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Finance and Psychology – A never-ending love story?!

Behavioural Finance and its impact on the credit crunch in 2009

Master's Thesis 2009 53 Pages

Business economics - Business Management, Corporate Governance

Excerpt

Table of Contents

ACKNOWLEDGEMENT

ABBREVIATIONS

1 INTRODUCTION
1.1 Background
1.2 Research Question
1.3 Structure

2 LITERATURE REVIEW
2.1 Efficient Market Hypothesis
2.2 Behavioural Finance
2.3 From Expected Utility Theory to Prospect Theory
2.4 General Analysis Methods
2.4.1 Technical Analysis
2.4.2 Fundamental Analysis
2.5 Rational versus Irrational Investors
2.6 The Limits of Arbitrage
2.7 Principal-Agent Problems

3 METHODOLOGY

4 ANALYSIS OF BEHAVIOURAL PATTERNS
4.1 Confirmation Bias
4.2 Conservatism Bias
4.3 Overconfidence
4.4 Mental Accounting
4.5 Representativeness
4.6 Anchoring
4.7 Herding and Stock Market Recommendations

5 EMPIRICAL EVIDENCE OF BEHAVIOURAL PATTERNS
5.1 Evaluation of Experiment
5.2 General Discussion of Behavioral Patterns

6 CONCLUSION

APPENDICES

REFERENCE LIST

Abstract

In the last decades many financial crises have emerged, like the stock crash of 1987, the Asian crisis in 1997 and the global financial crisis that started in 2008. Although those crises occurred for different reasons, they all proved financial markets to be inefficient. Not all traders think rationally. Behavioural patterns cause irrationality amongst traders. Even after decades of research in this field, financial crises like the latest one in 2008 still develop out of a combination of different behavioural patterns like herding. As a consequence those patterns deserve an indepth analysis that is conducted by the author in this work.

In order to find out to what extent behavioural finance influences the decision -making process of traders and investors the seven most relevant behavioural patterns have been identified and analysed through qualitative research in form of primary research. The informal interview with the sophisticated trader Thomas Vittner serves as empirical evidence for the significance of the determined behavioural patterns. To find out, whether public investors and traders showed a herding behaviour towards analysts’ stock recommendations in the financial crisis and its recovery, quantitative research has been made by conducting an experiment. Stocks performances in relation to analysts’ recommendations were analysed and evaluated.

The author’s selected behavioural patterns are influencing traders’ and investors’ decision-making processes to a large extent as their majority trades irrationally. The herding behaviour to follow analysts’ stock recommendations only holds partially in the crisis and in the recovery phase. The results show that whereas 100% of analysts’ recommendations matched with market trends before the crisis, only 50% matched during the crisis and its recovery. People tended to follow the general signals of the market, rather than to recommendations given by analysts.

Acknowledgement

This work evolved in St. Andrews, Scotland in the summer of 2009. During the three months developing process of this dissertation, my supervisor Donnie Macleod, has always been a great support and guiding advisor for my ideas and thoughts. I wish him all the best.

Completing the Master’s Degree in St. Andrews would not have been possible without the aid of my family that supported me mentally, financially and with love throughout the whole year, above all my mother. I would like to thank them for believing in my abilities and encouraging me to pursue this postgraduate year at the University of St. Andrews.

Furthermore I would like to thank my flat mates, Julio Guerrero and Alexander Pablo Steingass, with whom I studied together in Cologne for three years and with whom I shared an incredible fourth year in Scotland. I wish them all the best for their future careers and I am sure that they are going to succeed in life, the way they plan to.

A special “thank you” goes out to Thomas Vittner. I really appreciate that he found the time and patience to do an interview with me. The outcome of the interview helped significantly in putting the empirical evidence for this dissertation together. I hope that he is going to continue to be a successful trader in the future and I wish him great success for his book “Das Trading-Coaching”.

