系統識別號 | U0002-3006201611233600 |
---|---|
DOI | 10.6846/TKU.2016.01078 |
論文名稱(中文) | 利用群組遺傳演算法探勘群組股票投資組合之研究 |
論文名稱(英文) | A Study on Mining Group Stock Portfolio by Using Grouping Genetic Algorithms |
第三語言論文名稱 | |
校院名稱 | 淡江大學 |
系所名稱(中文) | 資訊工程學系碩士在職專班 |
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) | 林政邦 |
研究生(英文) | Cheng-Bon Lin |
學號 | 702410126 |
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 | 66頁 |
口試委員 |
指導教授
-
陳俊豪(chchen@mail.tku.edu.tw)
委員 - 許輝煌 委員 - 洪宗貝 |
關鍵字(中) |
資料探勘 遺傳演算法 分組遺傳演算法 股票投資組合 分組問題 股票投資組合最佳化 |
關鍵字(英) |
Data mining Genetic algorithms Grouping genetic algorithms Stock portfolio Grouping problem Stock portfolio optimization |
第三語言關鍵字 | |
學科別分類 | |
中文摘要 |
由於金融市場的多變性,金融資料探勘一直是一個吸引人且具有挑戰性的研究議題。例如,股價預測與股票投資組合探勘等都是金融資料探勘的範疇。其中,股票投資組合探勘問題不難理解是一個最佳化問題,故在過去幾十年,很多基於最佳化技術的演算法被提出來解決股票投資組合的問題。然而,現有方法的癥結點在於只提供一組股票投資組合。只提供一組投資組合,在現實應用上則會遇到許多問題。例如:投資者認為股票價格到達相對高點而不願意購買或該股票漲停無法買入。另外,探勘投資組合除了需滿足客觀的目標外,投資者亦常會有個人的主觀需求。客觀目標是指投資組合需要風險低且報酬高,而投資者預計購買股票家數與總投資金額等為投資者的主觀需求。 故本論文提出了二個演算來解決上述問題。首先,我們提出群組遺傳演算法為基礎的群組股票投資組合(Group stock portfolio, GSP)探勘技術,其目標為將n家股票分成K個群組。在群組股票投資組合中,同一個群組中股票是具有相似的特性。為達此目的,每個染色體是由三個部分組成,分別為群組、股票和股票投資組合。群組與股票部分表示如何將n家股票分成K個群組。每一群組在股票投資組合部分則利用兩實數表示是否為購買群組與購買單位。而評估函數則由染色體的群組平衡(Group balance)及投資組合的滿意度(Portfolio satisfaction)組成。群組平衡是用來測量每個群組中的股票家數是否相似,投資組合滿意度則是用於評估群組股票投資組合是否滿足客觀需求與投資者的主觀需求。由此,探勘所得的群組股票投資組合可以提供多種不同的投資組合給投資者。接著,為了提升群組的相似度與獲利穩定度,我們接著提出第二個演算法。在第二個演算法中,額外設計染色體的價格平衡(Price balance)與購買單位平衡(Unit balance),主要目標分別為量測群組中股票的股價與購買單位是否相似。 最後,所提出的兩個方法透過台灣50中選出來的31家股票進行驗證,包含:群組投資組合分析、適合度函數對於群組投資組合的影響與投資報酬分析。實驗結果顯示,所提的兩個方法都可以探勘出高於標準利潤並提供有用的群組投資組合。 |
英文摘要 |
Due to variance of financial market, the financial data mining is always an attractive research issue and a real challenge to researches. For example, stock price prediction and stock portfolio mining are topics of financial data mining. It is easily to understand that the stock portfolio mining problem is an optimization problem. Hence, in the past decades, based on genetic algorithms, many approaches were proposed to deal with it. However, the problem of them is that only one stock portfolio is suggested. When only one stock portfolio is provided, some problems may happen in real application. For example, investors may think the price of the suggested stock is too high to buy, or the stock price reach the daily limit such that investor cannot buy it. Besides, stock portfolio mining should not only consider the objective criteria but also investors' subjective criteria. The objective criteria are return on investment (ROI) and value at risk (VaR). This thesis two approaches for solving the mentioned problems. Firstly, we propose a grouping genetic algorithm based approach for mining group stock portfolio (GSP) and its goal is to divide n stocks into K groups. Stocks in the same group means that they have similar properties. To achieve this goal, a chromosome consists of three parts. They are grouping part, stock part and stock portfolio parts. The grouping and stock parts are used to represent how n stock are divided into K groups. For each group in the stock portfolio part uses two real number to indicate whether it is purchased group and it’s purchased units. Each chromosome is then evaluated by the group balance and portfolio satisfaction. The group balance is used to make the number of stocks in groups can as similar as possible. The portfolio satisfaction is utilized to measure the satisfaction degree of objective and subjective criteria of a chromosome. As a result, the derived GSP can provide various stock portfolios to investors. Then, to improve the similarity of groups and profit stability, the second algorithm has been proposed. In second approach, the price balance and unit balance are designed to make the stock prices of stocks and purchased units in groups can as similar as possible. At last, the two proposed algorithms are verified on 31 stocks which are selected from Taiwan 50 ETF, including the derived GSP analysis, impact of the fitness functions to the derived GSPs and the ROI of the derived GSPs. The experimental results show that the two proposed algorithms can mine GSPs that provide higher ROI than benchmark. |
