Multifactor Models Do Not Explain Deviations from the CAPM

A number of studies have presented evidence rejecting the validity of the Capital Asset Pricing Model (CAPM). This evidence has spawned research into possible explanations. These explanations can be divided into two main categories - the risk based alternatives and the nonrisk based alternatives. The risk based category includes multifactor asset pricing models developed under the assumptions of investor rationality and perfect capital markets. The nonrisk based category includes biases introduced in the empirical methodology, the existence of market frictions, or explanations arising from the presence of irrational investors. The distinction between the two categories is important for asset pricing applications such as estimation of the cost of capital. This paper proposes to distinguish between the two categories using ex ante analysis. A framework is developed showing that ex ante one should expect that CAPM deviations due to missing risk factors will be very difficult to statistically detect. In contrast, deviations resulting from nonrisk based sources will be easy to detect. Examination of empirical results leads to the conclusion that the risk based alternatives is not the whole story for the CAPM deviations. The implication of this conclusion is that the adoption of empirically developed multifactor asset pricing models may be premature.

Asset Pricing Models: Implications for Expected Returns and Portfolio Selection

Implications of factor-based asset pricing models for estimation of expected returns and for portfolio selection are investigated. In the presence of model mispricing due to a missing risk factor, the mispricing and the residual covariance matrix are linked together. Imposing a strong form of this link leads to expected return estimates that are more precise and more stable over time than unrestricted estimates. Optimal portfolio weights that incorporate the link when no factors are observable are proportional to expected return estimates, effectively using an identity matrix as a covariance matrix. The resulting portfolios perform well both in simulations and in out-of-sample comparisons.

When are Contrarian Profits Due to Stock Market Overreaction?

The profitability of contrarian investment strategies need not be the result of stock market overreaction. Even if returns on individual securities are temporally independent, portfolio strategies that attempt to exploit return reversals may still earn positive expected profits. This is due to the effects of cross-autocovariances from which contrarian strategies inadvertently benefit. We provide an informal taxonomy of return-generating processes that yield positive [and negative] expected profits under a particular contrarian portfolio strategy, and use this taxonomy to reconcile the empirical findings of weak negative autocorrelation for returns on individual stocks with the strong positive autocorrelation of portfolio returns. We present empirical evidence against overreaction as the primary source of contrarian profits, and show the presence of important lead-lag relations across securities.

Data-Snooping Biases in Tests of Financial Asset Pricing Models

We investigate the extent to which tests of financial asset pricing models may be biased by using properties of the data to construct the test statistics. Specifically, we focus on tests using returns to portfolios of common stock where portfolios are constructed by sorting on some empirically motivated characteristic of the securities such as market value of equity. We present both analytical calculations and Monte Carlo simulations that show the effects of this type of data-snooping to be substantial. Even when the sorting characteristic is only marginally correlated with individual security statistics, 5 percent tests based on sorted portfolio returns may reject with probability one under the null hypothesis. This bias is shown to worsen as the number of securities increases given a fixed number of portfolios, and as the number of portfolios decreases given a fixed number of securities. We provide an empirical example that illustrates the practical relevance of these biases.

Stock Market Prices Do Not Follow Random Walks: Evidence From a Simple Specification Test

In this paper, we test the random walk hypothesis for weekly stock market returns by comparing variance estimators derived from data sampled at different frequencies. The random walk model is strongly rejected for the entire sample period (1962-1985) and for all sub-periods for a variety of aggregate returns indexes and size-sorted portfolios. Although the rejections are largely due to the behavior of small stocks, they cannot be ascribed to either the effects of infrequent trading or time-varying volatilities. Moreover, the rejection of the random walk cannot be interpreted as supporting a mean-reverting stationary model of asset prices, but is more consistent with a specific nonstationary alternative hypothesis.

An Econometric Analysis of Nonsynchronous Trading

We develop a stochastic model of nonsynchronous asset prices based on sampling with random censoring. In addition to generalizing existing models of non-trading our framework allows the explicit calculation of the effects of infrequent trading on the time series properties of asset returns. These are empirically testable implications for the variances, autocorrelations, and cross-autocorrelations of returns to individual stocks as well as to portfolios. We construct estimators to quantify the magnitude of non-trading effects in commonly used stock returns data bases and show the extent to which this phenomenon is responsible for the recent rejections of the random walk hypothesis.

The Size and Power of the Variance Ratio Test in Finite Samples: A Monte Carlo Investigation

We examine the finite sample properties of the variance ratio test of the random walk hypothesis via Monte Carlo simulations under two null and three alternative hypotheses. These results are compared to the performance of the Dickey-Fuller t and the Box-Pierce Q statistics. Under the null hypothesis of a random walk with independent and identically distributed Gaussian increments, the empirical size of all three tests are comparable. Under a heteroscedastic random walk null, the variance ratio test is more reliable than either the Dickey-Fuller or Box-Pierce tests. We compute the power of these three tests against three alternatives of recent empirical interest: a stationary AR(1), the sum of this AR(1) and a random walk, and an integrated AR( 1). By choosing the sampling frequency appropriately, the variance ratio test is shown to be as powerful as the Dickey-Fuller and Box-Pierce tests against the stationary alternative, and is more powerful than either of the two tests against the two unit-root alternatives.

Econometric Models of Limit-Order Executions

This paper attempts to assess whether money can generate persistent economic" fluctuations in dynamic general equilibrium models of the business cycle. We show that a small" nominal friction in the goods market can make the response of output to monetary shocks large" and persistent if it is amplified by real wage rigidity in the labor market. We also argue that" given the level of real wage rigidity that is observed in developed countries nominal stickiness might be sufficient for money to produce economic fluctuations as persistent" as those observed in the data.

An Ordered Probit Analysis of Transaction Stock Prices

We estimate the conditional distribution of trade-to-trade price changes using ordered probit, a statistical model for discrete random variables. Such an approach takes into account the fact that transaction price changes occur in discrete increments, typically eighths of a dollar, and occur at irregularly spaced time intervals. Unlike existing continuous-time/discrete-state models of discrete transaction prices, ordered probit can capture the effects of other economic variables on price changes, such as volume, past price changes, and the time between trades. Using 1988 transactions data for over 100 randomly chosen U.S. stocks, we estimate the ordered probit model via maximum likelihood and use the parameter estimates to measure several transaction-related quantities, such as the price impact of trades of a given size, the tendency towards price reversals from one transaction to the next, and the empirical significance of price discreteness.

Vertical integration and firm boundaries : the evidence

Since Ronald H. Coase's (1937) seminal paper, a rich set of theories has been developed that deal with firm boundaries in vertical or input–output structures. In the last twenty-five years, empirical evidence that can shed light on those theories also has been accumulating. We review the findings of empirical studies that have addressed two main interrelated questions: First, what types of transactions are best brought within the firm and, second, what are the consequences of vertical integration decisions for economic outcomes such as prices, quantities, investment, and profits. Throughout, we highlight areas of potential cross-fertilization and promising areas for future work