系統識別號 | U0002-3006201612163900 |
---|---|
DOI | 10.6846/TKU.2016.01081 |
論文名稱(中文) | 最大多樣性的群組股票投資組合探勘技術之研究 |
論文名稱(英文) | A Study on Maximally Diverse Group Stock Portfolio Mining Techniques |
第三語言論文名稱 | |
校院名稱 | 淡江大學 |
系所名稱(中文) | 資訊工程學系碩士班 |
系所名稱(英文) | Department of Computer Science and Information Engineering |
外國學位學校名稱 | |
外國學位學院名稱 | |
外國學位研究所名稱 | |
學年度 | 104 |
學期 | 2 |
出版年 | 105 |
研究生(中文) | 呂承諭 |
研究生(英文) | Cheng-Yu Lu |
學號 | 603410530 |
學位類別 | 碩士 |
語言別 | 英文 |
第二語言別 | |
口試日期 | 2016-06-17 |
論文頁數 | 100頁 |
口試委員 |
指導教授
-
陳俊豪
委員 - 洪宗貝 委員 - 許輝煌 |
關鍵字(中) |
資料探勘 群組遺傳演算法 群組股票投資組合 分組問題 最大多樣性分群問題 投資組合最佳化 |
關鍵字(英) |
Data mining grouping genetic algorithm group stock portfolio grouping problem maximally diverse grouping problem portfolio optimization |
第三語言關鍵字 | |
學科別分類 | |
中文摘要 |
金融市場中充斥著眾多的因素會影響投資的獲利程度,故許多探勘投資組合的最佳化方法不斷的被提出。又每個投資者有不同的考量,若只提供單一投資組合是無法滿足投資者的各種需求,所以能夠產生多個組合的群組股票投資組合(Group stock portfolio)探勘技術也接著被提出。然而,在現存方法中並沒有考量群組的多樣性。故為了改善既有方法的群組多樣性,亦即讓群組中的股票產業別可以多樣化,本論文利用群組遺傳演算法提出兩個探勘最大多樣性的群組股票投資組合方法。 第一個方法中,為了計算每個群組的多樣性,首先在適應度函數裡加入多樣性指標(Diversity factor)來促使每個群組都盡可能有相似數量的股票產業別。接著,為了提高群組股票投資組合的報酬穩定性,根據股票現金股利設計了穩定指標(Stability factor)來保留獲利能力較好的股票並移除風險較高的股票。此外,購買單位平衡(Unit balance)和價格平衡(Price balance)亦被用來增加群組中股票價格與購買單位的相似度。演算法中結合上述的指標,設計了兩個適應度函數來評估染色體並設計合適的基因運算來產生新的染色體,包含,兩階段交配、兩階段突變和反轉運算。 第二個方法則提出了更精密的群組遺傳探勘演算法來避免高風險的股票存活於群組股票投資組合中。為了達到這樣的目標,染色體編碼除了原有的群組、股票和股票投資組合三個部分外,又新增了活躍股票部分(Active stock part)用於將股票分成活躍和非活躍兩類。接著亦修改第一個方法中的投資組合滿意度(Modified portfolio satisfaction)並設計出一般化多樣性指標(Generalized diversity factor)。結合舊有與上述兩新增指標,演算法亦利用兩個適應度函數來評估染色體的優劣並透過三階段的交配、三階段的突變和反轉運算來產生下一世代的染色體。 最後,實驗使用兩個真實股市資料,透過不同的分析來驗證方法的有效性與顯示其優點,包含:群組股票投資組合分析,適應度函數對群組股票投資組合的影響以及投資報酬率的分析。 |
英文摘要 |
Since many factors may influence the returns of portfolios, lots of algorithms were proposed for deriving near-optimal stock portfolios. Since providing a stock portfolio to investor may not enough, algorithms for mining a group stock portfolio (GSP) which can generate various stock portfolios were then proposed. However, the diversity of groups, which is an important property of grouping problem, is not considered in that approach. To improve the diversity of group which means number of stock categories in a group should be as more as possible, hence this thesis proposes two approaches for mining a diverse GSP by grouping genetic algorithm. In the first approach, to measure the diversity of groups, the diversity factor is designed to make the numbers of stock categories as similar as possible between groups, and used as a part of fitness function. Then, to increase the profit of the derived GSP, the stability factor is then designed based on cash dividends to keep good quality companies in groups and remove high risk companies from groups. The unit and price balances are also used to increase the similarity of groups. Combining them, two fitness functions are designed to evaluate chromosomes. Genetic operations, including two-phase crossover, two-phase mutation and inversion, are executed to generate new offspring. In the second approach, a more sophisticated GGA-based approach for mining diverse GSP is proposed to avoid high risk stocks appear in a GSP. To reach the goal, it uses not only grouping, stock and stock portfolio parts but also active stock part to encode a diverse GSP into a chromosome. The active stock part divides stocks into inactive and active stocks. Then, utilizing the modified portfolio satisfaction, the generalized diversity factor and factors presented in the first approach, two fitness functions are designed to measure the quality of chromosomes. Then, three-phase crossover, three-phase mutation and inversion are executed to generate chromosomes for next population. Finally, experiments were made on two real financial datasets to show the advantages of the proposed approaches, including the derived GSP analysis, impact of the fitness functions and the ROI of the derived GSPs. |
第三語言摘要 | |
論文目次 |
