Once upon a time, two economists were walking together when one of them saw something that caught his attention. “Look,” he exclaimed, “here’s a great research topic!” “Nonsense,” the other one said, “If it were, someone would have written a paper on it by now.” For a long time this attitude governed the view of academics toward the stock market. They simply believed that the stock market was not a proper subject for serious study. That attitude has long since changed. The amount of research information available today is overwhelming. The problem has become how to interpret it so that it becomes useful for decision-making, whether from the point of view of a financial services firm or that of an investor.
By offering a brief introduction to the basic concepts used in research on the stock market – specifically market efficiency and behavioral aspects of investing – we hope to help an investor define his or her approach to equity investing and give aspiring financial services professionals some food for thought in regard of both portfolio policy and marketing strategy.
The Pre-History: Statistical Research
Most of the early statistical research of the stock market centered on the question of whether security prices are serially correlated (i.e., are there trends in stock prices) or follow a “random walk,” changing to reflect new information. A number of studies concluded that successive daily changes in stock prices are mostly independent. There seemed to be no pattern that could predict the future direction of price movements.
In 1959 Roberts plotted the results of a series of randomly-generated numbers to see whether any patterns that were known to technical analysts would be visible. Figure 1 provides an example of Roberts’ plot:
Simulated stock price path
Roberts noted that it was virtually impossible to tell if his plots were generated using random numbers or actual stock market data. Today, anyone can replicate Roberts’ results using a common spreadsheet program. (See Benninga for detailed instructions).
The Origin of the Efficient Market Hypothesis
Next came the introduction of the term “efficient market.” Fama defined an efficient market as:
Note that this description is very similar to that of a perfectly competitive market out of a microeconomics textbook where every seller earns a normal profit, i.e., the amount of profit sufficient to stay in business, but insufficient to attract a competitor.
If we assume that this is true of the stock market, it follows that any new information that becomes available to the market will be very quickly reflected in the prices. Otherwise, there will be opportunities for abnormal returns. Three forms of the efficient market hypothesis have been proposed and studied. The weak form holds that current stock prices reflect historical price and volume data. The semi-strong form of the hypothesis holds that prices include not only historical data, but also all publicly-known available information about the company. The strong form of the hypothesis is the most stringent and holds that stock prices fully reflect all information, both pubic and private.
Tests of Market Efficiency in the 1960s
A number of different approaches were used to test the efficient market hypothesis. One was to do more studies on serial correlation of security prices. A variation of this approach tested various trading strategies recommended by technical analysts to see if they have any investment value. Both invariably came back with mostly negative results.
An important breakthrough in testing market efficiency came with the advent of the “event study” methodology. In an event study, researchers take a sample of similar events that occurred in different companies at different times and determine how, on average, this event impacted the stock price. The event study techniques were first applied to stock splits, but quickly expanded into other areas. Some of the research designs are quite clever. For example, Johnson, Magee, Nagarajan, and Newman studied stock market reaction to executive deaths and found that unexpected CEO deaths are associated with stock price decreases unless the CEO was the company founder. Then the stock price tends to increase, begging the inference that the ability to create a business is different from the ability to run one.
By 1975, the preponderance of evidence argued that markets were efficient. Statistical studies showed that technical analysis did not add value. Event studies found that the market quickly reacts to new information and studies of professional investors’ performance made a strong case for the strong form market efficiency.
Tests of Market Efficiency after 1975
As more and more researchers tested the efficient market hypothesis, some rather controversial evidence began to appear. Rozeff and Kinney found that January stock returns were higher than in any other month. Gibbons and Hess reported “the Monday effect” – stock prices tended to go down on Mondays. Both of these findings were clearly inconsistent with the weak form of market efficiency. Interestingly enough, Gibbons and Hess noticed that the Monday effect seemed to decrease over time. It appears that the effect was known to some market participants for a while, and they were taking advantage of this private information, which, in turn, caused their gains to decrease over time.
Grossman and Stiglitz argued that if all relevant information were reflected in market prices, market agents would have no incentive to acquire the information on which prices are based. (This line of reasoning came to be known as Grossman-Stiglitz paradox and, along with his other contributions, earned Joseph Stiglitz his Nobel prize in 2001.)
Rendelman, Jones, and Latane studied earnings surprises and their effect on the stock price. They divided their sample into ten groups (deciles) according to how positive or negative the earnings surprise was. Then they calculated averaged price paths for stocks in each decile. Figure 2 presents a summary of their findings.
