The Journal of Finance

Multi‐Beta CAPM or Equilibrium‐APT?: A Reply

Published: 09/01/1985   |   DOI: 10.1111/j.1540-6261.1985.tb02371.x

JAY SHANKEN

Testing Portfolio Efficiency when the Zero‐Beta Rate is Unknown: A Note

Published: 03/01/1986   |   DOI: 10.1111/j.1540-6261.1986.tb04506.x

JAY SHANKEN

A lower bound on the distribution function of the likelihood ratio test of portfolio efficiency is derived. An empirical application demonstrates that the bound may sometimes be used to infer rejection of the null hypothesis without appeal to asymptotic statistical approximations. A procedure for incorporating partial information about the zero‐beta intercept, in the multivariate framework, is also developed and applied.

The Current State of the Arbitrage Pricing Theory

Published: 09/01/1992   |   DOI: 10.1111/j.1540-6261.1992.tb04671.x

JAY SHANKEN

This paper provides a simple proof of a recent theorem presented by Reisman (1992), concerning the use of proxies for the factors in the return‐generating process of the arbitrage pricing theory (APT). In the single‐factor case, the theorem asserts that any variable correlated with the factor can serve as the benchmark in an approximate APT expected return relation. The significance of this result is considered and a new direction for empirical work on “arbitrage pricing” is outlined.

On the Exclusion of Assets from Tests of the Mean Variance Efficiency of the Market Portfolio: An Extension

Published: 06/01/1986   |   DOI: 10.1111/j.1540-6261.1986.tb05039.x

JAY SHANKEN

This paper extends Kandel's [3] analysis of the testability of the mean‐variance efficiency of a market index when the return on some component of the index is not perfectly observable. In addition to information about the mean and variance of the missing asset, considered by Kandel, we explore the usefulness of information about the beta of the missing asset on the observed sub‐portfolio in an economy with a riskless asset. The results are somewhat more supportive of the notion that mean‐variance efficiency is testable on a subset of the assets.

Nonsynchronous Data and the Covariance‐Factor Structure of Returns

Published: 06/01/1987   |   DOI: 10.1111/j.1540-6261.1987.tb02565.x

JAY SHANKEN

Evidence is presented that indicates that the standard estimator of the covariance matrix of daily returns provides a distorted view of the true covariance‐factor structure. An alternative estimator, based on a model of the price‐adjustment delay process, reveals roughly twice as much covariation in individual security returns. The number of factors identified also appears to increase when this estimator is employed. Since the linear space spanned by the estimated factor‐loading vectors is quite sensitive to the estimator used, it is important that the consistent estimator be considered in the usual two‐stage empirical investigations of the APT.

The Arbitrage Pricing Theory: Is it Testable?

Published: 12/01/1982   |   DOI: 10.1111/j.1540-6261.1982.tb03607.x

JAY SHANKEN

This paper challenges the view that the Arbitrage Pricing Theory (APT) is inherently more susceptible to empirical verification than the Capital Asset Pricing Model (CAPM). The usual formulation of the testable implications of the APT is shown to be inadequate, as it precludes the very expected return differentials which the theory attempts to explain. A recent competitive‐equilibrium extension of the APT may be testable in principle. In order to implement such a test, however, observation of the return on the true market portfolio appears to be necessary.

Comparing Asset Pricing Models

Published: 02/08/2018   |   DOI: 10.1111/jofi.12607

FRANCISCO BARILLAS, JAY SHANKEN

A Bayesian asset pricing test is derived that is easily computed in closed form from the standard F‐statistic. Given a set of candidate traded factors, we develop a related test procedure that permits the computation of model probabilities for the collection of all possible pricing models that are based on subsets of the given factors. We find that the recent models of Hou, Xue, and Zhang (2015a, 2015b) and Fama and French (2015, 2016) are dominated by a variety of models that include a momentum factor, along with value and profitability factors that are updated monthly.

Learning, Asset‐Pricing Tests, and Market Efficiency

Published: 12/17/2002   |   DOI: 10.1111/1540-6261.00456

Jonathan Lewellen, Jay Shanken

This paper studies the asset‐pricing implications of parameter uncertainty. We show that, when investors must learn about expected cash flows, empirical tests can find patterns in the data that differ from those perceived by rational investors. Returns might appear predictable to an econometrician, or appear to deviate from the Capital Asset Pricing Model, but investors can neither perceive nor exploit this predictability. Returns may also appear excessively volatile even though prices react efficiently to cash‐flow news. We conclude that parameter uncertainty can be important for characterizing and testing market efficiency.

Pricing Model Performance and the Two‐Pass Cross‐Sectional Regression Methodology

Published: 02/15/2013   |   DOI: 10.1111/jofi.12035

RAYMOND KAN, CESARE ROBOTTI, JAY SHANKEN

Over the years, many asset pricing studies have employed the sample cross‐sectional regression (CSR) R2 as a measure of model performance. We derive the asymptotic distribution of this statistic and develop associated model comparison tests, taking into account the impact of model misspecification on the variability of the CSR estimates. We encounter several examples of large R2 differences that are not statistically significant. A version of the intertemporal capital asset pricing model (CAPM) exhibits the best overall performance, followed by the Fama–French three‐factor model. Interestingly, the performance of prominent consumption CAPMs is sensitive to variations in experimental design.

Another Look at the Cross‐section of Expected Stock Returns

Published: 03/01/1995   |   DOI: 10.1111/j.1540-6261.1995.tb05171.x

S. P. KOTHARI, JAY SHANKEN, RICHARD G. SLOAN

Our examination of the cross‐section of expected returns reveals economically and statistically significant compensation (about 6 to 9 percent per annum) for beta risk when betas are estimated from time‐series regressions of annual portfolio returns on the annual return on the equally weighted market index. The relation between book‐to‐market equity and returns is weaker and less consistent than that in Fama and French (1992). We conjecture that past book‐to‐market results using COMPUS‐TAT data are affected by a selection bias and provide indirect evidence.