My last gratitude goes out to Dr. Fritz W. Noelle, who found the time to proof-read my bachelor’s thesis last year. He has always been a valuable friend and I want to thank him for his enduring support throughout my academic career.

Abbreviations

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1 Introduction

What registers in the stock market ’ s fluctuations are not the events themselves but the human reactions to these events, how millions of individual men and women feel these happenings may affect the future. Above all else, in other words, the stock market is people.

(Bernard Baruch)

1.1 Background

In the last decades many financial crises have emerged, like the stock crash of 1987, the Asian crisis in 1997 and the global financial crisis that started in 2008. Although those crises occurred for different reasons, they all proved financial markets to be inefficient. Not all traders think rationally, as the statement, given by Bernard Baruch who is a highly successful stock trader and former advisor to American presidents, indicates psychology has a significant impact on the decision-making process. Behavioural patterns cause irrationality amongst traders.

In the 1970’s, Fama French’s Efficient Market Hypothesis reached its height of dominance. The idea that stock prices always fully incorporate all available information matched very well with the theoretical approaches at that time. In the 1980’s excess volatility entailed that traders and scholars of psychology and finance started to question Fama French’s approach. In the 1990’s academic discussion shifted away from econometric analyses towards developing models of human psychology and how they relate to financial markets.

Even after decades of research in this field, financial crises like the latest one in 2008 still develop out of a combination of different behavioural patterns like herding. As a consequence those patterns deserve an in-depth analysis that is conducted by the author in this work.

1.2 Research Question

The inefficiency of markets leads to the main purpose of this master’s dissertation. This work seeks to find the answer to the question: “To what extent does behavioral finance influence traders’ and investors’ decision making processes?” The current financial crisis leads to a further question that is analysed in this work: “Does the traders’ and investors’ herding behaviour to follow analyst’s stock recommendations still hold in the financial crisis?

Research methods used in order to answer these questions are inductive as well as deductive. The information needed to answer the above mentioned questions appropriately are gathered through quantitative and qualitative research.

1.3 Structure

The second chapter first contrasts the classical Efficient Market Hypothesis (EMH), which was introduced by Fama French in the 70’s with neoclassic behavioural finance that was first analysed by Kahneman, Tversky and Slovic in the 80’s. EMH argues that market prices that fully reflect all available information are called efficient. Contrary behavioural finance argues that some asset prices are deviations that brought about the presence of irrational traders and therefore mispricings are not immediately corrected by rational traders.

The next subchapter leads from expected utility to prospect theory. The utility function is based on the assumption that an individual pursues to select an alternative out of a series of choices with the highest expected utility for him. Prospect theory argues that possible outcomes are heuristically ranked with the result that low results are seen as losses and higher results as gains. Subsequently the alternative with the highest prospect is then selected.

Chapter two furthermore provides an in-depth analysis of the two contrary analysis tools that provide the basis for traders’ and investors’ decisions. The aim of technical analysis is to filter necessary information from the past performance of an asset, in graphic form, in order to determine the future value of an asset. Fundamental Analysis studies a company’s financial strength, based on historical data; sector and industry position; management; dividend history; capitalization; last its potential for future growth.

The last part of the literature review presents the two types of traders that are present on the market. Rational traders or arbitrageurs trade to ensure that if a security has a perfect substitute, which generates the same returns, then both securities have the equal price. Irrational traders or noise traders have different beliefs and therefore trade through noise they perceive. Noise trading makes financial markets possible as its whole structure is based on liquid markets in the shares of individual firms, but it also makes them imperfect.

The third chapter outlines the methodologies the author to collect the necessary information in order to tackle the research questions. The author uses qualitative as well as quantitative research methods with the aim of pursuing an inductive and deductive strategy.

In the fourth chapter, the seven main behavioural patterns that influence the decision-making process of traders and investors, namely confirmation bias, conservatism bias, overconfidence, mental accounting, representativeness, anchoring and herding are identified and analysed.

