All trades executed by thirty-seven large investment management firms from July 1986 to December 1988 are used to study the price impact and execution cost of the entire sequence('package') of trades that the authors interpret as an order. The authors find that market impact and trading cost are related to firm capitalization, relative package size, and, most importantly, to the identity of the management firm behind the trade. Money managers with high demands for immediacy tend to be associated with larger market impact. Copyright 1995 by American Finance Association.
Preface What Is a Growth Stock? A Hypothesis Regarding the Market's Pricing of Growth Stocks Market Expectations and Responses to New Information Using Fundamental Analysis to Segregate Mispriced Growth Stocks Competitive Analysis Implementing the Strategy Market Anomalies of Importance to Trading Diversification, Risk and Market Efficiency Some Concluding Thoughts Notes Bibliography Index
The authors examine differences in structural characteristics that lead firms of different sizes to react differently to the same economic news. They find that a small firm portfolio contains a large proportion of marginal firms–firms with low production efficiency and high financial leverage. The authors construct two size-matched indices designed to mimic the return behavior of marginal firms and find that these return indices are important in explaining the time-series return difference between small and large firms. Furthermore, risk exposures to these indices are as powerful as log(size) in explaining average returns of size-ranked portfolios.
Journal of Financial and Quantitative Analysis200742(4), 893-913
Abstract Analysts' earnings forecasts are influenced by their desire to win investment banking clients. We hypothesize that the equity bull market of the 1990s, along with the boom in investment banking business, exacerbated analysts' conflicts of interest and their incentives to strategically adjust forecasts to avoid earnings disappointments. We document shifts in the distribution of earnings surprises and related changes in the market's response to surprises and forecast revisions. The evidence for shifts is stronger for growth stocks, where conflicts of interest are more pronounced. However, shifts are less notable for analysts without ties to investment banking and in international markets.
We examine whether sample selection bias explains the difference in returns between ‘value’ stocks (high book-to-market ratios) and ‘glamour’ stocks (low book-to-market ratios). Selection bias on Compustat is not a severe problem: for CRSP primary domestic firms, the proportion missing from Compustat is not large and the average return is not very different from the Compustat sample. Mechanical problems with matching Cusip identifiers account for much of the discrepancy between CRSP and Compustat. The superior performance of value stocks is confirmed for the top quintile of NYSE-Amex stocks, using a sample free from selection bias.
Review of Financial Studies199912(5), 937-974open access
We evaluate the performance of models for the covariance structure of stock returns, focusing on their use for optimal portfolio selection. We compare the models' forecasts of future covariances and the optimized portfolios' out-of-sample performance. A few factors capture the general covariance structure. Portfolio optimization helps for risk control, and a three-factor model is adequate for selecting the minimum-variance portfolio. Under a tracking error volatility criterion, which is widely used in practice, larger differences emerge across the models. In general more factors are necessary when the objective is to minimize tracking error volatility.
[We evaluate the performance of models for the covariance structure of stock returns, focusing on their use for optimal portfolio selection. We compare the models' forecasts of future covariances and the optimized portfolios' out-of-sample performance. A few factors capture the general covariance structure. Portfolio optimization helps for risk control, and a three-factor model is adequate for selecting the minimum-variance portfolio. Under a tracking error volatility criterion, which is widely used in practice, larger differences emerge across the models. In general more factors are necessary when the objective is to minimize tracking error volatility.]
[We examine the intraday relationship between returns and returns volatility in the stock index and stock index futures markets. Our results indicate a strong intermarket dependence in the volatility of the cash and futures returns. Price innovations that originate in either the stock or futures markets can predict the future volatility in the other market. We show that this relationship persists even during periods in which the dependence in the returns themselves appears to weaken. The findings are robust to controlling for potential market frictions such as asynchronous trading in the stock index. Our results have implications for understanding the pattern of information flows between the two markets.]
ABSTRACT This paper analyzes the tax‐loss selling hypothesis as an explanation of the January seasonal in stock returns and argues that rational tax‐loss selling implies little relation between the January seasonal and the long‐term loss. Empirical results show that the January seasonal is as strongly related to the long‐term loss as it is to the short‐term loss. The evidence is inconsistent with a model that explains the January seasonal by optimal tax trading.