臺灣博碩士論文加值系統

本研究欲探討在台灣證券交易市場上，不同類型之投資人是否會有其特定之股票特性之偏好。透過股票交易紀錄之分析，本文採用了統計回歸與資料採礦兩種方法以探討在不同構面下，例如性別、財富水準或交易頻率之不同，投資人是否會有其偏好之股票特性，例如股票之每股盈餘、股利率、市價淨值比及每股價格。實證結果發現一般而言，台灣投資人較偏好上市期間較短、高系統風險、低股利率、每股盈餘較高、本益比低以及高市值之股票。然而，女性投資人、北部投資人與財富水準低、交易頻率低及交易經驗少的投資人皆較喜愛交易高風險、波動率高、未來成長潛力高之股票。

This thesis investigates whether there are specific trading preferences for stock characteristics among various types of investors in Taiwan stock markets. By examining the stock trading, we apply the methods of statistical regression and data mining to investigate whether the investors with different characteristics, such as gender, wealth levels, and trading frequency show any preferences with respect to different stock characteristics, such as EPS, dividend yield, market-to-book, and prices. The results show that in general, investors in Taiwan show greater trading preferences for newly listed stocks and stocks with higher beta, lower dividend yield, greater EPS, lower prices, higher P/E ratios, and larger sizes. However, female investors, less wealthy investors, investors living in the North region, investors with less trading frequency, and less experienced investors are more likely to trade stocks that are more risky and have more volatility and growth potential in the future.

Index
致謝辭 i
Abstract ii
摘要 iii
1. Introduction 1
1.1. Research Background 1
1.2. Research Objectives 3
2. Literature Review 6
2.1. Investor Preferences for Stock Characteristics 6
2.1.1. Domestic Institutional Investors 6
2.1.2. Foreign Institutional Investors 7
2.1.3. Comparison of Individual vs. Institutional Investors 9
2.2. Analysis of Investor Characteristics Affecting Security Portfolio Preference 11
2.2.1. Gender 11
2.2.2. Wealth 12
2.2.3. Local Preference 13
2.2.4. Experience 14
2.3. Data Mining 15
2.3.1. Data Mining: A brief explanation 15
2.3.2. Models of Data Mining 16
2.3.3. Methods of Clustering 17
2.3.3.1. The Hierarchical Clustering 17
2.3.3.2. The Nonhierarchical Clustering 18
2.3.4. Self Organizing Map 19
2.3.5. The Decision Tree 20
2.4. Related Work 21
2.4.1. Application in Forecasting Stock Market Returns 22
2.4.2. Application in Knowledge Discovery in Financial Investments 22
2.5. Summary 23
3. Research Methodology 25
3.1. Data 25
3.2. The Measure of Trading Preferences 26
3.3. Stock Characteristics 28
3.4. Investor Characteristics 31
3.4.1. Gender 31
3.4.2. Wealth 31
3.4.3. Region 33
3.4.4. Trading Frequency 33
3.4.5. Trading Experience 34
3.5. Regression Model 35
3.6. Data Mining 36
3.6.1. Self Organizing Map 36
3.6.1.1. Data Mining Tool 36
3.6.1.2. Data Mining Process 36
3.6.2. The Decision Tree 37
3.7. The Diagram of Process 38
4. Empirical Results and Analysis from Statistical Regressions 39
4.1. Trading Amount vs. Investor Characteristics 39
4.2. Trading Amount vs. Stock Characteristics 40
4.3. Stock Preferences of Various Investors 41
4.3.1. Institutional Investors vs. Individual Investors 41
4.3.2. Gender 44
4.3.3. Wealth Level 44
4.3.4. Region 46
4.3.5. Trading Frequency 47
4.3.6. Trading Experience 48
4.4. Summary 49
5. Empirical Results and Analysis from Data Mining 51
5.1. Self Organizing Map 51
5.2. Decision Tree 51
5.3. Summary 54
6. Conclusions and Future Work 57
6.1. Trading Volumes vs. Investor Characteristics 57
6.2. Trading Volume vs. Stock Characteristics 58
6.3. Trading Preferences for Stock Characteristics among Various Investors: Results from Statistical Regression 59
6.4. Trading Preferences for Stock Characteristics among Various Investors: Results from Data Mining 64
6.5. Comparison of Regression and Data Mining 66
6.6. Future Work 67
Appendix 69
Reference 85

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