The presence of regularly occurring anomalies in conventional economic theory was a big contributor to the formation of behavioural finance. These so-called anomalies, and their continued existence, directly violate modern financial and economic theories, which assume rational and logical behaviour. A relevant point of criticism, levied against traditional models in economics and finance, is that they are often formulated as if the typical decision-maker were an individual with unlimited cerebral RAM. Such a decision-maker would consider all relevant information and come up with the best choice under the circumstances in a process known as constrained optimisation.

Normal humans are imperfect and information requirements are for some financial models egregious. A well-known example is that capital asset pricing model, the famous model important enough that William Sharpe won the 1990 Nobel Prize for Economics Sciences for this contribution. This model assumes that investors are capable of studying the universe of securities in order to come up with all required model inputs. These inputs include expected returns and variances for all securities, as well as covariances among different securities. Only then is the investor able to make appropriate portfolio decisions.

The dictionary definition for heuristics refers to the process by which people find things out for themselves usually by trial and error. Trial and error often leads people to develop “rules of thumb”, but this process often leads to other errors. Heuristics can also be defined as the “use of experience and practical efforts to answer questions or to improve performance”. Due to the fact that more and more information is spread faster and faster, life for decision-makers in financial markets has become a mostly inevitable approach, but not always beneficiary.

Heuristics may help to explain why the market sometimes acts in an irrational manner, which is opposite to the model of perfectly informed markets. The interpretation of new information may require heuristic decision-making rules, which might later have to be reconsidered.

There is a large number of identified heuristics and biases from psychology and they come in all shapes and sizes. One dichotomy is between those heuristics that are reflexive, autonomic, and noncognitive, and economise on effort (Type A); and others, which are cognitive in nature (Type B). Type A heuristics are appropriate when a very quick decision must be made or when the stakes are low (e.g. “I choose a burger over a pizza because I usually prefer them”). Type B heuristics are more effortful and are appropriate when the stakes are higher. In some cases, an initial reaction using Type A heuristic can be overruled or corroborated using Type B heuristic (e.g. “No, I will choose the pizza today because it is prepared a bit differently and I like to try new things”). In this book we shall focus on both types, but limit ourselves to only the most relevant for decision-making relevant for financial investments.

Financial behaviour stemming from familiarity

In this section we explore a series of related heuristics that induce investors to exhibit preferences unrelated to objective considerations. One example is that investors are more comfortable with the familiar. They dislike ambiguity and normally look for ways to avoid unrewarded risk. Investors tend to stick with what they have rather than investigate other options. They put off undertaking new initiatives, even if deep down they know the effort could be worthwhile. All of these observations point to a tendency to seek comfort.

As an example, people are more likely to accept a gamble if they feel they have a better understanding of the relevant context, i.e. if they feel more competent. Heath & Tversky (1991) demonstrated based on an experimental questionnaire-based study that when people felt they had some competence on the question, they were more likely to choose a gamble based on this competence rather than a random lottery. This is evidenced by the positive relationship between judged probability of beingright on the questions and the percentage choosing the competence bet. The logical conclusion is that people have a preference for the familiar.

Home bias - domestic investors hold domestic securities

Although preferences are slowly changing in this regard, it continues to be truethat domestic investors hold mostly domestic securities, i.e. German investors hold mostly German securities; Japanese investors hold mostly Japanese securities and so on, as reported by French & Poterba (1991). Referring to the first column of table 1, we see displayed the aggregate market value of the six biggest stock markets in the world. The United States as of 1989 had 47.8% of the world market, Japan 26.5% etc. Nevertheless, a typical U.S. investor held 93.8% in U.S. stocks; a typical Japanese investor held 98.1% in Japanese stocks etc. Thus, domestic investors overweight domestics stocks. This behaviour is often referred to as the “Home bias”.

Bias toward the home country contradicts evidence indicating that diversifying internationally allows investors to reduce risk without surrendering return. This is particularly true since stock markets in different countries still are not highly correlated. Ackert & Deaves (2010) report that the average pairwise correlation coefficient for the countries listed in table 1during 1975-1989 was 0.502, which attests to the gains from diversification. One reason why investors might hold domestic securities is because they are optimistic about their markets relative to foreign markets. Another behavioural explanation is along the lines of comfort-seeking and familiarity. Investors tend to favour that which is familiar; German investors are more familiar with German stocks and markets, and so they are more comfortable investing in German securities. The same holds equally true for other foreign investors.


arket value weights

U.S. investors

Japanese investors

U.K. investors

U.S.

47.8%

93.8%

1.3%

5.9%

Japan

26.5%

3.1%

98.1%

4.8%

U.K.

13.8%

1.1%

0.2%

82.0%

France

4.3%

0.5%

0.1%

3.2%

Germany

3.8%

0.5%

0.1%

3.5%

Canada

3.8%

0.1%

0.1%

0.6%

Tablel: Estimated country weights among international investors (adapted from French & Poterba, 1991)



Investing in your employer or brands you know

There is abundant evidence that investors overweight the stocks of companies whose brands are familiar or that they work for. Regarding the first, Frieder & Subrahmanyam (2005) looked at a survey data on perceived brand quality, brand familiarity and brand recognition, and asked whether these attributes impacted investor preferences. To answer this question, they correlated institutional holdings with these factors. Note that high institutional holding in a stock implies low retail holding in that same stock. Frieder & Subrahmanyam (2005) found that institutional holdings are significant and negatively related to brand recognition, but no discernible impact was present for brand quality. The former implies that retail investors have a higher demand for firms with brand recognition, which is consistent with comfort-seeking and familiarity.

