The Study Of Investor Behavior And Informed Trading In Limit Order Driven Market

The interrelationship between market status and investor behaviors, and the measurement of informed trading are two prevailing and difficult topics in limit order driven market. This dissertation consists of three parts, and each part includes two chapters for discussions in details. The paper states the influence of market status on investors'behaviors, the impact of investors'behaviors on market status inversely, and the informed trading measurement. The details are as followed.First, Chapter Two studies investors'risk attitude and risk factors from a micro-level, based on high-frequency trading data. Owning to rational individual, spread and depth represent investors'common behaviors, which are formed by limit order book. This chapter investigates the real reason of investor behaviors change, adopting LSB spread separation model. The results indicate that liquidity and volatility change ratio have more impact on investors'behaviors; the investors mainly have concerns over asymmetry information.Second, Chapter Three discusses the investors'arrival intensity and the influencing factors, adopting EKOP model. The arrival rates are time-variation, and largely depend on different market situation. The uninformed traders focus on some macro variables, including market return, total volume and market volatility. While informed traders have concern on some micro variables, such as stock return, average volume, relative spread, market depth and market elasticity besides of those macro variables.Third, Chapter Four investigates reaction of investor behaviors on volatility, one of market situations by time series. This chapter, which adopts VAR model, studies the relationship of volume and volatility in different market situations, consisting of volume and volatility as endogenous variables, duration and trading direction as exogenous variables. Then it moves further into the persistent impact of unexpected trading on volatility through impulse respond analysis. Comparing to bear market, there is more private information in bull market, one unexpected trading could result in larger change on volatility, and the private information could be absorbed totally by longer time.Fourth, Chapter Five investigates reaction of investor behaviors on volatility, one of market situations by cross-sectional angle. Firstly, all the stocks in Shanghai Stock Exchange are divided into several portfolios based on capitalizations or industries. Then, it measures heterogeneous beliefs of investors using cross-sectional expected return dispersion (ERD). Lastly, it verifies whether ERD includes the new information about volatility. In bull market, expected return dispersion affects volatility of portfolios and individual stock fiercely. However, this kind of effect is not significant in bear market. In bear market, return volatility is decided by time-series conditional variance of portfolios and index.Fifth, PIN index in EKOP model was adopted to investigate the probability of informed trading in many literatures. However, most of these studies did not directly test the veracity. Chapter Six suggests the extensional EKOP model and investigates the validity of PIN from two methods: the returns of informed and uninformed traders; the behaviors of PIN in different periods before and after announcement. The empirical results show that the traditional PIN index computed by numbers of trading is worse than extensional model that computed by volume or value.Sixth, Chapter Seven studies different investors'informed trading proportion with return variance decomposition method to decompose the variance of returns, based on heterogeneous investors. It subdivides investors into three categories: the traders who acquire announcement information in advance (the first type of informed traders, insider trader); the traders who analyze information privately (the second type of informed traders) and the uninformed traders. Comparing the difference of informed trading before and after announcement, it discovers insider trading percentage is small, which accounts for 2.78%. The independent analysts, for example institutional investors, are 36% on average.This dissertation provides theoretical foundation for investors on their trading decisions and presents a valuable access for regulators on understanding security market.