We derive the process followed by trading volume, in a market with finite depth and constant investment opportunities, where a representative investor, with a long horizon and constant relative risk aversion, trades a safe and a risky asset. Trading volume approximately follows a Gaussian, mean-reverting diffusion, and increases with depth, volatility, and risk aversion. The model generates an endogenous ban on leverage and short-selling.
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This paper develops a multi-period rational expectations model of stock trading in which investors have differential information concerning the underlying value of the stock. Investors trade competitively in the stock market based on their private information and the information revealed by the market-clearing prices, as well as other public news. We examine how trading volume is related to the information flow in the market and how investors' trading reveals their private information.
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In this paper we study the dynamic relationship between trading volume, volatility, and stock returns at the international stock markets. First, we examine the role of volume and volatility in the individual stock market dynamics using a sample of ten major developed stock markets. Next, we extend our analysis to a multiple market framework, based on a large sample of cross-listed firms. Our analysis is based on both semi-nonparametric (Flexible Fourier Form) and parametric techniques. Our major findings are as follows. First, we find no evidence of the trading volume affecting the serial correlation of stock market returns, as predicted by Campbell et.al (1993) and Wang (1994). Second, the stock market volatility has a negative and statistically significant impact on the serial correlation of the stock market returns, consistent with the positive feedback trading model of Sentana and Wadhwani (1992). Third, the lagged trading volume is positively related to the stock market volatility, supporting the information flow theory. Fourth, we find the trading volume to have both an economically and statistically significant impact on the price discovery process and the co-movement between the international stock markets. Overall, these findings suggest the importance of the trading volume as an information variable.
We find that trading volume not only contributes positively to the contemporaneous volatility, as indicated in previous literature, but also contributes negatively to the subsequent volatility. And this pattern between trading volume and volatility is consistently held among individual stocks, volume-based portfolios, size-based portfolios, and market index, and among daily data and weekly data. These empirical findings tend to support that the Information-Driven-Trade (IDT) hypothesis is more pervasive and powerful in explaining trading activities in the stock market than the Liquidity-Driven-Trade (LDT) hypothesis. Our additional tests obtain three interesting findings, 1) liquidity and the degree of information asymmetry influence the relation between volume and subsequent volatility, 2) the effect of volume on subsequent volatility and volume size have a non-linear relationship, which is consistent with Barclay and Warner (1993, JFE)'s finding, 3) the effect of volume on subsequent volatility is asymmetry when the stock price moves up and when the stock price moves down, and we attribute this asymmetry to the short-selling constraints.