As for overweighting companies that one works for, while the same sort of familiarity versus informational advantage debate is possible, the extent to which some investors invest in these companies seems to transcend an informational explanation. According to Ackert & Deaves (2010), many employee-investors put a very high percentage of their investible wealth in their employer’s stock, thus foregoing a significant amount of possible diversification.

Diversification heuristic - "the 1/N buffet rule"

The diversification heuristic suggests that people in general like to try a bit of everything when choices are not mutually exclusive. A common behaviour among buffet diners is to sample most (if not all!) dishes. To concentrate on one or two runs the risk of not liking your selections and/or missing out on a good thing. Such behaviour is similar to that reported by Simonsen (1992), who reports shoppers are more likely to choose a variety of items when they must make multiple purchases for future consumption, versus the case when they make single purchases just prior to each consumption decision. Simonsen (1992) argues that certain factors drive such behaviour. First, many people have a hardwired preference for variety and novelty. This preference is much more salient when multiple purchases are made. Second, future preferences embody some uncertainty. Spreading purchases over different categories reduces risk in the same fashion that spreading your money over different stocks accomplishes the same risk-reduction goal in a well-diversified portfolio. A final motivation for variety-seeking is it makes your choice simpler, thus saving time and reducing decision conflict.

One popular form of naive diversification amongst investors is the 1/N strategy. The 1/N strategy entails equal division of investment money between the available funds. For example when given a choice of five funds for pension investments people will often divide their pension contributions equally between the funds. Siebenmorgen & Weber (2003) found that financial advisers were also prone to recommending 1/N strategies, and to ignoring correlations between investments when estimating portfolio risk. The 1/N strategy is often seen as irrational behaviour since it involves the loss of the benefits of Markowitz diversification in standard finance.

Financial behaviour stemming from representativeness

One of the more common heuristics is judging things by how they appear rather than how statistically likely they are. The classic example comes from works by Kahneman & Tversky (1973). It concerns Linda, a 31-year-old who is single, out spoken and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and equality. Which is more likely?

  • Linda works in a bank

  • Linda works in a bank and is active in the feminist movement.

Analarmingly highpercentage of people go for the second option. However, it can’t possibly be true, as it represent saconjunction fallacy. That is, there must always be more people who work in banks than there are who work in banks and are active in the feminist movement. So why do somany people get this question wrong? The answer seems to be that the description is biased, it sounds like some one who might plausibly be involved in the feminist movement. People are driven by the narrative of the description rather than by the logic of the analysis.

Montier (2007) reports another example of representativeness: a health survey was conducted in a sample of adult males, in New Jersey, of all ages and occupations. Nearly 300 professional fund managers coming from all over the globe submitted themselves to the task of trying to answer these two questions:

  • What percentage of the men surveyed have had one or more heart attacks?

  • What percentage of the men surveyed are both over 55 and have had one or more heart attacks?

The question is a conjunction fallacy in the same way as the Linda problem. There are always going to be more men who had one or more heart attacks than there are men over 55 and one or more heart attacks. However, across the 300 fund managers the estimate daverage percentage of men suffering one or more heart attacks was 12.5%, while the estimated percentage of men over 55 and suffering one or more heart attacks was 16%. Of course, averages can hide all sorts of things, so looking at the full data setreveals that 40% of the sample suffered from representativeness in as much as they had higher estimates of the latter part of the question compared to the first section answer!

Good investments vs. good companies

There is a lot of empirical evidence in literature that representativeness and related biases induce inappropriate investment decisions. To casual observers it seems obvious that if a company has high-quality management, a strong image, and consistent growth in earnings, it must be a good investment. Students of finance, of course, know better. In valuation, future cash flows are forecasted and discounted back to the present using appropriate risk-adjusted discount rate. All the aforementioned attributes that make a company a good company should theoretically be reflected in these estimates of future cash flows (including the growth in cash flows) and the risk-adjusted discount rate, i.e. they should already be impounded in the stock price. In other words, good companies will sell at high prices, and bad companies will sell at low prices. But, once the market has adjusted, there is no reason to favour a good company over a bad company, or, for that matter, a bad company over a good company. Quite simply, it is a mistake to think that a good company is representative of a good investment, and yet, that is exactly what people often seem to believe.

In works by Shefrin & Statman (1995) some very revealing evidence is provided from a survey of senior executives on company attributes for a number of years. Executives are asked to assign values between “0” (poor) and “10” (excellent) to each company in their industry for the following items: quality of management; quality of products/services; innovativeness; long-term investment value: financial soundness; ability to attract, develop, and keep talented people; responsibility to the community and environment; and wise use of corporate assets. Because 82% of the respondents consider quality of management as the most important attribute of a company’s quality, Shefrin & Statman (1995) used it as their proxy for company quality. Results show that management quality (i.e. good company measure) and value as a long-term investment (i.e. good stock measure) are very highly correlated. The R2 value from the first regression of survey data suggests a correlation of 0.93. Executives apparently believe that good companies are good stocks. As discussed in the section above, it is important to understand that no company attributes should be associated with investment value. That is, all information on company quality should already be embedded in stock prices so that all companies (good and bad ones) on an ex ante basis are equally good investments.

