ABSTRACT We analyze the rationale for limit order trading. Use of limit orders involves two risks: 1) an adverse information event can trigger an undesirable execution, and 2) favorable news can result in a desirable execution not being obtained. On the other hand, a paucity of limit orders can result in accentuated short‐term price fluctuations that compensate a limit order trader. Our empirical tests suggest that trading via limit orders dominates trading via market orders for market participants with relatively well balanced portfolios, and that placing a network of buy and sell limit orders as a pure trading strategy is profitable.

The authors analyze the rationale for limit order trading. Use of limit orders involves two risks: (1) an adverse information event can trigger an undesirable execution, and (2) favorable news can result in a desirable execution not being obtained. On the other hand, a paucity of limit orders can result in accentuated short-term price fluctuations that compensate a limit order trader. The authors' empirical tests suggest that trading via limit orders dominates trading via market orders for market participants with relatively well-balanced portfolios, and that placing a network of buy and sell limit orders as a pure trading strategy is profitable.

We analyze the rationale for limit order trading. Use of limit orders involves two risks: 1) an adverse information event can trigger an undesirable execution, and 2) favorable news can result in a desirable execution not being obtained. On the other hand, a paucity of limit orders can result in accentuated short-term price fluctuations that compensate a limit order trader. Our empirical tests suggest that trading via limit orders dominates trading via market orders for market participants with relatively well balanced portfolios, and that placing a network of buy and sell limit orders as a pure trading strategy is profitable.

The capital asset pricing model's (CAPM) primary empirical implication is a positively sloped linear relation between a security's expected rate of return and its relative risk (beta). Recent research indicates that inferences about the risk-return relation are sensitive to the choice of the return measurement interval. The authors perform multivariate tests of the Sharpe-Lintner CAPM using monthly and annual returns on market-value-ranked portfolios. The CAPM is rejected using monthly returns, a result consistent with previous research. In contrast, the authors fail to reject the CAPM when annual holding period returns are used.

This paper considers the Arbitrage Pricing Theory when investors have incomplete information on the parameters generating asset returns. Each asset in the economy may have a different amount of information available on it. Bayesian investors use their prior beliefs in conjunction with the total available information to assign an expected return and a set of factor betas to each asset. The assigned expected returns are shown to be linear in their associated factor betas. However, the factor betas and prices of assets differ from those under complete information. Specifically, risky assets with high (low) information are priced relatively higher (lower). On the other hand, factor betas of high (low) information assets are relatively lower (higher). The analysis has econometric implications for testing the APT. In this paper's framework, maximum likelihood estimates of factor betas, which are based on normality assumptions, are too high (low) for high (low) information assets. In addition, sequentially increasing the sample size by adding new securities to a factor analysis procedure can result in the detection of apparent additional priced factors when they do not really exist.

This paper develops an equilibrium factor pricing theory when investors have heterogeneous beliefs about asset payoffs generated by the Ross linear factor model. Investors receive private information about the unknown parameters of the payoff process. They use this private information and equilibrium prices to predict asset payoffs. The paper develops a closed form price function for a noisy rational expectations equilibrium and relates it to the general solution. Even though we allow for a large number of investors, diversity of beliefs and parameter uncertainty both persist in equilibrium. We show that investors' beliefs about expected payoffs are approximately linear in the asset's betas, thus establishing the APT. As investors prefer to hold high information assets in equilibrium, the relative weight of these assets in the APT pricing bound is higher. The reverse is true for low information assets.

The size effect is sensitive to the length of the return interval used in estimating betas. Beta changes with the return interval because an asset's covariance with the market and the market's variance do not change proportionately as the return interval is changed. We document beta sensitivity to the return interval. Evidence from cross-sectional regressions of returns on monthly and annual betas is inconsistent with beta changes stemming only from the higher standard errors of the longer-interval betas. We provide evidence that the size effect becomes statistically insignificant when risk is measured by betas estimated using annual returns.

ABSTRACT The capital asset‐pricing model's (CAPM) primary empirical implication is a positively sloped linear relation between a security's expected rate of return and its relative risk (beta). Recent research indicates that inferences about the risk‐return relation are sensitive to the choice of the return measurement interval. We perform multivariate tests of the Sharpe‐Lintner CAPM using monthly and annual returns on market‐value‐ranked portfolios. The CAPM is rejected using monthly returns, a result consistent with previous research. In contrast, we fail to reject the CAPM when annual holding period returns are used.