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.

Trading constraints with unspecified constraint horizon are imposed on the shares held by the state in the IPO of each listed firm in China Stock Market. In 2005, a so-called Split Share Structure Reform (also known as Division Reform) was launched in which the holders of restricted shares give up a proportion of their shares to purchase the right to terminate the trading constraint. From the size of the compensation, we infer the value of restricted shares and find that their price discounts are negatively affected by the restriction looseness captured by our proposed new multi-dimensional measure and positively affected by the bargaining power of the holders of freely-traded shares.

The paper examines the short-run spillover effect of daily stock returns and volatilities between the S&P 500 in the U.S. and Shanghai SSE composite in China. First, we find that a structural break happened in the SSE stock return mean in December 2005. Second, analyzing modified GARCH (1,1)-M models, we find evidence of a symmetric and asymmetric volatility spillover effect from the U.S. to the China stock market in the post-break period. Third, the symmetric volatility spillover effect from China to the U.S. is also observed in the post-break period.

In this paper, we propose a new approach to test the presence of GARCH Effects of China stock market. Our method is based on Maximal Overlap Discrect Wavelet Transform (MODWT)that provides a natural platform to investigate the volatility behavior at different time scales without losing any information.The empirical results show that GARCH effects are more significant at short time horizons as compared to long. Furthermore, when compared the modeling results of GARCH-t with that of EGARCH-t, it yields very higher effectiveness to capture the leverage effect of financial time series at relavant time scales.

We investigate considerations (compensations) paid in on-going Division Reform, a process of releasing trading constraint, in China Stock Market, and link this event with existing literature of restricted asset by inferring implied illiquidity discount of restricted shares from consideration. We also propose a new measure of restriction capturing multiple dimensions of restriction finding it together with the scale of restriction well explain the ratio of considerations and the implied illiquidity discount. We also use theoretical models to justify the 20% implied illiquidity discount and find it is below the 48.67% upper bound suggested by Longstaff (1995) and it falls within the range between 12.18% and 23.82% suggested by Lonstaff (2001) concluding the considerations paid in Division Reform is adequate and relative wealth of two classes of shareholders remains after the trading constraint released.

The short history and market segmentation characteristic of China stock markets not surprisingly make the market indicators behave in certain way. In this paper, we tabulate the belief that the regulatory and instrumental policy changes in China structurally break the market indices. This is proven and break points are detected with a focus on Shanghai Stock Exchange in the first part of this paper. Whereas, the stochastic trend nature of the market remains even when the structural breakpoints are detected and after it is tested against various kinds of deterministic trends. It, to some extent, implies the efficiency of Shanghai market with regards to unpredictability. The second part of this paper dedicates to analyzing the interaction between A and B share markets. As a contrast to the past literature, the change in trading volume of B share market is found to be a much more sensitive leading indicator to the change in A share market, in the sense of Granger causality with a VAR fashion. This finding may further reveal the unbalanced investor structure in A and B share markets.