Other regressions from the same survey by Shefrin & Statman(1995) reveal that two company characteristics, size and the book-to-market ratio, are strongly associated with perceived management quality. Specifically, big companies and those that have low book-to-market ratios (where the latter are considered growth companies) are seen to be good companies. This is not overly surprising. Big companies have often become big because they are good (i.e. well managed), and growth should come from quality. Additionally, size and book-to-market, even

after accounting for their impact on management quality, are observed to interdependently influence investment value. Big firms are viewed as good investments, and growth companies are viewed as good investments. In other words, big high-growth firms are perceived as representative of good investments. Interestingly, as discussed earlier, the empirical evidence points in the exact opposite direction. It is small-cap value firms that have historically outperformed. Indeed, the tendency for individuals to use representativeness in the context of investments may have contributed to the small-firm and value anomalies. We will address this further in chapter 4.

Chasing winners

Research has also shown that investors choose securities and investment funds based on past performance.

To those with this view, investment performance in recent past is representative of future investment performance. This form of representativeness is often referred to as the “recency bias”. Such trend- following or momentum chasing, has long been a popular strategy, and, coupled with detecting turning points, it is the heart of technical analysis. Trend-following is indeed an international phenomenon in all stock markets. So is there any evidence in favour of the popular notion that momentum-chasing is profitable? Ackert & Deaves (2010) answers both yes and no to this question. There is evidence that risk- adjusted returns are positively correlated for three to twelve month return intervals. For longer periods of three years or more, however, the evidence favours reversals or negative serial correlation.

Gambler's fallacy in investing

It’s not hard to imagine that under certain circumstances, investors or traders can easily fall prey to the gambler’s fallacy being the erroneous belief that additional observations should be such that a sample will closely resemble the underlying distribution. For example, some investors believe that they should liquidate a position after it has gone up in a series of subsequent trading sessions because they don’t believe that the position is likely to continue going up. Conversely, other investors might hold on to a stock that has fallen in multiple sessions because they view further declines as “improbable”. Nevertheless, as students of finance will know: just because a stock has gone up on six consecutive trading sessions does not mean that it is less likely to go up on during the next session.

It’s important to understand that in the case of independent events, the odds of any specific outcome happening on the next chance remains the same regardless of what preceded it. With the amount of noise inherent in the stock market, the same logic applies: Buying a stock because you believe that the prolonged trend is likely to reverse at any second is irrational. One could suggest the investors to base their decisions on fundamental and/or technical analysis before determining what will happen to a trend.



Anchoring

Anchoring refers to the decision-making process where quantitative assessments are required and where these assessments may be influenced by suggestions. The concept of anchoring draws on the tendency to attach our thoughts to a reference point, even though it may have no logical relevance to the decision at hand. People have in their mind some reference points (i.e. anchors) for example of previous stock prices. When they get new information they adjust this past reference insufficiently to the new information acquired. Anchoring describes how individuals tend to focus on recent behaviour and give less weight to longer time rends.Although it may seem an unlikely phenomenon, anchoring is even fairly prevalent in situations where people are dealing with concepts that are novel.

Values in speculative markets, like the stock market, are inherently ambiguous. It is hard to tell what the value of e.g. the Dow Jones Industrial Average should be as there is no agreed-upon economic theory that would provide an answer to this question. In the absence of any better information, past prices are likely to be important determinations of prices today. Therefore, the anchor is the most recent remembered price. The tendency of investors to use this anchor enforces the similarity of stock prices from one day to the next. Other possible anchors are remembered historical prices, and the tendency of past prices to serve as anchors may explain the observed tendency for trends in individual stocks prices to be reversed. For individual stocks, price changes may tend to be anchored to the price changes of other stocks, and price-earnings ratios may be anchored to other firms’ price-earnings levels. This kind of anchoring may explain why individual stock prices move together as much as they do, and thus why stock price indices are as volatile as they are. Likewise, it may help to explain why the averaging across stocks that is inherent in the construction of the index does not more solidly dampen its volatility. It may also explain why stocks of companies that are in different industries, but are headquartered in the same location, tend to have more similar price movements than stocks of companies that are in the same industry, but headquartered in different countries. This obviously being contrary to one’s expectation that the industry would defined the fundamentals of the company better than the location of its headquarters.

Anchoring can indeed be a source of frustration in the financial world, as investors base their decisions on irrelevant figures and statistics. For example, some investors invest in the stocks of companies that have fallen considerably in a very short amount of time. In this case, the investor is anchoring on a recent “high” that the stock has achieved and, consequently, believes that the drop in price provides an opportunity to buy the stock at a discount. While, it is true that the fickleness of the overall market can cause some stocks to drop substantially in value, allowing investors to take advantage of this short-term volatility, stocks most often decline in value due to changes in their underlying fundamentals.

As an example, suppose that stock X had a very strong revenue in the last year, causing its share price to shoot up from 25€ to 80€. Unfortunately, one of the company’s major customers, who contributed to 50% of X’s revenue, has decided not to renew its purchasing agreement with X. This change of events causes a drop in X’s share price from 80€ to 40€. By anchoring to the previous high of 80€ and the current price of 40€, the investor erroneously believes that X is undervalued. Keep in mind that X is not being sold at a discount, instead the drop in share value is attributed to a change to X’s fundamentals (loss of revenue from a big customer). In this example, the investor has fallen prey to the dangers of anchoring.

