Modern themes and insights in behavioral finance (Part 2)

Chanel Holden

2022-02-15 14:41:00 Tue ET

Modern themes and insights in behavioral finance


Lee, C.M., Shleifer, A., and Thaler, R.H. (1990). Anomalies: closed-end mutual funds. Journal of Economic Perspectives 4(4): 153-164.

Lee, Shleifer, and Thaler (1990) view the closed-end fund discount as a 4-part anomaly:

  1. New closed-end funds first appear at a premium and then move rapidly to a discount.
  2. Closed-end funds usually trade at substantial discounts relative to their net asset values.
  3. Closed-end fund discounts are subject to wide variation both over time and across funds.
  4. When closed-end funds get terminated through a merger, liquidation or conversion to an open-end fund, the initial discount disappears.


DeLong, Shleifer, Summers, and Waldmann (1990) and Lee, Shleifer, and Thaler (1991) have explored a possible explanation for the closed-end fund discount based on a model of noise traders. In particular, DeLong et al provide a mathematical model with rational arbitrageurs and noise traders. The rational traders have unbiased expectations, whereas, the noise traders make systematic errors in their investment decisions. Noise trader sentiment shifts over time: sometimes noise traders are excessively optimistic about the future, and other times they are excessively pessimistic. This variability in noise trader sentiment creates a new source of risk in the markets where the risk-averse rational arbitrageurs meet the noise traders. In turn, the additional risk from time-varying noise trader sentiment could deter rational investors from attempting aggressive arbitrage strategies.

Closed-end funds are a good illustration of the above model. If there is a high concentration of noise traders in the ownership of closed-end funds than in the ownership of these funds’ assets, pessimistic noise traders tend to drive down the prices of closed-end funds below the net asset values. Rational investors may not buy such funds at bargain prices because a trader faces a couple of risks associated with buying closed-end funds at a discount. The first risk is that the net asset value of the fund may underperform the market. Further the second risk is that the discount may widen when the rational trader seeks to sell the fund (perhaps because noise traders have become even more pessimistic). This analysis implies that rational traders are willing to purchase closed-end funds only at a substantial discount. Indeed, this discount serves as compensation for noise trader risk. Lee, Shleifer, and Thaler (1991) report evidence in support of this noise trader model. Closed-end fund discounts tend to vary over time and move together. Also the discount disappears when the closed-end fund gets terminated since noise trader risk evaporates in a merger, liquidation, or conversion to an open-end fund.

Lee, Shleifer, and Thaler (1990) contend that the law of one price may not always hold true. Mispricing can occur or can even persist because no riskless arbitrage opportunity exists. In this case, the rational traders face limits or constraints on long-term bets against noise trader sentiment. The key lesson from the above analysis is that the demand for securities can affect market prices even if that demand arises from irrational beliefs.



Stout, L. (2005). Inefficient markets and the new finance model. Journal of Financial Transformation 14(1): 95-105. 

Stout (2005) discusses the key lessons of the new finance based on behavioral finance:

  1. The empirical evidence against market efficiency induces us to question the relationship between security price and fundamental value.
  1. Even if the relationship between price and value is more indirect or more complex than what the orthodox efficient market hypothesis predicts, we should be able to model the relationship between price and value.
  1. The limits of arbitrage such as short-sale constraints could result in persistent deviations from economic value.


Miller (1977) explores how the market sets security prices given 3 more realistic assumptions: investor disagreement, short-sale constraints, and risk aversion. The model predicts that even at a very high price, a company can sell a few shares of stock to a few very optimistic traders. A drop in the stock price boosts demand partly because less optimistic investors who do not already own the stock become interested to buy it. Also, the extant, more optimistic but risk- averse investors become willing to put more eggs in one basket. As a consequence, we would observe a downward-sloping demand function for this stock.

