The work presented in this dissertation can be grouped around three major themes. The first theme relates to risk, the second theme relates to asset pricing, whereas the third theme relates to serial correlation of asset returns. The three chapters of this dissertation investigate these themes Chapter Two analyses the behavior over time of market risk, aggregate idiosyncratic risk and correlations in portfolio of Taiwan listing stocks and studied pattern of aggregate correlation between the 3 most important Taiwan stock index and Taiwan value-weighted index. We find (1) Idiosyncratic risk is trended upwards; (2) The conditional stock returns correlation process is asymmetric. The implication of our finding is (1) It takes more stocks to achieve a given level of diversification; (2) Diversification strategies perform poorly in bear markets. Chapter Three investigates the role of the asset co-skewness and conditioning information in asset pricing. First, I estimate long-run predictive regressions of asset returns to test whether aggregate idiosyncratic risk is a price factor of industrial returns. Then I use data on Taiwan 19 industry portfolios to fit various assets pricing models. I find (1) the cross-sectional ctional correlation between 2 i £] (the gamma coefficient from the 3M-CAPM equation) and 3 i ϕ (the interaction coefficient from the CCAPM equation) is positive and fairly large. (2) The firm-level volatility is a good proxy for cay as conditioning information variable. (3) The gamma coefficient can pick up the extent of beta co-vary with the market wide excess-return over the business cycle. (4)among 19 industrial returns, the 2 industrial returns can be explained by 3M-CAPM; the 7 industrial returns can be explained by CCAPM; the 5 industrial returns can be explained by 3M-CAPM+CCAPM, Others can¡¦t be explained by either of three models. Chapter Four examines the impact of positive feedback trading behavior of the investors on the short-term dynamics of return for four Taiwan index futures contracts by utilizing the framework of the model developed by Sentana & Wadhwani(1992). Use of the Asymmetric Nonlinear Smooth Transition GARCH Model demonstrates that positive feedback trading of investors is the main determinant of short-term dynamics of return for Taiwan index futures contracts. Moreover, it shows that positive trading is more intense during market declines than it is during market advances due to extensive use of spot-loss trading for investors. Finally it is shown that the sophisticated professional investors intend to take positive feedback trades wave so that they lead to increase positive feedback trading in Taiwan index futures since the government opened the enterprises for managed futures.

What do investors' searches for public information reveal about their subsequent trading strategies? Does their search for information support the hypothesis of market efficiency or does it lend support to the idea that investors have behavioral biases. Using Google Trends, we find that the volume of Google searches about feeder cattle is associated with re-enforcement of momentum trading in a manner consistent with a positive feedback mechanism. Further, we find evidence that search volume for "cattle" is associated with higher volatility and thus amplifies the positive feedback trading mechanism, while the search volume for "corn", a major input to cattle production, is associated with a reduction in volatility.

What do investors' searches for public information reveal about their subsequent trading strategies? Does their search for information support the hypothesis of market efficiency or does it lend support to the idea that investors have behavioral biases. Using Google Trends, we find that the volume of Google searches about feeder cattle is associated with re-enforcement of momentum trading in a manner consistent with a positive feedback mechanism. Further, we find evidence that search volume for "cattle" is associated with higher volatility and thus amplifies the positive feedback trading mechanism, while the search volume for "corn", a major input to cattle production, is associated with a reduction in volatility.

Abstract Based on the sequential auction model of Kyle (1985) and embedded the formulation of positive feedback traders in De Long et al. (1990), our model formulates a recursive market game of insiders, noise traders, and market makers. In particular, the submitted demands of positive feedback inertial traders are influenced by previous own trading quantities. I prove the existence and uniqueness of a recursive linear equilibrium with positive feedback inertial trades. Further, the equilibrium calibrates that the strategies of insider and market makers are also influenced by positive feedback trades. Finally, we conduct a simulation analysis to get a price-volume pattern with some empirical interesting implications. Finally, this thesis takes trading strategies to trade the individual stock in TSEC. Although the market mechanism of TSEC has no market makers, it is still expected that these trading strategies are useful for traders which implies the information is filtrated by these trading strategies.