When it comes to avoiding anchoring, there’s no substitute for rigorous critical thinking. Be especially careful about which figures you use to evaluate a stock’s potential. Successful investors don’t just base their decisions on one or two benchmarks. They evaluate each company from a variety of perspectives in order to derive the truest picture of the investment landscape.

Herding

A fundamental observation about human society is that people who communicate regularly with one another think similarly. This naturally also goes for investors. It is important to understand the origins of this similar thinking, so that one can judge the plausibility of theories of speculative fluctuations that ascribe price changes to faulty thinking. There are two primary reasons why herd behaviour happens.

The first is the social pressure of conformity indeed being a powerful force. This is because most people are very sociable and have a natural desire to be accepted by a group, rather than be branded as an outcast. Therefore, following the group is an ideal way of becoming a member. The second reason is the common rationale that it’s unlikely that such a large group could be wrong. After all, even if you are convinced that a particular idea or course of action is irrational or incorrect, you might still follow the herd, believing they know something that you don’t. This is especially prevalent in situations in which an individual has very little experience.

Part of the reasons why people’s judgements are similar at similar times is that they are reacting to the same information. The social influence has an immense power on individual judgement. When people are confronted with the judgement of a large group of people, they tend to change their “wrong” answers.

They simply think that all the other people could not be wrong. They are reacting to the information that a large group of people had reached a judgement different form theirs. This is a rational behaviour also viewed in terms of evolution. In everyday living we have learned that when a large group of people is unanimous in its judgement, they are certainly right. Herding and anchoring are thus closely related.

People are influenced by their social environment and they feel pressure to conform. Fashion is a mild form of herd behaviour while an example of the strong form is fads that constitute speculative bubbles and crashes. Herd behaviour may be the most generally recognised observation on financial markets in a psychological context. Herd behaviour can play a role in the generation of speculative bubbles as there is a tendency to observe “winners” very closely, particularly when good performance repeats itself a couple of times. It seems plausible to make distinction between voluntary and enforced herd behaviour. Many players on financial markets might think that a currency or equity is not correctly priced, but they refrain, nevertheless, from a contrary financial exposure. These people simply feel that it is not worthwhile to combat the herd. This is an example of enforced herd behaviour. They follow the herd, not voluntarily, but to avoid being trampled and are therefore enforced into following the herd.

Even otherwise completely rational people can participate in herd behaviour when they take into account the judgements of others, and even if they know that everyone else is behaving in a herd-like manner.

The behaviour, although individually rational, produces group behaviour that is irrational and causes fluctuations in the market. The “noise trading” theory stems from the fact that investors with a short time horizon are influencing the stock prices more than the long-term investors are. Investors, with no access to inside information, irrationally act on noise as if it were information that would give them an edge.

Another important variable to herding is the word of mouth. People generally trust friends, relatives and working colleagues more than they do the media. The conventional media, printed information, televisions, and radio have a profound capability for spreading ideas, but their ability to generate active behaviours is still limited. Talking to other people and other kinds of interpersonal communication are among the most important social connections humans have. It is therefore likely that news about buying opportunity will rapidly spread. In a study by Shiller & Pound (1989) private investors were asked what first draw their attention to a company they recently had invested in. Only six percent of the respondents specified newspapers and periodicals. Even if people read a lot, their attention and actions appear to be more stimulated by interpersonal communication.

A strong herd mentality can even affect financial professionals. The ultimate goal of an investment manager is obviously to follow an investment strategy to maximise a client’s invested wealth. The problem lies in the amount of scrutiny that investment managers receive from their clients whenever a new investment fad pops up. For example, a wealthy client may have heard about an investment gimmick that’s gaining notoriety and inquires about whether the investment manager employs a similar strategy.In many cases, it’s tempting for an investment manager to follow the herd of investment professionals. After all, if the aforementioned gimmick pans out, his clients will be happy. If it doesn’t, the money manager can justify his poor decision by pointing out just how many others were led astray.

Herd behaviour is usually not a very profitable investment strategy and the cost of being led astray can often be very high. Investors that employ a herd-mentality investment strategy constantly buy and sell their investment assets in pursuit of the newest and hottest investment trends. For example, if a herd investor hears that internet stocks are the best investments right now, he will free up his investment capital and then dump it on internet stocks. If biotech stocks are all the rage six months later, he’ll probably move his money again, perhaps before he has even experienced significant appreciation in his internet investments. Keep in mind that all this frequent buying and selling incurs a substantial amount of transaction costs, which can eat away at available profits. Furthermore, it’s extremely difficult to time trades correctly to ensure that you are entering your position right when the trend is starting. By the time a herd investor knows about the newest trend, most other investors have already taken advantage of this news, and the strategy’s wealth-maximising potential has probably already peaked. This means that many herd-following investors will probably be entering into the game too late and are likely to lose money as those at the front of the pack move on to other strategies.

