumann-Morgenstern Utility Concept Index under Risky Situations:

Making use of Bernoulli’s idea that under risky and uncertain prospects as in betting, gambling and purchasing lottery tickets etc., a rational individual will go by the expected utilities rather than expected money values, Neumann and Morgenstern in their now famous work ” Theory of Games and Economic Behaviour gave a method of numerically measuring expected utility from winning prizes. On the basis of such utility index, called N-M index rational decisions are made by the individuals in case of risky situations.

Thus, Neumann- Morgenstern method seeks to assign a utility number or in other words, construct N-M utility index of the total utility of money which a person gets as his stock of money wealth increases. The choices by an individual under risky and uncertain situations depend on N-M utility index (i.e. expected numerical utilities) and with changes in money income.

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• Utility Theory and Attitude toward Risk (Explained With Diagram)
• Risk Aversion and Insurance (Explained With Diagram)
• Risk Preference and Gambung: Why Do Some Individuals Gamble?
• Preference Hypothesis and Strong Ordering (Explained With Diagram)

Opinion Types on Social Media: A Review of Approaches to What Opinions Are in Social vs. Computational Science

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• Svetlana S. Bodrunova   ORCID: orcid.org/0000-0003-0740-561X 26  

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Today’s public opinion research has rapidly transformed to include opinions expressed within new forms of mediatized communication, including those on social networks and messengers. Computational communication studies have provided for new approaches in opinion mining and detection, and many new approaches to how ‘opinions’ are seen in research have emerged. The growth of the academic field that (re)conceptualizes opinion as a term, including for automated and AI-based opinion detection, has gradually turned from a spurring advantage to an inhibitory obstacle for further public opinion studies. The diversity of this field, being a problem itself, also produces scientific sub-problems that we try to address. The three of them are: (1) the widening differences between the ‘traditional’ (pre-computational) opinion studies, already highly diverse, and computational opinion mining; (2) the shared methodological basis of the computational communication studies that mostly reduce ‘opinion’ to the lexical-semantic levels of speech, largely ignoring social-group, political, cultural, temporal, and narrative aspects of opinion formation; (3) our unawareness of whether the gap between human and AI-created human-like representation of opinions can ever be overcome, and, if not, how the machine sees opinions and why they cannot reach the logic of human judgment on opinionated content. In this paper, we review the three periods of formation of opinion studies, which have brought on the ‘traditional’ (very diverse), computational, and human-like computational views on how opinions look like in academic representation. Our main focus is on how the pre-computational and computational methods of opinion detection could enrich each other and whether full similarity between opinion formulation and/or representation by artificial intelligence and human beings could ever be reached. In the conclusion, we hint on the future directions of conceptual research on the nature and essence of human and human-like opinion.

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Van Atteveldt, W., Peng, T.Q.: When communication meets computation: opportunities, challenges, and pitfalls in computational communication science. Commun. Methods Meas. 12 (2–3), 81–92 (2018)

Article   Google Scholar  

Lazarsfeld, P.F.: The People’s Choice. Columbia University Press, New York (1944)

Google Scholar  

Katz, E.: The two-step flow of communication: an up-to-date report on an hypothesis. Public Opin. Q. 21 (1), 61–78 (1957)

Noelle-Neumann, E.: The spiral of silence a theory of public opinion. J. Commun. 24 (2), 43–51 (1974)

Lutovinova, O.V.: Internet as a new ‘oral-written’ communication system. News RSPU: AI Herzen 71 , 58–65 (2008)

Kennedy, C.: Two kinds of subjectivity. In: Meier, C., van Wijnbergen-Huitink, V. (eds.) Subjective Meaning: Alternatives to Relativism, pp. 105–126. De Gruyter, Berlin – Boston (2016)

