I will let Chicago Tribune give you the story in detail, that sort of fits anyway. Just to give you a gist

The steep sell-off in United’s shares came after a news service in Florida distributed an old story posted on the South Florida Sun-Sentinel Web site six years ago. Monday’s recirculated story gave the appearance that United had filed for bankruptcy protection again. In fact, the story was originally published Dec. 10, 2002, by the Chicago Tribune, marking the airline’s decision at that time to seek protection from creditors.

And more importantly for us, moments after a headline for the story hit Bloomberg, shares in United stock fell from about $12 a share to a low of $3, prompting a halt in trading of United stock.

Of course, once the truth was out the stock got back to a slightly lower $10 odd and will probably now reach its market value of $12 and might start trading in a regular manner.

What Happened Here?

What we saw was a perfect simulation of Panic Selling and to a certain extent noise trading. I intend to cover these two in details later (so watch this space for that).

The fact was that the atmosphere was already volatile with the Lehman going bankrupt and Meryll Lynch’ed’. And so when  people were  already on shaky grounds a news of another big one falling was natural to have this effect. They had gone from 65 a share to 26 cents with Lehman obviously they didn’t want any further dampener.

The result - everyone wanted to get out as soon as possible. What they trusted in was the credibility of a news source like Bloomberg, and took an action that if true would have been the right one.

Ok, but how would have I made money?

On the face of it, you would have had you bought the stock at $3 or bought more while still holding at $12. But that’s not easy, you would be stupid to do that when a news like that stares right at your face.

Behavioral Finance would have helped you much before this news. So that when the news came you would have known that it is not the fact. What the proponents of behavioral finance tell you always is to look at the inherent value fo the stock and identify when human emotions more than fundamentals of finance are at play.

Researching the stock well and knowing where it is headed in terms of its business plans and where it has put its money would have helped a lot of people make the most of this opportunity. And these are exactly the step that behavioural finance abides by. Think about it, $3 for a $12 share! You could have bought 4 times more share than otherwise.

Of course, this is all in hindsight, and interestingly even hindsight bias is the common behavioural biases that we investors show.

Read this blog to perhaps avoid regret in hindsight in the future, now that you already know overconfidence is bad from me.

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Though research and development of financial models based on behavioural finance started very recently the idea of behavioural finance is not new. Many investors have long considered that psychology plays a key role in determining the behaviour of markets. However it is only in the past couple of decades that a series of concerted formal studies have been undertaken in this area. Paul Slovic’s (1972) paper on individual’s misperceptions about risk and Amos Tversky and Daniel Kahneman’s papers on heuristic driven decision biases and decision frames (1979) being some of the major works in the field. The results of these studies were significantly different from the rational, self-interested decision-maker that by traditional finance models explained.

Yet as said before, Most of the ideas in behavioural economics are not new and actually they return to the roots of neoclassical economics.

When economics first became identified as a distinct field of study, psychology did not exist as a discipline. Many economists moonlighted as the psychologists of their times. Adam Smith, who is best known for the concept of the “invisible hand” and The Wealth of Nations, wrote a less well-known book The Theory of Moral Sentiments, which laid out psychological principles of individual behaviour that are arguably as profound as his economic observations.

The book is bursting with insights about human psychology, many of which presage current developments in behavioural economics. E.g. He commented that “we suffer more… when we fall from a better to a worse situation, than we ever enjoy when we rise from a worse to a better.” This concept is now understood as loss aversion, a judgmental bias.

Jeremy Bentham, whose utility concept formed the foundation of neoclassical economics, wrote extensively about the psychological underpinnings of utility, and some of his insights into the determinants of utility are only now starting to be appreciated. Francis Edgeworth’s Theory of Mathematical Psychics, which introduced his famous “box” diagram showing two-person bargaining outcomes, also included a simple model of social utility, in which one person’s utility was affected by another person’s payoff, which is a springboard for modern theories.