Overconfidence and excessive trading

The key behavioural factor and perhaps the most robust finding in the psychology of financial judgement needed to understand market anomalies is overconfidence. Investors tend to exaggerate their talents and underestimate the likelihood of bad outcomes over which they have no control. The combination of overconfidence (i.e. overestimating or exaggerating one’s ability to successfully perform a particular task) and optimism causes people to overestimate the reliability of their knowledge, underestimate risks and exaggerate their ability to control events, which leads to excessive trading volume and speculative bubbles. The greater confidence a person has in himself, the more risk there is of overconfidence. This applies, in particular, to areas where people are not well-informed. Self-confidence, interestingly, usually bears no relation to the relationship between overconfidence and competence. March & Shapira (1987) demonstrated as one of many examples that portfolio managers overestimate the probability of success in particular when they think of themselves as experts.

In a 2007 study Montier found that 74% of the 300 professional fund managers surveyed believed that they had delivered above-average job performance. Of the remaining 26% surveyed, the majority viewed themselves as average. Incredibly, almost 100% of the survey group believed that their job performance was average or better. Clearly, only (slightly less than) 50% of the sample can be above average, suggesting the irrationally high level of overconfidence these fund managers exhibited. Clearly, overconfidence is not a trait that applies only to fund managers.

In terms of investing, overconfidence can be detrimental to the individual’s stock-picking ability in the long run. In a 1998 study Odean found that overconfident investors generally conduct more trades than their less-confident counterparts. Odean found that overconfident investors and traders tend to believe they are better than others at choosing the best stocks and the best times to enter/exit a position. Unfortunately, Odean (1998) also found that traders that conducted the most trades tended, on average, to receive significantly lower yields than the market.Keep in mind that professional fund managers, who have access to the best investment/industry reports and computational models in the business, can still struggle at achieving market-beating returns. High trading volumes and the pursuit of active investment strategies thus seems inconsistent with common knowledge of rationality.

Apparently, many investors feel that they do have speculative reasons to trade often, and apparently this have to do with a tendency for each individual to have beliefs that he or she perceives better than others’ beliefs. It is as if most people think that they are above average. Shiller (1987) observed in a survey of the market crash in 1987, a surprisingly high confidence among investors in intuitive feelings about the direction the market would take after the crash. Therefore, overconfidence may help to explain possible general market overreactions as well as excess volatility and speculative asset prices. It may also explain why investment professionals hold actively managed portfolios with the intention of being able to choose winners and why pension funds hire active equity managers.

Evidence from the field of trading

Are the predications of overconfidence and excessive trading corroborated by evidence from the real world? Barber & Odean (2000) investigated the performance of individual investors by examining the trading histories of more than 60,000 U.S. discount brokerage investors between 1991 and 1996. Their goal was to see if the trades of these investors were justified in the sense that they led to improvements in portfolio performance. There is an important point to consider in respect to why and when a market transaction would make sense at all. Suppose, for example, you sell one stock and use the proceeds to buy another, and in doing so incur 100€ in transaction costs. This transaction is only logical if you expect to generate a higher portfolio return, i.e. high enough to at least offset the transaction cost. To be sure, individual investors do a lot of trading. Barber & Odean (2000) found that, on average, U.S. professional investors turn over 75% of their portfolio annually. This means that, for a typical investor who holds a 100,000€ portfolio, 75,000€ worth of stock is traded in a given year.

Barber & Odean (2000) divided their sample of individual investors into five equal groups (quintiles). Specifically, the 20% of investors who traded the least were assigned to the lowest turnover quintile (no. 1), the 20% of investors who traded the next least were assigned to quintile 2, and so on all the way to quintile no. 5, which was reserved for those investors trading the most. To put this into perspective, those trading the least only turned over 0.19% of their portfolio per month being a total of less than 3% per year. Those trading the most turned over 21.49% of their portfolio per month, being more than 300% per year. Referring to figure 3, we see for each quintile the gross average monthly return and the net (i.e. after transaction costs) average monthly return. The returns for all quintiles (both gross and net) were fairly high during the period (even for those trading excessively) because the overall stock market was performing well in the analysed period.

Figure 3: Gross and net returns for groups with different trading intensities (based on Barber & Odean, 2000)

Returning to the central question: Was this trading worthwhile? Was it based on superior information, or was it based on the perception of superior information (i.e. misinformation)? An inspection of figure 3 reveals that while the additional trading did lead to a very slight improvement in gross performance, net performance suffered. The evidence reported by Barber & Odean (2000) suggest that the trades were not based on superior information, but rather were often conducted because of misinformation. While it is impossible to prove without a doubt that overconfidence was the culprit, the view appears to be a reasonable one.

While figure 3 is in terms of raw returns, sometimes returns are high because greater risk is taken and investors are merely being properly rewarded for the risk borne. If an investor earns high average returns only because high risk has been borne, this does not imply any sort of stock-picking skill. After risk- adjusting returns, Barber & Odean (2000) found that their results were quite similar to those displayed in figure 3. For all investors, the net risk-adjusted annual return (i.e. after taking into account transaction costs, bid-ask spreads, and differential risk) was below the market return by well over 3%. The 20% of investors who traded the most underperformed the market (again on a net risk-adjusted basis) by close to 10% per year.