Narrog, H.: Three types of subjectivity, three types of intersubjectivity, their dynamicization and a synthesis. In: Olmen, D., Cuyckens, H., Ghesquière, L. (eds.) Aspects of grammaticalization: (Inter)subjectification and directionality, pp. 19–46. De Gruyter Mouton, Berlin – Boston (2016)

De Cock, B.: Subjectivity, intersubjectivity and non-subjectivity across spoken language genres. Span. Context 12 (1), 10–34 (2015)

Article   MathSciNet   Google Scholar  

Buchanan, J.T., Henig, E.J., Henig, M.I.: Objectivity and subjectivity in the decision-making process. Ann. Oper. Res. 80 , 333–345 (1998)

Liu, B.: Sentiment analysis and subjectivity. In: Indurkhya, N., Damerau, F.J. (eds.) Handbook of natural language processing, 2nd edn., pp. 627–666. Routledge, London (2010)

Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Lang. Resour. Eval. 39 , 165–210 (2005)

Katiyar, A., Cardie, C.: Investigating LSTMs for joint extraction of opinion entities and relations. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. vol. 1, pp. 919–929. Long Papers (2016)

Habermas, J.: Political communication in media society: does democracy still enjoy an epistemic dimension? The impact of normative theory on empirical research. Commun. Theory 16 (4), 411–426 (2006)

Jezierska, K.: With Habermas against Habermas: Deliberation without consensus. J. Deliberative Democracy 15 (1), 13 (2019). https://delibdemjournal.org/article/id/598/

Van Dijk, T.A.: Opinions and ideologies in the press. In: Bell, A., Garrett, P. (eds.) Approaches to Media Discourse, pp. 21–63. Blackwell, Oxford (1998)

Choi, Y., Breck, E., Cardie, C.: Joint extraction of entities and relations for opinion recognition. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 431–439 (2006)

Yang, B., Cardie, C.: Joint inference for fine-grained opinion extraction. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 1640–1649. Long Papers (2013)

Nigmatullina, K., Bodrunova, S.S., Polyakov, A., Kasymov, R.: Narrative communities on social networks and the roles of legacy media in them: the case of user complaints in Russian regions. In: Proceedings of the International Conference on Human-Computer Interaction, pp. 271–286. Springer Nature Switzerland, Cham (2023)

Bastos, M.T., Raimundo, R.L.G., Travitzki, R.: Gatekeeping Twitter: message diffusion in political hashtags. Media Cult. Soc. 35 (2), 260–270 (2013)

Castells, M.: Communication, power and counter-power in the network society. Int. J. Commun. 1 (1), 238–266 (2007)

Newman, M.E.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103 (23), 8577–8582 (2006)

Lippmann, W.: Public Opinion. Routledge, London (1922/2017)

Lippmann, W.: The Phantom Public. Routledge, London (1925/2017)

Katz, E., Lazarsfeld, P.F.: Personal Influence: The Part Played by People in the Flow of Mass Communications. The Free Press, Glencoe (IL) (1955)

Merton, R.K.: Patterns of Influence: a study of interpersonal influence and communications behavior in a local community. In: Lazarsfeld, P.F., Stanton, F.N. (eds.) Communications Research, 1948–9, pp. 180–219. Harper and Brothers, New York (1949)

Menzel, H., Katz, E.: Social relations and Innovation in the medical profession. Public Opin. Q. 19 , 337–352 (1955)

Noelle-Neumann, E., Petersen, T.: The spiral of silence and the social nature of man. In: Kaid, L.L. (ed.) Handbook of Political Communication Research, pp. 339–356. Routledge, London (2004)

Lassiter, M.D.: The Silent Majority. Princeton University Press (2013)

Mustafaraj, E., Finn, S., Whitlock, C., Metaxas, P.T.: Vocal minority versus silent majority: discovering the opinions of the long tail. In: Proceedings of the 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing, pp. 103–110. IEEE (2011)