At the core of behavioural economics is the conviction that increasing the realism of the psychological underpinnings of economic analysis will improve economics on its own terms

– generating theoretical insights, making better predictions of field phenomena, and suggesting better policy. This conviction does not imply a wholesale rejection of the neoclassical approach to economics based on utility maximization, equilibrium, and efficiency. The neoclassical approach is useful because it provides economists with a theoretical framework that can be applied to almost any form of economic (and even non-economic) behaviour, and it makes refutable predictions.

The rejection of academic psychology by economists began with the neoclassical revolution, which constructed an account of economic behaviour built up from assumptions about the nature—that is, the psychology—of homo-economicus.

At the turn of the 20th century, economists hoped their discipline could be like a natural science. Psychology was just emerging at that time, and was not very scientific. The economists thought it provided too unsteady a foundation for economics. Their distaste for the psychology of their period, as well as dissatisfaction with the hedonistic assumptions of Benthamite utility, led to a movement to erase the psychology from economics.

Expunging psychology from economics happened slowly. In the early part of the 20th century, the writings of economists such as Irving Fisher and Vilfredo Pareto still included rich speculations about how people feel and think about economic choices. Later John Maynard Keynes very much appealed to psychological insights, but by the middle of the century discussions of psychology had largely disappeared.

Throughout the second half of the century, many criticisms of the positivistic perspective took place in both economics and psychology. In economics, researchers like George Katona, Harvey Leibenstein, Tibor Scitovsky, and Herbert Simon wrote books and articles suggesting the importance of psychological measures and bounds on rationality. These commentators attracted attention, but did not alter the fundamental direction of economics.

However, things were soon set right and many simultaneous developments on the fallacies of standard theory led to the emergence of behavioural economics. One of them was the rapid acceptance by economists of the expected utility and discounted utility models as normative and descriptive models of decision making under uncertainty and inter-temporal choice, respectively. Scientists questioned anomalies in existing theories using compelling experiments.

As economists began to accept anomalies as counterexamples that could not be permanently ignored, developments in psychology identified promising directions for new theory.

Beginning around 1960, cognitive psychology became dominated by the metaphor of the brain as an information-processing device replacing the behaviourist conception of the brain as a stimulus-response machine. The information-processing metaphor permitted a fresh study of neglected topics like memory, problem solving and decision making. These new topics were more obviously relevant to the neoclassical conception of utility maximization than behaviourism had appeared to be, thus sowing the seeds of behavioural finance.

Perhaps the two most influential contributions in this context were published by Tversky and Kahneman. They argued that heuristic short-cuts created probability judgments which deviated from statistical principles. Their 1979 paper “Prospect theory: decision making under risk” documented violations of expected utility and proposed an axiomatic theory, grounded in psychophysical principles, to explain the violations. It triggered the radical advance in behavioural finance in the decade that followed. I have written about Prospect Theory and the impact it had on Behavioral Finance in depth before.

Thanks to this award winning contribution, behavioural finance then took rapid strides in the field of economics and finance, representations of which were seen in the amount of discussions and debate it generated. In 1986 in a conference at the University of Chicago, an extraordinary range of social scientists presented papers. Ten years later, in 1997, a special issue of the Quarterly Journal of Economics was devoted to behavioural economics, both of which showed the influence behavioural finance was having on the financial world.

And now we have grown so much into it that we have a blog on behavioural finance here.

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By definition, if two events are independent, the occurrence of one event does not affect the occurrence of the second. For example, if a fair coin is flipped twice, the occurrence of a head on the first flip does not affect the outcome of the second flip. What if a coin is flipped five times and comes up heads each time. Is a tail “due” and therefore more likely than not to occur on the next flip?

Since the events are independent, the answer is “no.”

The gambler’s fallacy believing the answer is “yes.”

Getting Practical About the Gambler in You

The Gambler’s Fallacy is committed when a person assumes that a departure from what occurs on average or in the long term will be corrected in the short term. The form of the fallacy is as follows:

1. X has happened.
2. X departs from what is expected to occur on average or over the long term.
3. Therefore, X will come to an end soon.

