| Since the middle of June 2015, the Chinese A-Share market has experienced the plunging market in a short period of time, the total market value of A-Share has lost nearly 17 trillion in June and the bull market based on financial leverage has accidentally ended ahead of time. The cataclysmic of the market also makes investors have a deeper understanding of the fact that when chasing the interest, we should know the importance of grasping the market discipline, and avoiding following the sheep at the same time. We can grasp the law of stock market from the various aspects and the fully understanding of lead-lag relationship of the stock market is one of the most important. Lead-lag relationship is a special sequence of cross-correlation. Mastering the rise and fall discipline of market stock price of different types of stocks can effectively reduce the investment risk for investors.Based on the importance of the stock market to the national economy and life, the lead-lag relationships of the stock have been the focus for the scholars at home and abroad to study. The source of lead-lag relationship in stocks comes from three areas:predictability of the portfolio return, asynchronous transactions and information delay, and most scholars agree that lead-lag relationship is due to the difference of the information adjustment. From the perspective of earnings predictability, which means the portfolio return is affected by auto-correlation factor, that lead-lag relationship of portfolio return is derived from the auto-correlation and internal correlation of the group (Conrad and Kual,1988). From the perspective of the asynchronous transactions, asynchronous transactions is caused by the difference of trading system, such as the different closing time of Shanghai and Shenzhen stock market, which may cause desynchroneity. The asynchronous transactions have a significant impact on the current return of stocks (Fisher,1966; Cohen and Kalman.,1986). From the perspective of the information delay, there are three main factors:the first one of them is the size factor, the increasing information gathering cost with the size causes the lead-lag relationships of the stock return (Lo and MacKinlay,1990); the second is volume factor, due to the different response to the information and different transaction density, the volume zoom in and out, and there exists a negative correlation relationship between the current return and inter-temporal stock return(Wang, 1993); the third is the industry factor, the lead-lag relationship is related to the industry size, the competition degree, and so on, and what’s more, the lead-lag relationship is more evident within industry (Hou,2007). In addition to the above factors, domestic and foreign scholars from other different angles, such as expert coverage, institutional investors holding and the information structure and so on, to make an empirical analysis of the lead-lag relationship in the stock.From the research perspective, the research scholars at home and abroad mainly from the auto-correlation factor, size factor and the volume factor to study the lead-lag relationship of the stock. Compared with this, researches about the industry which the stocks belong to are less, and research conclusions of the lead-lag relationship between each country are not entirely consistent. From the point of view of factors such as size, Some scholars believe that that portfolio return of small size companies in is leading the portfolio return of big size companies both in the short run and long term (Hodgson et al.,1999), but some just hold the opposite opinions (Nabeel,2000). What factors influence the lead-lag relationship in Chinese stock market, the degree between factors, as well as different performance of factors in a bear market and bull phases?Based on previous researches, this article has carried on an empirical research on the size and the industry factors which have influence on the lead-lag relationship of the stock. The reasons of choosing the two factors, partly is that the research literature, connected to the self-related factors and the volume influencing lead-lag relationship, is much. And the study of research exploring the relationship between volume and price is more thorough and conclusions are consistent; in comparison to this, the study of lead-lag relationship between different industries or within the industry is less, and taking into account the importance of size impact on stock returns, so the article chooses the size factor and the industry factor to study the lead-lag relationship, which has certain theoretical and practical significance.In the research process, this article selects the data from July 1,2010 to June 15,2015 in Shanghai and Shenzhen A-share market. The study period is divided into a bear market and bull market phase, bear market phase for 2010 on July 1 to July 24,2014, the Shanghai index fluctuate in a low position, low minimum during the down to 1849.65. The bull market phase will be on July 25,2014 to June 15, 2015, the Shanghai composite index rose from 2105.06 to 5178.19 points, up 145.99%. From the perspective of research tools, this article refers to a variety of measurement methods, such as unit root test, co-integration test and granger causality test, to study the lead-lag relationship in price and portfolio return, and build a vector autoregressive model (VAR) or error correction model (VECM) to analyze. In the process of empirical analysis, this paper mainly uses the Excel to do the data processing, and the R software to estimate variables.In order to meet the demands of research, this paper uses a large number of graphs to show the statistical