Stock price paths around earnings announcement by decile.
While the market did react to earnings surprises quickly, the prices also drifted in the direction of the earnings surprise following the announcement. In other words, the market underreacted to the quarterly earnings announcements. However, in a somewhat puzzling twist, there were other studies which suggested that the stock market actually overreacts. Shiller found that stock prices change too much to be justified by subsequent changes in dividends. This phenomenon came to be known as “excess volatility”. De Bondt and Thaler concluded that the stock market tends to overreact to long series of bad news. So by 1985, there were enough anomalies discovered to seriously doubt the validity of the efficient market hypothesis.
Reconciling Theory and Reality
This is a good point at which to consider the efficient market hypothesis and identify those assumptions that may be inconsistent with reality as we know it. First of all, as ironic as it sounds, there is no way to test market efficiency per se. We can only test a joint hypothesis stating that, first, the market is efficient in equating asset prices with their intrinsic values, and, second, we know what the intrinsic values are; i.e., we have a perfect asset pricing model. Whenever an anomaly is found, we don’t know (and have no way of knowing) which part of this joint hypothesis did not work.
Jensen argued that an efficient market should adjust prices within limits imposed by the cost of trading. He insisted that if, for example, transactional costs are 1%, an abnormal return of 1% must be considered within the bounds of efficiency. Indeed, if inefficiency cannot be exploited for profit net of costs, is the market really inefficient? This, of course, begs another question: What is the level of transactional costs at which we can no longer call a market efficient in spite of its being within the bounds of efficiency? There may also be some effects caused by the way security prices are reported (market microstructure effects). A typical research assumption has been that trades can be executed at the closing price as recorded by a data provider. However, the average NYSE-AMEX stock has a quoted bid-ask spread of about 3%, rising to about 6% for stocks priced under $5.
Then there is a short-selling issue. In an efficient market, short sales are unrestricted. In reality, 70% of mutual funds state in their prospectus that they will never engage in a short sale. Interestingly enough, recent empirical evidence (e.g., Finn, Fuller and Kling) seems to suggest that, while undervalued investments are hard to come by, overvalued ones are much more common. Finally, there is the unavoidable issue of investor heterogeneity. One obvious example is tax status. Tax-exempt, tax-deferred, and taxable investors acting rationally will often choose different courses of action when presented with the same problem.
What then should the investor do? Is Fama-style rational profit maximization the best – or only – model of investor behavior? If it is not, what else is there, and what does that mean for understanding how investors act?
An Alternative Behavioral Model?
Since the early 1980s, there has been a movement toward incorporating more behavioral science into finance. The proponents of behavioral finance cite several key areas where the reality seems to be most at odds with the efficient market hypothesis. One is the excess volatility problem discussed above. A related puzzle is that of trading volume. If everyone knows that everyone (including himself or herself) is rational, then every trader might wonder what information the seller has that the buyer doesn’t, and vice versa.
Next is the great dividend puzzle. In a perfect world according to Modigliani and Miller, investors should be indifferent between dividends and capital gains. In the real world, because of the structure of the U.S. tax system, rational investors should prefer capital gains to dividends, and companies should prefer share repurchases to dividends. At the same time, most large companies do pay dividends. In addition, stock prices tend to rise when dividends are increased or initiated. Another puzzle is that of the equity premium. Historically, this benefit has been much greater than can be explained by risk alone. Finally, it seems that future returns can, at least partially, be predicted on the basis of various historic measures such as price-earnings and price-to-book ratios, earnings surprises, dividend changes, or share repurchases.
However, in spite of all these irregularities, real-world portfolio managers are still having a hard time trying to beat the market. Moreover, good performance this year consistently fails to predict good performance next year.
With this in mind, let’s examine the case for behavioral finance.
First of all, what is behavioral finance? In short, it postulates that investors have cognitive biases — imperfections in their perceptions of reality. (Have you ever noticed how much bigger the moon looks when it is just above the horizon compared to when it is high?) Here are a few of the most common cognitive biases in finance.
Mental accounting. Dividends are perceived as an addition to disposable income; capital gains usually are not.
Biased expectations. People tend to be overconfident in their predictions of the future. . Between 1973 and 1990, earnings forecast errors by security analysts have been anywhere between 25% and 65% of actual earnings.
Reference dependence. Investment decisions seem to be affected by an investor’s reference point. If a certain stock was once trading for $20, then dropped to $5 and finally recovered to $10, the investor’s propensity to increase holdings of this stock will depend on whether the previous purchase was made at $20 or $5.
