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Market Crowd Trading Conditioning, Agreement Price, and Volume Implications

Leilei Shi, Liyan Han, Yiwen Wang, Ding Chen, Yan Piao, and Chengling Gou

posted on 04 March 2012

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We propose a notion of trading conditioning in terms of operant conditioning in psychology and measure the intensity of market crowd trading conditioning by accumulative trading volume probability at a price in a time interval. Then, we develop a market crowd independent trading price model, a market crowd stationary equilibrium price model, and a market crowd trading conditioning model according to a price-volume probability wave equation. We test market crowd agreement on price(s) by regression model(s) and study their psychological behaviors in learning by correlation analysis, using tick by tick high frequency data on a daily basis in China stock market. We find that 1) market crowd behave coherence and reach agreement on a stationary equilibrium price in interaction widely; 2) when stationary equilibrium price jumps from time to time, market crowd adapt to the reference price point themselves and keep coherence no matter whether it is obviously underestimated, overestimated, or approximately equal to fundamental value; they are boundedly rational; 3) they show significantly behavioral “anomalies” over time and occasion: while significant disposition and herd anomalies disappear simultaneously by learning experience in a certain circumstance, buy and hold, panic sell, and maladjustment between judgment and trading activity may be pronounced significantly in decision making. The relation between return and volume indicates market crowd expectation on return in response to information and events by trading action. The behavioral annotation on accumulative trading volume probability suggests key links to potentially better applications in sociology and psychology and new directions for market crowd trading activity, price volatility, equilibrium, and game research in behavioral economics and finance, particularly when we combine the normative probability wave equation with the descriptive prospect theory together in a unified framework.