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| Title: | 利用集成學習建構股市最適投資組合
Using Ensemble Learning to Construct The Optimal Portfolio in Stock Market |
| Authors: | 林晏緯
Lin, Yen-Wei |
| Contributors: | 黃泓智
Huang, Hong-Chih
林晏緯
Lin, Yen-Wei |
| Keywords: | 股市漲跌
集成學習
極限梯度提升
多層感知器
支持向量迴歸
Stock trend
Ensemble learning
XGBOOST
MLP
SVR |
| Date: | 2021 |
| Issue Date: | 2021-08-04 14:55:57 (UTC+8) |
| Abstract: | 本研究使用台灣上市公司之財報資料以集成學習概念進行台灣股市個股漲跌預測，並建立最適投資組合。本研究使用多個不同的機器學習模型如極限梯度提升模型（XGBOOST）、多層感知器（MLP）、支持向量迴歸模型（SVR）等模型進行建模。為了使模型訓練結果更為穩定與準確，本研究使用上述模型進行多次訓練，選出各模型中上漲機率高的股票並對其進行綜合評分，接著組成股票投資清單，將評分高的股票進行權重配置建立投資組合。實證結果發現，相較於使用單一種模型做一次的訓練，使用多種模型進行多次訓練後建立的投資組合能夠有更穩定的結果，且整體績效也優於單一種模型。
This dissertation aims to use ensemble learning to predict the trend of stocks in Taiwan stock market and build an optimal portfolio. The machine learning models used in the study include XGBOOST, MLP, and SVR. To make the model training results more stable and accurate, this study uses the above models for multiple trainings, and selects the stocks with high rising probability in each model and rate each of them comprehensively. Consequently, the optimal portfolio is built by allocating stocks with high rating appropriately. The empirical results demonstrate that using several models which are trained for multiple times will lead to steadier outcome and greater performance compared to using single model. |
| Reference: | 1. Alberg, J., & Lipton, Z. C. (2017). Improving factor-based quantitative investing by forecasting company fundamentals. arXiv preprint arXiv:1711.04837.
2. Basak, S., Kar, S., Saha, S., Khaidem, L., & Dey, S. R. (2019). Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 47, 552-567.
3. Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
4. Clarke, R. G., De Silva, H., & Thorley, S. (2006). Minimum-variance portfolios in the US equity market. The journal of portfolio management, 33(1), 10-24.
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6. Emerson, S., Kennedy, R., O`Shea, L., & O`Brien, J. (2019, May). Trends and applications of machine learning in quantitative finance. In 8th international conference on economics and finance research (ICEFR 2019).
7. Gardner, M. W., & Dorling, S. R. (1998). Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric environment, 32(14-15), 2627-2636.
8. Khaidem, L., Saha, S., & Dey, S. R. (2016). Predicting the direction of stock market prices using random forest. arXiv preprint arXiv:1605.00003.
9. Markowitz, H. (1952). The utility of wealth. Journal of political Economy, 60(2), 151-158.
10. Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and computing, 14(3), 199-222.
11. Tokat, Y., & Wicas, N. W. (2007). Portfolio rebalancing in theory and practice. The Journal of Investing, 16(2), 52-59. |
| Description: | 碩士
國立政治大學
風險管理與保險學系
108358026 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0108358026 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202100851 |
| Appears in Collections: | [風險管理與保險學系] 學位論文
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