Contents CHAPTER 1 INTRODUCTION 1 1.1 Problem Definition and Motivation 1 1.2 Contributions 4 1.3 Reader’s Guide 5 CHAPTER 2 RELATED WORK 6 2.1 Maximally Diverse Grouping Problem 6 2.2 Review of Stock Porfolio Mining Approaches 7 CHAPTER 3 A GGA-BASED ALGORITHM TO MINE DIVERSE GROUP STOCK PORTFOLIO 10 3.1 Motivation 10 3.2. Elements of the proposed algorithm 13 3.2.1 Encoding Scheme 13 3.2.2 Initial Population 15 3.2.3 Fitness Evaluation 17 3.2.4 Genetic Operations 23 3.2.4.1 Two-Phase Crossover 24 3.2.4.2 Two-Phase Mutation and Inversion 26 3.3 Proposed GGA-based for mining diverse GSP 26 3.4 An Example 30 CHAPTER 4 MINING DIVERSE GROUP STOCK PORTFOLIO WITH ACTIVE AND INACTIVE STOCKS 39 4.1 Motivation 39 4.2 Elements of the proposed algorithm 41 4.2.1 Encoding Scheme 41 4.2.2 Initial Population 44 4.2.3 Fitness Evaluation 46 4.2.4 Genetic Operations 53 4.2.4.1 Three-Phase Crossover 53 4.2.4.2 Three-Phase Mutation and Inversion 55 4.3 Proposed approach for mining diverse GSP from active stocks 56 4.4 An Example 60 CHAPTER 5 EXPERIMENTAL RESULTS 70 5.1 Experimental Results for Method (I) 70 5.1.1 Data Descriptions 71 5.1.2 The Analysis of the Derived Diverse GSP 74 5.1.3 Comparing Proposed Approach with Existing Approaches 79 5.1.4 Effectiveness of the Stability Factor 82 5.2 Experimental Results for Method (II) 85 5.2.1 Data Descriptions 86 5.2.2 The Analysis of the Derived Diverse GSP 87 5.2.3 Comparison of the Proposed Approach and Existing Approaches 91 5.2.4 Effectiveness of Different Active Rates 94 CHAPTER 6 CONCLUSION AND FUTURE WORKS 96 REFERENCES 98 List of Figures Figure 1. Encoding scheme of chromosome Cq. 13 Figure 2. An example of encoding scheme. 14 Figure 3. Encoding scheme of chromosome Cq. 42 Figure 4. An example of encoding scheme. 43 Figure 5: The 30 stock price series of the dataset 71 Figure 6: The stock price series of the second dataset 73 Figure 7. The stock-price series of the diverse GSP by f1 77 Figure 8. The stock-price series of the diverse GSP by f2 78 Figure 9. The average diversity values of the previous and proposed approaches 79 Figure 10. The stock-price series of the diverse GSP by f1 on the second dataset 90 Figure 11. The stock-price series of the diverse GSP by f2 on the second dataset 91 List of Tables Table 1. The given two GSPs A and B 10 Table 2. Stock information 11 Table 3. Cash dividend yields of two companies 16 Table 4. Cash dividend (yi) of each stock 16 Table 5. Proportion of average cash dividend of each group to all groups 17 Table 6. Stocks used in this example 30 Table 7. The portfolio satisfactions of all chromosomes 34 Table 8. The group balances of all chromosomes 34 Table 9. The dissimilarity matrix of all stocks 35 Table 10. The diversity value of all chromosomes 36 Table 11. The fitness values of all chromosomes 36 Table 12. The given two GSPs C and D 40 Table 13. Stock price information 40 Table 14. Cash dividend yields of two companies 44 Table 15. Cash dividend (yi) of each stock 45 Table 16. Proportion of average cash dividend of each group to all groups 45 Table 17. Stocks used in this example 60 Table 18. The portfolio satisfactions of all chromosomes 64 Table 19. The group balances of all chromosomes 65 Table 20. The dissimilarity matrix of all stocks 65 Table 21. The diversity value of all chromosomes 66 Table 22. The fitness values of all chromosomes 67 Table 23. Information of the dataset 72 Table 24. Initial and final best diverse GSP using fitness function f1. 75 Table 25. The derived diverse GSP using fitness functions f1 and f2. 76 Table 26. Returns and diversity of the derived diverse GSP on the first dataset 80 Table 27. Returns and diversity of the derived diverse GSP on the second dataset 81 Table 28. Returns and diversity of the derived diverse GSP using the first dataset (30 companies) with different h as training data 82 Table 29. Returns and diversity of the derived diverse GSP using the second dataset (31 companies) with different h as training data 84 Table 30. The derived diverse GSPs on the first dataset. 87 Table 31. The derived diverse GSPs on the second dataset. 88 Table 32. Returns of the derived diverse GSP on the first dataset 92 Table 33. Returns of the derived diverse GSP on the second dataset 93 Table 34. Comparison of the derived GSPs in terms of number of industries 93 Table 35. ROI of the proposed approach with f2 using different active rates 95 |
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