The fifth chapter presents empirical evidence of the determined behavioural patterns in chapter four. This deductive approach is conducted with an interview of Thomas Vittner, a sophisticated Austrian trader (see Appendix 5). His most relevant statements for this work are covered in the discussion part of this chapter. The second research question is answered by an experiment the author conducted. Its results are presented in this chapter.

The last chapter concludes this master’s dissertation. It provides the answer to the research questions by outlining the main aspects of this work.

2 Literature Review

The Literature Review chapter serves to submit a general knowledge about the most important theories and hypotheses dealing with the master’s dissertation topic. It forms the basis for the empirical evidence part of this work.

2.1 Efficient Market Hypothesis

Eugene F. Fama was the first to introduce the Efficient Market Hypothesis (EMH) in his influential article in the Journal of Finance in 1970. By his definition it is “the ideal is a market in which prices provide accurate signals for resource allocation: that is, a market in which firms can make production-investment decisions, and investors can choose among the securities that represent ownership of firms’ activities under the assumption that security prices at any time “fully reflect” all available information. A market in which prices always “fully reflect” available information is called efficient.” In order to prove whether the EMH holds Fama used the weak form test, where only historical prices are taken into consideration, the semi-strong form test, where he tested if prices efficiently adjust to other information that is available to the public and finally the strong form test which includes whether investors or groups have monopolistic access to any information relevant for price formation. In any of these cases it is impossible for investors to have a higher return than the market. (Fama, 1970) A more in-depth analysis of the EMH is beyond the scope of this work.

Fama’s assumption is based on the fact that investors are rational and hence in terms of market inequalities act as arbitrageurs to bring the market equilibrium back. In his work “Noise ” , published in the Journal of Finance 1986, Fischer Black introduced a second type of traders. Beside the known rational traders which are the basis of Fama’s work in the 70s, Fischer Black argues that there are noise traders. On the one hand noise is the reason for markets to be inefficient, but on the other hand often simultaneously prevents people from taking advantage of inefficiencies. He argues that noise is the peoples’ belief to trade on information, while they are in fact trading on noise rather than information. He calls them irrational traders. A more comprehensive analysis of both types is given in a later chapter. (Black, 1986)

By the start of the twenty-first century economists began to believe that the stock-price determination also included psychological and behavioural elements, always with the use of technical and fundamental analysis. These two general analysis methods are described in more details in yet a later chapter. (Malkiel, 2003)

2.2 Behavioural Finance

Under the above mentioned EMH actual prices reflect fundamental values. It states that no investment strategy can earn superior average returns than guaranteed for the risk taken by the investor.

“Behavioural finance argues that some features of asset prices are most plausibly interpreted as deviations from fundamental value, and that these deviations are brought about by the presence of traders who are not fully rational.” The theory of behavioural finance argues that mispricing not necessarily means that it is immediately corrected by rational traders. Strategies designed to correct mispricing can be to too costly and risky so that they are not interesting anymore. Thus, the mispricing remains. (Barberis & Thaler, 2005)

Nofsinger argues that “Behavioural Finance studies how people actually behave in a financial setting. Specifically, it is the study of how psychology affects financial decisions, corporations, and the financial markets.” (Nofsinger, 2002)

The emergence of behavioural finance goes back to 1982 with the work of Kahneman, Slovic, and Tversky and their book “Judgment under uncertainty ” where the authors have presented several behavior patterns that influence investment decisions. In the last decades as the subject became more popular, scholars in the field of psychology and economics discovered some new behavioural patterns. The early works of Kahneman, Slovic, and Tversky as well as later findings are discussed more in-depth in a later chapter.

2.3 From Expected Utility Theory to Prospect Theory

Beginning with the early work of Bernoulli in 1738, first reviewed and continued by Oskar Morgenstern and John von Neumann 1944 in their book Theory of Games and Economic Behaviour and then by Milton Friedman and L.J. Savage in 1948 the Expected Utility Theory (EUT) dominated the analysis of decision-making under risk and was generally accepted as a model of rational choice.

