Trading Conditioning, Price Volatility, and Return Patterns in Chinese Equities

Summary

The paper presents a theoretical model that links trading conditioning intensity with security price volatility and returns. It uses distributions of transaction volume and price to represent uncertainty and intensity, framing the relationships in terms of market psychology. The authors apply the model to high-frequency data from China’s stock market.

They report that changes in conditioning intensity are generally positively associated with mean returns. This relationship is not statistically significant in periods immediately before and after bubble crashes, while a significant negative association appears during a rising Shanghai Composite bull market interval. The authors argue that the framework can examine disposition effects and herding together and help explain excess trading volume and other market anomalies. The supplied summary does not detail the sample design, model validation, or robustness checks, so the findings should be read as reported associations rather than proof of causation.

Key ideas

• The model uses transaction volume and price distributions to represent volatility uncertainty and trading intensity.
• The authors test the framework on high-frequency Chinese stock market data.
• Changes in trading conditioning intensity are generally positively associated with mean returns.
• The reported association is insignificant around bubble crashes and negative during a rising bull market interval.
• The framework is proposed as a way to examine disposition effects, herding, and excess trading.

Tags

# A Security Price Volatile Trading Conditioning Model


# A Security Price Volatile Trading Conditioning Model









We develop a theoretical trading conditioning model subject to price volatility and return information in terms of market psychological behavior, based on analytical transaction volume-price probability wave distributions in which we use transaction volume probability to describe price volatility uncertainty and intensity. Applying the model to high frequent data test in China stock market, we have main findings as follows: 1) there is, in general, significant positive correlation between the rate of mean return and that of change in trading conditioning intensity; 2) it lacks significance in spite of positive correlation in two time intervals right before and just after bubble crashes; and 3) it shows, particularly, significant negative correlation in a time interval when SSE Composite Index is rising during bull market. Our model and findings can test both disposition effect and herd behavior simultaneously, and explain excessive trading (volume) and other anomalies in stock market.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.