The Impact Of Margin Trading On Stuck Market Volatility From The Perspective Of Counterfactual Thinking

China’s securities market started late compared with the overseas developed,short-selling transactions have been banned since it was founded in 1990,so investors can only earn profits by low-buy and high-selling.On March 31,2010,the margin trading put into effect officially in the domestic A-share market,marking the end of the unilateral market-making status and the beginning of bilateral transaction in China’s securities market.The policy has made great progress rapidly.At the beginning,there were only 90 securities and only 6 securities companies that could carry out this policy.As time goes on,the underlying stocks were eliminated and expanded many times,by the end of December 31,2017,the number of securities was 970,accounting for about one-third of the number of A-shares.The number of brokers reached 94,accounting for about three-quarters of the total number of securities firms.Many investors and medias criticized the policy since the crash of 2015,people thought it would aggravate the volatility of stock market and began to focus on this policy.Since then,a series of measures restricting the transaction have begun to be implemented.What is really the role of the market trading?Based on this,this paper selects the latest expansion of margin trading as the research background,constructed the counterfactual volatility of stocks by the controlled based on the panel data approach of program evalution proposed by Hsiao et al.(2012),then the difference between true volatility and counterfactual volatility is the treatment effect that can be used to measure the effect of margin trading,then a t-test to show whether the impact is significant or not;The single factor analysis to carry out on whether the function is related to the characteristics of stocks.Finally,based on the regression analysis method,controlling the stock market value stock index futures trading,the fixed effect model is used to explain the effect of margin trading.The conclusions are as follows:(1)It can be used by the control group instead of common factors for parameter estimation according to the Hsiao et al.(2012)method to construct a multi-factor model of volatility,since the stock volatility is driven by unobservable common factors such as sudden events,the factor model indicates that the influence of common factors on the treated group is unchanged if not because of policy intervention,that is to say,the parameters were unchanged and then the treated stocks are constructed according to the post-policy data and the obtained parameters.(2)77 stocks included in the list of margin trading On December 12,2016 were treatment group,the others were control group and made selection furtherly according to the beta value.The volatility treatment effect and t-test results of 59 stocks were obtained because of the lack of data.It shows that the number of significant negative treatment effect is 27,accounting for 46%;21 are significantly positive,accounting for 36%.It can be seen that the volatility of stocks reflects the policy.Although there are differences,the treatment effect has a large proportion of negative share,indicating that margin trading help to restraint on stock volatility.Then,the stock volatility is weighted by the market capitalization and total market value respectively to obtain the market daily volatility,and the market daily counterfactual volatility can be calculated correspondingly.The t-test results show that the market daily volatility treatment effect is significantly negative regardless of the market capitalization or total market capitalization,indicating that the implementation of the policy reduces market volatility and helps stabilize stock market.(3)The single factor results show that the degree of stock volatility affected is related to the characteristics of stocks.The combination of the larger market capitalization within the sample interval,the combination of the P/E ratio,the P/B ratio and the turnover ratio,the greater the decline in the volatility of individual stocks after the introduction of the policy.Among them,the stocks with large market capitalization have a 0.55%increase in the volatility than those with smaller market capitalization after the policy;the stocks with smaller P/E ratios have increased by about 0.3%after the policy;the stocks with smaller P/B ratios have a 0.3%increase in stocks after the policy,and stocks with smaller turnover ratios have a 0.77%increase after the policy.(4)The results of fixed-effects model show that the policy have significant negative effects on stock volatility in our sample interval.It plays the role of price buffer to some extent.Compared with the financing trading,the scale of the securities trading was small,but the effect was more significant.This conclusion is still established after controlling the influence of a series of variables such as stock characteristics and stock index futures trading and so on.The possible innovations in this paper are the application and improvement of research methods.Different from the previous,GARCH model or DID or propensity score matching,the article is based on the panel data approach of program evalution.The research sample is the stocks of recent expansion;furtherly,in the estimation of the parameters of the volatility multi-factor model,in order to avoid over-fitting and simplify calculations,Hsiao et al.(2012)is not used but by stepwise regression to obtain optimal explanatory variables.Finally,there may be some bias in the research results in view of the availability of data,due to the use of high-frequency daily data for research.Except for the variables selected in this paper,there are many other factors affecting stock volatility and they are ignored,so the conclusion needs to be consolidated furtherly.