Gambler’s fallacy is better explained with this example.

“Bill is playing against Doug in a WWII tank battle game. Doug has had a great “streak of luck” and has been killing Bill’s tanks left and right with good die rolls. Bill, who has a few tanks left, decides to risk all in a desperate attack on Doug. He is a bit worried that Doug might wipe him out, but he thinks that since Doug’s luck at rolling has been great Doug must be due for some bad dice rolls. Bill launches his attack and Doug butchers his forces. “

There are two common ways this fallacy is committed. In both cases a person is assuming that some result must be “due” simply because what has previously happened departs from what would be expected on average or over the long term.

The first involves events whose probabilities of occurring are independent of one another. For example, one toss of a fair (two sides, non-loaded) coin does not affect the next toss of the coin. So, each time the coin is tossed there is (ideally) a 50% chance of it landing heads and a 50% chance of it landing tails. Suppose that a person tosses a coin 6 times and gets a head each time. If he concludes that the next toss will be tails because tails “is due”, then he will have committed the Gambler’s Fallacy. This is because the results of previous tosses have no bearing on the outcome of the 7th toss. It has a 50% chance of being heads and a 50% chance of being tails, just like any other toss.

The second involves cases whose probabilities of occurring are not independent of one another. For example, suppose that a boxer has won 50% of his fights over the past two years. Suppose that after several fights he has won 50% of his matches this year, that he his lost his last six fights and he has six left.

If a person believed that he would win his next six fights because he has used up his losses and is “due” for a victory, then he would have committed the Gambler’s Fallacy. After all, the person would be ignoring the fact that the results of one match can influence the results of the next one. For example, the boxer might have been injured in one match which would lower his chances of winning his last six fights.

Why The Heck do People Gamble?

The tendency for people to gamble has provided a puzzle for the theory of human behaviour under uncertainty, since it means that we must accommodate both risk-avoiding behaviours (as evidenced by people’s willingness to purchase insurance) with an apparent risk-loving behaviour. Friedman and Savage proposed that the co-existence of these behaviours might be explained by utility functions that become concave upward in extremely high range, but such an explanation has many problems.

For one thing, people who gamble do not appear to be systematically risk seekers in any general sense; instead they are seeking specific forms of entertainment or arousal.

Moreover, the gambling urge is compartmentalized in people’s lives; it tends to take for each individual only certain forms: people specialize in certain games. The favored forms of gambling tend to be associated with a sort of ego involvement: people may feel that they are especially good at the games they favor or that they are especially lucky with these.

The complexity of human behaviour exemplified by the gambling phenomenon has to be taken into account in understanding the etiology of bubbles in speculative markets. Gamblers may have very rational expectations, at some level, for the likely outcome of their gambling, and yet have other feelings that drive their actual behaviour.

Economists tend to speak of quantitative “expectations” as if these were the only characterization of people’s outlooks that mattered. It is my impression, from interviews and survey results, that the same people who are highly emotionally involved with the notion that the stock market will go up may give very sensible, unexciting, forecasts of the market if asked to make quantitative forecasts.

It should be noted that not all predictions about what is likely to occur are fallacious. If a person has good evidence for his predictions, then they will be reasonable to accept. For example, if a person tosses a fair coin and gets nine heads in a row it would be reasonable for him to conclude that he will probably not get another nine in a row again.

This reasoning would not be fallacious as long as he believed his conclusion because of an understanding of the laws of probability. In this case, if he concluded that he would not get another nine heads in a row because the odds of getting nine heads in a row are lower than getting fewer than nine heads in a row, then his reasoning would be good and his conclusion would be justified. Hence, determining whether or not the Gambler’s Fallacy is being committed often requires some basic understanding of the laws of probability.

In investing, gambler’s fallacy victims say the market will head down after it’s been up for a while, and go up as soon as it’s been down. The market eventually changes direction, of course, but having a sense of when is something difficult even for an expert to evaluate. Knowing the market’s next move is crucial only if you’re timing the market—a fool’s game that involves holding investments for as little as a few hours based on short-term market predictions.

