The Impact Of Financial Investor Behavior On Stock And Futures Markets

The traditional financial theories did not give the answer that how investor behavior affect the financial market. However, under the framework of behavioral finance, investor behavior is an important factor affect s the prices and the operation of financial market. Accordingly, the researches of this subject have many contents. Systematic study of investors behavior and the influence of investors in the financial market make investors more clearly their bias in forecasting stock price and may be useful to investors obtain excess returns with these bias. As well as the research of investor behavior will contribute to better understanding of return, help investor in deep understanding the dynamic interaction mechanism between return and trading volume, volatility. Further, it is also beneficial for regulators to better grasp the psychological characteristics of investors. Provide regulators a theoretical basis for their more efficiency control the market.The contents of this paper are as follows:Firstly, using Google trends records for S&P 500 stocks, we explore whether analysts’ forecasts influence investor attention, Google search trends, a typical user-generated content platform and whether Google search trends have an impact on the effects of analysts’ forecasts on future stock returns. We find that search volume is related to analyst earning forecast updates, percentage of negative forecasts, total number of forecasts and forecast dispersion. Consistent with previous studies, we find that stocks with higher analysts’ forecast dispersion earn significantly lower returns than otherwise similar stocks. However this dispersion effect disappears if Google abnormal search volume is high. Our evidence suggests that information asymmetry associated with analysts’ forecast dispersion impacts both investor attention and the dispersion effect on stock returns.Secondly, this paper examines weekday and intra-day liquidity effect of CSI300 Stock Index Futures. Empirical results show that weekday liquidity pattern exhibits a reverse V-shaped for the CSI300 index futures, while the intra-day liquidity pattern exhibits a reverse J-curve in morning and a reverse U-curve in afternoon. Furthermore, this paper investigates the influencing factors of stock index futures liquidity and gives some recommendations for the trading regime of Chinese Stock Index Futures market.Thirdly, this paper presents price index and index futures lead lag relationship for empirical analysis based on high-frequency data during three different periods, high investor sentiment, low investor sentiment and flat investor sentiment respectively. Using the unit root test, cointegration test, VECM estimates, Granger causality test method, we explored the lead lag relationship between CSI300 spot index and index futures with 5 minutes high-frequency data. The empirical results show that with the increase of transaction time, stock index futures in a larger extent guiding spot index futures, price discovery function of stock index futures market are better.Fourth, due to the volume is often divided into a small sum of investors, and the volume itself can not show the direction of a transaction, order imbalance is better to reflect trading behavior. This paper selects nine futures contracts from Dalian Commodity Exchange(DCE) and Zhengzhou Commodity Exchange(ZCE) as data example to study trading activity and the price behavior based on the high frequency data from 2010 to 2015. In this paper, we find that contemporaneous order imbalances are positively related to returns. Order imbalanc e caused by price pressure last more than one day indicating difficult y in absorbing excess buy and sell orders. We also find that lagged order imbalance can predict current returns and that the effect of order imbalance on liquidity is limited. These results are consistent with the explanation that speculative trading and herd effect not liquidity hinders the Chinese agricultural futures markets to accommodate excess order imbalance.In this paper, we used a variety of statistical and econometric analysis method, including: time series regression analysis, cointegration test, VECM model estimation, Granger causality test and the Fama-Macbeth cross-sectional regression model. The main characteristics of this paper are regard of leading, system, market and guiding for regulatories.