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題名 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作者 許育愷
Hsu, Yu-Kai貢獻者 黃泓智
許育愷
Hsu, Yu-Kai關鍵詞 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日期 2021 上傳時間 4-Aug-2021 14:55:43 (UTC+8) 摘要 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.參考文獻 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), 17-237. 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. 描述 碩士
國立政治大學
風險管理與保險學系
108358025資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108358025 資料類型 thesis dc.contributor.advisor 黃泓智 zh_TW dc.contributor.author (Authors) 許育愷 zh_TW dc.contributor.author (Authors) Hsu, Yu-Kai en_US dc.creator (作者) 許育愷 zh_TW dc.creator (作者) Hsu, Yu-Kai en_US dc.date (日期) 2021 en_US dc.date.accessioned 4-Aug-2021 14:55:43 (UTC+8) - dc.date.available 4-Aug-2021 14:55:43 (UTC+8) - dc.date.issued (上傳時間) 4-Aug-2021 14:55:43 (UTC+8) - dc.identifier (Other Identifiers) G0108358025 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136380 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 風險管理與保險學系 zh_TW dc.description (描述) 108358025 zh_TW dc.description.abstract (摘要) 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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 第一章 緒論 6第一節 研究動機 6第二節 研究目的 7第三節 研究流程 7第二章 文獻回顧 9第一節 投資組合文獻探討 9第二節 基本面分析文獻探討 10第三節 集成學習文獻探討 10第三章 研究方法 12第一節 研究架構 12第二節 基本面篩選 13第三節 集成學習模型 16第四節 權重配置方法 23第五節 績效分析指標 24第四章 實證分析 26第一節 SVR_MLP模型實證結果 26第二節 5MLP模型實證結果 28第三節 SMX模型實證結果 30第五章 結論與建議 32參考文獻 33 zh_TW dc.format.extent 1461680 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108358025 en_US dc.subject (關鍵詞) 基本面分析 zh_TW dc.subject (關鍵詞) 集成學習 zh_TW dc.subject (關鍵詞) 支持向量回歸模型 zh_TW dc.subject (關鍵詞) 多層感知器 zh_TW dc.subject (關鍵詞) 報酬率預測 zh_TW dc.subject (關鍵詞) 投資組合 zh_TW dc.subject (關鍵詞) Fundamental Analysis en_US dc.subject (關鍵詞) Ensemble Learning en_US dc.subject (關鍵詞) SVR en_US dc.subject (關鍵詞) MLP en_US dc.subject (關鍵詞) Return Predict en_US dc.subject (關鍵詞) Portfolio en_US dc.title (題名) 結合基本面分析及集成學習模型建構最適投資組合 zh_TW dc.title (題名) Combining Fundamental Analysis and Ensemble Learning to Construct the Optimal Portfolio en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 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), 17-237. 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. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202100808 en_US