When investors disagree, the stock price could deviate away from its fundamental value by a wide margin. If there are practical limits on short sales, only optimists’ views affect the stock price. Miller’s model predicts that speculative stocks could trade at higher prices than average. Similarly, this model suggests that greater uncertainty increases the dispersion of stock price forecasts. As a result, the wider dispersion could boost the stock price even when the average investor’s opinion of value remains invariant.

Stout (2005) asserts that Miller’s model helps explain a number of market phenomena apart from bubbles: under-diversification (Stout, 1997), the price impact of new share issues and repurchases (Stout, 1990), the large premium required for takeovers (Stout, 1990), and a large number of market anomalies (Stout, 1997). Stout contends that behavioral finance opens a new avenue for empirical research on these anomalies.

A good example of the recent research on market anomalies relates to the “equity premium puzzle”. Bernartzi and Thaler (1995) suggest that the equity premium reflects a psychological phenomenon, “loss aversion”, or an odd human tendency to neurotically dislike decreases in wealth so much that otherwise risk-averse actors would be willing to accept extra risk to gain a chance of avoiding losses. Bernartzi and Thaler note that investors demand a very large premium to hold stocks because stocks carry a much higher probability of a substantial drop in value in a particular year (relative to risk-free investments). This prospect of intermittent loss is so psychologically painful that investors tend to shun stocks. Hence loss aversion leads investors to be less inclined to hold stocks unless these investors receive a disproportionately large return on their stock investments.



Fama, E. (1998). Market efficiency, long-run returns, and behavioral finance. Journal of Financial Economics 49(1): 283-306. 

Fama reviews the empirical literature on a number of market anomalies and then provides an affirmative response to the efficient markets hypothesis. In particular, Fama argues that price underreaction is about as frequent as overreaction in an efficient market where market prices fully embed information. If the market anomalies split randomly between underreaction and overreaction, these so-called anomalies are consistent with the notion of market efficiency. A roughly even split between apparent overreaction and underreaction is a good description of the menu of the extant anomalies. In Fama’s words, this roughly even split between apparent overreaction and underreaction is a pyrrhic victory for the efficient markets hypothesis.

In addition to the above, Fama contends that some of the long-term anomalies are sensitive to methodology. In other words, these anomalies tend to disappear when they get exposed to different models for abnormal returns or when the analyst uses different statistical methods to measure abnormal returns. As a result, most long-term return anomalies could reasonably be attributed to chance.

Fama notes that the empirical work on market anomalies rarely tests a specific alternative to the efficiency markets hypothesis. Like all models, the efficient markets hypothesis is a faulty description of price formation. But this hypothesis can only be replaced by a better specific model of price formation, which needs to be potentially rejectable by empirical tests. On this basis, Fama offers a forceful set of alternative statistical results that help defend the efficient markets hypothesis.



Rubinstein, M. (2001). Rational markets: yes or no? The affirmative case. Financial Analysts Journal 57(3): 15-29. 

Rubinstein (2001) argues that the security markets are “minimally rational” since it is not easy to exploit abnormal profit opportunities. To support his argument, Rubinstein re-examines some of the strongest evidence against market rationality: excess volatility, the risk premium puzzle, the size anomaly, closed-end fund discounts, the calendar effect, and the 1987 stock market crash. In his review of the recent research on anomalies, Rubinstein carefully follows the prime directive of financial economics:

“Explain asset prices by rational models. Only if all attempts fail, resort to irrational investor behavior or market inefficiency.”

Rubinstein contends that the behavioralist would attribute market anomalies to some human tendency or systematic biases to overreact or underreact to some recent events. In this sense, Rubinstein suggests that it is not adequate to extrapolate from studies of individual decision- making done in narrow or limited conditions to the more complex and more subtle security markets. Further, Rubinstein finds some of the behavioral explanations to appear concocted to explain ex post observations (much like the medievalists used to suppose that a different angel provides the motive power for each planet). For instance, Rubinstein thinks that many behavioralists find some more convoluted way to explain short-run price reversals, medium- term price momentum, or longer-run price reversals, from irrational investor behavior.