properties and regression results intuitively.Based on the above research methods, the full text is divided into six chapter altogether. The first chapter is the introduction part, mainly introduce the research background, research significance, and the research methods of this article, and introduce the research contents and innovative points of the article. The second chapter is literature review. This paper explains the meaning of the lead-lag relationship at first, and classifies the factors of the lead-lag relationship of the stock. Finally, the latest research of the scholars at home and abroad in related fields are summarized. The third chapter is the research design. Based on related research both at home and abroad, together with the present situation of Chinese stock market, this article chooses the size and industry factor to analyze the lead-lag relationship of stocks.The fourth chapter is the size factor analysis. In the research of size factor, this article picks 20 stocks from the Shanghai and Shenzhen a-share market (among them,10 big-size companies, and 10 small-size companies), to construct big-size portfolio returns and small-size portfolio returns. Controlling the autocorrelation and eliminating the influence of asynchronous transaction, the paper has three considerations in the process of analyzing the size factor, one is eliminating the autocorrelation, another one is introducing the book/market ratio and the last is bring in the asymmetric information (good news and bad news), based on which we make empirical analysis on the specific influence of size factor. In addition, considering the research object selection problem, on one hand the article uses logarithm return of the Cninfo market index to evade less sample selection problem; on the other hand, constructs the weekly portfolio return series to do the robustness test.The fifth chapter is analysis of industry factor. In industry factors research, this paper mainly selects these industries which have the relationship between upstream and downstream industry to study the lead-lag relationship between and within the industries. To study the lead-lag relationship within the industry, this article takes the auto industry as an example, grouping the auto companies according to the market size, and also constructs the weekly portfolio return series to do the robustness test. To research about the lead-lag relationship between the industries, first the paper studies the industry index, then does the industry group study with industries which have the granger causality relationship to explore the size cross effect of industry factor and size factor.The sixth chapter is conclusion. Empirical study shows that:(1)From the results of size factor, in full stage the big-size portfolio return and small-size portfolio return has a two-way granger causality relationship, while in a bear market and bull market phase causality relationship is evident in the one-way. In a bear market phase, small-size portfolio return is a granger cause of large portfolio returns, the shares prices of small companies adjust more quickly, and portfolio return of the lagging small-size has a prediction effect on big-size portfolio return. In the bull market phase, big-size portfolio return is the granger reason of small-size portfolio return, and big-size portfolio return has a prediction effect on the small-size portfolio return. (2) From the results of industry factor, there is no granger causality relationship of portfolio return formed on size within the auto industry, and the lead-lag relationship within the industry mainly is characterized by the co-integration relationship of price. In addition to the auto industry lag issue of returns for the iron and steel industry can be used to predict the current return, lead-lag relationship between the auto industry and its upstream and downstream industry is not obvious.Compared with the previous research, the innovation of this article is embodied in the following three aspects:first of all, the article introduces the B/M ratio in the size factor analysis, by clarifying the stocks according to the B/M ratio and size, to explore their cross effect, then has a deep research on the size factor by controlling the B/M ratio. Secondly, this article introduces a sequence of weekly portfolio return and logarithmic return of Cninfo market index to do robustness analysis. Finally, in the study of industry factor, the industries which have important economic, ties upstream and downstream industries, with the automotive industry are screened and their causality is analyzed to investigate the lead-lag relationship caused by their cross influence.However, due to the limited theory and research ability, there are still many shortcomings in the article, the future research directions of this article can be expanded from the following two aspects. On one hand, in the study of the size factor, the article does not take into the cross influence with other important factors, in the future cross portfolio can be constructed with factors such as volume, expert coverage and so on to explore their interaction relationship between influencing factors. On the other hand, in the study of the industry factor, the article only selects automotive industry and its upstream and downstream sectors to do the empirical analysis, and there are some limitations. In the later study, clarify listed companies by their industry more scientifically, and differentiate them according to the upstream and downstream industry chain, thus we can get a more systematic study of the lead-lag relationship in the whole stock market caused by the industry factor. |
PDF Full Text Request