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研究生： PHAM TRAN BAO QUYEN
PHAM TRAN BAO QUYEN
論文名稱： 以啟發式人工智慧基於盈利預測的多目標優選營建投資組合策略研析
Multiobjective-optimized construction stock portfolio investment strategy based on profitability prediction
指導教授： 周瑞生
Jui-Sheng Chou
口試委員: 曾惠斌
Hui-Ping Tserng
周子銓
Tzu-Chuan Chou
楊亦東
I-Tung Yang
周瑞生
Jui-Sheng Chou
學位類別： 碩士
Master
系所名稱： 工程學院 - 營建工程系
Department of Civil and Construction Engineering
論文出版年： 2023
畢業學年度： 111
語文別： 英文
論文頁數： 270
外文關鍵詞： portfolio investment management, multiobjective metaheuristics algorithm, stock preselection, multicriteria decision-making
相關次數： 點閱：121  下載：0 
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    • TABLE OF CONTENTS ABSTRACT ........................................... i ACKNOWLEDGEMENT ................... iii TABLE OF CONTENTS ..................... iv LIST OF TABLES ............................. viii LIST OF FIGURES ............................. xii LIST OF ABBREVIATIONS ........... xvii CHAPTER 1 INTRODUCTION .......... 1 1.1 Research background and motivation ...................................... 1 1.2 Research purpose and expected contribution ........................... 2 1.3 Research process and organization .......................................... 4 CHAPTER 2 LITERATURE REVIEW ............................................ 7 2.1 Fundamental analysis in portfolio investment management .... 7 2.2 Application of artificial intelligence techniques and metaheuristic optimization algorithms in portfolio investment management . 8 2.2.1 Machine learning techniques for stock preselection ......... 8 2.2.2 Machine learning techniques and metaheuristics algorithms for portfolio allocation ... 9 CHAPTER 3 RESEARCH METHODOLOGY ............................... 12 3.1 Automatic data collection and financial indicator calculation ............................................ 13 3.1.1 Automated data collection from Taiwan marketplace websites ................................... 13 3.1.2 Automated calculation of financial indicators ................ 15 3.2 Stock preselection methodology ............................................ 17 3.2.1 eXtreme Gradient Boosting (XGBoost) .......................... 17 3.2.2 Forensic-based Investigation optimization algorithm (FBI) ........................................ 18 3.2.3 Hybrid machine learning model (FBI-XGBoost) for prediction .................................. 22 3.2.4 Performance evaluation metrics ...................................... 25 3.3 Portfolio optimization methodology ...................................... 27 3.3.1 Mean-variance model (MV) and Equal-weight scheme (EW) ..................................... 27 3.3.2 Multi-objective approach for Portfolio optimization ...... 28 3.3.3 Multi-objective Forensic-based Investigation based on Decomposition algorithm (MOFBI/D) .................................. 31 3.3.4 Multi-criteria decision-making technique ....................... 47 3.3.5 Performance evaluation criteria ...................................... 50 CHAPTER 4 FORENSIC-BASED INVESTIGATION OPTIMIZED MACHINE LEARNING FOR STOCK PRESELECTION ......... 53 4.1 Stock preselection procedure .... 53 4.2 Experimental design .................. 54 4.2.1 Data and data processing .... 54 4.2.2 Alternative models and benchmarks ............................... 60 4.3 Performance of the prediction task for stock preselection ..... 61 4.4 Experimental result of the stock preselection phase .............. 63 4.5 Conclusion remarks ................... 66 CHAPTER 5 MULTI-OBJECTIVE FORENSIC-BASED INVESTIGATION ALGORITHM FOR PORTFOLIO OPTIMIZATION 68 5.1 Procedure of the portfolio optimization stage ........................ 68 5.2 Experimental design .................. 69 5.2.1 Data and data processing .... 69 5.2.2 Parameter setting of MOFBI/D algorithm ...................... 72 5.2.3 Parameter setting of TOPSIS .......................................... 72 5.3 Performance of the optimization task ..................................... 73 5.4 Experimental result of the Portfolio optimization phase ........ 73 5.5 Conclusion remarks ................... 76 CHAPTER 6 PERFORMANCE ANALYSIS OF FINANCIAL INVESTMENT STRATEGY FROM THE DEVEOPED MODEL ... 77 6.1 Baseline model and stocks for analyses and comparison ....... 77 6.2 Analyses and experimental results of the AID-FXMM model for portfolio investment management .................................... 77 6.2.1 Analysis of the minimum expected ROI for each portfolio candidate ......................... 80 6.2.2 Analysis of the maximum number of assets in the portfolio ........................................ 80 6.3 Analysis result ........................... 81 6.3.1 Analysis results related to expected ROI threshold ........ 81 6.3.2 Analysis results involved in maximum number of assets ............................................ 94 6.4 Conclusion marks .................... 120 CHAPTER 7 CONCLUSIONS AND RECOMMENDATIONS .. 121 7.1 Discussion and conclusion ...... 121 7.1.1 Contributions .................... 121 7.1.2 Theoretical implications ... 122 7.1.3 Practical implications .................................................... 122 7.2 Limitations and recommendations for future research ......... 123 References ......................................... 124 APPENDIX A: Result of predicted return on investments and ranks of construction companies for stock preselection stage ............... 130 APPENDIX B. Performance of MOFBI/D algorithm in mathematical test functions for multi/many-objective problems ......... 145 APPENDIX C: Transaction information in the back-testing evaluation .................................... 152 APPENDIX C-I: Transaction information of the analysis related to expected ROI ............... 152 APPENDIX C-II: Transaction information of the analysis of maximum number of assets ... 162 APPENDIX D: PROGRAMMING CODE .................................... 182 APPENDIX E: AID-FXMM MODEL TUTORIAL ...................... 233

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