Rubinstein revisits and provides reasonable answers to some of the most serious anomalies:

  1. Asset prices vary far too much relative to fundamental values.
  2. Aggregate consumption is not sufficiently variable to justify the 7 percent realized return premiums from investing in stocks in the United States.
  3. Contrary to the CAPM’s key prediction that the market premium serves as the only valid pricing factor for stock returns, the size and book-to-market factors can help explain the variation in stock returns.
  4. Closed-end funds often sell at significant discounts to their net asset values.
  5. Although the U.S. stock market has risen at about 10 percent per year since the late 1920s, the Friday close to Monday close 3-year return has been negative.
  6. On October 19, 1987, the stocks on the NYSE fell 29 percent in a single day (apparently absent any significant fundamental news).



Kahneman, D. (2003). Maps of bounded rationality: psychology for experimental and behavioral economics. American Economic Review 93(5): 1449-1475. 

Kahneman (2003) seeks to obtain a map of bounded rationality by exploring the systematic biases that separate people’s choices from the optimal decisions assumed in rational models. This map of bounded rationality extends the conceptual framework pioneered by H. Simon (1955, 1979). Kahneman’s study of human perception has a general property that perceptual systems are often designed to enhance the accessibility of changes or differences. Perception is reference-dependent. In other words, the external attributes of a focal stimulus reflect the contrast between that stimulus and a context of prior and concurrent stimuli. Also, intuitive evaluations of outcomes are reference-dependent in this sense.

Kahneman and Tversky (1979) propose their prospect theory as an alternative to Bernoulli’s (1738) expected utility theory, which assumes that the utility of outcomes depends solely on the final state of endowment regardless of the initial reference point. This alternative theory suggests that human preferences are closely related to attitudes to gains or losses (relative to a reference point). In turn the main carriers of utility or value are gains and losses or changes of wealth rather than final states of wealth. The main predictions of prospect theory follow from the shape of the value function. The value curve is concave in the domain of gains and so favors risk aversion. Also, the value curve is convex in the domain of losses and so favors risk seeking. More importantly, the value curve is sharply kinked at the reference point. This pattern suggests “loss aversion”: the average person demands compensation at least 2 to 2.5 times a loss to be indifferent to a gain of the same size (Kahneman et al, 1991; Tversky and Kahneman, 1992). Thaler (1980) uses this idea of loss aversion to explain riskless choices. In particular, loss aversion helps explain a main violation of consumer theory that Thaler labels as the “endowment effect”: the value of a good is higher to a person when that person views the good as something that could be lost or given up than when the person treats the same good as a potential gain (Kahneman et al, 1990, 1991; Tversky ad Kahneman, 1991). Further, another violation of standard microeconomic theory pertains to the “framing effects”: some insignificant variations in the description of outcomes can affect human preferences (Arrow, 1982; Tversky and Kahneman, 1981, 1986). For instance, it is disproportionately attractive to save people for sure, whereas, it is disproportionately aversive to accept the certain death of people.

Tversky and Kahneman (1974) suggest that people rely upon a limited number of heuristics such as representativeness, availability, and anchoring adjustment. Such heuristics help reduce the complex tasks of assessing probabilities or predicting values to much simpler judgmental operations. In general, such heuristics are quite useful, but sometimes they lead to severe and systematic errors. In other words, these heuristics could be useful mental shortcuts in a series of cases, but the resultant human choices that arise from these heuristics often deviate from the central predictions of rational choice models.

While it is a daunting task to include a common sense psychology of the intuitive agent into economic models, many behavioral economists have risen up to this challenge over the past 3 decades. Some recent behavioral models add realistic assumptions about cognitive limits to the basic architecture of the rational model. In turn, these models could account for a variety of anomalies that rational models fail to explain. Therefore, the new behavioral models show much promise and are likely to come to fruition in time.



Tversky, A. and Kahneman, D. (1974). Judgment under uncertainty: heuristics and biases. Science 185(4157): 1124-1131. 

