Forecasting Crashes: Trading Volume, Past Returns and Conditional Skewness in Stock Prices
Forecasting Crashes: Trading Volume, Past Returns and Conditional Skewness in Stock Prices
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DOI：
10.2139/ssrn.194948
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发表时间：
1999-12
期刊：
影响因子：
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通讯作者：
Joseph Chen;Harrison G. Hong;Jeremy C. Stein
中科院分区：
文献类型：
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作者：
Joseph Chen;Harrison G. Hong;Jeremy C. Stein
本文研究股票收益不对称的决定因素。我们开发了一系列的横截面回归规范，试图预测个股的日收益的偏度。负偏度在以下股票中最为明显：1)过去六个月相对于趋势的交易量增加；2)前36个月的正回报。第一个发现与Hong and Stein（1999）的模型一致，该模型预测，当投资者之间的意见分歧很大时，负不对称更有可能发生。后者的发现符合许多理论，最著名的是布兰查德和沃森（1982）对股价泡沫的诠释。当我们试图预测总体股票市场的偏度时，也会得到类似的结果，尽管我们在这种情况下的统计能力是有限的。
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.