Better-than-average effect

Numerous studies have asked people to rate themselves relative to average on certain positive personal attributes such as athletic skill or investor ability, and, consistent with the “better-than-average effect”, many rate themselves as above average on those attributes. Obviously, of course, only (slightly fewer than) 50% of the people in any pool can truly be superior. Similarly, people are likely to see themselves as “less than average” for negative traits. When subsequently asked how biased they themselves were, subjects rated themselves as being much less vulnerable to those biases than the average person.

One factor that facilitates a better-than-average belief is that often the exact definition of excellence or competence is unclear. Naturally enough, people have in the backs of their minds the definition that will make them look best. Some investors might see “best” as most adept at taking losses; others might see it as most competence at anticipating trends in technical analysis; while still others might see it as being most skilful at diversifying their portfolio. Both motivation and cognitive mechanisms are likely behind the better-than-average effect. On the motivational side, thinking that you are better than average enhances self-esteem. On the cognitive side, performance criteria that most easily come to mind are often those that you are best at.

Hindsight bias and confirmation bias

In social science, attribution theory investigates how people make causal attributions, i.e. how they come up with explanations for the causes of actions and outcomes. Certain persistent errors occur. For example, people, when 3.4.3 Hindsight bias and confirmation bias

In social science, attribution theory investigates how people make causal attributions, i.e. how they come up with explanations for the causes of actions and outcomes. Certain persistent errors occur. For example, people, when observing others, tend to over-attribute behaviour to dispositional (as opposed to situational) factors. In other words, if someone seems to be behaving badly, we naturally believe them to be of bad character, rather than searching out environmental details that may be explanatory. It’s often said that “seeing is believing”. While this is often the case in certain situations, what you perceive is not necessarily a true representation of reality. This is not to say that there is something wrong with ones senses, but rather that our minds have a tendency to introduce biases in processing certain kinds of information and events.

It can be difficult to encounter something or someone without having a preconceived opinion. This first impression can be hard to shake because people also tend to selectively filter and pay more attention to information that supports their opinions, while ignoring or rationalising the rest. This type of selective thinking is often referred to as the “confirmation bias”. In investing, the confirmation bias suggests that an investor would be more likely to look for information that supports his or her original idea about an investment rather than to seek out information that contradicts it. As a result, this bias can often result in faulty decision-making because one-sided information tends to skew an investor’s frame of reference, leaving him or her with an incomplete picture of the situation. Consider, for example, an investor that hears about a hot stock from an unverified source and is intrigued by the potential returns. That investor might choose to research the stock in order to prove whether or not its touted potential is real. What ends up happening is that the investor finds all sorts of green flags about the investment (such as growing cash flow or a low debt/equity ratio), while glossing over financially disastrous red flags, such as loss of critical customers or dwindling markets.

Confirmation bias represents a tendency for us to focus on information that confirms some pre-existing thought. Part of the problem with confirmation bias is that being aware of it isn’t good enough to prevent you from doing it. One solution to overcoming this bias would be finding someone to act as a “dissenting voice of reason”. That way you’ll be confronted with a contrary viewpoint to examine.

observing others, tend to over-attribute behaviour to dispositional (as opposed to situational) factors. In other words, if someone seems to be behaving badly, we naturally believe them to be of bad character, rather than searching out environmental details that may be explanatory. It’s often said that “seeing is believing”. While this is often the case in certain situations, what you perceive is not necessarily a true representation of reality. This is not to say that there is something wrong with ones senses, but rather that our minds have a tendency to introduce biases in processing certain kinds of information and events.

It can be difficult to encounter something or someone without having a preconceived opinion. This first impression can be hard to shake because people also tend to selectively filter and pay more attention to information that supports their opinions, while ignoring or rationalising the rest. This type of selective thinking is often referred to as the “confirmation bias”. In investing, the confirmation bias suggests that an investor would be more likely to look for information that supports his or her original idea about an investment rather than to seek out information that contradicts it. As a result, this bias can often result in faulty decision-making because one-sided information tends to skew an investor’s frame of reference, leaving him or her with an incomplete picture of the situation. Consider, for example, an investor that hears about a hot stock from an unverified source and is intrigued by the potential returns. That investor might choose to research the stock in order to prove whether or not its touted potential is real. What ends up happening is that the investor finds all sorts of green flags about the investment (such as growing cash flow or a low debt/equity ratio), while glossing over financially disastrous red flags, such as loss of critical customers or dwindling markets.

Confirmation bias represents a tendency for us to focus on information that confirms some pre-existing thought. Part of the problem with confirmation bias is that being aware of it isn’t good enough to prevent you from doing it. One solution to overcoming this bias would be finding someone to act as a “dissenting voice of reason”. That way you’ll be confronted with a contrary viewpoint to examine.Another common perception bias is the “hindsight bias”, which tends to occur in situations where a person believes (after the fact) that the onset of some past event was predictable and completely obvious, whereas in fact, the event could not have been reasonably predicted. Many events seem obvious in hindsight. Psychologists attribute hindsight bias to our innate need to find order in the world by creating explanations that allow us to believe that events are predictable. While this sense of curiosity is useful in many cases (take science, for example), finding erroneous links between the cause and effect of an event may result in incorrect oversimplifications. The hindsight bias appears to be especially prevalent when the focal event has well-defined alternative outcomes (e.g. an election or the European Cup final), when the event in question has emotional or moral overtones, or when the event is subject to process of imagination before its outcome is known.

