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研究生:楊千億
研究生(外文):Michael-Nayat Young
論文名稱:行為投資理論建構下投資組合之研究
論文名稱(外文):Portfolio Selection Applying Behavioral Portfolio Theory
指導教授:張國華張國華引用關係
指導教授(外文):Chang, Kuo-Hwa
學位類別:碩士
校院名稱:中原大學
系所名稱:工業工程研究所
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:91
中文關鍵詞:行為投資學心理帳戶SP/A理論展望理論投資組合最佳化
外文關鍵詞:prospect theorySP/A theorymental accountBehavior portfolio theoryportfolio optimization
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描述投資人在投資上有別於一般假設的投資行為的研究稱之為行為投資學(behavior portfolio theory, BPT)。 行為財務自提出展望理論(prospect theory)的經濟學家Kahneman於2002年獲得諾貝爾經濟學獎之後已逐漸獲得重視。在當今競爭激烈且強調客製化服務的金融市場,研究行為投資學下的投資組合最佳化也更顯重要, 再加上電腦計算工具的功能性與使用方便性的快速提高, 也使得行為投資學下的投資組合最佳化更加可行。
本論文的目的是考量投資人的投資行為與投資心理層面並藉由將它們量化以建構更貼切投資人屬性個別最佳化的投資組合。 本論文探討模式以分三階段量化上述投資的行為: 我們量化投資人不理性的行為以及其對投資標的物的影響; 我們以個人主觀機率分布量化投資人對未來景氣的評估; 我們以數理規劃模式量化不同心理帳戶之下最佳化個別的投資組合。在過去的研究中並未有將三階段整合一起討論的研究。
我們以線性迴歸分析投資人不理性的行為對投資標的物的關係,並以迴歸分析式子模擬產生未來投資標的物的報酬率; 我們以SP/A 理論量化個人主觀機率分布; 我們將心理帳戶分為風險愛好心理帳戶、一般風險心理帳戶以及風險規避心理帳戶。由測試的結果得知使用考慮不理性行為模擬資料的投資組合會較使用原始歷史資料的投資組合會有較好的表現,也會更貼切各個心理帳戶對風險的要求。在另一方面,測試結果一般而言也較大盤表現的好。
現在由於資訊與投資產品多元下,投資者會更有機會找到貼切自己個人需要的投資組合。行為投資學理論可幫助投資人能夠更精確的找到所需要的投資組合。本研究的結果將可提供投資者在建構個人投資組合時有可行的參考架構。

In this study, we determine the market period using the Relative Strength Index and tested the top 150 securities in the MSCI Taiwan Index for the significant effects of irrational behaviors during Bull, Bear, and Normal Market Periods. From the top 150 securities, we narrow down our investment objectives to those securities showing effects of at least 1 irrational behavior. Considering the significant irrational behavior indexes, we use linear regression equations to generate 20000 scenarios for the investment objectives, then through SP/A theory and our developed estimator for fear and hope levels we assign probabilities to the possible return scenarios of the securities and the market. We then divide our investment into 3 mental accounts (MA-1, MA-2, and MA-3) which respectively have risk-seeking, risk neutrality, and risk aversion attitudes towards different goals. We used the inverse of the safety-first model, mean-variance model, and safety first model respectively for each mental account to calculate our portfolios. We use the generated scenarios and the historical data and applied mixed-integer programming by means of the AIMMS solver to test our models. We call the portfolios using historical data as RS-HD and SF-HD for the risk-seeking and safety-first mental account respectively. In the end, our portfolios outperformed the market as well as the portfolios using the historical data. Through the 100 weeks of test period, although RS-HD have more instances of returns at least 5% but overall our MA-1 account outperformed their RS-HD counterparts by winning 80% of their total comparison; our MA-3 portfolios also outperformed their SF-HD counterparts in terms of satisfying the threshold limit of having returns less than -5% at 5%, using our MA-3 portfolio selection model we only had 2 instances of returns less than -5% while the SF-HD had 6 instances. Our MA-3 portfolios also won 81.82 of the total comparison with SF-HD. Finally, our MA-1 and MA-3 portfolios were also able to outperform the market in terms of mean return and cumulative returns.
Table of Contents

摘要 i
Abstract ii
Table of Contents iii
List of Tables v
List of Figures vii
CHAPTER 1 1
Introduction 1
1.1 Background and Motivation 1
1.2 Objective 3
1.3 Thesis Flow 3
CHAPTER 2 4
Related Literature Review 4
2.1 Mean-Variance Theory 4
2.2 Safety-First Portfolio Models 4
2.3 SP/A Theory 5
2.4 Behavioral Portfolio Theory 7
2.4.1 Portfolio Selection in BPT-SA 7
2.4.2 Portfolio Selection in BPT-MA 8
2.5 Related Researches on Portfolio Selections 9
CHAPTER 3 15
Behavioral Portfolio Selection Framework 15
3.1 Estimation of Future Returns 15
3.1.1 Determining Future Market Periods 15
3.1.2 Estimating Future Returns 16
3.2 Changing of Probability Measure 17
3.2.1 Ranking 20000 Scenarios 17
3.2.2 Determining the Parameters of Fear and Hope 18
3.3 Separating Mental Accounts 19
3.3.1 Risk-Seeking Model 20
3.3.2 Risk-Neutrality Model 21
3.3.3 Risk-Aversion Model 21
CHAPTER 4 24
Empirical Results 24
4.1 Data Description 24
4.2 Determining Market Periods 24
4.3 Estimating Future Returns 25
4.4 Determining qs and qp 26
4.5 Back Test Results 28
4.5.1 MA-1 (Risk-Seeking Portfolios) 28
4.5.2 MA-2 (Risk-Neutral Portfolios) 32
4.5.3 MA-3 (Risk-Averse Portfolios) 35
4.5.4 Aggregate Portfolios 39
CHAPTER 5 42
Conclusions 42
Reference 44
Appendix A 47
Appendix B 49
Appendix C 60
Appendix D 62
Appendix E 65
Appendix F 81