Bodrunova, S.S.: Cumulative deliberation: new normativity in studying public spheres online [Kumulyativnaya deliberatsiya: nobaya normativnost’ v izuchenii publichnyh sfer onlain], Vestnik Moskovskogo universiteta, Seriya 10: Zhurnalistika 1 (48), 87–122 (2023)

Bodrunova, S.S.: The concept of cumulative deliberation: linking systemic approaches to healthier normativity in assessing opinion formation in online discussions. J. Assoc. Inform. Sci. Technol. 1–13 (2023). https://doi.org/10.1002/asi.24850

Margetts, H., John, P., Hale, S., Yasseri, T.: Political Turbulence: How Social Media Shape Collective Action. Princeton University Press (2015)

Qazi, A., Raj, R.G., Hardaker, G., Standing, C.: A systematic literature review on opinion types and sentiment analysis techniques: tasks and challenges. Internet Res. 27 (3), 608–630 (2017)

Scheufele, D.A.: Deliberation or dispute? An exploratory study examining dimensions of public opinion expression. Int. J. Public Opin. Res. 11 (1), 25–58 (1999)

Murakami, K., et al.: Automatic classification of semantic relations between facts and opinions. In: Proceedings of the Second Workshop on NLP Challenges in the Information Explosion Era (NLPIX 2010), pp. 21–30 (2010)

Carrillo-de-Albornoz, J., Aker, A., Kurtic, E., Plaza, L.: Beyond opinion classification: extracting facts, opinions and experiences from health forums. PLoS ONE 14 (1), e0209961 (2019)

Liang, P.W., Dai, B.R.: Opinion mining on social media data. In: 2013 IEEE 14 th International Conference on Mobile Data Management, vol. 2, pp. 91–96. IEEE (2013)

Păvăloaia, V.D., Teodor, E.M., Fotache, D., Danileţ, M.: Opinion mining on social media data: sentiment analysis of user preferences. Sustainability 11 (16), 4459 (2019)

Qiu, J., Lin, Z., Shuai, Q.: Investigating the opinions distribution in the controversy on social media. Inf. Sci. 489 , 274–288 (2019)

Blei, D.M., Lafferty, J.D.: Topic models. In: Text Mining, pp. 101–124. Chapman and Hall/CRC (2009)

Stoyanov, V., Cardie, C.: Topic identification for fine-grained opinion analysis. In: Proceedings of the 22nd International Conference on Computational Linguistics (CoLing-2008), pp. 817–824 (2008)

Najadat, H.M., Alzu’bi, A.A., Shatnawi, F., Rawashdeh, S., Eyadat, W.: Analyzing social media opinions using data analytics. In: Proceedings of the 2020 11th International Conference on Information and Communication Systems (ICICS), pp. 266–271. IEEE (2020)

Kobayashi, N., Inui, K., Matsumoto, Y., Tateishi, K., Fukushima, T.: Collecting evaluative expressions for opinion extraction. In: Keh-Yih, Su., Jun’ichi Tsujii, Jong-Hyeok Lee, Oi Yee Kwong, (eds.) IJCNLP 2004. LNCS (LNAI), vol. 3248, pp. 596–605. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-30211-7_63

Chapter   Google Scholar  

Toprak, C., Jakob, N., Gurevych, I.: Sentence and expression level annotation of opinions in user-generated discourse. In: Proceedings of the 48 th Annual Meeting of the Association for Computational Linguistics, pp. 575–584 (2010)

Maynard, D., Gossen, G., Funk, A., Fisichella, M.: Should I care about your opinion? Detection of opinion interestingness and dynamics in social media. Future Internet 6 (3), 457–481 (2014)

Dhawan, P., Bhardwaj, G., Kaushal, R.: Analysis and Classification of Multi-opinionated Content in the Era of Cyber Activism. In: Alexandrov, D.A., Boukhanovsky, A.V., Chugunov, A.V., Kabanov, Y., Koltsova, O. (eds.) DTGS 2017. CCIS, vol. 745, pp. 31–44. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69784-0_3