Over and under-reaction in the market

One consequence of having emotion in the stock market is the overreaction toward new information. According to market efficiency, new information should more or less be reflected instantly in a security’s price. For example, good news should raise a business’ share price accordingly, and that gain in share price should not decline if no new information has been released since. Reality, however, tends to contradict this theory. Oftentimes, participants in the stock market predictably overreact to new information, creating a larger-than-appropriate effect on a security’s price. Furthermore, it also appears that this price surge is not a permanent trend. Although the price change is usually sudden and sizable, the surge erodes over time.

De Bondt & Thaler (1985) show that people tend to overreact to unexpected and dramatic news events. Consistent with the predictions of the overreaction hypothesis, portfolio of prior “losers” are found to outperform prior “winners”. In their study, they examined returns on the New York Stock Exchange for a three-year period. From these stocks, they separated the best 35 performing stocks into a “winners portfolio” and the worst 35 performing stocks were then added to a “losers portfolio”. De Bondt & Thaler (1985) then tracked each portfolio’s performance against a representative market index for three years. Surprisingly, it was found that the losers’ portfolio consistently beat the market index, while the winners’ portfolio consistently underperformed. In total, the cumulative difference between the two portfolios was almost 25% during the three-year time span. In other words, it appears that the original “winners” would became “losers”, and vice versa.

So what happened? In both the winners and losers portfolios, investors essentially overreacted. In the case of loser stocks, investors overreacted to bad news, driving the stocks’ share prices down disproportionately. After some time, investors realised that their pessimism was not entirely justified, and these losers began rebounding as investors came to the conclusion that the stock was underpriced. The exact opposite is true with the “winners’ portfolio”: investors eventually realised that their exuberance wasn’t totally justified.

Overreaction seems to be related to some deep-set of psychological phenomena. Ross (1987) argues that much overconfidence is related to a broader difficulty in making adequate allowance for the uncertainty in one’s own viewpoints. Kahnemann & Tversky showed in their 1974 paper that people have a tendency to categorise events as typical or representative of a well-known class, and then, in making probability estimates overstress the importance of such categorisation disregarding evidence of the underlying probabilities. One consequence of this phenomenon is for people to see patterns in data that is truly random, to feel confident, for example, that that a series which is in fact random walk is not a random walk. Price reactions to information are crucial for market behaviour. Recent empirical research in finance has uncovered two families of pervasive regularities: under-reaction of stock prices to news such as earnings announcements, and overreaction of stock prices to a series of good or bad news. The underreaction evidence shows that over horizons of one to twelve months, security prices under-react to news. As a consequence, news is incorporated only slowly into prices, which tend to exhibit positive autocorrelations over these horizons. A related way to make this point is to say that current good news has power in predicting positive returns in the future. The overreaction evidence shows that over longer horizons of three to five years, security prices overreact to consistent patterns of news pointing in the same direction. That is, securities that have had a long record of good news trend to become overpriced and have low average returns afterwards. The under-reaction evidence in particular is consistent with anchoringreferring to the phenomenon according to which people mistrust new data and give too much weight to prior probabilities of events in a given situation. Edwards concluded in a 1968 study that “it takes anywhere from two to five observations to do one observation’s worth of work in inducing a subject to change his opinion”. According to this principle, people are slow to change their opinions. For this reason, it takes some time before investors begin to conclude that a trend, such as price increases in connection with a speculative bubble, will not continue. Further, it is over- and under-reaction that is one of the primary causes of trends, momentum and fads.

Under-diversification and excessive risk taking

Another investor error likely related to overconfidence is the tendency to be under-diversified. Underdiversified people are too quick to overweight/underweight securities when they receive a positive/ negative signal, and insufficient diversification then results. Another factor is that most investors, lacking the time to analyse a large set of securities, will stop after a few. As long as they believe they have identified a few “winners” in this group, they are content. After all, if they are so sure that certain stocks are good buys, why dilute their portfolio with stocks that they have not studied?

In a study by Kelly (1995), the portfolio composition of more than 3,000 U.S. individuals was examined.

Most individuals indentified held no stocks at all. Of those households that did hold stocks (more than 600), Kelly (1995) found that the median number of stocks in their portfolios was only one. And only about 5% of stock-holding households held 10 or more stocks. Most evidence says that to achieve a reasonable level of diversification, one has to hold more than 10 different stocks and preferably in different sectors of the economy. Thus, it seems clear that many individual investors are quite under-diversified.

In their study, Groetzmann & Kumar (2005) sought to ascertain who were most prone to being underdiversified. Not surprisingly, they found that under-diversification increased with income, wealth, and age.

Those who traded the most also tended to be the least diversified. This is likely because overconfidence is the driving force behind both excessive trading and under-diversification. Also less diversified were those people who were sensitive to price trends and those who were influenced by home bias.

Related to under-diversification is excessive risk taking. This is actually tautological, in that underdiversification is tantamount to taking on risk for which there is no apparent reward. It is done, of course, in the hope of finding undervalued securities. The disposition effect, as presented in section 2.3.1, is sometimes associated with overconfidence. An overconfident trader, overly wedded to prior beliefs, may discount negative public information that pushes down prices, thus holding on to looser and taking excessive risk. Indeed, there is evidence that especially futures traders exhibit this behaviour. As indentified by Groetzmann & Kumar (2005), traders with mid-day losses increase their risk and perform poorly subsequently.

