市场人群的交易行为、协议价格和交易量的影响

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

长期以来,金融学术界的文献主要关注价格和回报,而很少关注交易量。在过去的二十年里,它已经将价格和交易量与经济基本面联系起来,并探讨了交易量的行为含义,如投资者对风险的态度、过度自信、分歧和关注等。然而,令人惊讶的是,我们对交易量的了解是如此之少。本文从行为分析的角度证明了交易量概率代表了市场人群交易行为的频率,并检验了两个与中国股市价格相关的交易量不确定性相关的自适应假设。实证研究表明,除了简单的启发式外,市场人群交易股票的效率低下适应,逐渐倾向于在交易日内对某一结果或某一资产价格达成广泛的一致,并在相互作用和竞争中经常产生这样一个平稳的均衡价格,无论它是被高度高估还是低估。这表明,资产价格不仅包括基本价值,还包括私人信息、投机、情绪、关注、赌博和娱乐价值等。此外,市场人群在任意两个连续交易日与环境的交互作用中,通过交易量的显著增减来适应盈亏。我们的研究结果表明,在三期反馈循环中,信息与新闻、交易行为和回报结果之间的相互作用如何产生过多的交易量,其中包括各种内部和外部原因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Market Crowds' Trading Behaviors, Agreement Prices, and the Implications of Trading Volume
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.
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