So How to Avoid Gambler’s Falacy?

The best thing to do top avoid gambler’s fallacy is to simply stop gambling or in the investment world stop day trading. According to an independent study by the North American Securities Administrators Association and publicized by the Federal Trade Commission, more than 70 percent of day traders lost everything they invested.

Build a portfolio that relies on quality investments, diversification and a long-term investing horizon. Choose investments that, as a group, will ride through the market’s inevitable changes.

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Unlike much psychology, prospect theory has a solid mathematical basis — making it comfortable for economists to play with. However, unlike Expected Utility theory which concerns itself with how decisions under uncertainty should be made (a prescriptive approach), prospect theory concerns itself with how decisions are actually made (a descriptive approach).

Prospect theory was created by two psychologists, Kahneman and Tversky, who wanted to build a parsimonious theory to fit a number of violations of classical rationality that they and others had uncovered in empirical work.

Prospect theory has probably had more impact than any other behavioral theory on economic research. Prospect theory is very influential despite the fact that it is still viewed by much of the economics profession at large as of far less importance than expected utility theory. Among economists, prospect theory has a distinct, though still prominent, second place to expected utility theory for most research.

However, Prospect theory is a mathematically-formulated alternative to the theory of expected utility maximization, an alternative that is supposed to capture the results of such experimental research.

Prospect Theory may be represented in a number of ways but in essence, it describes several states of mind that can be expected to influence an individual’s decision making processes. To postulate the theory Kahneman and Tversky conducted an experiment, by presenting to a groups of people a number of problems.

One group of subjects was presented with this problem.

1. In addition to whatever you own, you have been given $1,000. You are now asked to choose between:
A.    A sure gain of $500.
B.    A 50% chance to gain $1,000 and a 50% chance to gain nothing.

Another group of subjects was presented with another problem.

2. In addition to whatever you own, you have been given $2,000. You are now asked to choose between:
A. A sure loss of $500.
B. A 50% chance to lose $1,000 and a 50% chance to lose nothing.

In the first group 84% chose A. In the second group 69% chose B. The two problems are identical in terms of net cash to the subject, however the phrasing of the question causes the problems to be interpreted differently. This formed the foundation of prospect theory, which in turn gave a huge impetus to the growth of behavioral finance.

In fact it was with this award winning theory that the Efficient Market Hypothesis was proved to be wrong leading to numerous achievement for the field of behavioral finance. So better investment decisions, because of behavioural or behavioral finance can be easily linked to the Prospect theory.

The key concepts addressed by the theory include:

• Loss aversion
• Framing or frame dependence
• Regret aversion
• Mental accounting
• Ambiguity aversion

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In investing, quasi magical thinking can be felt or seen in the way investors sometimes decide to buy or sell stocks. They may do a certain action before making a purchase, take God’s name before making any transaction, wear their left socks first, though they do realize that they do this for their own satisfaction and it does not bear any effect i.e. information-wise in the result of the trade. They do it with the belief that such an action can correct any mistake or help them in not making a mistake, though it seldom happens that way.

Quasi-magical thinking appears to operate more strongly when outcomes of future events, rather than historical events, are involved. Langer showed that people place larger bets if invited to bet before a coin is tossed than after (where the outcome has been concealed), as if they think that they can better influence a coin not yet tossed.

It appears likely that such quasi-magical thinking explains certain economic phenomena that would be difficult to explain the basis of strictly rational behavior. Such thinking may explain why people vote, and why shareholders exercise their proxies. In most elections, people must know that the probability that they will decide the election must be astronomically small, and they would thus rationally decide not to vote.

Quasi-magical thinking, thinking that in good societies people vote and so if I vote I can increase the likelihood that we have a good society or a good company, might explain such voting. The ability of labor union members or oligopolists to act in concert with their counterparts, despite an incentive to free-ride, or defect, may also be explained by quasi-magical thinking.

