The Dynamic Relation Between Stock Returns, Trading Volume, and Volatility

We examine the dynamic relation between returns, volume, and volatility of stock indexes. The data come from nine national markets and cover the period from 1973 to 2000. The results show a positive correlation between trading volume and the absolute value of the stock price change. Granger causality tests demonstrate that for some countries, returns cause volume and volume causes returns. Our results indicate that trading volume contributes some information to the returns process. The results also show persistence in volatility even after we incorporate contemporaneous and lagged volume effects. The results are robust across the nine national markets.     

Individual Investor Trading and Stock Returns

This paper investigates the dynamic relation between net individual investor trading and short‐horizon returns for a large cross‐section of NYSE stocks. The evidence indicates that individuals tend to buy stocks following declines in the previous month and sell following price increases. We document positive excess returns in the month following intense buying by individuals and negative excess returns after individuals sell, which we show is distinct from the previously shown past return or volume effects. The patterns we document are consistent with the notion that risk‐averse individuals provide liquidity to meet institutional demand for immediacy.     

Insider Trading Activity,Different Market Regimens,and Abnormal Returns

This paper show that corporate insiders earn abnormal returns by adjusting their own firm's stock trading to future market movements. Insider trading activity in bear markets is characterized by decreases in insider sales and increases in purchases, consistent with the view that those markets are followed by improved economic conditions. Conversely, insider sales increase and purchases decrease in bull markets, consistent with the view that inferior market conditions tend to follow those periods.     

Trading Volume and Cross-Autocorrelations in Stock Returns

This paper finds that trading volume is a significant determinant of the lead-lag patterns observed in stock returns. Daily and weekly returns on high volume portfolios lead returns on low volume portfolios, controlling for firm size. Nonsynchronous trading or low volume portfolio autocorrelations cannot explain these findings. These patterns arise because returns on low volume portfolios respond more slowly to information in market returns. The speed of adjustment of individual stocks confirms these findings. Overall, the results indicate that differential speed of adjustment to information is a significant source of the cross-autocorrelation patterns in short-horizon stock returns.     

投资者分歧、异常交易量和股票横截面收益率预测——基于中国股票市场的经验证据

本文定义月度异常交易量为本月与上个月交易金额的比值,发现中国市场月度收益率与滞后一个月的异常交易量显著负相关。在控制了公司规模、账面市值比、流动性以及动量效应等指标后仍然具有显著的解释作用。进一步研究表明,在出现高异常交易量后的12个月内,换手率和特质性波动率都有大幅上升。本文认为,交易量上升代表着市场分歧程度和受关注程度的增加,在卖空约束下会使得股票价值高估,从而造成未来收益率下降。     

The Puzzle in Post-Listing Common Stock Returns

Prior studies indicate that common stocks tend to earn negative returns immediately following listing on the NYSE. The authors document the phenomenon in detail and investigate a number of possible explanations. No full explanation is discovered, although several are ruled out.     

On Stock Market Returns and Returns on Investment

This article presents general conditions under which it is possible to obtain asset pricing relations from the intertemporal optimal investment decision of the firm. Under the assumption of linear homogeneous production and adjustment cost functions (the Hayashi (1982) conditions), it is possible to establish, state by state, the equality between the return on investment and the market return of the financial claims issued by the firm. This result proves to be, in essence, robust to the consideration of very general constraints on investment and the inclusion of taxes.     

Trading and Returns under Periodic Market Closures

This paper studies how market closures affect investors' trading policies and the resulting return-generating process. It shows that closures generate rich patterns of time variation in trading and returns, including those consistent with empirical findings: (1) U-shaped patterns in the mean and volatility of returns over trading periods, (2) higher trading activity around the close and open, (3) more volatile open-to-open returns than close-to-close returns, (4) higher returns over trading periods than over nontrading periods, (5) more volatile returns over trading periods than over nontrading periods. It also shows that closures can make prices more informative about future payoffs.     

中国股市股票交易信息与股票横截面收益研究

本文基于沪深股市1993-2008年剔除了金融类股的所有A股数据,研究了股票动量因素、反转因素和换手率等股票交易信息对股票收益横截面的影响。研究结果表明,股票的一年期的动量因素对股票的横截面收益的影响不显著,股票的三个月的短期反转因素对股票的横截面收益有显著影响,股票的三个月的累计收益越低,其在接下来一期获得的收益就可能越高。股票的换手率对股票的横截面收益的影响也显著,股票的上一期的换手率越高,在接下来的一期获得的收益就可能越低。     

Stock Returns,Expected Returns,and Real Activity

Measuring the total return variation explained by shocks to expected cash flows, time-varying expected returns, and shocks to expected returns is one way to judge the rationality of stock prices. Variables that proxy for expected returns and expected-return shocks capture 30% of the variance of annual NYSE value-weighted returns. Growth rates of production, used to proxy for shocks to expected cash flows, explain 43% of the return variance. Whether the combined explanatory power of the variables—about 58% of the variance of annual returns—is good or bad news about market efficiency is left for the reader to judge.     

Stock Market Returns and Inflation Forecasts

This study uses data from the Livingston survey of expectations to examine the Fisher hypothesis as a model relating expected stock returns and expected inflation. We show that the Fisher hypothesis holds much better for ex ante expectations than ex post realizations.     

Trading Mechanisms and Stock Returns: An Empirical Investigation

This paper examines the effects of the mechanism by which securities are traded on their price behavior. We compare the behavior of open-to-open and close-to-close returns on NYSE stocks, given the differences in execution methods applied in the opening and closing transactions. Opening returns are found to exhibit greater dispersion, greater deviations from normality and a more negative and significant autocorrelation pattern than closing returns. We study the effects of the bid-ask spread and the price-adjustment process on the estimated return variances and covariances and discuss the associated biases. We conclude that the trading mechanism has a significant effect on stock price behavior.     

The Dynamic Relation Between Returns and Idiosyncratic Volatility

We claim that regressing excess returns on one-lagged volatility provides only a limited picture of the dynamic effect of idiosyncratic risk, which tends to be persistent over time. By correcting for the serial correlation in idiosyncratic volatility, we find that idiosyncratic volatility has a significant positive effect. This finding seems robusrt for various firm size portfolios, sample periods, and measures of idiosyncratic risk. Our findings suggest stock markets mis-price idiosyncratic risk. There may be some measurement problems with idiosyncratic risk. There may be some measurement problems with idiosyncratic risk that could be related to nondiversifiable risk.