Tversky and Kahneman (1974) suggest that people rely upon a limited number of heuristics such as representativeness, availability, and anchoring adjustment. Such heuristics help reduce the complex tasks of assessing probabilities or predicting values to much simpler judgmental operations. In general, such heuristics are quite useful, but sometimes they lead to severe and systematic errors. In other words, these heuristics could be useful mental shortcuts in a series of cases, but the resultant human choices that arise from these heuristics often deviate from the central predictions of rational choice models.



People often rely upon the representativeness heuristic to evaluate probabilities by the degree to which A resembles or is representative of B. When A seems to be highly representative of B, people would judge the probability that A originates from B to be higher than average. On the other hand, if A is not similar to B, people would judge the probability that A arises from B to be lower than average. People who adopt the representativeness heuristic are often quite insensitive to sample size. For instance, people often mistakenly think that a deviation from a probabilistic average is more likely in a larger sample. In contrast, probability theory suggests that such a deviation is more likely in a smaller sample. This fundamental notion of statistics is typically not part of people’s repertoire of intuitions.

Another good example of representativeness relates to the gambler’s fallacy. After observing a long run of red on the roulette wheel, for instance, many people often erroneously believe that black is now due, because the occurrence of black may result in a more representative sequence than the occurrence of an additional red. Many people are inclined to view chance as some self-correcting process where a deviation in one direction induces a deviation in the opposite direction to restore the equilibrium. Indeed, any deviations could persist as a chance process unfolds.



People often rely on the availability heuristic to assess the probability of an event by the ease with which instances or occurrences can be brought to mind. For instance, one person may assess the risk of heart attack among old people by recalling such occurrences among his or her acquaintances. In this case, availability could be a useful clue for assessing the frequency or probability of an event, because people tend to recall instances of large classes better and faster than instances of less frequent classes. However, the reliance on availability can lead to predictable biases because factors apart from frequency and probability affect availability. A good example pertains to familiarity. When people judge the size of a class by the availability of its instances, a class whose instances are easily retrievable tends to appear more numerous than a class of equal frequency whose instances are less retrievable. In addition to familiarity, some other factors, such as salience, could affect the retrievability of instances. For example, the impact of seeing a house on fire on the subjective probability of such accidents could be greater than the impact of reading about a fire in the local newspaper. Further, some recent occurrences are likely to be more available than earlier occurrences.

Availability provides a natural account for illusory correlation. How often a couple of events co-occur can be closely related to the strength of the associative bond between them. When there is a strong association between these events, people are likely to deduce that the events could have occurred together frequently.


Anchoring adjustment

In many situations, people make estimates by starting from some initial value that then gets adjusted to yield the final answer. The initial value or starting point may arise from a partial computation or the formulation of the problem. In either case, adjustments tend to be quite insufficient. This anchoring adjustment process implies that different initial values yield very different estimates. In other words, people tend to systematically bias their estimates towards the initial values.

A good example of this anchoring adjustment relates to the computation of 1x2x3x4x5x6x7 x8 versus 8x7x6x5x4x3x2x1. A study of intuitive numeric estimation by high school students shows that the median estimate for the ascending sequence is 512 while the median estimate for the descending sequence is 2,250. Both results underestimate the correct answer (40,320). This example illustrates that people tend to anchor their intuitive judgments to some starting point. But any adjustments around this starting point tend to be insufficient. In other words, this mental shortcut often leads to systematic errors in predication or estimation.

It is not surprising that people retain heuristics such as representativeness or availability even though such mental shortcuts often lead to systematic biases. What is somewhat surprising is the failure of people to infer from lifelong experiences the fundamental statistical rules such as regression towards the mean or the effect of sample size on sampling variability. A better understanding of these heuristics could improve judgments in situations of uncertainty.



Tversky, A. and Kahneman, D. (1986). Rational choice and the framing of decisions. Journal of Business 59(4): S251-S227. 