Representativeness heuristic. Investors mistake good companies for good stocks. Good companies are well-known and in most cases fairly valued. Their stocks, therefore, may not have a significant upside potential.
 (See Footnote 13 for more information on behavioral finance.)
Why is Behavioral Finance Important? As marketers know, any product has its unique set of utilitarian and value-expressive characteristics. Do investments have value-expressive characteristics? If they do, we should not be surprised that pricing differences exist between otherwise identical investments, based entirely on their value-expressive characteristics. A casual look at advertising by stock exchanges and the extent of branding efforts in the mutual funds industry suggests a positive answer.
Is a Compromise in Sight? Are the differences between traditional finance and behavioral finance irreconcilable? Not necessarily. One the one hand, sensible proponents of behavioral finance recognize its limitations. On the other hand, advocates of traditional finance research are beginning to study the evidence of various behavioral effects. (See, for example, Chen, Hong and Stein for treatment of opinion differences.)
We believe the current state of research warrants two conclusions. From the standpoint of an individual investor, it is important to remember that without clear indication of an equity portfolio manager’s ability to beat the market, the investor may be better off with a passive strategy (“When in doubt, index”). Since there is rarely such indication in the equity markets — the evidence of being able to outperform the market is usually mixed, especially when considered on a risk-adjusted basis — individual investors should give a serious thought to indexing their equity holdings and seeking skill-based return enhancement elsewhere. However, it pays to understand that what is true in the stock market may not necessarily be so in other markets (e.g., in the bond market). Finally, it is imperative for individual investors to keep in mind that they themselves may be subject to various cognitive biases.
To a financial services firm, we would recommend, at least for the time being, using behavioral finance to develop and validate marketing ideas, rather than investment strategies. Behavioral research gives us valuable clues about the personality of the client, but the market impact of behavioral phenomena so far appears to be factored into prices.
 Roberts, Harry (1959), “Stock Market ‘Patterns’ and Financial Analysis: Methodological Suggestions,” Journal of Finance, Vol. XIV, No. 1, 1-10.
 Benninga, Simon (2000), Financial Modeling (Cambridge, Massachusetts: MIT Press)
 Fama, Eugene (1965a), “Random Walks in Stock Market Prices,” Financial Analysts Journal, vol. 21, no. 5 (September/October), 55-59.
Fama, Eugene (1965b), “The Behavior of Stock Market Prices,” The Journal of Business, vol. 38 (January), 34-105.
 Johnson, W. Bruce, Robert P. Magee, Nandu J. Nagarajan, and Harry A. Newman (1985), “An Analysis of the Stock Price Reaction to Sudden Executive Deaths,” Journal of Accounting and Economics, 1985, 151-174.
 Rozeff, M. and W. Kinney (1976), “Capital Market Seasonality: The Case of Stock Returns,” Journal of Financial Economics 3, 379-402.
 Gibbons, M., and P. Hess, (1981) “Day of the Week Effects and Assets Returns,” Journal of Business, vol. 54, 579-596.
 Grossman, Sanford, and Joseph Stiglitz (1980), “On the Impossibility of Informationally Efficient Markets,” American Economic Review 70, 393-408.
 Rendelman, Richard J., Charles P. Jones, and Henry A. Latane (1982), “Empirical Anomalies Based on Unexpected Earnings and the Importance of the Risk Adjustments,” Journal of Financial Economics, vol. 10, no. 3, 269-287.
 Shiller, Robert (1981), “Do Stock Prices Move Too Much to Be Justified by Subsequent Changes in Dividends?” American Economic Review , vol. 71, no. 3 (June), 421-436.
 De Bondt, Werner, and Richard Thaler (1985), “Does the Stock Market Overreact?” Journal of Finance, vol. 40, no. 3 (July), 793-808.
 Jensen, Michael (1978), “Some Anomalous Evidence Regarding Market Efficiency,” Journal of Financial Economics, vol. 6, nos. 2/3, 95-101.
 Finn, Mark, Russell Fuller, and John Kling (1999), “Equity Mispricing: It’s Mostly on the Short Side,” Financial Analysts Journal, vol. 55, no. 6 (November/December), 117-126.
 Chen, Joseph, Harrison Hong, and Jeremy Stein (2000), “Forecasting Crashes: Trading Volume, Past Returns and Conditional Skewness in Stock Prices,” NBER Working Paper No. 7687 (Cambridge, Massachusetts: National Bureau of Economic Research).