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Masterarbeit, 2009, 55 Seiten
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.
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)
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.
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
Abbildung in dieser Leseprobe nicht enthalten
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)
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.
“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)
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 decision-making. 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.
DPR = Abbildung in dieser Leseprobe nicht enthalten x 100
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.
DYR = Abbildung in dieser Leseprobe nicht enthalten x 100
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. Investors may wish to compare the returns from shares with the returns from other forms of investment. By using the DYR investors can “gross up” those other forms of investment to make a comparison.
The Earnings per Share (EPS) ratio evaluates a company’s earning power. In order to compute the ratio the financial statement is used to collect the relevant data. (Atrill & McLaney, 2008)
Malkiel argues that 40 percent of the “variance of future returns for the stock market as a whole can be predicted on the basis of the initial dividend yield of the market index”. Until the 1980s empirical data computed by Malkiel proved that investors have earned a higher rate of return from the stock market if they purchased stocks with relatively high dividend yield. However, since the 1980s, dividend yields indicated very low predicted returns and proved wrong more and more. This could be due to the fact that companies nowadays are more likely to institute a share repurchase program rather than increase their dividends. (Malkiel, 2003)
Although the dividend yield is a very useful ratio it should be considered with care. Fluck, Malkiel and Quandt found out, that investors who simply purchase a portfolio of individual stocks with the highest dividend yields in the market will not earn superior rates of return. They proved this by putting the ten most successful stocks of the Dow Jones Industrial Average measured by dividend yield into one portfolio and found out that during 1995-1999 the portfolio underperformed the market averages. (Fluck, Malkiel, & Quandt, 1997)
Investment analysts regard the EPS as a fundamental measure of share performance. The investment potential of a share is expressed by the trend of the above mentioned ratio over time.
EPS = Abbildung in dieser Leseprobe nicht enthalten
By ordinary shareholders increasing their investment in the business, it is, on the one hand, possible to make total profit rise, but on the other hand this will not necessarily mean that the profitability per share will rise, too. Investment analysts usually do not compare the EPS of one business to that of another as differences in the equity constituents can render comparisons meaningless.
The Price-Earnings ratio (P/E) relates the share price to the above calculated EPS. Expressed in a formula it looks as follows:
P/E = Abbildung in dieser Leseprobe nicht enthalten
A relative low P/E indicates that a stock is “cheap”, whereas a high P/E shows that a stock is unfavourable. Interest rates, the P/E of comparable stocks and P/E averages of historical data are used as a standard of comparison. Especially fast growing companies tend to have even higher price increase potential, although they have a high P/E. This particularly holds if contrary to the market participants’ opinion, higher corporate profits occur despite those already absorbed in the current share price.
P/E can be helpful when comparing different businesses as they provide useful information on market confidence concerning the future. However different accounting policies can lead to different profit and EPS figures. Consequently this can distort comparisons. (Atrill & McLaney, 2008)
In his article, published in the Journal of Finance in 1977, Basu analysed the correlation between P/E and the development of the stock price. He showed this correlation by empirical evidence. By analyzing different portfolios with low and high P/E from 1957 to 1971 his studies proved that portfolios with low P/E performed better than P/E with high valuation. P/E ratio information was not “fully reflected” in security prices. This leads to a violation of Fama’s above mentioned EMH. (Basu, 1977)
To conclude this chapter of comparing technical versus fundamental analysis some key points have to be mentioned.
Technical analysis concentrates on the study of market action, whereas fundamental analysis focuses on the economic forces of supply and demand. Both approaches try to solve the problem of market direction, but from different angles. “The fundamentalist studies the cause of market movement, while the technician studies its effect. “
The fundamental approach determines the intrinsic value of the market. If this value is below the current stock price then the stock is overpriced and vice versa. Technical traders are free to pick their stocks and choose their markets, while fundamentalists, who specialize in only one group, lack flexibility. Technicians have the “big picture” as they follow many different markets and hence develop a certain intuition of market directions in a broader view. (Murphy, 1998)
Rational investors are called arbitrageurs. By simple definition, arbitrageurs are “simultaneously purchasing and selling the same, or essentially similar, securities in two different markets for advantageously different prices. “Arbitrageurs are crucial market participants as their purpose is to bring prices to fundamental values and keep markets efficient. (Shleifer & Robert, 2005)
They trade to ensure that if a security has a perfect substitute that generates the same returns then both prices are equal. If the price of the security falls below the price of the substitute, arbitrageurs sell the substitute and buy the security until the prices are equal again, and vice versa. If the substitute is exactly the same then arbitrageurs face a riskless profit and as a consequence provide perfect elasticity of demand for the security at the price of its substitute portfolio. This works particularly well in the derivative market, but not for stocks and bonds as a whole as they do not have close substitute portfolios. The corollary is that mispriced portfolios no longer offer riskless hedging for arbitrageurs. (Summers & Shleifer, 1990)
Most arbitrageurs are agents for investors; do not manage their own money and borrow money and securities from intermediaries to put on their trades. As prices can move against them through noise traders for example, they face the risk of liquidation. This can happen if prices are driven down by pessimistic noise traders and arbitrageurs have to liquidate their position before the price recovers for instance. Another possibility is: if noise traders are very optimistic and put the price up arbitrageurs must take a position that accounts for the risk of a further price increase, when they have to buy back the asset. Their horizon is very short; this means that they only use a limited time frame for their trades as market equilibrium is usually redressed rapidly. (Shleifer, Inefficient Markets: An Introduction to Behavioral Finance, 2000)
Irrational investors are called noise traders. This term was introduced by Black in his article from 1986. But why do people trade on noise? He argues that one possible answer is that they like to do it and the second one is that they do not know that they actually trade on noise. 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. It is based on the fact that investors have different beliefs through different noise they perceive. If both parties involved in a trade had the same perception, no party would be willing to take the counter part of the trade. Noise traders think that they trade on information. In most cases they actually trade on noise. It is sometimes difficult to distinguish between information traders and noise traders. Information traders can never be sure if in reality they trade on noise as information is already absorbed by the share price. Black argues that there will always be an ambiguity about who is an information trader and who is a noise trader. (Black, 1986)
In the 1950s and 1960s irrational traders could not yet be considered as a problem for market efficiency. Milton Friedman and Eugene Fama both pointed out that irrational investors are met in the market by rational arbitrageurs, who trade against them. How this exactly works will be explained in the next chapter.