Tversky and Kahneman (1986) offer a lot of experimental evidence to show that alternative descriptions of a problem often result in different human preferences. This result is contrary to the principle of invariance that underlies the rational theory of risk choice. For this reason, the deviations of actual human behavior from the normative model are too widespread to be left outside the rational choice framework, too systematic to be dismissed as random errors, and too fundamental to be addressed by relaxing the normative system.

A crucial condition for the rational theory of choice is the principle of invariance: different representations of the same problem should give rise to the same preference (Arrow, 1982). However, a number of experiments show that variations in framing decision problems yield systematic violations of invariance that cannot be defended on normative grounds. Tversky and Kahneman suggest that invariance would hold if all formulations of the same prospect were transformed to a canonical representation (such as a cumulative probability distribution of the same random variable) because the various versions would then all be evaluated in the same manner. The apparent failures of invariance indicate that people do not spontaneously aggregate concurrent prospects or transform all outcomes into a common frame.

Kahneman and Tversky (1979) offer prospect theory as an alternative to the rational theory of choice. Prospect theory posits that a preliminary analysis of the decision problem frames the effective acts, contingencies, or outcomes. This framing phase depends on norms, habits, and expectancies of the decision maker. The next step involves the evaluation of the various prospects in a specific frame. The decision maker then attempts to optimize the overall value of the prospect. Kahneman and Tversky note that the value function resembles the shape of the letter S. This value function is concave above the reference point and convex below it.

A significant property of the value function is loss aversion: the human response to losses is more extreme than the response to gains. The common reluctance to accept a fair bet on the toss of a coin implies that the displeasure of losing a sum of money exceeds the pleasure of winning the same amount. Therefore, the above value function depends upon gains or losses relative to a reference point, is concave for gains and convex for losses, and is usually steeper for losses than for gains (Kahneman and Tversky, 1979; Tversky and Kahneman, 1981).

Loss aversion presents an obstacle to mutual negotiation whenever the participants evaluate their own concessions as losses and the concessions obtained from the other party as gains. When diplomats negotiate over missiles, for instance, the loss of regional security associated with dismantling a missile may often loom larger than the increment of security produced by a similar action on the adversary’s part. Agreement would be much easier to achieve by both negotiators who trade in “bargaining chips” that carry equal values to both sides. In this case, loss aversion tends to wane in effect as both sides can evaluate their prospects or outcomes in common grounds (Kahneman and Tversky, 1984).

In most cases, incentives can improve the quality of decisions, and the forces of arbitrage or competition can nullify the effects of human error or illusion. But whether these factors help ensure rational choices in any particular situation in an open empirical issue. Indeed prospect theory serves as an attempt to articulate the main principles of perception and judgment that constrain the rationality of choice. This new paradigm opens a bright avenue for behavioral economists.



Kahneman, D. Knetsch, J., and Thaler, R.H. (1991). Anomalies: the endowment effect, loss aversion, and status quo bias. Journal of Economic Perspectives 5(1): 193-206. 

Thaler (1980) reports the “endowment effect” that people often demand much more to give up an object than they would be willing to pay to acquire it. This endowment effect is closely related to the “status quo bias” or a preference for the current state of affair (Samuelson and Zeckhauser, 1988). The above anomalies are a manifestation of an asymmetry of value that Kahneman and Tversky (1984) label as “loss aversion”. Loss aversion refers the fact that the disutility of giving up an object is greater than the utility associated with acquiring the object.

Kahneman, Knetsch, and Thaler (1990) run a number of experiments to determine whether the endowment effect survives when subjects face market discipline and so have a chance to learn. One of the experiments involves the market for mugs that are given to a small number of undergrad students at random. These students then get to trade the mugs with their class- mates. In this case standard economic theory predicts that the objects would gravitate toward the subjects who value these objects most (since transaction costs are insignificant). However, the evidence suggests that the median selling prices are about twice the median buying prices. In other words, people who own a good tend to value it more than others who want to buy the good (Loewenstein and Kahneman, 1991). The above endowment effect shows a human tendency to be disproportionately averse to losses.