Quasi magical thinking also brings into the picture the concept of magical thinking, which we will discuss soon.

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In an efficient market, there is “no free lunch”: no investment strategy can earn excess risk-adjusted average returns, or average returns greater than are warranted for its risk. 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.

Suppose that the fundamental value of a share of Ford is $20. Imagine that a group of irrational traders becomes excessively pessimistic about Ford’s future prospects and through its selling, pushes the price to $15. Defenders of the EMH argue that rational traders, sensing an attractive opportunity, will buy the security at its bargain price and at the same time, hedge their bet by shorting a “substitute” security, such as General Motors, that has similar cash flows to Ford in future states of the world. The buying pressure on Ford shares will then bring their price back to fundamental value.

In essence, it is based on two assertions.

• First, as soon as there is a deviation from fundamental value – in short, a mis-pricing – an attractive investment opportunity is created.
• Second, rational traders will immediately snap up the opportunity, thereby correcting the mis-pricing.

Behavioural finance does not take issue with the second step in this argument: when attractive investment opportunities come to light, it is hard to believe that they are not quickly exploited. Rather, it disputes the first step. The argument is that even when an asset is wildly mis-priced, strategies designed to correct the mis-pricing can be both risky and costly, rendering them unattractive. As a result, the mis-pricing can remain unchallenged.

The risks associated with correcting the mis-pricing are:

1. Fundamental Risk
2. Noise Trader Risk

Fundamental risk

The most obvious risk an arbitrageur faces if he buys Ford’s stock at $15 is that a piece of bad news about Ford’s fundamental value causes the stock to fall further, leading to losses. Of course, arbitrageurs are well aware of this risk, which is why they short a substitute security such as General Motors at the same time, that they buy Ford. The problem is that substitute securities are rarely perfect, and often highly imperfect, making it impossible to remove all the fundamental risk. Shorting General Motors protects the arbitrageur somewhat from adverse news about the car industry as a whole, but still leaves him vulnerable to news that is specific to Ford – news about defective tires, say.

Noise trader risk

Noise trader risk, an idea introduced by De Long and studied further by Shleifer and Vishny, is the risk that the mis-pricing being exploited by the arbitrageur worsens in the short run. Even if General Motors is a perfect substitute security for Ford, the arbitrageur still faces the risk that the pessimistic investors causing Ford to be undervalued in the first place become even more pessimistic, lowering its price even further. Once one has granted the possibility that a security’s price can be different from its fundamental value, then one must also grant the possibility that future price movements will increase the divergence.

Noise trader risk is a topic I would like to explore further at a later stage, because it has many practical and real world implication besides theoretical value. Would venture into it once we start discussing personal finance and the impact behavioural finance has in it.

To Conclude

The above two are amongst the most basic limits to arbitrage that exists in the market. As is evident the concept of limits to arbitrage is nothing but restriction placed on arbitrageurs because of inefficiency of the markets in summing up information. The concept nullifies the view that when there is an opportunity to arbitrage the market agents identify it immediately and correct it. What we just saw was that, even when an opportunity is identified, sometimes players can’t do anything about it because the market risks.

Till then to a world of behavioural concepts and better investment ideas.

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Miscalibration

A good starting point for a list of psychological factors that affect decision-making is miscalibration i.e overconfidence in our own abilities. Miscalibration Studies that analyze assessments of uncertain quantities using the fractile method usually find that people’s probability distributions are too tight.

For e.g. People are asked to answer questions with two answer alternatives. After that, they are asked to state the probability that their answer is correct. The usual finding is that for all questions assigned a given probability the proportion of correct answers is lower than the assigned probability. Meaning that people used to say they think their answer is correct while observations more often than not showed they were wrong.

