https://rady.ucsd.edu/faculty/directory/valkanov/pub/classes/mfe/docs/fama_french_jfe_1993.pdf

Common risk factors in the returns on stocks and bonds

Two easily measured variables, size and book-to-market equity, combine to capture the cross-sectional variation in average stock returns associated with market 3, size, leverage, book-to-market equity, and earnings-price ratios. Moreover, when the tests allow for variation in 3 that is unrelated to size, the relation between market /3 and average return is flat, even when 3 is the only explanatory variable. THE ASSET-PRICING MODEL OF Sharpe (1964), Lintner (1965), and Black (1972) has long shaped the way academics and practitioners think about average returns and risk. The central prediction of the model is that the market portfolio of invested wealth is mean-variance efficient in the sense of Markowitz (1959). The efficiency of the market portfolio implies that (a) expected returns on securities are a positive linear function of their market O3s (the slope in the regression of a security's return on the market's return), and (b) market O3s suffice to describe the cross-section of expected returns. There are several empirical contradictions of the Sharpe-Lintner-Black (SLB) model. The most prominent is the size effect of Banz (1981). He finds that market equity, ME (a stock's price times shares outstanding), adds to the explanation of the cross-section of average returns provided by market Os. Average returns on small (low ME) stocks are too high given their f estimates, and average returns on large stocks are too low. Another contradiction of the SLB model is the positive relation between leverage and average return documented by Bhandari (1988). It is plausible that leverage is associated with risk and expected return, but in the SLB model, leverage risk should be captured by market S. Bhandari finds, howev er, that leverage helps explain the cross-section of average stock returns in tests that include size (ME) as well as A. Stattman (1980) and Rosenberg, Reid, and Lanstein (1985) find that average returns on U.S. stocks are positively related to the ratio of a firm's book value of common equity, BE, to its market value, ME. Chan, Hamao, and Lakonishok (1991) find that book-to-market equity, BE/ME, also has a strong role in explaining the cross-section of average returns on Japanese stocks.

http://www.business.unr.edu/faculty/liuc/files/badm742/fama_french_1992.pdf

The Cross‐Section of Expected Stock Returns

Using a sample free of survivor bias, I demonstrate that common factors in stock returns and investment expenses almost completely explain persistence in equity mutual funds' mean and risk-adjusted returns Hendricks, Patel and Zeckhauser's (1993) "hot hands" result is mostly driven by the one-year momentum effect of Jegadeesh and Titman (1993), but individual funds do not earn higher returns from following the momentum strategy in stocks The only significant persistence not explained is concentrated in strong underperformance by the worst-return mutual funds The results do not support the existence of skilled or informed mutual fund portfolio managers PERSISTENCE IN MUTUAL FUND performance does not reflect superior stock-picking skill Rather, common factors in stock returns and persistent differences in mutual fund expenses and transaction costs explain almost all of the predictability in mutual fund returns Only the strong, persistent underperformance by the worst-return mutual funds remains anomalous Mutual fund persistence is well documented in the finance literature, but not well explained Hendricks, Patel, and Zeckhauser (1993), Goetzmann and Ibbotson (1994), Brown and Goetzmann (1995), and Wermers (1996) find evidence of persistence in mutual fund performance over short-term horizons of one to three years, and attribute the persistence to "hot hands" or common investment strategies Grinblatt and Titman (1992), Elton, Gruber, Das, and Hlavka (1993), and Elton, Gruber, Das, and Blake (1996) document mutual fund return predictability over longer horizons of five to ten years, and attribute this to manager differential information or stock-picking talent Contrary evidence comes from Jensen (1969), who does not find that good subsequent performance follows good past performance Carhart (1992) shows that persistence in expense ratios drives much of the long-term persistence in mutual fund performance My analysis indicates that Jegadeesh and Titman's (1993) one-year momentum in stock returns accounts for Hendricks, Patel, and Zeckhauser's (1993) hot hands effect in mutual fund performance However, funds that earn higher

