Market Crowds' Trading Behaviors, Agreement Prices, and the Implications of Trading Volume

TL;DR

This paper investigates trading volume in the Chinese stock market, revealing how market crowds adapt their trading behaviors, often reach agreement on prices, and how trading volume interacts with information, sentiment, and external factors.

Contribution

It introduces a behavioral analysis of trading volume probability and tests adaptive hypotheses, highlighting the role of crowd interactions and information in volume fluctuations.

Findings

Market crowds tend to reach agreement on asset prices during trading days.

Trading volume reflects a combination of fundamental, private, and sentiment values.

Interaction between information, news, and trading actions produces excessive volume.

Abstract

It has been long that literature in financial academics focuses mainly on price and return but much less on trading volume. In the past twenty years, it has already linked both price and trading volume to economic fundamentals, and explored the behavioral implications of trading volume such as investor's attitude toward risks, overconfidence, disagreement, and attention etc. However, what is surprising is how little we really know about trading volume. Here we show that trading volume probability represents the frequency of market crowd's trading action in terms of behavior analysis, and test two adaptive hypotheses relevant to the volume uncertainty associated with price in China stock market. The empirical work reveals that market crowd trade a stock in efficient adaptation except for simple heuristics, gradually tend to achieve agreement on an outcome or an asset price widely on a trading day, and generate such a stationary equilibrium price very often in interaction and competition among themselves no matter whether it is highly overestimated or underestimated. This suggests that asset prices include not only a fundamental value but also private information, speculative, sentiment, attention, gamble, and entertainment values etc. Moreover, market crowd adapt to gain and loss by trading volume increase or decrease significantly in interaction with environment in any two consecutive trading days. Our results demonstrate how interaction between information and news, the trading action, and return outcomes in the three-term feedback loop produces excessive trading volume which includes various internal and external causes.