The utility function is based on the assumption that an individual pursues to select an alternative out of a series of choices with the highest expected utility for him. There are four axioms of the EUT that define a rational decision from an individual. The first one is called Completeness and says that either choice A is better than choice B, B is better than A or choice A equals choice B. The second axiom is Transitivity which provides the assumption that if an individual prefers choice A over choice B and B over C then he also must prefer A over C. The third one, Independence, states that the preference order of two gambles mixed with a third one maintains the same preference order as when the two are mixed independently. The last axiom Continuity says that even if the preferences are A over B and B over C that there are possible combinations of A and C to equal B.

Impartially from the expected utility formulation investors can be classified as risk-neutral, risk- averse and risk-seeking. The majority tends to be risk-neutral. To give a simple example: people tend to accept $400 with certainty rather than an equal chance of gaining $600 or $200. Friedman and Savage furthermore argue that in some cases there are anomalies in the behavior of individuals. They call it the insurance and gambling effect. When considering insurances many people are willing to pay a small amount (insurance premium) in order to prevent larger losses that only have a very small probability without getting any expected return for the insurance premium. The opposite is referred to as the gambling effect where people pay a small amount (lottery ticket) in order to get a large amount (the prize) with a very small probability.

In general it can be said that EUT states that when choosing among alternatives no matter if risk is involved or not, an individual has a set of preferences that have a numerical value attached to it depending on the utility. The individual chooses in accordance with a system of preferences that have been mentioned above in the four axioms. (von Neumann & Morgenstern, 1944) (Friedmann & Savage, 1948)

In their influential article of 1979 Kahneman and Tversky introduced a psychologically more realistic alternative to the EUT. They argued that the theory as “it s commonly interpreted and applied is not an adequate descriptive model”. Their alternative is called prospect theory. In order to prove the EUT which weighs the utilities of outcomes by their probabilities as wrong, the authors did a survey with students in which their preferences systematically violated the above mentioned principle. The number of respondents who answered each problem is noted by N, and the percentages that choose each option is given in brackets. The test was designed as follows:

Problem 1: Choose between

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Problem 2: Choose between

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In these two problems that are connected together over three-quarter of the respondents do not follow the EUT as they violate one axiom. The axiom states that if B is preferred over A then any mixture of B must be preferred to the mixture A. The respondents did not follow this axiom.

Another interesting scenario where the axiom is violated is the following:

Problem 7: Choose between

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Problem 8: Choose between

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In problem 7 the respondents chose the prospect where winning is more likely to occur, whereas in problem 8 where the probabilities of winning are fractional respondents tend to choose the prospect that offers the larger gain. Consequently the above mentioned problems provide attitudes toward risk that cannot be captured by the EUT.

Kahneman and Tversky did a second test by reversing the signs of the outcome. The results are that in terms of losses the majority of respondents were risk-seeking. For example if we consider Problem 7’ this result becomes clearer.

Problem 7’: Choose between

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To summarize the second test Kahneman and Tversky found out that in each of the problems the preference between negative prospects is the mirror image of the preference between positive prospects. They called this finding the reflection effect. The test showed that people prefer high expected value and small variance. Certainty increases the desirability of gains and the aversiveness of losses.

The incurred prospect theory argues that the decision-making process is divided in two steps called editing and evaluation. First of all the possible results are heuristically ranked. Similarities and reference points are determined, with the result that low results are seen as losses and higher results as gains. Subsequently based on the potential results and their probability values are linked with these points. The alternative with the highest prospect is then selected. (Kahneman & Tversky, 1979)

2.4 General Analysis Methods

When it comes to forecasting markets analysts prefer to use certain data to predict the movement in the market. The most popular analysis methods are the technical analysis where past stock price movements of a stock are analyzed and the fundamental analysis where ratios are taken into consideration in order to forecast the movement of the stock.