第三語言摘要 | |
論文目次 |
Contents CHAPTER 1 INTRODUCTION 1 1.1 Problem Definition and Motivation 1 1.2 The Contributions 3 1.3 Reader's Guide 4 CHAPTER 2 EREVIEW OF RELATED WORK 5 2.1 Grouping Genetic Algorithm and Grouping Problem 5 2.2 The M-V Model based Approaches 7 2.3 Non M-V Model based Approaches 8 CHAPTER 3 MINING GROUP STOCK PORTFOLIO BY USING GROUPING GENETIC ALGORITHM 10 3.1 Encoding scheme 10 3.1.1 Initial population 11 3.1.2 Fitness Evaluation 13 3.1.3 Genetic operations 15 3.1.3.1 Two-Phase Crossover 16 3.1.3.2 Two-Phase Mutation and Inversion 17 3.2 The GGA-based algorithm for mining GSP 17 3.3 An Example 20 CHAPTER 4 THE ENHANCED GGA-BASE ALGORITHM FOR MINING GROUP STOCK PORTFOLIO 25 4.1 Enhanced Encoding scheme 25 4.1.1 Initial population 26 4.1.2 Fitness Evaluation 28 4.1.3 Genetic operations 32 4.1.3.1 Two-Phase Crossover 32 4.1.3.2 Two-Phase Mutation and Inversion 33 4.2 The enhanced GGA-based algorithm for mining GSP 34 4.3 An Example 36 CHAPTER 5 EXPERIMENTAL RESULTS 43 5.1 Experimental Results for Method (Ⅰ) 43 5.1.1 Dataset Descriptions 43 5.1.2 The analysis of the derived group stock portfolio 44 5.2 Experimental Results for Method (Ⅱ) 45 5.2.1 Convergence of the proposed approach 46 5.2.2 Impact of the designed fitness functions 47 5.2.3 Profits of the derived GSP 52 CHAPTER 6 CONCLUSIONS AND FUTURE WORKS 55 References 57 APPENDIXES: ENGLISH PAPER 59 I. INTRODUCTION 60 II. A. GGA AND GROUPING PROBLEMS 60 III. STOCK PORTFOLIO OPTIMIZATION APPROACHES 61 IV. COMPONENTS OF THE PROPOSED APPROACH 62 V. PROPOSED MINING ALGORITHM 64 VI. EXPERIMENTAL RESULTS 65 VII. CONCLUSION AND FUTURE WORK 66 List of Figures Figure 1. Encoding scheme of Cq. 10 Figure 2. An example of encoding scheme. 11 Figure 3.Encoding scheme of Cq. 25 Figure 4. An example of encoding scheme. 26 Figure 5: The stock price series of the dataset 46 Figure 6. The convergence of the proposed approach with the four fitness functions 47 Figure 7. The average group balance values of the proposed approach by using fitness functions f1 to f4. 48 Figure 8. The average unit balance values of the proposed approach by using fitness functions f1 to f4. 49 Figure 9. The average price balance values of the proposed approach by using fitness functions f1 to f4. 50 Figure 10. The stock price series in groups derived by f1. 51 Figure 11. The stock price series in groups derived by f3. 51 List of Tables Table 1. Cash dividend yields of two companies 12 Table 2. Cash dividend (yi) of each stock 12 Table 3. Proportion of average cash dividend of each group to all groups 13 Table 4 Stocks used in this example 20 Table 5. The portfolio satisfactions of all chromosomes 22 Table 6. The group balances of all chromosomes 23 Table 7. The fitness values of all chromosomes 23 Table 8. Cash dividend yields of two companies 27 Table 9. Cash dividend (yi) of each stock 27 Table 10. Proportion of average cash dividend of each group to all groups 28 Table 11. Twelve stocks used in this example 37 Table 12. The portfolio satisfactions of all chromosomes 39 Table 13. The group balances of all chromosomes 39 Table 14. The unit balances of all chromosomes 40 Table 15. The price balance of all chromosomes 40 Table 16. The fitness values of all chromosomes 40 Table 17.Information of the dataset 44 Table 18. Initial and final best group stock portfolios 44 Table 19.The average purchased units of groups 49 Table 20. Comparing the proposed approach with the benchmark in terms of ROI 53 Table 21.The derived GSP by using fitness function f4 54 |
參考文獻 |
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