This paper develops a semiautoregression approach to estimate factors of the arbitrage pricing theory that has the advantage of providing a simple asymptotic variance-covariance matrix for the factor estimates, which makes it easy to adjust for measurement errors. Using the extracted factors, the author confirms the finding that the arbitrage pricing theory describes asset returns slightly better than the capital asset pricing model, although there is still some mispricing in the arbitrage pricing theory model. The author finds that not only are the factors priced by the market, but the factor premiums move over time in relation to business cycle variables.

[In this article we break assets' betas with common factors into components attributable to news about future cash flows, real interest rates, and excess returns. To achieve this decomposition, we use a vector autoregressive time-series model and an approximate log-linear present value relation. The betas of industry and size portfolios with the market are largely attributed to changing expected returns. Betas with inflation and industrial production reflect opposing cash flow and expected return effects. We also show how asset pricing theory restricts the expected excess return components of betas.]

This article develops a new framework for measuring financial and real economic linkages between countries. Using U.S. and U.K. data from 1957 to 1989, the authors find closer financial linkages after the Bretton Woods currency arrangement was abandoned and Britain suspended exchange controls. In a pairwise application to fifteen countries over a shorter period, they also find that news about future dividend growth is more highly correlated between countries than contemporaneous output measures. This suggests that there are lags in the international transmission of economic shocks and that contemporaneous output correlation may understate the magnitude of integration.

This paper uses an autoregressive approach to test a multi-factor model with time-varying risk premiums. A quasi-differencing approach is used to eliminate the unobservable factors in the model. It is found that the model is capable of capturing the “size effect” and the “dividend yield effect, ” but is incapable of explaining the “book-to-market effect” and the “earnings-price ratio effect.” Thus, it is concluded that a constant-beta multi-factor model will not be able to explain the cross-sectional variation in expected returns.

[This article applies the methodology of Bai and Ng (2002, 2004) for decomposing panel data into systematic and idiosyncratic components to both stock returns and turnover panels. This approach works well for both returns and turnover, despite the presence of severe heteroscedasticity and nonstationarity of individual stocks' turnover. We test the mutual fund separation model of Lo and Wang (2000). Trading due to systematic risk in returns can account for 66% of systematic turnover. Thus, portfolio rebalancing due to systematic risk is a very important motive for stock trading. Finally, several common turnover measures may understate the impact of stock trading.]

ABSTRACT This paper developes a semiautoregression (SAR) approach to estimate factors of the arbitrage pricing theory (APT) that has the advantage of providing a simple asymptotic variance‐covariance matrix for the factor estimates, which makes it easy to adjust for measurement errors. Using the extracted factors, I confirm the finding that the APT describes asset returns slightly better than the CAPM, although there is still some mispricing in the APT model. I find that not only are the factors “priced” by the market, but the factor premiums move over time in relation to business cycle variables.

This paper uses real estate investment data for major groups of U.S. financial institutions—commercial banks, thrifts, and life insurance companies—to evaluate their investment timing performance over the 1970–1989 period. Our major finding is that real estate investments by commercial banks and thrifts have largely been driven by past real estate and market returns rather than by future expected returns. This apparent “trend-chasing” investment strategy—of buying high and selling low—offers an explanation for the poor performance of their real estate investments. We argue that imposing market value accounting on such institutions may actually reinforce their “trend-chasing” behavior.

In this article we break assets’ betas with common factors into components attributable to news about future cash flows, real interest rates, and excess returns. To achieve this decomposition, we use a vector autoregressive time-series model and an approximate log-linear present value relation. The betas of industry and size portfolios with the market are largely attributed to changing expected returns. Betas with inflation and industrial production reflect opposing cash flow and expected return effects. We also show how asset pricing theory restricts the expected excess return components of betas.

In this article we break assets’ betas with common factors into components attributable to news about future cash flows, real interest rates, and excess returns. To achieve this decomposition, we use a vector autoregressive time-series model and an approximate log-linear present value relation. The betas of industry and size portfolios with the market are largely attributed to changing expected returns. Betas with inflation and industrial production reflect opposing cash flow and expected return effects. We also show how asset pricing theory restricts the expected excess return components of betas.