2.4.1 Technical Analysis

“Technical Analysis is the science of recording, usually in graphic form, the actual history of trading (price changes, volume of transaction, etc.) in a certain stock or in the Averages and then deducting from that pictured history the probable future trend.”

Some people say that by using technical analysis and being provided with enough accurate data you can ignore the company’s financial fundamentals as well as the industry completely. (Edwards & Magee, 2001)

The aim of technical analysis is to filter the regularities in the time series of prices by taking out nonlinear patterns from manipulated data like noise. Identifying key price movements, in order to form specific patterns of a stock and filtering out random fluctuations is crucial to compute a sound technical analysis. The main benefit of this analysis is that in most cases the human eye can perform this differentiation quickly and accurately. (Lo, Mamaysky, & Wangm, 2000)

A very basic item of information provided for individual stocks by the stock markets are price fields which define a security’s price and volume. On an ordinary trading day you can use the following information for your technical analysis:

Open: This is the price of the first trade of the day for a certain stock High: This is the highest price the stock was traded on the day Close: This is the last price that the stock was traded on the day Volume: This is the number of shares that were traded during the period. The relationship between prices and volume is important.

Although the collected data are very simple it is possible to draw charts (e.g. bar charts) and to interpret the results. (Achelis, 1995) Generally technical analysis is based on three premises:

The first premise is that the market action discounts everything. This has to be taken for granted and means that anything that can affect the stock price fundamentally, psychologically or politically has already been included in the stock price and as a consequence a study of the price action is all that is required. It goes back to the economic roots of supply and demand and is the basis for all fundamental forecasting. When prices rise for example this means that the market is bullish and contrary to the one when prices go down and the market is bearish. By studying price charts the investor tries to find out in which way the market will move.

This leads us to the second premise. The purpose of chart reading is to identify trends at an early stage to go with the waves as long as possible to create higher returns. In theory technicians believe that it is more likely that the trend continues than that it changes direction. This most applies to trend-followers, who identify and follow existing trends.

The third and last premise stated by Murphy is that history repeats itself. If somebody does not believe in this, then technical analysis will not make sense for him. Charts are based on the pattern of human psychology which tends not to change. Murphy argues that “the key to understanding the future lies in the study of the past, or that the future is just a repetition of the past.” (Murphy, 1998)

The building blocks of technical analysis go back to William Peter Hamilton who applied the Dow Theory (named by Charles Dow, founder and editor of The Wall Street Journal) and enhanced it over the period 1902 - 1929. By analyzing the stock market in that period Hamilton verified the Dow Theory that simply argues that focusing on the movements of the Dow Jones Industrial Average will determine the movement of the market in the future. The classic theory developed by Dow argues that the market always has three parallel movements. The primary movement has the longest observation period and Hamilton also calls it the “main movement”. Here the major trend of the stock is evaluated and may includes a time frame from less than one year up to several years. The secondary movement or “medium swing” can last from a few days up to three months. The third movement is also called the “short swing” that considers the development of a stock in the last hours up through some days.

The Dow Theory conforms to the above mentioned EMU in terms of the theory that the stock price incorporates any new available information. Furthermore Dow and Hamilton argue that trends are confirmed by volume in the short term and the above mentioned primary movement in the long term. They represent the true market view. If the traded volume of a stock increases significantly price movements are to be expected. (Hamillton, 1998)

After Hamilton’s death in 1929 many scholars concentrated on analyzing the theory further. Cowles provided evidence in his article “Can Stock market forecasters forecast?” from 1933 that out of 100 assumptions of the market direction given by Hamilton only approximately half of his position changes proved to be right. Cowles argued that flipping a coin whether to buy or sell a stock would have had the same effect as using the Dow Theory. He analyzed that a buy-hold strategy of a well diversified portfolio would have given him an average annual return of 15.5% instead of a 12% using Hamilton’s advice during the same time frame. (Cowles, 1933)