In a model developed by De Long, Shleifer, Summers and Waldman in 1989, the scholars found out that noise traders can earn higher expected returns solely by bearing more risk that they themselves create and as a consequence leave risk-averse arbitrageurs out of the trade. If noise traders overestimate returns or underestimate risks, they tend to invest in risky asset and drive prices up. As already mentioned, this occurs to their false belief that they have special information about the future development of an asset. This belief can emerge from false signals from technical analysts, stock brokers, or economic consultants. (De Long, Shleifer, Summers, & Waldmann, 1990)
As already mentioned in the previous chapter, arbitrage is not always riskless. In reality some problems can occur for arbitrageurs that limit their profit or even lead to losses. One problem that limits arbitrage is called fundamental risk. It is simply the risk that an arbitrageur is wrong about the position he is in. For instance, if stocks are selling over the expected value of dividends, an arbitrageur sells them short and automatically faces the risk that his evaluation might be wrong. It could be that the realization of dividends in fact is better than expected, which would lead to a loss for an arbitrageur. Fear of such losses limits the original position and might reduce his position. As a consequence he lacks liquidity to drive the price down to fundamentals again. Beside psychology, the limits of arbitrage are the building blocks of behavioural finance.
If we suppose arbitrageurs had an infinite horizon then they could sit these losses out until the equilibrium is recovered. In reality, arbitrageurs have a very short horizon. As efficient arbitrage requires high capital coverage to move the market back, it is very likely that in most cases they do not trade on their own money but borrow cash and securities to implement their trades, and as a result must pay the lenders fees. These fees cumulate over the time of the open position and can add up to large amounts. As already explained in the previous chapter arbitrageurs sometimes do not always fully trade with their own fortune, but also manage other people’s money. This leads to the principal-agent problem that is discussed in the next chapter. A further aspect, that indicates the short horizon of arbitrageurs, is that money managers’ performance is evaluated usually once every few months but at least once a year and the fear of losses limits their attitude towards holding positions for a long horizon.
We already determined that arbitrageurs know that noise traders are in the market and that their perception can vary greatly from each other and from the arbitrageur’s point of view. This noise trader risk results in margin risks for rational traders due to the time shift of redressing the equilibrium. If a position moves against them, just when the potential returns are increasing, they find themselves having to reduce their exposure to meet the margin payment. Liquidity is crucial in this case as a short, but large upcoming imbalance could force arbitrageurs to close their position with the knowledge that the price will nevertheless move back to the equilibrium in the near future. (Summers & Shleifer, 1990)
Montier argues that “arbitrage is the province of a relatively small number of highly sophisticated and specialized investors…. running funds for others.” Scholars have recognized that the separation between principals and agents can affect market dynamics. Montier presents some important implications for arbitrage.
Transferred into the world of specialized arbitrageurs this means that there is a separation between capital provision and information. Generally outside investors (the capital providers) have very little knowledge about the nature of the market, the arbitrageur trades in. This implication leads on the one hand to the tendency that outside investors, due to their lack of knowledge, perform more risk-averse and provide arbitrageurs with limited funds, but on the other hand that arbitrageurs are capital-constrained. The second implication is that unsophisticated capital providers measure the arbitrageur’s performance by past results and consequently allocate funds to them. This means that an agent whose results are poor will have problems to raise money in the future.
These implications are crucial in extreme circumstances and entail that main qualities of efficient arbitrage are captivated and at least powerful in situations, when it is needed most, namely when prices and fundamentals diverge the widest. Of course entering the market at this point would lead to the highest returns for them but in most cases capital providers do not understand these circumstances.
Logically, it would be very beneficial for rational traders to enter markets that are very volatile as mispricing can be exploited to a large extent, but in reality due to the above mentioned problem, arbitrageurs seek to minimize the risk by only specializing into secure markets.
Montier argues that if arbitrageurs are funded by debt rather than equity and the position moves against them, due to fundamental or noise trader risk, the value of securities will decline. As a consequence this erosion of the arbitrageur’s capital basis increases leverage, but decreases the probability of borrowing. In-depth analysis of markets and focusing on markets with a low level of fundamental risk is crucial for arbitrageurs to prevent asset fire sales. This term is defined as a “situation whereby arbitrageurs are forced to liquidate their positions at a time when the best potential buyers (other arbitrageurs) have limited resources, and prices fall way below the level justified by the fundamentals.” (Montier, 2002)