A key implication of loss aversion is that individuals are often inclined to remain at the status quo. This status quo bias persists because the disutility of leaving the status quo looms larger than the utility of seeking an alternative (Samuelson and Zeckhauser, 1988). A good example can be the U.S. claim against any unilateral action on either side of the Taiwan Strait to alter the status quo. Another example pertains to the notion of fairness. Supreme Court Justice O. W. Holmes (1897) states the principle succinctly: “It is in the nature of a man’s mind. A thing that a man enjoyed or used as his own for a long time, whether property or opinion, cannot be torn away without his resenting the act…The law can ask no better justification than the deepest instincts of man.”

In general, a given difference between 2 options has a much greater impact if it is viewed as a difference between 2 disadvantages than if it is seen as a difference between 2 advantages. The status quo bias is a natural consequence of this asymmetry, that is, the disadvantages of a change loom larger than its advantages. Subjects are often more sensitive to the dimension in which they are losing relative to their reference point.

Kahneman, Knetsch, and Thaler (1991) suggest that loss aversion can be an essential part of behavioral models of risky choice. Just as a person’s response to gains can differ from his or her response to losses, the market’s general response to increases in the price can differ from the market’s response to decreases in the price. The possibility of loss aversion suggests that the treatment of responses to changes in economic value should routinely separate the cases of favorable vis-à-vis unfavorable changes. Such separation could help improve the precision of estimates at a tolerable price in terms of greater complexity.



Rabin, M. and Thaler, R.H. (2001). Anomalies: risk aversion. Journal of Economic Perspectives 15(1): 219-232. 

Rabin and Thaler (2001) focus on a key aspect of utility theory, risk aversion, which refers to the hesitation over risky or uncertain monetary prospects even when they involve an average gain. In conventional utility theory, risk aversion comes solely from the concavity of a person’s utility defined over wealth levels. But a number of experiments provide evidence against the basic idea of constant or stable relative risk aversion. In particular, these experiments suggest that an average person’s marginal utility for wealth must decline very rapidly. A good example relates to the U.S. equity premium. Some studies report that the typical investor’s relative risk aversion parameter could be as high as 40 to justify the large U.S. equity premium.

Rabin (2000) and Rabin and Thaler (2001) argue that most people do not display a consistent coefficient of relative risk aversion. It would therefore be a waste of time to try to measure a constant coefficient of relative risk aversion. An alternative explanation for a typical person’s aversion to modest risk is related to “loss aversion” and “mental accounting”.

In Kahneman and Tversky’s (1979) prospect theory, loss aversion is the tendency to feel the pain of a loss more acutely than the pleasure of a gain of the same size. In this more realistic context, decision-makers react to changes in wealth, instead of absolute levels of wealth, and are roughly twice as sensitive to losses relative to gains. In other words, most people perceive prospects as gains or losses relative to the status quo or some reference point, and losses are weighted roughly twice as much as gains. This theory explains why most people would reject coin-flip bets that offer less than 2-to-1 odds (Samuelson, 1963). Therefore, a core aspect of prospect theory, loss aversion, directly explains why people tend to turn down small gambles with a positive average return.

Mental accounting pertains to the way individuals and households keep track of and evaluate financial transactions. Most people’s aversion to small-scale risk often appears to derive from the tendency to assess risks in isolation rather than in a broader perspective. In fact, if small- scale gambles were recast in a broader view, people would be more likely to accept the small gambles that offer a positive average return. People would realize that their gains would tend to outweigh their losses in the long run. Moreover, the stakes of the bet would seem small to most people if they tried to consider the bet in conjunction with their total wealth.

Benartzi and Thaler (1995) use the above concept of mental accounting to characterize their explanation or the equity premium puzzle. Should investors focus on the long-run returns of U.S. stocks, the investors would recognize the relatively small risk associated with U.S. stocks compared to bonds. In turn, such investors would be happy to hold stocks at a much smaller premium. Instead, most investors tend to focus on the high short-run volatility of U.S. stock returns with frequent mental accounting losses. As a consequence, these investors demand a substantial equity premium as compensation for high short-term risk.



