Forecasting Crashes: Trading Volume, Past Returns and Conditional Skewness in Stock Prices
University of California, Davis - Graduate School of Management
Harrison G. Hong
Columbia University, Graduate School of Arts and Sciences, Department of Economics; National Bureau of Economic Research (NBER)
Jeremy C. Stein
Harvard University - Department of Economics; National Bureau of Economic Research (NBER)
This paper is an investigation into the determinants of asymmetries in stock returns. We develop a series of cross-sectional regression specifications which attempt to forecast skewness in the daily returns of individual stocks. Negative skewness is most pronounced in stocks that have experienced: 1) an increase in trading volume relative to trend over the prior six months; and 2) positive returns over the prior thirty-six months. The first finding is consistent with the model of Hong and Stein (1999), which predicts that negative asymmetries are more likely to occur when there are large differences of opinion among investors. The latter finding fits with a number of theories, most notably Blanchard and Watson's (1982) rendition of stock-price bubbles. Analogous results also obtain when we attempt to forecast the skewness of the aggregate stock market, though our statistical power in this case is limited.
Number of Pages in PDF File: 47
JEL Classification: G1
Date posted: January 3, 2000
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