本研究提出以基因規劃法與機器學習之雙層機制構建合成股票因子及投資組合，從 2006 年至 2020 年每年以基因規劃法生成股票因子形成Alpha因子池，累積15年共包含150個因子，爾後以嵌入法選取因子並預測股票上漲機率用於建構投資組合。在樣本外績效當中，實證結果顯示本研究使用的三個預測模型之中，其中兩個Boosting模型之Sharpe比率與年化報酬率皆優於Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX)報酬指數，不過最大虧損與年化波動率為略高，特別的是，當股票數量逐漸增加時，eXtreme Gradient Boosting (XGBoost) 在兩個風險指標上趨近於 TAIEX報酬指數。資訊比率方面，即使Light Gradient Boosting Machine (LightGBM)的值為所有模型當中最高者，不過，整體而言XGBoost相對穩定。最後，在樣本外穩健性測試中，測試結果發現在因子池當中有7個Alpha因子與未來20天的報酬具有統計顯著性相關。

This paper proposes a double-selection method for systematically constructing synthesized stock factors and portfolios. In this procedure, which builds an Alpha factor pool using Genetic Programming for a time period spanning from 2006 to 2020, including a total of 150 Alpha factors, and thereby select the Alpha factors through embedded method to predict the upward probability of individual stocks for constructing portfolios. For the out-of-sample performance, the empirical results show that the Sharpe ratios and the annualized returns for the two Boosting models are greater than for the TAIEX Total Return Index, but the maximum drawdowns and the annualized volatilities tend to underperform. In particular, the XGBoost is nearly identical to the TAIEX Total Return Index in both risk indicators when the number of the stocks increases in the portfolios. For the Information ratio, the LightGBM is the largest model among all models. However, the XGBoost is relatively stable on the whole. Finally, in the case of the out-of-sample robustness test, seven Alpha factors from my Alpha factor pool are statistically significant correlated with returns over the following 20 trading days.

論文審定書 i
中文摘要 ii
Abstract iii
Table of Contents iv
List of Figures vi
List of Tables vii
Chapter 1 Introduction 1
Chapter 2 Literature Review 4
2.1 Efficient-market Hypothesis 4
2.2 Margin Trading and Institutional Investors 5
2.3 Evolutionary Algorithms 6
2.4 Asset Pricing via Machine Learning 7
2.5 Summary 8
Chapter 3 Methodology 10
3.1 Data Sources and Research Design 10
3.2 Genetic Programming 11
3.3 Factor Selection and Portfolio Construction 20
3.4 Performance Measurement 26
3.5 Alpha Factor Test 28
Chapter 4 Empirical Results 30
4.1 Alpha Factors Construction and Selection 30
4.2 Portfolio Performance 30
4.3 Robustness Test of Alpha Factors 36
Chapter 5 Conclusion 41
5.1 Conclusions 41
5.2 Further Suggestions 41
References 43
Appendix A 48
Appendix B 58
Appendix C 61

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