THE EMPIRICAL RELATIONSHIP BETWEEN TRADING VOLUME, RETURNS AND VOLATILITY

TLDR

The study empirically investigates how trading volume relates to returns and volatility in the Australian stock market, testing hypotheses on volume‑price dynamics and the influence of information arrival on return formation. The authors analyze volume‑price change relationships and test a hypothesis linking return formation to information arrival, using US overnight returns as a proxy for expected news and daily trading volume as a proxy for news arrival. They find a significant positive relationship between trading volume and returns, and between volume and volatility, with an asymmetric volume‑price slope for negative returns; at the individual‑stock level the evidence is weaker, and accounting for information arrival reduces volatility persistence, shedding light on return generation and its inference from volume data.

Abstract

Abstract This paper presents an empirical analysis of the relationship between trading volume, returns and volatility in the Australian stock market. The initial analysis centres upon the volume‐price change relationship. The relationship between trading volume and returns, irrespective of the direction of the price change, is significant across three alternative measures of daily trading volume for the aggregate market. This finding also provides basic support for a positive relationship between trading volume and volatility. Furthermore, evidence is found supporting the hypothesis that the volume‐price change slope for negative returns is smaller than the slope for non‐negative returns, thereby supporting an asymmetric relationship which is hypothesised to exist because of differential costs of taking long and short positions. Analysis at the individual stock level shows weaker support for the relationship. A second related hypothesis is tested in which the formation of returns is conditional upon information arrival which similarly affects trading volume. The hypothesis is tested by using the US overnight return to proxy for expected “news” and trading volume to proxy for news arrival during the day. The results show a reduction in the significance and magnitude of persistence in volatility and hence are consistent with explaining non‐normality in returns (and ARCH effects) through the rate of arrival of information. The findings in this paper help explain how returns are generated and have implications for inferring return behaviour from trading volume data.