[I present a methodology that uses the mixed jump-diffusion model for stock returns to estimate the separate effects of information surprises and strategic trading around corporate events. Using simulation techniques, I show that for events with multiple announcements spread over a long time, the estimators derived from the mixed jump-diffusion model are more powerful compared to the traditional cumulative abnormal return estimators. The new methodology is used to study the separate effects of information surprises and strategic trading associated with blockholdings and subsequent targeted repurchases. I find that for more than 93 percent of the firms in our sample the mixed jump-diffusion model is statistically superior to the pure diffusion model in describing stock returns. More important, I find a statistically significant negative effect due to trading, while the average effect around announcements is statistically insignificant. In contrast, the standard cumulative abnormal return is not statistically different from zero.]

[This paper shows that S&P 500 stock betas are overstated and the non-S&P 500 stock betas are understated because of liquidity price effects caused by the S&P 500 trading strategies. The daily and weekly betas of stocks added to the S&P 500 index during 1985-1989 increase, on average, by 0.211 and 0.130. The difference between monthly betas of otherwise similar S&P 500 and non-S&P 500 stocks also equals 0.125 during this period. Some of these increases can be explained by the reduced nonsynchroneity of S&P 500 stock prices, but the remaining increases are explained by the price pressure or excess volatility caused by the S&P 500 trading strategies. I estimate that the price pressures account for 8.5 percent of the total variance of daily returns of a value-weighted portfolio of NYSE/AMEX stocks. The negative own autocorrelations in S&P 500 index returns and the negative cross autocorrelations between S&P 500 stock returns provide further evidence consistent with the price pressure hypothesis.]

We consider the multiperiod relationship between a client and a portfolio manager and the resulting problem of motivating a manager of unknown ability to acquire valuable information. We explore the contractual forms and the optimal retention policy of the client and find that the optimal initial set of contracts features a smaller performance-based fee component paid to the manager than in a first-best contract, and the contract choice elicits only partial information about the manager. As a result, ex post performance measurement is critical to future recontracting. In general, managers are retained only if the returns on their portfolio exceed the benchmark by an appropriate amount.

[We consider the multiperiod relationship between a client and a portfolio manager and the resulting problem of motivating a manager of unknown ability to acquire valuable information. We explore the contractual forms and the optimal retention policy of the client and find that the optimal initial set of contracts features a smaller performance-based fee component paid to the manager than in a first-best contract, and the contract choice elicits only partial information about the manager. As a result, ex post performance measurement is critical to future recontracting. In general, managers are retained only if the returns on their portfolio exceed the benchmark by an appropriate amount.]

I present a methodology that uses the mixed jump diffusion model for stock returns to estimate the separate effects of information surprises and strategic trading around corporate events. Using simulation techniques, I show that for events with multiple announcements spread over a long time, the estimators derived from the mixed jump-diffusion model are more powerful compared to the traditional cumulative abnormal return estimators. The new methodology is used to study the separate effects of information surprises and strategic trading associated with block holdings and subsequent targeted repurchases. I find that for more than 93 percent of the firms in our sample the mixed jump-diffusion model is statistically superior to the pure diffusion model in describing stock returns. More important, I find a statistically significant negative effect due to trading while the average effect around announcements is statistically insignificant. In contrast, the standard cumulative abnormal return is not statistically different from zero.

We show that entrepreneurs may prefer to allow insider trading even when it is not socially optimal. We examine a model in which an insider/manager allocates resources on the basis of his private information and outside information conveyed through the secondary-market price of the firm's shares. If the manager is allowed to trade, he will compete with informed outsiders, reducing the equilibrium quality of outside information. While the benefits to production of outside information are the same for society and entrepreneurs, we show that the social and private costs are different. Thus, entrepreneurs and society may disagree on the conditions under which insider trading restrictions should be imposed.

This paper shows that S&P 500 stock betas are overstated and the non–S&P 500 stock betas are understated because of liquidity price effects caused by the S&P 500 trading strategies. The daily and weekly betas of stocks added to the S&P 500 index during 1985–1989 increase, on average, by 0.211 and 0.130. The difference between monthly betas of otherwise similar S&P 500 and non–S&P 500 stocks also equals 0.125 during this period. Some of these increases can be explained by the reduced nonsynchroneity of S&P 500 stock prices, but the remaining increases are explained by the price pressure or excess volatility caused by the S&P 500 trading strategies. I estimate that the price pressures account for 8.5 percent of the total variance of daily returns of a value-weighted portfolio of NYSE/AMEX stocks. The negative own autocorrelations in S&P 500 index returns and the negative cross autocorrelations between S&P 500 stock returns provide further evidence consistent with the price pressure hypothesis.

[We show that the positive volatility-volume relation documented by numerous researchers actually reflects the positive relation between volatility and the number of transactions. Thus, it is the occurrence of transactions per se, and not their size, that generates volatility; trade size has no information beyond that contained in the frequency of transactions. Our results suggest that theoretical research needs to entertain scenarios in which (i) both the frequency and size of trades are endogenously determined, yet (ii) the size of trades has no information content beyond that contained in the number of transactions.]

We show that the positive volatility-volume relation documented by numerous researchers actually reflects the positive relation between volatility and the number of transactions. Thus, it is the occurrence of transactions per se, and not their size, that generates volatility; trade size has no information beyond that contained in the frequency of transactions. Our results suggest that theoretical research needs to entertain scenarios in which (1) both the frequency and size of trades are endogenously determined, yet (2) the size of trades has no information content beyond that contained in the number of transactions. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.