A great number of psychological studies have demonstrated that test subjects regularly overestimate their abilities, especially relative to others. Studies also show that people tend to overestimate the accuracy of information. With respect to factual information, research subjects consistently overestimated the probability that their answer to a question was correct. People often tend to show, in experimental settings, excessive confidence about their own judgments. Lichtenstein, Fischhoff and Philips (1977) asked subjects to answer simple factual questions (e.g., “Is Florida the capital of Russia?”) and then asked them to give the probability that their answer was right: subjects tended to overestimate the probability that they were right, in response to a wide variety of questions.

Such studies have been criticized as merely reflecting nothing more than a difference between subjective and frequentist definitions of probability, i.e., critics claimed that individuals were simply reporting a subjective degree of certainty, not the fraction times they are right in such circumstances.

However, in reaction to such criticism, the experiment was repeated asking the subjects for probability odds that they are right and very clearly explaining what such odds mean, and even asking them to stake money on their answer. The overconfidence phenomenon persisted. Moreover, in cases where the subjects said they were certain they were right, they were in fact right only about 80% of the time: there is no interpretation of subjective probability that could reconcile this result with correct judgments.

Better than average effect

People genuinely think that they are above average. A Taylor and Brown document in their survey that people have unrealistically positive views of the self. One important manifestation is that people judge themselves as better than others with regard to skills or positive personality attributes. One of the most cited examples states that 82 % of a group of students rank themselves among the 30 percent of drivers with the highest driving safety (Svenson (1981)).

Illusion of control

Along with unrealistic optimism Illusion of control is another ovreconfidence barometer. Langer (1975) defines illusion of control as “an expectancy of a personal success probability inappropriately higher than the objective probability would warrant”. Closely related is the phenomenon of unrealistic optimism about future life events (Weinstein (1980)). Presson and Benassi (1996) note in their survey and meta-analysis that after Langer’s article was published, illusion of control “has become a catch phrase in studies in which researchers manipulate conditions that lead people to make nonveridical judgments of control, contingency, prediction ability, etc.” In other words, there is no precise definition of illusion of control in the psychological literature.

Most of the ‘illusion of control’ studies analyze how different manipulated variables such as choice, outcome sequence, task familiarity, or active involvement are related to illusion of control. Certain studies stress that almost all studies do not measure the degree of control. Instead, most studies measure prediction ability or judgments of contingency so that the studies suggest that the phrase “illusionary judgment” would better summarize the various operations of illusion of control in the literature although they admit that “there is some question as to whether illusion of control researchers have examined a single underlying construct.”

The Study of Overconfidence

The questions whether there are stable individual differences in the degree of overconfidence has long been unexplored. Recent psychological research tries to find out whether there are stable individual differences in reasoning or decision making competence.

Furthermore, the question whether the above mentioned concepts - miscalibration, the better than average effect, illusion of control, and unrealistic optimism - are related is mainly unexplored. Some argue that these manifestations are related , others argue that this need not to be the case or even deny a logical link.

Most of the studies that analyze these various facets of overconfidence try to figure out which variables or stimuli induce overconfidence and under which circumstances overconfidence is reduced. However, these studies do not analyze whether the above mentioned concepts are related.

All said and done I am sure all of us when we sit back and reflect have seen traces of overconfidence in our day to day decisions sometimes. And like I always say, investing decisions are no different from regular ones.

Image courtesy: StaneStane

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Thus practitioners of behavioural finance and finance advisors need to understand the level or the risk perception that their client carry in their mind and need to design investment methods on the same basis. Hence, an understanding of risk and its characteristics is an integral part of behavioural finance.

The Presence of Risk

Whether the activity is driving a car or investing in the stock market, everyday we are exposed to all forms of risk. Risk can have different meanings to different individuals. Among experts and society there is no absolute or formally established meaning of risk. Since, scholars do not have the same opinion about how to define risk nor measure risk, a wide range of descriptions and measurements have developed over time.

While risk is a topic applied collectively and universally across a vast array of circumstances, it does not represent the same meaning across various disciplines, organizations, or individuals. For instance, an investment professional may consider risk as the potential of losing a client, while the client may consider risk as the possibility of losing their principal or a portion of it, or just not earning as much as with some safe investment.

