隨著行動網路的興起，資訊的交流變得愈來愈頻繁，近年來已有許多研究透過社會網路分析抑或是資訊網路來分析資訊的擴散與社會互動是否會對投資人的行為帶來影響，甚至是造成資產價格的改變。然而在過去的實證研究中，大多僅以結點(投資人)的角度來分析市場，幾乎沒有充分運用到整個網路結構的特質，故本研究嘗試去探究網路的內部結構究竟是如何對資產價格與市場品質造成影響。我們基於Ozsoylev et al. (2014)所提出的定義提出了投資人於個別股票上的資訊網路，當兩投資人短期間內於特定股票上具有同方向與相似價格的交易行為，且在一定期間內發生次數超過特定的門檻則代表他們彼此之間有資訊交流存在。依循這樣的規則建構網路，本文並透過網路參數，如網路的中心性 (centrality) 、平均距離 (distance) 以及模組化程度 (modularity) 來捕捉網路結構，並授予其經濟意義。
本研究主要的發現有三，(1)崩盤期間的資訊網路明顯比平常時期更為密集，代表投資人間的資訊交換更為頻繁。(2)另外，當網路結構參數的數值較高時，代表當下的資訊風險也開始增加，因此投資人會要求更高的報酬來彌補其所承擔的額外風險。(3)最後，我們可以發現到網路結構與市場品質之間存在一交互影響的關係，其中網路的平均距離與模組化程度皆會促使市場的波動性與不流動性的增加。

In recent studies, social network analysis or more generally, information networks provide a hopeful tool to help us explain the information diffusion of each investor. However, most empirical studies simply analyze the market in terms of the node level (investor), and don’t fully utilize the feature of network topology. Therefore, this study aims to investigate how asset price and market quality depend on the network’s general topological properties. We propose a methodology based on Ozsoylev et al. (2014) to define the information network of each stock. Two investors will have information linkage in a specific stock, if they trade at the same direction and similar order price within a short period. Furthermore, this behavior must exceed a certain threshold. According to this rule, we will build a network, and provide the economic meaning of network parameters, such as centrality, distance, and modularity.
The main findings of this study are as follows: (1) The information networks in crisis periods are more cluster than usual. (2) A higher degree of network parameters represents information risks are higher, hence investors would require a higher return for taking on extra risk. (3) There exists an interactive relationship between network topology and market quality. Modularity and distance both increase the volatility or illiquidity.

論文審定書 i
摘要 ii
ABSTRACT iii
I. INTRODUCTION 1
1.1 Background Information 1
1.2 Research Purpose 5
1.3 Research Structure 8
1.4 Research Contribution 10
II. LITERATURE REVIEW 11
2.1 Social Network Analysis 11
2.2 Information Network 15
III. METHODOLOGY 19
3.1 Data Description 19
3.2 Information Network 21
3.3 Measuring the Investor Performance 25
3.4 Network Topology and Asset Pricing 27
3.5 Network Topology and Market Quality 29
IV. EMPIRICAL RESULTS 31
4.1 Descriptive Statistics 31
4.2 Centrality and Investor Performance 45
4.3 Network Topology and Asset Pricing 48
4.4 Network Topology and Market Quality 58
V. CONCLUSION 64
5.1 Conclusion 64
5.2 Suggestions for future research 67
REFERENCES 68
Appendix 73

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