The stock price crash constitutes one part of the complexity in the stock market. We aim to verify the threshold effect of leveraged trading on the stock price crash risk from the perspective of feedback trading. We empirically demonstrate that leveraged trading has a threshold effect on the stock price crash risk on the basis of monthly data on leveraged trading in the Chinese stock market from January 2014 to December 2016. At a low leverage ratio, leveraged trading reduces the stock price crash risk; however, as the leverage ratio increases and exceeds a certain threshold, leveraged trading asymmetrically increases the stock price crash risk. These findings provide new insights in understanding the complexity in the Chinese stock market.

Leveraged trading exhibits the characteristics of “strong margin trading and weak short selling” in the Chinese stock market. On the basis of monthly data on leveraged trading in the Chinese stock market from January 2014 to December 2016, we aim to empirically examine the relationship between leveraged trading and investor sentiment, and analyze the characteristics of investor sentiment contained in the leverage ratio. The results show that (1) as the leverage ratio increases, the pattern of investor trading changes from the positive feedback trading of “chasing up and down” to the negative feedback trading of “selling high and buying low”; (2) leveraged trading has the typical characteristics of irrational sentiment; (3) inverse arbitrage strategies based on leverage ratios is effective in one month in the Chinese market. The findings in this paper provide empirical support for clarifying the influence mechanism between leveraged trading and investor sentiment, and can serve as a useful reference for reducing the impact of leveraged trading on volatility and maintaining the sustainability of the stock market.

This paper discusses the impact of noise trader risk on total consumption and investor consumption. The model predicts that: (1) If noise traders show optimistic beliefs, they will have a restraining effect on the total consumption when the noise trading intensity is high enough, they will expand consumption at t = 1 and reduce consumption at t = 2, and rational investors will reduce consumption at t = 1 and expand consumption at t = 2; (2) if the beliefs of noise traders do not show bias, the consumption of rational investors is always higher than that of noise traders and exceeds the market benchmark; (3) the relative consumption of rational investors and noise traders depends on the risk, risk aversion, fundamental risk and market ratio of noise traders; (4) based on the reasonable range of noise traders’ beliefs, the lifetime consumption of noise traders will be higher than that of rational investors and the market, and the excess consumption will change with a series of parameters.

Sentiment and extrapolation are ubiquitous in the financial market, and they are not only the embodiment of human nature, but also the primary drivers of asset price bubbles. In this study, we first constructed a theoretical model that included fundamental traders and extrapolated investors, and we assessed the time series characteristics of asset prices under different types of information shocks. According to the research results, good news about the fundamentals can lead to positive asset price bubbles, and correspondingly, bad news can lead to negative asset price bubbles; however, the decrease in asset prices in the case of negative bubbles is not as substantial as the increase in prices in the case of positive bubbles, and the time for prices to reverse is also long, which can be explained by the short-selling constraints. According to the comparative static analysis, the scales of the positive and negative foams depend on the proportion of investors in the market and the extrapolation coefficient. We verified the conclusion of the theoretical model from two aspects: (1) we analyzed the relationship between investor sentiment and the prevalence of informed trading, and according to the results, the increase (decrease) in investor sentiment can reduce the information content of asset prices and increase price volatility; however, the impact of low sentiment is not substantial, which preliminarily tests the conclusion of the theoretical model; (2) we examined the relationship between the cumulative change in investor sentiment and future portfolio returns, and we found that the cumulative increase in investor sentiment can have a positive impact on future portfolio returns at the initial stage, and depress future portfolio returns in the long term, which forms positive asset price bubbles. The cumulative depression of investor sentiment can depress the future portfolio returns at the initial stage, and positively influence the future portfolio returns in the long term, which forms negative asset price bubbles. Moreover, these two nonlinear relationships exhibit cross-sectional differences in different types of asset portfolios, which further validates the key proposition of the theoretical model.

This article constructs a behavioral financial model that provides feedback on both historical prices and company fundamentals without considering asset liquidation to discuss the long-term impact of investor sentiment and feedback trading on asset price fluctuations. The research conclusion shows that the abnormal volatility of asset prices is captured by the value effect, the cognitive bias effect, the sentiment shock effect, and the trading inductive effect. The value effect is the volatility of asset prices that is completely determined by fundamental factors; the higher the degree of cyclical fluctuations in fundamental factors, the higher the volatility of prices. The bias effect refers to investors’ misreading of basic information and trends in asset prices; the greater the instability of emotional shocks, the greater the abnormal volatility of asset prices. The trading inductive effect is also called the Keynes effect, which reflects the role played by rational traders.

Feedback trading theory is one of the most primitive theories about financial market. But for a long time，researches and modelings on this topic are rarely seen. The model in this paper shows the effects of sentiment shocks on asset prices in a market characterized by feedback trading in the long run. We find that，generally，feedback trading will lead to cognitive bias effect and trading inducement effect. Cognitive bias effect increases with the feedback trading parameter ( FTP) . In our model，the abnormal volatility of asset prices is captured by cognitive bias effect，sentiment shock effect and trading inducement effect．