Paper: arXiv 2310.05322

Authors: Leilei Shi, Bing Han, Yingzi Zhu, Liyan Han, Yiwen Wang, Yan Piao

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.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper provides an interesting behavioral interpretation of trading volume in the Chinese stock market using adaptive hypotheses. However, it leans more heavily on conceptual narrative and basic empirical testing rather than dense mathematical derivations or advanced statistical rigor.

Research Flowchart

  flowchart TD
    A[Research Goal: Analyze behavioral implications of trading volume in China stock market] --> B[Data: Daily stock prices, trading volume, and market news/events]
    B --> C[Methodology: Formulate & test 2 adaptive hypotheses on price-volume relationship]
    C --> D[Compute market crowd interactions & volume uncertainty across consecutive days]
    D --> E[Outcome 1: Trades reflect adaptation to gain/loss & achieve stationary equilibrium prices]
    D --> F[Outcome 2: Three-term feedback loop drives excessive volume beyond fundamental value]