The Journal of Finance publishes leading research across all the major fields of finance. It is one of the most widely cited journals in academic finance, and in all of economics. Each of the six issues per year reaches over 8,000 academics, finance professionals, libraries, and government and financial institutions around the world. The journal is the official publication of The American Finance Association, the premier academic organization devoted to the study and promotion of knowledge about financial economics.
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DISCUSSION
Published: 07/01/1986 | DOI: 10.1111/j.1540-6261.1986.tb04520.x
ROBERT F. STAMBAUGH
Presidential Address: Investment Noise and Trends
Published: 07/18/2014 | DOI: 10.1111/jofi.12174
ROBERT F. STAMBAUGH
During the past few decades, the fraction of the equity market owned directly by individuals declined significantly. The same period witnessed investment trends that include the growth of indexing as well as shifts by active managers toward lower fees and more index‐like investing. I develop an equilibrium model linking these investment trends to the decline in individual ownership, interpreting the latter as a reduction in noise trading. Active management corrects most noise trader–induced mispricing, and the fraction left uncorrected shrinks as noise traders' stake in the market declines. Less mispricing then dictates a smaller footprint for active management.
On the Predictability of Stock Returns: An Asset‐Allocation Perspective
Published: 06/01/1996 | DOI: 10.1111/j.1540-6261.1996.tb02689.x
SHMUEL KANDEL, ROBERT F. STAMBAUGH
Sample evidence about the predictability of monthly stock returns is considered from the perspective of a risk‐averse Bayesian investor who must allocate funds between stocks and cash. The investor uses the sample evidence to update prior beliefs about the parameters in a regression of stock returns on a set of predictive variables. The regression relation can seem weak when described by usual statistical measures, but the current values of the predictive variables can exert a substantial influence on the investor's portfolio decision, even when the investor's prior beliefs are weighted against predictability.
Are Stocks Really Less Volatile in the Long Run?
Published: 03/27/2012 | DOI: 10.1111/j.1540-6261.2012.01722.x
ĽUBOŠ PÁSTOR, ROBERT F. STAMBAUGH
According to conventional wisdom, annualized volatility of stock returns is lower over long horizons than over short horizons, due to mean reversion induced by return predictability. In contrast, we find that stocks are substantially more volatile over long horizons from an investor's perspective. This perspective recognizes that parameters are uncertain, even with two centuries of data, and that observable predictors imperfectly deliver the conditional expected return. Mean reversion contributes strongly to reducing long‐horizon variance but is more than offset by various uncertainties faced by the investor. The same uncertainties reduce desired stock allocations of long‐horizon investors contemplating target‐date funds.
The Equity Premium and Structural Breaks
Published: 12/17/2002 | DOI: 10.1111/0022-1082.00365
Ľluboš Pástor, Robert F. Stambaugh
A long return history is useful in estimating the current equity premium even if the historical distribution has experienced structural breaks. The long series helps not only if the timing of breaks is uncertain but also if one believes that large shifts in the premium are unlikely or that the premium is associated, in part, with volatility. Our framework incorporates these features along with a belief that prices are likely to move opposite to contemporaneous shifts in the premium. The estimated premium since 1834 fluctuates between 4 and 6 percent and exhibits its sharpest drop in the last decade.
Portfolio Inefficiency and the Cross‐section of Expected Returns
Published: 03/01/1995 | DOI: 10.1111/j.1540-6261.1995.tb05170.x
SHMUEL KANDEL, ROBERT F. STAMBAUGH
The Capital Asset Pricing Model implies that (i) the market portfolio is efficient and (ii) expected returns are linearly related to betas. Many do not view these implications as separate, since either implies the other, but we demonstrate that either can hold nearly perfectly while the other fails grossly. If the index portfolio is inefficient, then the coefficients and R2 from an ordinary least squares regression of expected returns on betas can equal essentially any values and bear no relation to the index portfolio's mean‐variance location. That location does determine the outcome of a mean‐beta regression fitted by generalized least squares.
Costs of Equity Capital and Model Mispricing
Published: 05/06/2003 | DOI: 10.1111/0022-1082.00099
Ľuboš Pástor, Robert F. Stambaugh
Costs of equity for individual firms are estimated in a Bayesian framework using several factor‐based pricing models. Substantial prior uncertainty about mispricing often produces an estimated cost of equity close to that obtained with mispricing precluded, even for a stock whose average return departs significantly from the pricing model's prediction. Uncertainty about which pricing model to use is less important, on average, than within‐model parameter uncertainty. In the absence of mispricing uncertainty, uncertainty about factor premiums is generally the largest source of overall uncertainty about a firm's cost of equity, although uncertainty about betas is nearly as important.