The phrase risk clearly has a comprehensive presence in a wide range of the current literature on financial, economic, societal, and technological issues. The basic definition of risk generally carries a negative connotation such as the possibility of harm, loss, destruction, or an undesirable event.

Thus, to place oneself ‘at risk’ implies to partake either voluntarily or involuntarily in an activity or activities that might result in harm, loss, or an undesirable event. The k definition of risusually differs in regards to the specific activity, situation, or circumstance.

Defining Risk: The Judgment of it Existence

A dictionary definition of risk is that of “

a state in which the number of possible future events exceeds the number of actually occurring events, and some measure of probability can be attached to them.”

Such a definition does not sufficiently consider the complexity of risk in business.

Brehmer writes, “How risk is judged depends upon the context in which the judgments take place”.

For instance, risk assessments about hazardous activities (i.e. nuclear power) might evoke concerns over imminent hazard or danger. While, in another circumstance such as investing in a stock mutual fund risk might be considered a decision tailored to realize or failure to reach a potential investment objective.

Renn, focused on the human element of risk when he stated that risks refer to the possibility that human actions or events lead to consequences that affect aspects of what humans’ value. The human element of risk is highly significant if you assume on a micro-level that the decision maker is one of the most important aspects of defining and understanding risk instead of merely within a macro-level such as all the participants in the financial markets as a whole.

The Debate on Risk

The view point of standard economists totally disregards the psychological aspect of decision making. Their perception of risk and decision making is completely reliant on observations and calculations on a given set of data over a period of time, using various statistical instruments to quantitatively evaluate the risk. Standard economists believe in the objective definition of risk perception, where in risk is observed from historical data and is hence seen to be measurable.

The objective risk is summarized by Moore as; “The term risk commonly denotes only those future events in which probabilities of alternative possible outcomes are known.”

When a large number of observations are available, the most probable frequency generated by chance closely approximates the objective probability of an event.” E.g. tossing a coin, the possible outcomes are already known, and it is repetitive. Thus standard view refers risk as a measurable element, which is limited and hence can be easily taken into account.

On the other hand behavioural finance scholars are of the opinion contrary to the one floated by followers of classical and standard economics. According to literature in behavioural finance there is a difference in the objective aspect of risk and the understanding of it by the individual investor. They propose that, investors do not always seek profit maximization or a highest return to investment; rather they pursue maximization of a satisfying strategy.

This means under a given set of outcome about which an investor has limited knowledge, he chooses the most satisfying outcome from the point of view of his perception, even if it may not be the outcome that gives him the highest return. Thus according to behavioural finance risk is subjective in nature and not objective. When individuals focus on objective risk they have in mind risk that have been confirmed scientifically utilizing the best obtainable knowledge and data, on the other hand perceived risk is rooted in subjective factors.

The Risk Never Concludes

I will leave the argument of whether the objective assessment of risk is better than the subjective way or vice versa for a later post. However, in either case the impact that risk has in investor decision making is clearly evident. In purely behavioural finance parlance, the effect is seen in the way investors process information. And it is the way we process the information that we make our decision is it not?

We will continue this discussion further in the way risk reflects in information processing and decision making in the posts coming ahead. Till then if why don’t we put down some of the ways in which risk has affected our individual decision making process..

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Eugene Fama laid the EMH’s empirical foundations, defines efficient market hypothesis as “the simple statement that security prices fully reflect all available information. A precondition for this strong version of the hypothesis is that information and trading costs, the costs of getting prices to reflect information, are always zero”. This meant that any information regarding the stock is reflected in its price when the transaction costs are assumed to be nil, and that the market always knows any information about a stock.

The Roots of Efficient Market Hypothesis

The central theory behind the Efficient Market Hypothesis is that financial markets are efficient in the sense that investors within these markets process information instantaneously and the prices completely reflect all existing information. The following is a description of each the three different types of the market efficiency:

1) The weak form: The market is efficient with respect to the history of all past market prices and information is fully reflected in securities values.

