"...a blend of erudition (fascinating and sometimes obscure historical minutiae abound), popularization (mathematical rigor is relegated to appendices) and exposition (the reader need have little knowledge of the fields involved) ...and the illustrations include many superb examples of computer graphics that are works of art in their own right." Nature

The Fractal Geometry of Nature

Efficient Capital Markets: A Review of Theory and Empirical Work Author(s): Eugene F. Fama Source: The Journal of Finance, Vol. 25, No. 2, Papers and Proceedings of the Twenty-Eighth Annual Meeting of the American Finance Association New York, N.Y. December, 28-30, 1969 (May, 1970), pp. 383-417 Published by: Blackwell Publishing for the American Finance Association Stable URL: http://www.jstor.org/stable/2325486 Accessed: 30/03/2010 21:28

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Efficient capital markets: a review of theory and empirical work*

This paper tests the relationship between average return and risk for New York Stock Exchange common stocks. The theoretical basis of the tests is the "two-parameter" portfolio model and models of market equilibrium derived from the two-parameter portfolio model. We cannot reject the hypothesis of these models that the pricing of common stocks reflects the attempts of risk-averse investors to hold portfolios that are "efficient" in terms of expected value and dispersion of return. Moreover, the observed "fair game" properties of the coefficients and residuals of the risk-return regressions are consistent with an "efficient capital market"--that is, a market where prices of securities

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Risk, Return, and Equilibrium: Empirical Tests

Research in experimental psychology suggests that, in violation of Bayes' rule, most people tend to "overreact" to unexpected and dramatic news events. This study of market efficiency investigates whether such behavior affects stock prices. The empirical evidence, based on CRSP monthly return data, is consistent with the overreaction hypothesis. Substantial weak form market inefficiencies are discovered. The results also shed new light on the January returns earned by prior "winners" and "losers." Portfolios of losers experience exceptionally large January returns as late as five years after portfolio formation. As ECONOMISTS INTERESTED IN both market behavior and the psychology of individual decision making, we have been struck by the similarity of two sets of empirical findings. Both classes of behavior can be characterized as displaying overreaction. This study was undertaken to investigate the possibility that these phenomena are related by more than just appearance. We begin by describing briefly the individual and market behavior that piqued our interest. The term overreaction carries with it an implicit comparison to some degree of reaction that is considered to be appropriate. What is an appropriate reaction? One class,,of tasks which have a well-established norm are probability revision problems for which Bayes' rule prescribes the correct reaction to new information. It has now been well-established that Bayes' rule is not an apt characterization of how individuals actually respond to new data (Kahneman et al. [14]). In revising their beliefs, individuals tend to overweight recent information and underweight prior (or base rate) data. People seem to make predictions according to a simple matching rule: "The predicted value is selected so that the standing of the case in the distribution of outcomes matches its standing in the distribution of impressions" (Kahneman and Tversky [14, p. 416]). This rule-of-thumb, an instance of what Kahneman and Tversky call the representativeness heuristic, violates the basic statistical principal that the extremeness of predictions must be moderated by considerations of predictability. Grether [12] has replicated this finding under incentive compatible conditions. There is also considerable evidence that the actual expectations of professional security analysts and economic forecasters display the same overreaction bias (for a review, see De Bondt [7]). One of the earliest observations about overreaction in markets was made by J. M. Keynes:"... day-to-day fluctuations in the profits of existing investments,

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Does the Stock Market Overreact

https://teach.business.uq.edu.au/courses/FINM6900/files/module-1/readings/APT-Ross.pdf

The arbitrage theory of capital asset pricing

BIASES IN COMPUTED RETURNS An Application to the Size Effect

IN THE recent literature there has been a considerable interest in the theory of random walks in stock-market prices. The basic hypothesis of the theory is that successive price changes in individual securities are independent random variables. Independence implies, of course, that the past history of a series of changes cannot be used to predict future changes in any "meaningful" way. What constitutes a "meaningful" prediction depends, of course, on the purpose for which the data are being examined. For example, the investor wants to know whether the history of prices can be used to increase expected gains. In a random-walk market, with either zero or positive drift, no mechanical trading rule applied to an individual security would consistently outperform a policy of simply buying and holding the security. Thus, the investor who must choose between the random-walk model and a more complicated model which assumes the existence of an excessive degree of either persistence (positive dependence) or reaction (negative dependence) in successive price changes, should accept the theory of random walks as the better model if the actual degree of dependence cannot be used to produce greater expected profits than a buy-and-hold policy.' On the other hand, the statistician has different though equally pragmatic notions of what constitutes an important violation of the independence assumption of the random-walk model. He will

Filter Rules and Stock-Market Trading

In recent years, the number of downgrades in corporate bond ratings has exceeded the number of upgrades, leading some to conclude that the credit quality of U.S. corporate debt has declined. However, an alternative explanation of this apparent decline in credit quality is that the rating agencies are now using more stringent standards in assigning ratings. An ordered probit analysis of a panel of firms from 1978 through 1995 suggests that rating standards have indeed become more stringent, implying that at least part of the downward trend in ratings is the result of changing standards. BOND RATINGS PLAY A KEY ROLE in corporate financing and investment decisions. A corporation that can issue higher rated bonds usually receives better terms than one that can issue only lower rated bonds. By law or policy, some investors can purchase only bonds with an investment-grade rating, a restriction which in some asset pricing models would affect the relative prices of financial assets. Numerous articles in the popular press have presumed that the credit quality of the debt of U.S. corporations has been declining over the last couple of decades. The comprehensive study by Lucas and Lonski (1992) of Moody's rating changes of corporate debt is consistent with this presumption. To cite their statistics, in 1970 Moody's downgraded 21 issues and upgraded 23 issues, but over the following years the number of bonds downgraded began to exceed by substantial margins the number of bonds upgraded, and by 1990 Moody's downgraded 301 issues and upgraded only 61. Their study includes both investment and noninvestment grade bonds, but internal data from Donaldson Lufkin & Jenrette confirm that this trend also applies to investment grade bonds alone. In a somewhat different context, Grundy (1997) documents similar trends in the ratings of preferred shares over the 1965 to 1990 period. As the credit quality of a firm's corporate debt decreases, that firm will face a greater probability of financial distress, which at the extreme translates into bankruptcy. There is some debate as to the effect of financial dis

The Declining Credit Quality of U.S. Corporate Debt: Myth or Reality?

On the assessment of risk

A PREVIOUS STUDY [3] showed that estimated beta coefficients, at least in the context of a portfolio of a large number of securities, were relatively stationary over time. Nonetheless, there was a consistent tendency for a portfolio with either an extremely low or high estimated beta in one period to have a less extreme beta as estimated in the next period. In other words, estimated betas exhibited in that article a tendency to regress towards the grand mean of all betas, namely one. This study will examine in further detail this regression tendency.1 The next section presents evidence showing the existence of this regression tendency and reviews the conventional reasons given in explanation [1], [4], [5]. The following section develops a formal model of this regression tendency and finds that the conventional analysis of this tendency is, if not incorrect, certainly misleading. Accompanying this theoretical analysis are some new empirical results which show that a major reason for the observed regression is real non-stationarities in the underlying values of beta and that the so-called "order bias" is not of dominant importance.

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Marshall E. Blume

Papers

BIASES IN COMPUTED RETURNS An Application to the Size Effect

Filter Rules and Stock-Market Trading

The Declining Credit Quality of U.S. Corporate Debt: Myth or Reality?

On the assessment of risk

Betas and their regression tendencies