投資對使用者來說是一門深奧的學問，因為國際金融危機難以預測且政府政策亦常影響經濟甚鉅。在過去的研究中，已經有許多學者針對股票投資組合優化問題提出許多研究，其中有些學者提出群組股票投資組合 (Group Stock Portfolio, GSP)，將投資組合問題視為分群問題來為使用者提供多樣的股票替代方案。此外，以往的研究也發現群組內的產業多樣性會影響投資組合的最終結果，所以考慮多樣性之後衍伸出多樣性群組股票投資組合(Diverse Group Stock Portfolio, DGSP)。因此在本篇論文中，我們考慮產業多樣性，並設計兩個方法來尋找多樣性群組股票投資組合 (DGSP)。在第一個方法中，我們應用一個新的染色體代表方式並針對它設計新的適應函數，來尋找比以往具有更低風險的多樣性群組投資組合 (DGSP)，此外，我們使用結合模糊邏輯 (Fuzzy logic)的模糊群組基因演算法 (Fuzzy Grouping Genetic Algorithm ) 來動態調整所需的參數。在第二個方法中，我們透過多目標遺傳演算法以夏普比例與群組平衡當兩目標函數用以找出多種不同的解(柏拉圖解集合)。所提之演算法亦設計了個體修復機制調整不可行的染色體。透過上述改良使得所提的方法不僅能夠更專注於尋找最佳解，更可以提升搜尋速度。最後，透過兩真實資料亦驗證所提的方法是有效率及效能的演算法。

Investment is always an interesting and important issue for people since international financial crisis is hard to predict and government’s policy may have influence on economic. In the past, many scholars have proposed research on portfolio issues. In some of these studies, group stock portfolios (GSP) are utilized to provide various alternative stocks to an investor. Previous studies have also observed that diversity of industries within a group can affect the performance of a final GSP. Therefore, in this thesis, the diversity of industries was taken into consideration, and two approaches for finding the diverse group stock portfolio (DGSP) have been designed. In the first approach, a new chromosome representation and an enhanced fitness function are applied to find a better DGSP with lower risk than before; moreover, we design a fuzzy grouping genetic algorithm (FGGA) which utilizes fuzzy logic to dynamically tune the parameters in the evolution process for finding appropriate DGSPs. In the second approach, we employ the multi-objective genetic algorithm to find different solutions (Pareto solutions) based on the two objective functions, Sharpe ratio and group balance. In addition, a mechanism is also designed in the proposed approaches to repair non-feasible chromosomes in the population. Through the above improvements, the proposed approaches can not only focus on finding the best solution, but also speed up the evolution process. Finally, experiments made on two real datasets also show that the proposed approaches are effective and efficient.

論文審定書 i
論文公開授權書 ii
誌謝 iii
摘要 iv
Abstract v
Contents vi
List of Tables viii
List of Figures x
Chapter 1 Introduction 1
1.1 Background 1
1.2 Contribution 4
1.3 Thesis Organization 5
Chapter 2 Related Works 6
2.1 Stock Portfolio 6
2.2 Group Stock Portfolio (GSP) 10
2.3 Diverse Group Stock Portfolio (DGSP) 13
2.4 Fuzzy Logic 14
Chapter 3 Fuzzy GGA-based Approach for DGSP Optimization 17
3.1 Motivation 17
3.2 Elements of the Proposed Algorithm 17
3.2.1 Encoding Scheme 18
3.2.2 Initial Population 20
3.2.3 Fuzzy Logic Controller 21
3.2.4 Genetic Operations 26
3.2.5 Infeasible Solution Adjustment 28
3.2.6 Fitness Evaluation 30
3.3 The Proposed Approach 37
3.4 An Example 40
Chapter 4 MOGA-based Approach for GSP Optimization 58
4.1 Motivation 58
4.2 The proposed approach 58
Chapter 5 Experimental Evaluations 62
5.1 Dataset Descriptions 62
5.1.1 Training Datasets and Testing Datasets 62
5.1.2 Measurements for Comparison 70
5.2 Analysis of the First Approach 72
5.2.1 The Analysis of the Derived DGSPs Using FGGA 72
5.2.2 Evaluating The Proposed Approach in Terms of Fitness 77
5.2.3 Evaluating the Proposed Approach in Terms of Groups (K) and Desired Stock Number (D) 79
5.2.4 Evaluating The Proposed Approach in Terms of ROI and Fluctuation 81
5.3 Analysis of the Second Approach 83
Chapter 6 Conclusion and Future Works 86
References 88

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