2) The semi-strong form: The market is efficient in which all publicly available information is fully reflected in securities values.

3) The strong form: The market is efficient in regards to all information is fully reflected in securities prices.

EMH can be illustrated with the following example,

A student and his professor who are walking down the street. The student spots an Rs.100 note on the pavement and stoops down to pick it up. The professor then tells him not to waste his time for “Had the bill actually been there, it would have been picked up”. That, in essence, is the concept of the “Efficient Market Hypothesis.” Whenever any new information arises, it spreads so rapidly that stock prices almost instantaneously reflect the change. Thus, no study of stocks and careful stock selection should help over random stock selection. This is because all the effects of data being studied are already reflected in the information.

The efficient market idea contends that prices of securities in public capital markets always reflect all available information about the underlying businesses they represent. In its time the theory has been described as “dazzling” and as an “enormous theoretical and empirical success.” The entire field of academic finance was created on its basis, spreading to every department of every university, and ultimately penetrating trading, board, court and class rooms worldwide.

Despite that success, the EMH suffered from theoretical and empirical limitations or exceptions. This provided the foundation to the creation and the eventual growth of behavioural finance.

The theoretical challenges that EMH faced were:

• Difficult to sustain the case that, investors are fully rational. In fact investors are seen to be quasi rational in their behaviour;
• Assumptions made in deriving the theory, transaction cost in big trades can at times be very costly and can play an important role in the decision making.
• People react to irrelevant information trade on noise rather than information. This renders the point that all information is reflected in the price pointless, as most of it is just noise (rumors, false news, etc.);
• What investors actually do?
  Investors are seen not to use the information in an optimum way and they do not process the information correctly. They are seen to follow advice of financial gurus, fail to diversify, actively trade sell winning stocks, hold onto losing stocks, follow patterns etc.

The empirical challenges to EMH pointed fallacy at its every level

As to its claim that past prices give no profitable trading advantage (weak form efficiency), evidence comparing the performance of winning and losing portfolios shows that losers do way better and winners way worse than standard risk models (like CAPM) explain.

Also, EMH’s claim about public information (semi-strong form efficiency), is infected with anomalies in it. Stocks of smaller companies tend to outperform those of large; highly priced stocks are observed to get lower average returns in the future than those with lower prices. Observations like these imply that it is possible to get superior returns by buying the lower priced stocks.

This goes against the fundamental belief of EMH which states that investors cannot outperform the market, i.e. it cannot get returns higher than the market.

Concerning the more general EMH claim that there should be no reactions to non-information, plenty of evidence shows that all sorts of stock price movements cannot be explained in terms of changes in information about the related businesses. Consider an example that stocks selected for inclusion in a major stock index–such as the Standard & Poor’s 500, for example–tend to enjoy a price increase even though the inclusion alters nothing about their probable future business performance.

These anomalies that plagued EMH were the starting point for research into behavioural finance. It found an explanation to the deviations shown by the markets to the assumptions and beliefs of EMH. It plays a critical role in identifying such situations and making use of it to improve investor returns.

We will talk about how exactly Behavioural Finance grew using the imperfections in the EMH someday in detail. In the meanwhile you can use these places to read and know more about EMH:

• www.e-m-h.org
• www.investorhome.com
• www.capital-flow-analysis.com

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I see that a decent bunch of people of stumbled through this site today, and there while most of them had to rush after giving a thumbs up a few of you stayed back surprisingly long..at least that’s what my analytics tells me. So thank you for doing that and I hope the 2 articles I have written have added some value to you.

Just 2 posts in two months is not something I had aimed for when I started the blog, but circumstances play their games once in a while and I couldn’t be active here. I will certainly try and make amends and be regular here, but if I am not just be assured that it is not because I don’t care.

I hope to share whatever little I know about behavioural finance here (behavioral for my pals in the states), every time I get an opportunity. I will soon be add some stuff with which you can reach me and discuss stuff. Till then happy reading and I hope you enjoy it.

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