List of Tables
Table 1. Prediction vs Actual Market Period 25
Table 2. Partial Linear Regression Equation for Estimating Returns on December 13, 2013 26
Table 3. Weights and qs &; qp Estimates 26
Table 4. Risk-Seeking Portfolios Statistics Over 100 Weeks Test Period 28
Table 5. Risk Seeking Portfolios and Market Return Distribution 29
Table 6. Risk-Seeking Portfolios and Market Comparison 30
Table 7. MA-1 and RS-HD Comparison 30
Table 8. MA-1 and RS-HD Investment Statistics 31
Table 9. MA-1 and RS-HD Summary of Comparison 31
Table 10. MA-1 and Market Summary of Comparison 32
Table 11. Mean-Variance Portfolios Statistics Over 100 Weeks Test Period 32
Table 12. Risk-Neutral Portfolios and Market Return Distribution 33
Table 13. Risk-Neutral Portfolios and Market Comparison 34
Table 14. MA-2 Investment Statistics 34
Table 15. MA-2 and Market Summary of Comparison 35
Table 16. Risk-Averse Portfolios Statistics Over 100 Weeks Test Period 35
Table 17. Risk-Averse Portfolios and Market Return Distribution 36
Table 18. Risk-Averse Portfolios and Market Return Comparison 37
Table 19. MA-3 and SF-HD Return Comparison 38
Table 20. MA-3 and SF-HD Investment Statistics 38
Table 21. MA-3 and SF-HD Summary of Comparison 39
Table 22. MA-3 and Market Summary of Comparison 39
Table 23. Risk-Seeking, Risk-Neutral, Risk-Averse, and Aggregate Portfolios and Market Statistics 39
Table 24. Aggregate Portfolios Investment Statistics 40
Table 25. Company List 48
Table 26. Linear Regression Equations for Estimating Returns on January 13, 2012 50
Table 27. Linear Regression Equations for Estimating Returns on April 6, 2012 51
Table 28. Linear Regression Equations for Estimating Returns on June 22, 2012 52
Table 29. Linear Regression Equations for Estimating Returns on September 7, 2012 53
Table 30. Linear Regression Equations for Estimating Returns on November 23, 2012 54
Table 31. Linear Regression Equations for Estimating Returns on February 6, 2013 55
Table 32. Linear Regression Equations for Estimating Returns on May 3, 2013 56
Table 33. Linear Regression Equations for Estimating Returns on July 19, 2013 57
Table 34. Linear Regression Equations for Estimating Returns on October 4, 2013 58
Table 35. Linear Regression Equations for Estimating Returns on December 20, 2013 59
Table 36. Weekly Market Period, Investment Objectives and Significant Behavior(s) 61
Table 37. qp and qs estimator 64
Table 38. MA-1 NT$ 750K Weekly Portfolio – 1 66
Table 39. MA-1 NT$ 750K Weekly Portfolio – 2 67
Table 40. MA-1 NT$ 750K Weekly Portfolio – 3 68
Table 41. RS-HD NT$ 750K Weekly Portfolio – 1 69
Table 42. RS-HD NT$ 750K Weekly Portfolio – 2 70
Table 43. RS-HD NT$ 750K Weekly Portfolio – 3 71
Table 44. MA-2 NT$ 500K Weekly Portfolio – 1 72
Table 45. MA-2 NT$ 500K Weekly Portfolio – 2 73
Table 46. MA-2 NT$ 500K Weekly Portfolio – 3 74
Table 47. MA-3 NT$ 750K Weekly Portfolio – 1 75
Table 48. MA-3 NT$ 750K Weekly Portfolio – 2 76
Table 49. MA-3 NT$ 750K Weekly Portfolio – 3 77
Table 50. SF-HD NT$ 750K Weekly Portfolio – 1 78
Table 51. SF-HD NT$ 750K Weekly Portfolio – 2 79
Table 52. SF-HD NT$ 750K Weekly Portfolio – 3 80
Table 53. MA-1 and RS-HD Return Rate 82
Table 54. MA-2 Return Rate 83
Table 55. MA-3 and SF-HD Return Rate 84

List of Figures
Figure 1. The Basic Framework of Behavioral Portfolio Selection 3
Figure 2. Decumulative Probability Distribution 6
Figure 3. Our Behavioral Portfolio Selection Framework 23
Figure 4. Transformed Decumulative Probabilities for qp=qs=3.71 27
Figure 5. Transformed Decumulative Probabilities for qp=qs=3.22 27
Figure 6. Transformed Decumulative Probabilities for qp=qs=3.12 27
Figure 7. Risk-Seeking Portfolios and Market Return Distribution 29
Figure 8. Risk-Neutral Portfolios and Market Return Distribution 33
Figure 9. Risk-Averse Portfolios and Market Return Distribution 36

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