{"title":"Market Crowds' Trading Behaviors, Agreement Prices, and the Implications of Trading Volume","authors":"Leilei Shi, Bing Han, Yingzi Zhu, Liyan Han, Yiwen Wang, Yan Piao","doi":"arxiv-2310.05322","DOIUrl":null,"url":null,"abstract":"It has been long that literature in financial academics focuses mainly on\nprice and return but much less on trading volume. In the past twenty years, it\nhas already linked both price and trading volume to economic fundamentals, and\nexplored the behavioral implications of trading volume such as investor's\nattitude toward risks, overconfidence, disagreement, and attention etc.\nHowever, what is surprising is how little we really know about trading volume.\nHere we show that trading volume probability represents the frequency of market\ncrowd's trading action in terms of behavior analysis, and test two adaptive\nhypotheses relevant to the volume uncertainty associated with price in China\nstock market. The empirical work reveals that market crowd trade a stock in\nefficient adaptation except for simple heuristics, gradually tend to achieve\nagreement on an outcome or an asset price widely on a trading day, and generate\nsuch a stationary equilibrium price very often in interaction and competition\namong themselves no matter whether it is highly overestimated or\nunderestimated. This suggests that asset prices include not only a fundamental\nvalue but also private information, speculative, sentiment, attention, gamble,\nand entertainment values etc. Moreover, market crowd adapt to gain and loss by\ntrading volume increase or decrease significantly in interaction with\nenvironment in any two consecutive trading days. Our results demonstrate how\ninteraction between information and news, the trading action, and return\noutcomes in the three-term feedback loop produces excessive trading volume\nwhich includes various internal and external causes.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":"33 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - General Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2310.05322","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.