A Study Based On Pricing Efficiency Of Long-term And Short-term Behaviour Factor ——Evidence From Chinese A-listed Market

The Capital Asset Pricing Model(CAPM)is the research foundation of the linear single-factor pricing model.After half a century of development,the factor investment evolved from it has made it an empirical evidence today by virtue of its outstanding performance in asset allocation and obtaining excess returns.The key research object in the field of asset pricing.In the theoretical framework of traditional finance,the asset pricing model follows the principle of risk compensation for the expected return of individual stocks.It is believed that when the pricing factor faces risk exposure,it needs to obtain a risk premium higher than the transaction price,that is,a higher expected return.However,with the emergence of more income anomalies that cannot be explained by mainstream asset pricing models,the principle of risk compensation has been questioned by all parties.At the same time,theoretical analysis and empirical research have also found that traditional factor pricing models may have flaws in their construction logic.,The systemic risk in the stock market corresponds to the price premium,and the performance of the multi-factor pricing model is not good enough because the proxy variables of risk cannot fully cover the systemic risk that exists in the market.The assumption that investors are rational in the market when constructing pricing factors is also inconsistent with Chinese A-listed market.In the Shanghai and Shenzhen stock markets,there are relatively few investors who follow value investment,and individual investors account for more than institutional investors.At the same time,the turnover rate of stocks is generally high and the volatility is relatively high.These irrational investment characteristics are more obvious.One method of traditional pricing factor construction is based on the rational asset pricing theory under the efficient market hypothesis,and the other is to apply the structural constraints implicit in the rational asset pricing theory to make statistics on the return rate of assets or the ranking characteristics of asset portfolios.Analysis to extract factor characteristics,according to the construction principles can be divided into company fundamental characteristics factors and empirical technology factors,such construction methods and division methods lack of attention to investor behavior,ignoring the deviation caused by investors in investment behavior The impact on stock mispricing.Based on the above logic,the innovations and research contents of this article are as follows:Firstly,systematically combed the theoretical literature and empirical literature of traditional pricing factors,income anomalies,and mainstream multi-factor pricing models at home and abroad,and tried to argue that behavioral finance theory can be used to construct behavior in Chinese A-listed market through the analysis of literature data.Pricing factors,and strive to achieve innovation in the research ideology and methodology of the construction of Chinese stock pricing factors.Through systematic analysis of theoretical and empirical literature in the field of factor investment at home and abroad,it is found that the behavioral deviation of Chinese investors is one of the reasons for the mispricing of stocks by the market.Through the methodological research and psychology of constructing systematic pricing factors After cross-examination of the principles,it is found that the psychological principles of investor overconfidence and limited attention can be used as the theoretical support for the empirical research in this article,and can be used as proxy variables for behavioral pricing factors.Combining with the characteristics of Chinese A-listed market,this paper attempts to explain the mispricing of stocks from the perspective of investor behavior,and to construct and optimize systematic pricing factors.Secondly,based on the research of Daniel et al.(2020),optimized the construction of long-term and short-term behavioral pricing factors and positioning factor pricing models,and used the Chinese A-listed market from January 1,1997 to December 31,2020,The volume and price transaction data is used to test the pricing efficiency,which realizes the innovation in the use of factor investment tools and models in the field of empirical asset pricing in China.Based on the theory of behavioral finance,this article looks for proxy variables that can measure investor behavior deviations in the field of psychology:using Chinese A-listed companies' 5-year review equity issuance as a ranking variable to measure the overconfidence caused by investors The degree of deviation of the long-term mispricing of stocks is optimized to construct a long-term behavior factor;the cumulative abnormal return rate of 4 days before and after the disclosure date of the Chinese A-listed company's financial report is used as a sorting variable to capture investors' limited attention The short-term mispricing caused by insufficient response to the newly emerging earnings information in the stock market constitutes a short-term behavioral factor.In the statistical analysis with traditional pricing factors,it is found that the investment portfolio constructed by long-term behavior factors has a comparative advantage compared with the investment portfolio constructed by other pricing factors in balancing the relationship between market risk and the rate of return.In the correlation analysis with traditional pricing factors,it is found that there is no significant correlation between long-term behavioral factors and short-term behavioral factors,and between behavioral pricing factors and traditional pricing factors.The pricing factor is independent in its ability to catch mispricing.There are differences between the data used in constructing behavioral factors and previous studies:(1)Using behavioral factor loads as underpricing as proxy variables in the process of predicting individual stock returns;(2)Using the excess returns of sample stocks in the A-listed market within 1 month for daily the degree of time series sorting replaces the conventional 60-month stock volume and price data regression to calculate the factor loading of the behavior factor.In terms of predicting the return rate of individual stocks by behavioral factors,it is found that the factor loading of long-term behavioral factors has a positive and significant predictive ability on the future trend of individual stocks' returns.In stability testing,after controlling 24 conventional anomalous variables in 7 categories,the predictive ability still positive and stable.At the same time,it is found that the factor loadings of short-term behavior factors that cannot be