Analysts and excessive optimism

Abundant research has established that analysts tend to be excessively optimistic about the prospects of the companies that they are following. This is true both in the U.S. and in Europe. Table 2 shows the distribution of analyst recommendations among strong buy, buy, hold, sell, and strong sell for G7 countries adapted from a study by Jagadeesh & Kim (2006). It is clear from table 2 that analysts are much more likely to recommend a purchase rather than a sale. In the U.S., where this tendency was most pronounced, buys/sells were observed 52%/3% of the time. In Germany, where the tendency was least pronounced, the buy/sell ratio was 39%/20%. While excessive optimism is one interpretation, another is a conflict of interest induced by a perceived need to keep prospective issuers happy.

Table 2: Recommendation distributions in G7 countries during 1993-2002 (adapted from Jagadeesh & Kim, 2006)

Strong buy

Buy

Hold

Sell/Strong sell

U.S.

28.6%

33.6%

34.5%

3.3%

U.K.

24.3%

22.3%

41.7%

11.8%

Canada

29.4%

28.6%

29.9%

12.1%

France

24.7%

28.3%

31.1%

15.9%

Germany

18.3%

20.3%

41.5%

19.9%

Italy

19.2%

20.0%

47.1%

13.6%

Japan

23.6%

22.4%

35.7%

18.3%



Path-dependent behaviour

Decisions we make often have a path dependence to them. Path dependence exists if it is important to your decision how you got where you are rather than merely focusing on your current location. Such behaviour means that people’s decisions are influenced by what has previously transpired. It takes enormous mental discipline to simply look forward without agonising or gloating over what has transpired.

Thaler & Johnson (1990) provide evidence regarding how individual behaviour is affected by prior gains and losses. After a prior gain, people become more open to assuming risk. This observed behaviour is referred to as the house money effect, alluding to casino gamblers who are more willing to risk money that was recently won. This was briefly introduced in section 2.3.3. Gamblers call this “playing with the house’s money.” Since they don’t yet consider the money to be their own, they are willing to take more risk with it. The house money effect predicts that investors will be more likely to purchase risky stocks after closing out a profitable trade. After a prior loss, matters are not so clear-cut. On the one hand, people seem to value breaking even, so a person with a prior loss may take a risky gamble in order to break even. This observed behaviour is referred to as the “break-even effect”. On the other hand, an initial loss can cause an increase in risk aversion in what has been called the “snake-bit effect”.

Sequential decisions and prospect theory

Interestingly, at first, some of the findings on behaviour following gains and losses appear to contradict prospect theory. The house money effect suggests reduced risk aversion after an initial gain, whereas prospect theory makes no such prediction. It is notable, though, that the house money effect is not inconsistent with prospect theory, because prospect theory originally was developed to describe one-shot decisions. Recall the discussion of integration versus segregation in section 2.3.3. Under integration, an investor combines the results of successive gambles, whereas, under segregation, each gamble is viewed separately. Instead of presenting a challenge to prospect theory, the house money effect is best seen as evidence that sequential gambles are sometimes integrated rather than segregated. If one integrates after a large gain, one has moved safely away from the value function loss aversion kink in figure 1, serving to lessen risk aversion.

The evidence provided by Thaler & Johnson (1990) provides important insight into how individuals make sequential decisions. People do not necessarily combine the outcomes of different gambles. Financial theory is increasingly incorporating insights on individual behaviour provided by psychology and decision-making research on segregation vs. integration. For example, in the model by Barberis,

Huang & Santos (2001), investors receive utility from consumption and changes in wealth. In traditional models, people value only consumption. In this extension, investors are loss averse so that they are more sensitive to decreases than to increases in wealth, and thus, prior outcomes affect subsequent behaviour.

After a stock price increase, people are less risk averse because prior gains cushion subsequent losses, whereas after a decline in stock prices, people are concerned about further losses and risk aversion increases. Therefore, the model suggested by Barberis, Huang & Santos (2001) predicts that the existence of the house money effect in financial markets leads to greater volatility in stock prices. After prices rise, investors have a cushion of gains and are less averse to the risks involved in owning stocks. Indeed, as in this model, aspects of prospect theory are increasingly being embedded in modern financial models.

Despite some progress, it does not seem that our financial understanding of sequential behaviour in a market is complete. How does individual behaviour translate to a market setting? A recent experimental study by Ackert et al. (2006), which includes a market with sequential decision-making, provides some insight. Traders who are given a greater windfall of income before trading begins bid higher to acquire the asset, and, thus, the market prices are significantly higher. In fact, prices remain higher over the entirety of the three-period markets. As the house money effect would predict, people seem to be less risk averse after a windfall of money, as if the earlier gain cushions subsequent losses. Observed behaviour does not always suggest that traders will pay more to acquire stock after further increases in wealth. There is no evidence that traders become more risk taking if additional profits are generated by good trades when the market is open. The results also indicate that the absolute level of wealth has a dominant influence on subsequent behaviour so that changes in wealth are less important. This observed behaviour among traders could be because professional traders are trained to act in a more normative (i.e. less prospect theory-like, less emotional) fashion. Indeed, more work is required to allow us to better understand the dynamics of markets and whether individual behaviour adapts to or influences market outcomes.

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