本文提供多個委託單市場之微結構模型，以分析委託單策略、價格形成和委託簿資訊三者間的動態關係。本研究以漸近的方式推導三種模型以闡述這些動態關係，分別是：風險中立下單模型、風險趨避下單模型和以委託簿資訊為基礎的修正下單模型。這些推論可說明，委託單趨動市場會動態地調整買價和賣價以產生足夠的價格改善的報酬，來補償買賣雙方之限價單下單者的逆選擇風險和未成交風險在任何時刻的變動。
在風險中立下單模型中，該模型可分解風險中立偏好的委託單下單者所形成之逆選擇成本和買賣價差，本研究發現逆選擇成本包含三種成份：資訊交易者到達的機率、限價單之定價的成交機率和投資之成本對效益比率。在風險趨避下單模型中，該模型分析風險趨避的無資訊交易者之最佳化下單行為，本研究發現資產波動性是逆選擇風險和未成交風險的重要因素之一，藉此買賣價差也受到資產波動性影響。本模型創新的方法可以連結先前的模型Handa, Schwartz and Tiwari (2003)和Foucault (1999)，因為這些模型是本模型縮減型態的特別例子。
以委託簿資訊為基礎的修正下單模型，該模型動態地調整電子委託單市場之前期委託簿狀態，以分析逆選擇成本和買賣價差的價格形成。本研究發展出最佳的修正下單模型，市場會將即時資訊內含入買賣價差中，以管理逆選擇風險和未成交風險因即時資訊而造成的波動。本文使用台灣証券交易所的委託簿資料，實証結果証實下列發現：(1)委託簿狀態會顯著地影響下期的委託單積極度；(2)逆選擇成本和買賣價差與委託簿資訊的精確度呈反向關係；(3)委託簿對買賣價差的資訊效果可支持本文的模型。

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.

Abstract i

Chapter 1 Introduction 1
1.1 Order Strategy Model of Risk-Neutral Uninformed Traders 2
1.2 Order Strategy Model of Risk Averse Uninformed Traders 6
1.3 Revision Order Strategy Model Based on Order Book Information 9
1.4 Structure 13

Chapter 2 Literature Review 14
2.1 Order Strategy and Price Formation 14
2.2 Limit Order Book Information 18

Chapter 3 Order Strategy and Price formation Model 21
3.1 Order Strategy Model of Risk-Neutral Uninformed Traders 29
3.2 Order Strategy Model of Risk-Averse Uninformed Traders 39
3.3 Revision Order Strategy Model Based on Order Book Information 48
3.4 Improvements of Proposed Models 60

Chapter 4 Data and Methodology of Revision Order-Submission Model 67
4.1 Dynamic Behaviors of Uninformed Traders 67
4.2 State of Limit Order Book 68
4.3 Order Aggressiveness 72
4.4 Dynamic Behavior Hypotheses 74
4.5 Research Design 75
4.6 Description of the Market and the Dataset 77


Chapter 5 Empirical Results of Revision Order-Submission Model 83
5.1 State of Limit Order Book 83
5.2 Adverse Selection Cost and Price Formation 87
5.3 Dynamic Order-Submission Behavior 89

Chapter 6 Conclusions and Suggestions 91
6.1 Concluding Remarks 91
6.2 Suggestions for further researches 92

References 100

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