Predictive Systems: Living with Imperfect Predictors
Published: 07/16/2009 | DOI: 10.1111/j.1540-6261.2009.01474.x
ĽUBOŠ PÁSTOR, ROBERT F. STAMBAUGH
We develop a framework for estimating expected returns—a predictive system—that allows predictors to be imperfectly correlated with the conditional expected return. When predictors are imperfect, the estimated expected return depends on past returns in a manner that hinges on the correlation between unexpected returns and innovations in expected returns. We find empirically that prior beliefs about this correlation, which is most likely negative, substantially affect estimates of expected returns as well as various inferences about predictability, including assessments of a predictor's usefulness. Compared to standard predictive regressions, predictive systems deliver different expected returns with higher estimated precision.
Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle
Published: 05/01/2015 | DOI: 10.1111/jofi.12286
ROBERT F. STAMBAUGH, JIANFENG YU, YU YUAN
Buying is easier than shorting for many equity investors. Combining this arbitrage asymmetry with the arbitrage risk represented by idiosyncratic volatility (IVOL) explains the negative relation between IVOL and average return. The IVOL‐return relation is negative among overpriced stocks but positive among underpriced stocks, with mispricing determined by combining 11 return anomalies. Consistent with arbitrage asymmetry, the negative relation among overpriced stocks is stronger, especially for stocks less easily shorted, so the overall IVOL‐return relation is negative. Further supporting our explanation, high investor sentiment weakens the positive relation among underpriced stocks and, especially, strengthens the negative relation among overpriced stocks.
Mimicking Portfolios and Exact Arbitrage Pricing
Published: 03/01/1987 | DOI: 10.1111/j.1540-6261.1987.tb02546.x
GUR HUBERMAN, SHMUEL KANDEL, ROBERT F. STAMBAUGH
We characterize the sets of mimicking positions with returns that can serve in place of factors in an exact K‐factor arbitrage‐pricing relation for a set of N assets. All of the sets are K‐dimensional nonsingular linear transformations of each other. We interpret three examples of such transformations and discuss empirical considerations. We provide conditions under which the mimicking positions can be expressed as portfolios, and we characterize the relation between mimicking portfolios and the minimum‐variance frontier.
A Further Investigation of the Weekend Effect in Stock Returns
Published: 07/01/1984 | DOI: 10.1111/j.1540-6261.1984.tb03675.x
DONALD B. KEIM, ROBERT F. STAMBAUGH
This study uses a longer time period and additional stocks to further investigate the weekend effect. We find consistently negative Monday returns (1) for the S & P Composite as early as 1928, (2) for Exchange‐traded stocks of firms of all sizes, and (3) for actively traded over‐the‐counter (OTC) stocks. The OTC results are based on bid prices and therefore appear to reject specialist‐related explanations. For the 30 individual stocks of the Dow Jones Industrial Index, the average correlation between Friday and Monday returns is positive and the highest of all pairs of successive days. The latter finding is inconsistent with fairly general measurement‐error explanations.
Tests of Asset Pricing with Time‐Varying Expected Risk Premiums and Market Betas
Published: 06/01/1987 | DOI: 10.1111/j.1540-6261.1987.tb02564.x
WAYNE E. FERSON, SHMUEL KANDEL, ROBERT F. STAMBAUGH
Tests of asset‐pricing models are developed that allow expected risk premiums and market betas to vary over time. These tests exploit the relation between expected excess returns and current market values. Using weekly data for 1963 through 1982 on ten common stock portfolios formed according to equity capitalization, a single‐risk‐premium model is not rejected if the expected premium is time varying and is not constrained to correspond to a market factor. Conditional mean‐variance efficiency of a value‐weighted stock index is rejected, and the rejection is insensitive to how much variability of expected risk premiums is assumed.
Do Funds Make More When They Trade More?
Published: 03/19/2017 | DOI: 10.1111/jofi.12509
ĽUBOŠ PÁSTOR, ROBERT F. STAMBAUGH, LUCIAN A. TAYLOR
We model fund turnover in the presence of time‐varying profit opportunities. Our model predicts a positive relation between an active fund's turnover and its subsequent benchmark‐adjusted return. We find such a relation for equity mutual funds. This time‐series relation between turnover and performance is stronger than the cross‐sectional relation, as the model predicts. Also as predicted, the turnover‐performance relation is stronger for funds trading less‐liquid stocks and funds likely to possess greater skill. Turnover is correlated across funds. The common component of turnover is positively correlated with proxies for stock mispricing. Turnover of similar funds helps predict a fund's performance.