clearly defined also have the ability to predict the return of individual stocks.One of the reasons is that the factor loadings of short-term behavior factors may be systematically biased in the estimation.In the empirical study on the influence of behavioral factors on long and short returns in the return anomaly portfolio,it is found that the factor loading of long-term and short-term behavior factors can better explain the overestimated short return in the portfolio.Further,in the empirical research on the impact of behavioral factors on the forecasting efficiency of individual stocks,it is found that the trading frictions in the Chinese stock market have a significant effect on the efficiency of long-term behavioral factor stocks forecasting.At the same time,cognitive biases also have an impact on the efficiency of long-term behavioral factors in the forecast of individual stock returns.Using analyst coverage and analyst divergence as proxy variables for investors'cognitive biases in investment behavior,the study found that investors' cognitive biases The existence of the factor load of long-term behavior factors will significantly increase the sensitivity of individual stocks' expected returns.This article also verified that institutional investors did not play the role of price discovery in the investment process,but instead made bubble-ridden investment behaviors.As the proportion of institutional lists increased,the return rate of long-term behavioral factor portfolios increased significantly.It is concluded that institutional investors' riding behavior of stock price bubbles can significantly increase the expected return of long-term behavior factors of individual stocks,and that long-term behavior factors can explain institutional investors' riding behavior of stock price bubbles.In terms of the efficiency of explaining the anomaly of return in the A-listed market,the constructed behavior factor model performs well,especially in explaining the long-term return anomaly.It has a comparative advantage.Thirdly,the empirical test proves that there are long-term and short-term behavioral income anomalies in Chinese A-listed market,which has achieved innovation in the perfection of the classification of the A-listed market anomaly.In this paper,from the perspective of pricing factors to construct long-short investment portfolios to obtain abnormal returns,from the perspective of pricing factor correlation,the univariate portfolio spread method and the ability to explain each other between pricing factors are used in four aspects to systematically affect the long-term and short-term behavioral returns of the A-listed market.The existence of the elephant is empirically tested,and after further controlling 22 fundamental characteristic variables that affect the company's stock price changes,it proves that Chinese A-listed market has a long-term(usually 3-5 years)and short-term(less than 1 year)There is a stable behavioral income anomaly within the period of time.Fourthly,constructing a three-dimensional Logit model that can predict the pre-probability of stock price crashes,using long-term behavior factors and building pre-disruption probability models to predict the risk of stock price crashes,realizing the innovation of the combined use of them.This model can distinguish the probability of extreme negative returns,extreme positive returns and normal returns realized by stocks in the future.And it proves that it is effective to use the out-of-sample forecasting method to calculate the probability of stock price collapse.Through empirical testing,it is found that the greater the issuance of individual stocks for review equity,the higher the degree of overestimation of stock prices caused by investor behavior deviations,the smaller the expected future return of individual stocks,and the higher the probability of stock price collapse risks.Further research found that the extreme market risk of stock liquidity exhaustion during the stock market disaster will not affect the ability of long-term behavioral factors to predict stock price crash risks through pre-crash probability models,and the ability of long-term behavioral factors to predict stock price crashes is time-varying,which manifests itself as time-varying.The ability to predict progress has been significantly improved.After considering the impact of company size and value,it is found that long-term behavior factors have significantly improved the ability to predict the risk of stock price collapse,and the company size as a control variable provides a larger incremental contribution.In the empirical research on the ability of institutional investor behavior to predict stock price collapse by long-term behavioral factors,it is found that for the same group of stocks,the greater the CSI value of the rechecked equity issuance,the higher the risk of a stock price collapse with a high institutional listed holding ratio.Institutional investment the existence of the person can explain the long-term behavior factor's ability to predict the probability of a crash.Further research found that the prior probability of stock price collapse has no significant impact on long-term behavioral factors in obtaining future returns,and has no inherent correlation.On the whole,it has basically realized an innovative attempt in the methodology of stock price collapse probability model and behavioral factors to predict the risk of stock price collapse.Based on the theoretical basis of behavioral finance,this paper follows the mature empirical asset pricing research standard paradigm of the American securities market,and draws on the research methods of constructing systematic pricing factors,using the stock volume and price trading data of Chinese A-listed market to construct behavioral pricing factors.It also proves that there are behavioral returns anomalies in the long and short time horizons of the Chinese stock market.It is found that behavioral factors have a robust predictive ability for the future returns of individual stocks.Further studies have found that there are trading frictions and investor cognitive biases in the market.It has an impact on the efficiency of predicting the returns of individual stocks of behavioral factors,and the existence of institutional investors has an impact on the accuracy of predicting future returns of individual stocks of behavioral factors.It is proved that the combined use of the constructed pre-probability model of stock price crash and long-term behavior factors can quantitatively predict the risk of stock price crash.The thoughts and methods summarized in the demonstration process have enriched our country's existing empirical asset pricing research ideas,and the empirical results also have important reference value for factor investment practice.