政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/136380

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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/136380

Title:	結合基本面分析及集成學習模型建構最適投資組合 Combining Fundamental Analysis and Ensemble Learning to Construct the Optimal Portfolio
Authors:	許育愷 Hsu, Yu-Kai
Contributors:	黃泓智許育愷 Hsu, Yu-Kai
Keywords:	基本面分析集成學習支持向量回歸模型多層感知器報酬率預測投資組合 Fundamental Analysis Ensemble Learning SVR MLP Return Predict Portfolio
Date:	2021
Issue Date:	2021-08-04 14:55:43 (UTC+8)
Abstract:	本研究透過結合基本面分析以及集成學習的方式，在上市個股當中建立最適的投資組合，首先藉由兩個基本面的因子：本益比（PE Ratio）與股價淨值比（PB Ratio）篩選個股，接著，藉由包含支持向量迴歸模型（SVR）及多層感知器（MLP）的集成學習模型預測個股的數日後報酬，藉此挖掘出有潛力的個股進行投資組合的配置，並藉由歷史回測分析其績效。實驗結果顯示，在基本面篩選下的投資組合績效高於大盤指數績效，而加入集成學習模型後可以進一步提升其報酬績效。 The purpose of this study is trying to combine fundamental analysis and ensemble learning model to construct an optimal portfolio with listed stocks. First, use two fundamental factor, price-to-equity ratio and price-to-book ratio, to select potential stocks, and then predict the future return of each stock through ensemble learning model which including SVR and MLP to construct an optimal portfolio. Finally, analysis the performance according history stock data. The result shows that the portfolio constructed by only fundamental analysis outperforms Taiwan Capitalization Weighted Stock Index (TAIEX), and the portfolio constructed by fundamental analysis and ensemble learning outperforms the portfolio constructed by only fundamental analysis.
Reference:	1. 張家瑋（2017）。利用籌碼面分析與關聯規則建構最適投資組合。國立政治大學碩士論文。 2. 蕭鈞銓（2016）。以基本面分析建構最適資產配置流程。國立政治大學碩士論文。 3. Basu, S. (1977). Investment performance of common stocks in relation to their price‐earnings ratios: A test of the efficient market hypothesis. The journal of Finance, 32(3), 663-682. 4. Chan, L. K., Hamao, Y., & Lakonishok, J. (1991). Fundamentals and stock returns in Japan. The journal of finance, 46(5), 1739-1764. 5. Clemen, R. T. (1989). Combining forecasts: A review and annotated bibliography. International journal of forecasting, 5(4), 559-583. 6. Hegazy, O., Soliman, O. S., & Salam, M. A. (2014). A machine learning model for stock market prediction. International Journal of Computer Science and Telecommunications, 4(12), 17-23 7. Huang, C. S., & Liu, Y. S. (2019). Machine Learning on Stock Price Movement Forecast: The Sample of the Taiwan Stock Exchange. International Journal of Economics and Financial Issues, 9(2), 189-201. 8. Lev, B., & Thiagarajan, S. R. (1993). Fundamental Information Analysis. Journal of Accounting Research, 31(2), 190–215. 9. Markowitz H., (1952). Portfolio Selection. The Journal of Finance, 7(1), 77–91. 10. Mangalampalli, R., Pandey, V., Khetre, P., & Malviya, V. (2020). Stock Prediction using Hybrid ARIMA and GRU Models. International Journal of Engineering Research & Technology, 9(5), 737-743. 11. Sharpe, W. F. (1966). Mutual fund performance. The Journal of business, 39(1), 119-138. 12. Zhang, J., Li, L., & Chen, W. (2020). Predicting stock price using two-stage machine learning techniques. Computational Economics, 1-25.
Description:	碩士國立政治大學風險管理與保險學系 108358025
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0108358025
Data Type:	thesis
DOI:	10.6814/NCCU202100808
Appears in Collections:	[Department of Risk Management and Insurance] Theses

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