According to the empirical findings from evolution of liquidity, this dissertation constructs an optimal order submission strategy model within which a mixture of market and limit orders can be submitted by both informed and uninformed traders. In the Stacklberg Game Model, informed traders with short-lived private information are regarded as leaders, and uniformed traders with learning behaviors are referred as followers. Our theoretical findings conclude as follows: Firstly, the order strategies of all traders can be characterized as coming under one of seven regimes, pure market buy orders, a combination of market and limit buy orders, pure limit buy orders, a combination of limit buy and limit sell orders, pure limit sell orders, a combination of market and limit sell orders, and pure market sell orders. Traders will select their optimal trading strategy according to the regime within which their liquidation value falls. Parlour (1998) is a special case of this study. Secondly, an increase (reduction) in liquidation value will result in a non-linear increase in the optimal proportion of market order submissions by buyers (sellers). Thirdly, the probability of submitting limit orders for uniformed traders increases when information traders get large profit from the private information. The extreme case is uniformed traders only submit limit orders. This result is consistent with Foucault (1999). Fourthly, the price interval will be much wider when limit orders are submitted by uniformed traders than by informed traders. The reasons are that uniformed traders have no private information and that they are high risk aversion. Finally, numerical illustrations confirm the reliability of this model.

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This paper provides microstructure models of order-driven market to analyze the dynamic dependencies of order strategy, price formation and order book information. This study gradually derives three models to shed light on those dynamic dependencies: risk-neutral order-submission model, risk-averse order-submission model and revision order-submission model based on order book information. Those inferences support that the order-driven market dynamically adjusts the bid/ask at any moment to generate enough price improvement return in order to cover the fluctuations of the adverse selection risk and the non-execution risk faced by limit order submitters of both side. In risk-neutral order-submission model, the model anatomizes adverse selection cost and bid-ask spread under risk-neutral preference of order submitters. This study finds that adverse selection cost comprises three components: arrival probability of informed traders, execution probability of setting price of limit order, cost-to-benefit ratio of investment. In risk-averse order-submission model, the model analyzes the optimal order-submission behavior of risk-averse uninformed traders. This study finds that the asset volatility is the key determinant of the adverse selection risk and the non-execution risk, and thereby the bid-ask spread is associated with the asset volatility. The novelty approach of this model could connect both previous risk-neutral models of Handa, Schwartz and Tiwari (2003) and Foucault (1999), which are the special cases of the reduced form of this model. In revision order-submission model, the model analyzes adverse selection costs and price formation of bid-ask spread, dynamically adjusted by previous state of limit order book in an electronic limit order market. Using order book data from the Taiwan Stock Exchange, the empirical analysis corroborates the following findings: (1) the state of the limit order book significantly affects subsequent order aggressiveness; (2) adverse selection cost and spread are negatively associated with the precision of order book information; (3) information effects of limit order book on the bid-ask spread provide strong support for the model.

Taiwan Security Market is a typical order-driven market, and the business transactions are matched through the electronic trading system since 1988. In this work, we study the joint distributions of tick size changes of bid price and ask price, bid volume, and ask volume¡@for each matching order in Taiwan Stock Exchange (TSEC). Exponentially weighted moving average (EWMA) method is adopted to update the joint distribution of the incoming order variables aforementioned. Here we propose five methods to determine the update timing and consider three different initial matrices of the joint distributions. In empirical study, the daily matching data of two enterprises Uni-president Enterprises Corporation and Formosa Plastics Corporation in April, 2005 are considered. The goodness of fit for the joint distributions are determined by Chi-square Goodness of Fit Test. The results show that EWMA method provide good fit for most of the daily transaction data.