Market Crowd Trading Conditioning, Agreement Price, and Volume Implications
Complex System Research Group, Department of Modern Physics, University of Science and Technology of China (USTC)
Beihang University (BUAA) - School of Economic and Management Science
Tsinghua University - School of Economics & Management
University of Texas at Austin - McCombs School of Business
Beihang University (BUAA)
Complex System Research Group
April 15, 2013
It has been long that literature in finance focuses mainly on price and return but much less on trading volume, even completely ignoring it. There is no information on supply-demand quantity and trading volume in neoclassical finance models. Contrary to one of the clearest predictions of rational models of investment in a neoclassical paradigm, however, trading volume is very high on the world’s stock market. Here we extend Shi’s price-volume differential equation, propose a notion of trading conditioning, and measure the intensity of market crowd trading conditioning by accumulative trading volume probability in the price-volume probability wave equation in terms of classical and operant conditioning in behavior analysis. Then, we develop three kinds of market crowd trading behavior models according to the equation, and test them using high frequency data in China stock market. It is hardly surprising that we find: 1) market crowd behave coherence in interaction widely and reach agreement on a stationary equilibrium price between momentum and reversal traders; 2) market crowd adapt to stationary equilibrium price by volume probability increase or decrease in interaction with environment (information and events) in an open feedback loop, and keep coherence by conversion between the two types of traders when it jumps and results in an expected return from time to time, the outcome of prior trading action; 3) while significant herd and disposition “anomalies” disappear simultaneously by learning experience in a certain circumstance, other behavioral “anomalies”, for examples, greed and panic, pronounce significantly in decision making. Moreover, a contingency of return reinforcement and punishment, which includes a variety of internal and external causes, produces excessive trading volume. The behavioral annotation on the volume probability suggests key links and the new methods of mathematical finance for quantitative behavioral finance.
Number of Pages in PDF File: 56
Keywords: quantitative behavioral finance, differential equation, behavior analysis, trading conditioning, agreement price, adaptation, volume implications
JEL Classification: G12, G02, D53, C20, D70working papers series
Date posted: February 13, 2012 ; Last revised: April 15, 2013
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