Price Change and Trading Volume: Behavioral Heterogeneity in Stock Market

The well-known Wall Street adage that states, “It takes volume to make prices move” has long suggested that there exists a positive correlation between absolute changes in stock price and trading volume. To practitioners who use technical analysis as their trading tool, trading volume has always bee...

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Bibliographic Details
Published inComputational economics Vol. 61; no. 2; pp. 677 - 713
Main Authors Li, Changtai, Huang, Weihong, Wang, Wei-Siang, Chia, Wai-Mun
Format Journal Article
LanguageEnglish
Published New York Springer US 01.02.2023
Springer
Springer Nature B.V
Subjects
Behavior change
Behavioral/Experimental Economics
Causality
Change agents
Computer Appl. in Social and Behavioral Sciences
Economic crisis
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Economics and Finance
Financial market
Heterogeneity
International finance
Math Applications in Computer Science
Operations Research/Decision Theory
Prices
Pricing
Securities markets
Securities trading
Stock exchanges
Stock markets
Stock prices
Stocks
Time series
Trading
Volatility
Stylized facts
Stock market
Heterogeneous belief
Trading volume
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Summary:The well-known Wall Street adage that states, “It takes volume to make prices move” has long suggested that there exists a positive correlation between absolute changes in stock price and trading volume. To practitioners who use technical analysis as their trading tool, trading volume has always been treated as a key signal to price change. Although many studies have empirically examined the nonlinear relationship between price change and trading volume, very few studies are able to provide a persuasive explanation for such price-volume relationship. This paper fills this gap by providing an explanation for such relationship under a framework of heterogeneous agent model with evolutionary switching mechanism. With the support of US stock market data, we first summarize some stylized facts on stock return and trading volume. We then mimic these facts using our model. The comparison between simulated and “real” time series shows that our model is not only able to replicate the seemingly chaotic fluctuations of the financial market but also able to explain how stock prices and trading volumes co-evolve with agents’ belief.
ISSN:0927-7099
1572-9974
DOI:10.1007/s10614-021-10224-4