/pdf/on-persistence-in-mutual-fund-performance-hff1y6xdm1.pdf

On Persistence in Mutual Fund Performance

This paper documents that strategies which buy stocks that have performed well in the past and sell stocks that have performed poorly in the past generate significant positive returns over 3- to 12-month holding periods. We find that the profitability of these strategies are not due to their systematic risk or to delayed stock price reactions to common factors. However, part of the abnormal returns generated in the first year after portfolio formation dissipates in the following two years. A similar pattern of returns around the earnings announcements of past winners and losers is also documented

/pdf/returns-to-buying-winners-and-selling-losers-implications-47r2et8t86.pdf

Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency

In both corporate finance and asset pricing empirical work, researchers are often confronted with panel data. In these data sets, the residuals may be correlated across firms and across time, and OLS standard errors can be biased. Historically, the two literatures have used different solutions to this problem. Corporate finance has relied on clustered standard errors, while asset pricing has used the Fama-MacBeth procedure to estimate standard errors. This paper examines the different methods used in the literature and explains when the different methods yield the same (and correct) standard errors and when they diverge. The intent is to provide intuition as to why the different approaches sometimes give different answers and give researchers guidance for their use.

/pdf/estimating-standard-errors-in-finance-panel-data-sets-1rry6mh9tl.pdf

Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches

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

/pdf/risk-return-and-equilibrium-empirical-tests-3szncf1qyg.pdf

Risk, Return, and Equilibrium: Empirical Tests

THIS STUDY IS A descriptive analysis of how market prices of call options compare with prices predicted by the Black and Scholes [2], B-S, option pricing model. Although tests of alternative call option valuation models are not conducted, it is possible to relate some of the deviations which we observe between market and B-S model prices to predictions of other models, and thereby reconcile conflicting statements in the empirical literature regarding the relationship between market prices and B-S model prices. The B-S model: C = S-N(d1) - Xe-'-N(d2)

An Empirical Examination of the Black‐Scholes Call Option Pricing Model

IN THIS RESEARCH we use Cox's [7] and Cox and Ross' [8] constant elasticity of variance diffusion processes to model heteroscedasticity in returns to common stocks. The major goal of this paper is to test the Cox call option valuation model for constant elasticity of variance diffusion processes against the Black-Scholes [4] call option valuation model. We find that common stock prices do appear to be generated by constant elasticity of variance diffusion processes; moreover, we find that the Cox valuation model fits market prices of call options significantly better than the Black-Scholes model. Thus, our results have important implications for empirical analysis of call option data and may very- well have important implications for empirical analysis of common stock prices and prices of other financial instruments. At the theoretical level there are several plausible explanations for changes in stock return variances over time. First, firms may internally change their common stock return distribution through technological innovations and/or mergers and acquisitions. Another area of possible explanation for dynamic variances is contained in multiperiod consumption-investment theory (Rubinstein [16] and Fama [9]). If in each period aggregate consumer-investors plan their consumption and investment over multiple future periods, then the variances for securities may change over time as new information arises and new individuals (preferences) bid for risky assets in the capital markets. There is considerable evidence in the empirical literature that returns to common stocks are heteroscedastic. Blattberg and Gonedes [5] present evidence that a process in which the variance of returns changes randomly through time fits empirical data better than a stationary Stable Paretian process. Rosenberg [15] finds that the variance of monthly returns to the Standard and Poors 500 Index follows an autoregressive process through time. Using daily returns, Black [3] observes that the variance of returns varies inversely with the stock price. This inverse relationship between variance of returns and the stock price can be modeled by a constant elasticity of variance diffusion process.

James D. MacBeth

Papers

Risk, Return, and Equilibrium: Empirical Tests

An Empirical Examination of the Black‐Scholes Call Option Pricing Model

Tests of the Black-Scholes and Cox Call Option Valuation Models

Tests of the multiperiod two-parameter model

Long‐term growth in a short‐term market