Odean, T. (1998). Are investors reluctant to realize their losses? Journal of Finance 53(5): 1775- 1798. 

Odean (1998) analyzes the trading records for 10,000 accounts at a large discount brokerage house and reports evidence in support of the disposition effect: many investors tend to hold losing stocks too long and sell winning stocks too soon (Shefrin and Statman, 1985). Further, these investors demonstrate a strong preference for realizing winners rather than losers. This behavior does not appear to be motivated by a desire to rebalance portfolios or to avoid the higher trading costs of low-cap stocks. Neither does subsequent portfolio performance help explain the disposition effect.



Odean, T. (1998). Volume, volatility, price, and profit when traders are above average. Journal of Finance 53(6): 1887-1933. 

Odean (1998) examines financial market where price-taking traders, a strategic-trading insider, and risk-averse marketmakers are overconfident. Overconfidence often raises average trading volume, increases market depth, and decreases the average utility of overconfident investors. Overconfident investors can often cause financial markets to underreact to the information of rational traders. Financial markets also tend to underreact to abstract, statistical and highly relevant information, whereas, they tend to overreact to salient, anecdotal, and less relevant information.



Barber, B.M. and Odean, T. (2000). Trading is hazardous to your wealth: the stock investment performance individual investors. Journal of Finance 55(2): 773-806. 

Barber and Odean (2000) examine the investment performance of U.S. common stocks held directly by U.S. households by analyzing the data on trading activity for 78,000 households at a large discount brokerage firm over the 6 years ending in January 1997. This analysis tests 2 competing theories of trading activity. In the framework of rational expectations, Grossman and Stiglitz (1980) argue that investors trade only when the marginal benefit of an extra trade exceeds the marginal cost of the trade. In contrast, Odean (1998), Gervais and Odean (1998), and Caballe and Sakovics (1998) develop theoretical models of financial markets where most investors suffer from overconfidence. Such models predict that investors often tend to trade to their detriment.

Barber and Odean’s most dramatic empirical evidence supports the view that overconfidence leads to excessive trading activity. On one hand, those households that trade frequently with monthly turnover over 8.8 percent do not produce higher gross returns than the households that trade infrequently. In contrast, the former households earn a net annual geometric mean return of 11.4 percent, whereas, the latter households earn a net return of 18.5 percent. The above results are consistent with the models where extra trades emanate from investor over- confidence. But these results do not accord with the models based on rational expectations.

Barber and Odean also find that the households significantly underperform the benchmarks after accounting for trading costs. Whilst the average household earns a gross return of 18.7 percent, the investment in a value-weighted market index earns an annual geometric average return of 17.9 percent. In comparison, the net performance (after accounting for the bid-ask spread and commissions) of these households is below par. In brief, the average household earns a net return of 16.4 percent. Barber and Odean’s results suggest that excessive trading activity is hazardous to the average investor’s wealth. It is the total trading cost, not portfolio selection, that could explain the poor stock performance of the above households.

For brevity and clarify, we can summarize Barber and Odean’s main empirical results below:

  1. U.S. households trade common stocks frequently. The average household turns over 75 percent or more of its common stock portfolio on an annual basis.
  2. Trading costs are high. The average round-trip trade in excess of $1,000 costs 3 percent in commissions and 1 percent in bid-ask spread.
  3. Most households tilt their investments toward small and high-beta stocks. There is a less obvious tilt toward value or high book-to-market stocks.
  4. The average household underperforms the relevant market benchmarks in terms of net stock returns. Hence, excessive trading activity is hazardous to the investor’s wealth.



Barber, B. and Odean, T. (2001). The Internet and the investor. Journal of Economic Perspectives 15(1): 41-54. 