Wilson, T., Wiebe, J., Hwa, R.: Just how mad are you? Finding strong and weak opinion clauses. In AAAI 4 , 761–769 (2004)

Mikula, M., Machová, K.: Classification of opinions in conversational content. In: Proceedings of the 2015 IEEE 13th International Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 227–231. IEEE (2015)

Krippendorff, K.: Content Analysis: An Introduction to its Methodology. Sage Publications, London (2018)

Lee, M.J., Chun, J.W.: Reading others’ comments and public opinion poll results on social media: social judgment and spiral of empowerment. Comput. Hum. Behav. 65 , 479–487 (2016)

Ishida, T., Seki, Y., Kashino, W., Kando, N.: Extracting citizen feedback from social media by appraisal opinion type viewpoint. J. Nat. Lang. Process. 29 (2), 416–442 (2022)

Somasundaran, S., Wilson, T., Wiebe, J., Stoyanov, V.: QA with attitude: Exploiting opinion type analysis for improving question answering in on-line discussions and the news. In: Proceedings of ICWSM (2007)

Rajendran, P., Bollegala, D., Parsons, S.: Contextual stance classification of opinions: a step towards enthymeme reconstruction in online reviews. In: Proceedings of the Third Workshop on Argument Mining (ArgMining 2016), pp. 31–39 (2016)

Fidino, M., Herr, S.W., Magle, S.B.: Assessing online opinions of wildlife through social media. Hum. Dimens. Wildl. 23 (5), 482–490 (2018)

Alharbi, F.R., Khan, M.B.: Identifying comparative opinions in Arabic text in social media using machine learning techniques. SN Appl. Sci. 1 (3), 213 (2019)

Koopmans, R., Muis, J.: The rise of right-wing populist Pim Fortuyn in the Netherlands: a discursive opportunity approach. Eur J Polit Res 48 (5), 642–664 (2009)

Statham, P., Koopmans, R.: Political party contestation over Europe in the mass media: who criticizes Europe, how, and why? Eur. Polit. Sci. Rev. 1 (3), 435–463 (2009)

Fang, Wu., Huberman, B.A.: How public opinion forms. In: Papadimitriou, C., Zhang, S. (eds.) WINE 2008. LNCS, vol. 5385, pp. 334–341. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-92185-1_39

Scheufele, D.A., Eveland, W.P.: Perceptions of ‘public opinion’ and ‘public’ opinion expression. Int. J. Public Opin. Res. 13 (1), 25–44 (2001)

Alkhalifa, R., Kochkina, E., Zubiaga, A.: Opinions are made to be changed: Temporally adaptive stance classification. In: Proceedings of the 2021 Workshop on Open Challenges in Online Social Networks, pp. 27–32 (2021)

Liu, P., Joty, S., Meng, H.: Fine-grained opinion mining with recurrent neural networks and word embeddings. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1433–1443 (2015)

Blekanov, I.S., Tarasov, N., Bodrunova, S.S., Sergeev, S.L.: Mapping opinion cumulation: topic modeling-based dynamic summarization of user discussions on social networks. In: Proceedings of the International Conference on Human-Computer Interaction, pp. 25–40. Springer Nature Switzerland, Cham (2023). https://doi.org/10.1007/978-3-031-35915-6_3

Bodrunova, S.S., Blekanov, I.S., Tarasov, N.: ‘Opinion tree’: A method for mapping online discussions based on neural-network topic modeling and abstractive summarization [‘Derevo mneniy’: metod dinamicheskogo meppinga onlain-diskussiy na osnove neyrosetevogo tematicheskogo modelirovaniya i abstraktivnoy summarizatsii]. Accepted for publication in Monitoring Obshchestvennogo Mneniya: Ekonomicheskie i Sotsial’nye Peremeny for 2024.

