THE FAMA‐FRENCH MODEL,LEVERAGE, AND THE MODIGLIANI‐MILLER PROPOSITIONS

For a cost‐of‐equity model to conform to the Modigliani‐Miller cost‐of‐capital propositions, any sensitivity coefficients in the model must be related to the firm's leverage. In this paper I apply these principles to the Fama‐French model for the cost of equity and develop the relation between its sensitivity coefficients and firm leverage. I then examine an empirical process developed by Fama and French (1997) to model the evolution through time of their sensitivity coefficients and show that this empirical process is inconsistent with the Modigliani‐Miller propositions. Separable functions are proposed for these sensitivity coefficients that are consistent with the Modigliani‐Miller propositions.     

INSIDER TRADING,REGULATION, AND THE COMPONENTS OF THE BID–ASK SPREAD

In this article we investigate the relation between insider trading regulations and the bid–ask spread. We decompose the spread into its components before and after the enactment of strict new insider trading rules in New Zealand. We find that the enactment led to a significant decrease in the information asymmetry component of the spread, which is observed mainly in illiquid and high prechange information asymmetry companies. These findings are robust to model specification. In addition, we find a decrease in the contribution of information asymmetry to price volatility.     

DIFFERENCES OF OPINION,OVERCONFIDENCE, AND THE HIGH‐VOLUME PREMIUM

We argue that both differences of opinion and overconfidence lead to high‐volume shocks. However, a high‐volume shock induced mainly by differences of opinion (overconfidence) will lead to superior (inferior) stock returns. Empirically, Asian financial markets, in contrast to U.S. markets, reveal weaker and inconsistent high‐volume premiums. The inconsistency may be attributable to investor's overconfidence. Additional evidence based on U.S. data supports this view, as a high‐volume shock accompanied by increased institutional ownership yields substantially higher high‐volume premiums than otherwise, and high‐volume premiums generally are much stronger in down‐market states than up‐market states.     

VOLATILITY FORECASTS,TRADING VOLUME,AND THE ARCH VERSUS OPTION‐IMPLIED VOLATILITY TRADE‐OFF

We investigate empirically the role of trading volume (1) in predicting the relative informativeness of volatility forecasts produced by autoregressive conditional heteroskedasticity (ARCH) models versus the volatility forecasts derived from option prices, and (2) in improving volatility forecasts produced by ARCH and option models and combinations of models. Daily and monthly data are explored. We find that if trading volume was low during period t?1 relative to the recent past, ARCH is at least as important as options for forecasting future stock market volatility. Conversely, if volume was high during period t?1 relative to the recent past, option‐implied volatility is much more important than ARCH for forecasting future volatility. Considering relative trading volume as a proxy for changes in the set of information available to investors, our findings reveal an important switching role for trading volume between a volatility forecast that reflects relatively stale information (the historical ARCH estimate) and the option‐implied forward‐looking estimate.     

EXPECTED VOLATILITY,UNEXPECTED VOLATILITY,AND THE CROSS‐SECTION OF STOCK RETURNS

The existing literature finds conflicting results on the cross‐sectional relation between expected returns and idiosyncratic volatility. We contend that at the firm level, the sample correlation between unexpected returns and expected idiosyncratic volatility can cloud the true relation between the expected return and expected idiosyncratic volatility. We show strong evidence that unexpected idiosyncratic volatility is positively related to unexpected returns. Using unexpected idiosyncratic volatility to control for unexpected returns, we find expected idiosyncratic volatility to be significantly and positively related to expected returns. This result holds after controlling for various firm characteristics, and it is robust across different sample periods.