Barber and Odean (2001) focus on how technological advances associated with the Internet are likely to affect individual investors and financial markets. The Internet has changed how investors receive and act on information. In particular, the Internet has lowered the fixed and marginal costs of producing financial services and so enables newer or smaller companies to challenge the extant providers of these services. Lower costs and more alternatives appear to benefit many investors. However the new investment environment also may have a dark side. Many of today’s investors are new to the market. The ability to place trades directly online, rather than through a broker, could give these investors an exaggerated sense of control over the outcome of their trades. The vast amount of online investment data available can enable investors to confirm their prior beliefs and may lead such investors to become overconfident in their ability to pick stocks or other securities. Fast feedback may focus investors’ attention on recent performance. Furthermore, investors may have in recent years put themselves at a greater risk by concentrating their trades in e-commerce companies. Financial markets where valuations are uncertain and investors are active, could be prone to speculative bubbles.

Barber and Odean (2000) show that those investors who switched from phone-based to PC- based trading systems tend to trade both more actively and more speculatively. Also, Barber and Odean (2000, 2001) report that such investors tend to earn lower returns. In particular, a subsample of active investors at a U.S. nationwide discount broker lags the market return by 6 percent per annum. Barber and Odean argue that the Internet appears to have contributed to a number of conditions that the literature on experimental economics suggests to be most conducive to speculative bubbles or prolonged mispricings (Smith, Suchanek, and Williams, 1988; Caginalp, Porter, and Smith, 2000; Shiller, 2000):

  1. when the ratio of inexperienced to experienced investors is high;
  2. when there is greater uncertainty about the future value of a security;
  3. when investors have more cash to invest relative to the value of the security.


Barber and Odean (2000) contend that the Internet has brought changes to the investment environment. Such changes may bolster the overconfidence of online investors by providing an illusion of knowledge and an illusion of control. In general, a greater volume and variety of information is more likely to feed such illusions. When people who initially disagree on a topic are given arguments on either side of the issue, these people become further polarized in their beliefs (Lord, Ross, and Lepper, 1979). Most people tend to favor the arguments that accord with their prior views. Also most people are inclined to discount opposing views. Not only are people more impressed by the arguments that agree with their prior beliefs, but such people also actively seek out confirmatory evidence. Hence, investors are more likely to visit chatrooms of like-minded investors. These investors are often likely to “follow the herd”. In addition, online investors are likely to become overconfident. Such investors may believe that they are more able to perform tasks such as stock selection than the average investor. Some theoretical models predict that overconfident investors tend to trade both more actively and more speculatively than the average investor (Odean, 1998). Further, overconfident investors tend to hold underdiversified portfolios, have lower average utilities, and contribute to higher market volatility.



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This free ebook, AYA Analytica, shares new economic insights, investment memes, and stock portfolio strategies through both blog posts and patent specifications on our AYA fintech network platform. AYA fintech network platform is every investor's social toolkit for profitable investment management. We can help empower stock market investors through technology, education, and social integration.

We hope you enjoy the substantive content of this essay! AYA!


Andy Yeh

Chief Financial Architect (CFA) and Financial Risk Manager (FRM)

Brass Ring International Density Enterprise (BRIDE) © 



Do you find it difficult to beat the long-term average 11% stock market return?

It took us 20+ years to design a new profitable algorithmic asset investment model and its attendant proprietary software technology with fintech patent protection in 2+ years. AYA fintech network platform serves as everyone's first aid for his or her personal stock investment portfolio. Our proprietary software technology allows each investor to leverage fintech intelligence and information without exorbitant time commitment. Our dynamic conditional alpha analysis boosts the typical win rate from 70% to 90%+.

Our new alpha model empowers members to be a wiser stock market investor with profitable alpha signals! The proprietary quantitative analysis applies the collective wisdom of Warren Buffett, George Soros, Carl Icahn, Mark Cuban, Tony Robbins, and Nobel Laureates in finance such as Robert Engle, Eugene Fama, Lars Hansen, Robert Lucas, Robert Merton, Edward Prescott, Thomas Sargent, William Sharpe, Robert Shiller, and Christopher Sims.


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