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Bodrunova, S.S. (2024). Opinion Types on Social Media: A Review of Approaches to What Opinions Are in Social vs. Computational Science. In: Coman, A., Vasilache, S. (eds) Social Computing and Social Media. HCII 2024. Lecture Notes in Computer Science, vol 14705. Springer, Cham. https://doi.org/10.1007/978-3-031-61312-8_6

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What Is Weak Form Efficiency and How Is It Used?

Thomas J Catalano is a CFP and Registered Investment Adviser with the state of South Carolina, where he launched his own financial advisory firm in 2018. Thomas' experience gives him expertise in a variety of areas including investments, retirement, insurance, and financial planning.

What Is Weak Form Efficiency?

Weak form efficiency claims that past price movements, volume, and earnings data do not affect a stock’s price and can’t be used to predict its future direction.

Weak form efficiency is one of the three different degrees of efficient market hypothesis (EMH) .

Key Takeaways

• Weak form efficiency states that past prices, historical values, and trends can’t predict future prices.
• Weak form efficiency is an element of efficient market hypothesis.
• Weak form efficiency states that stock prices reflect all current information.
• Advocates of weak form efficiency see limited benefit in using technical analysis or financial advisors.

The Basics of Weak Form Efficiency

Weak form efficiency, also known as the random walk theory , states that future securities' prices are random and not influenced by past events. Advocates of weak form efficiency believe all current information is reflected in stock prices and past information has no relationship with current market prices.

The concept of weak form efficiency was pioneered by Princeton University economics professor Burton G. Malkiel in his 1973 book, "A Random Walk Down Wall Street." The book, in addition to touching on random walk theory, describes the efficient market hypothesis and the other two degrees of efficient market hypothesis:  semi-strong form efficiency and strong form efficiency . Unlike weak form efficiency, the other forms believe that past, present, and future information affects stock price movements to varying degrees.

Uses for Weak Form Efficiency

The key principle of weak form efficiency is that the randomness of stock prices make it impossible to find price patterns and take advantage of price movements. Specifically, daily stock price fluctuations are entirely independent of each other; it assumes that price momentum does not exist. Additionally, past earnings growth does not predict current or future earnings growth.

Weak form efficiency doesn’t consider technical analysis to be accurate and asserts that even fundamental analysis, at times, can be flawed. It’s therefore extremely difficult, according to weak form efficiency, to outperform the market, especially in the short term. For example, if a person agrees with this type of efficiency, they believe that there’s no point in having a financial advisor or active portfolio manager. Instead, investors who advocate weak form efficiency assume they can randomly pick an investment or a portfolio that will provide similar returns.

Real-World Example of Weak Form Efficiency

Suppose David, a swing trader , sees Alphabet Inc. ( GOOGL ) continuously decline on Mondays and increase in value on Fridays. He may assume he can profit if he buys the stock at the beginning of the week and sells at the end of the week. If, however, Alphabet’s price declines on Monday but does not increase on Friday, the market is considered weak form efficient.

Similarly, let’s assume Apple Inc. ( APPL ) has beaten analysts’ earnings expectation in the third quarter consecutively for the last five years. Jenny, a buy-and-hold investor, notices this pattern and purchases the stock a week before it reports this year’s third quarter earnings in anticipation of Apple’s share price rising after the release. Unfortunately for Jenny, the company’s earnings fall short of analysts’ expectations. The theory states that the market is weakly efficient because it doesn’t allow Jenny to earn an excess return by selecting the stock based on historical earnings data.

Investopedia does not provide tax, investment, or financial services and advice. The information is presented without consideration of the investment objectives, risk tolerance, or financial circumstances of any specific investor and might not be suitable for all investors. Investing involves risk, including the possible loss of principal.

Burton Gordon Malkiel. " A Random Walk Down Wall Street: The Time-tested Strategy for Successful Investing ," Page 100. W.W Norton & Company, 2007.

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