In 1998 Brown, Goetzman and Kumar analyzed both approaches with today’s widely known risk adjustment methods that both Hamilton and Cowles could not have known at that time. They found out that although Cowles’ portfolio would have done better, measured by riskiness and volatility the Dow Theory Portfolio had higher risk-adjusted returns as both figures were lower than in Cowles’ portfolio. (Brown, Goetzmann, & Alok, 1998)

One of the greatest benefits of technical analysis is its adaptability to any trading medium and time dimension. By observing many markets Murphy argues that technicians have the “big picture” and feel what markets are doing in general, rather than having a very narrow view that he calls the “tunnel vision”. As some markets tend to have built-in economic relationships and react to similar factors, price action in one market could indicate future movements in other markets.

Fundamental and technical analysis mostly differ from each other in the early stage of an important market movement as at that stage fundamentals do not support or explain what the market seems to be doing. Murphy’s explanation is that “market price acts as a leading indicator of the fundamentals.” This means that the technical analysis includes the fundamentals and is therefore, if only one approach has to be chosen, the preferred one used by analysts to determine the initial direction of markets. (Murphy, 1998)

2.4.2 Fundamental Analysis

The question whether technical or fundamental analysis is the better approach depends on the individual, who needs to determine his/ her natural behavior. In the 70’s fundamental analysis was considered as the main tool for analysts. This changed during the huge price trends in the commodity inflation period at the end of the 70’s. Whereas trend-following systems developed by technicians worked quite well at that time, fundamental analysis often led to wrong decisionmaking. In the 80’s technical analysis was seen as the primary tool for investors, but this changed again in 1987, when the stock market crash penalized trend-following systems. At that time it seemed that portfolio managers achieving the best performances were those who were primarily fundamentally oriented. (Schwager & Turner, 1997)

Fundamental Analysis is defined as “the study of a company’s financial strength, based on historical data; sector and industry position; management; dividend history; capitalization; and the potential for future growth.”

The starting point of a fundamental analysis is the financial statement of a company. It provides a first view on the past performance in order to confirm an existing trend or to show deviation from that trend. It is a long-term tendency that reflects how a company’s’ financials change, how related accounts emerge and how an already established direction might change in the future. (Thomsett, 2006)

In order to believe in fundamental analysis an investor needs to be rational and not trading on noise. The crucial point here is not to be biased by gossip and to evaluate figures in an objective manner. This implements that by using fundamental analysis it is very likely that you cannot beat the average market performance as same data are provided to every investor about a company. When using this approach it is useful to pursue a buy-hold strategy, hence it indicates whether the corporation’s basis is solid. One of the main benefits of fundamental analysis is that you can test, whether a stock is under or overpriced in comparison to the financial figures. If it is underpriced, it might be a good time to buy and if overpriced a good time to sell the stock respectively. (Thomsett, Mastering Fundamental Analysis: How to sport trends and pick winning stocks like pros, 1998)

Scholars of finance have proved in numerous empirical tests that fundamental-to-price ratios have a positive correlation towards the future development of a stock. The most common ones are described below:

The Dividend Payout Ratio (DPR) measures the proportion of earnings that a company pays out to their shareholders in form of dividends. This ratio is normally expressed as a percentage.

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The Dividend Yield Ratio (DYR) relates the cash return from a share to its current share price. This ratio helps investors to assess the cash return on their pursued investment. Like the ratio mentioned above this ratio is expressed in percentage, too.

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In the UK, investors that receive a dividend from a business also receive a tax credit (t). This tax credit can be offset against any tax liability arising from the dividends received.

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Details

Pages
53
Year
2009
ISBN (eBook)
9783656270577
ISBN (Book)
9783656271512
File size
26.7 MB
Language
English
Catalog Number
v200201
Institution / College
University of St Andrews
Grade
1,2
Tags
Behavioural Finance Finance Behavioral Finance Psychology

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Title: Finance and Psychology – A never-ending love story?!