| dc.contributor.advisor | 江彌修 | zh_TW |
| dc.contributor.advisor | Chiang, Mi-Hsiu | en_US |
| dc.contributor.author (Authors) | 陳昱安 | zh_TW |
| dc.contributor.author (Authors) | Chen, Yu-An | en_US |
| dc.creator (作者) | 陳昱安 | zh_TW |
| dc.creator (作者) | Chen, Yu-An | en_US |
| dc.date (日期) | 2020 | en_US |
| dc.date.accessioned | 3-Aug-2020 17:38:21 (UTC+8) | - |
| dc.date.available | 3-Aug-2020 17:38:21 (UTC+8) | - |
| dc.date.issued (上傳時間) | 3-Aug-2020 17:38:21 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0107352021 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/130991 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 金融學系 | zh_TW |
| dc.description (描述) | 107352021 | zh_TW |
| dc.description.abstract (摘要) | 本研究使用了三種傳統多因子選股模型以及結合了因子選股的兩種集成學習法為基礎之機器學習分類模型Extreme Gradient Boosting (XGBoost)、Random Forest來建構選股模型，並且比較了傳統多因子選股模型以及機器學習因子選股模型之策略績效，同時觀察兩種機器學習模型之間預測效果以及策略績效的差異性。而本研究所採用之資產標的為台灣股票市場之上市股票，樣本回測期間採自2010/1/1 至2020/1/1之所有台灣上市股票，因子特徵選取方面撇除掉過去研究常用之基本面數據，採用價量面以及台灣市場獨有之籌碼面資料，實證結果顯示，兩種機器學習模型在樣本內回測期間大幅優於傳統多因子選股模型，而在樣本外回測期間策略績效表現亦較傳統多因子選股模型出色，顯示了機器學習模型挖掘出資產報酬趨勢的能力。仔細比較XGBoost和Random Forest的策略績效後，可以發現到XGBoost優於Random Forest，表示帶有懲罰項係數防止決策樹過度擬合之XGBoost模型在樣本外表現上優於Random Forest，完整體現了金融市場隨著時間不斷變化的特性，模型優化了過度擬合歷史數據的缺點，提升模型在樣本外的性能。 | zh_TW |
| dc.description.abstract (摘要) | In this paper, we implement three traditional multi-factor stock selection models and two ensemble-learning models including Extreme Gradient Boosting (XGBoost) and Random Forest combined with stock factors. We compared the strategy performance of traditional factor stock selection model and ensemble-learning factor stock selection model, and the difference between the prediction effectiveness and strategy performance of the two ensemble-learning models. The assets used in this research are the listed stocks in Taiwan stock market and the backtesting period is from 2010/1/1 to 2020/1/1. The stock factors include technical data and the unique trading volume data of Taiwan stock market and exclude fundamental data that commonly used in the research before. The empirical results show that the two ensemble-learning models outperform the traditional multi-factor stock selection model during the whole period. This results prove the ability of ensemble-learning models to capture asset return trend. In the results, we can also find that XGBoost outperforms Random Forest, indicating the model with penalty coefficients to prevent over-fitting outperforms, fully reflecting the time-varying of financial market, the model optimizes the disadvantage in over-fitting historical data and improves the performance of the model outside the sample. | en_US |
| dc.description.tableofcontents | 第一章 緒論 1第二章 文獻探討 5第一節 因子選股相關研究 5第二節 集成學習法之相關研究 6第三章 研究方法 7第一節 研究標的與採用之資料 7第二節 監督式分類機器學習 14第三節 本研究採用之監督式分類機器學習概念 16第四節 模型與因子選股回測設計 21第五節 策略績效衡量指標 22第四章 實證結果 27第一節 傳統多因子選股策略 28第二節 機器學習多因子選股策略 35第三節 因子選股策略績效總結 43第五章 結論與建議 46第一節 結論 46第二節 未來策略可參考建議 47參考文獻 48 | zh_TW |
| dc.format.extent | 2961745 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0107352021 | en_US |
| dc.subject (關鍵詞) | 因子選股 | zh_TW |
| dc.subject (關鍵詞) | 機器學習 | zh_TW |
| dc.subject (關鍵詞) | 集成學習 | zh_TW |
| dc.subject (關鍵詞) | 隨機森林 | zh_TW |
| dc.subject (關鍵詞) | 台股市場 | zh_TW |
| dc.subject (關鍵詞) | Factors stock selection | en_US |
| dc.subject (關鍵詞) | Machine learning | en_US |
| dc.subject (關鍵詞) | Ensemble-learning | en_US |
| dc.subject (關鍵詞) | XGBoost | en_US |
| dc.subject (關鍵詞) | Random forest | en_US |
| dc.subject (關鍵詞) | Taiwan stock market | en_US |
| dc.title (題名) | 資產配置基於集成學習的多因子模型－以台灣股市為例 | zh_TW |
| dc.title (題名) | Asset Allocation Based on Ensemble-Learning Assisted Multi-Factor Models– Taiwan Stock Market as an Example | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | [1] Ahmad I., Basheri M., Iqbal M J., Raheem A. (2017). Improving Factor-Based Quantitative Investing by Forecasting company fundamentals.[2] Boivin J., S Ng. (2006). Are more data always better for factor analysis. Journal of Econometrics. 169-194.[3] Breiman L., J Friedman., C Stone. (1984). Classification and regression Trees. Wadsworth and Brooks Monetarey.[4] Breiman L. (2001). Random Forests. Machine Learning. 5-32.[5] Cao L., Tay F H. (2003). Support vector machine with adaptive parameters in financial time series forecasting. Neural Netw.[6] Deroski S., Enko B. (2004). Is combining classifiers with stacking better than selecting the best one? Machine Learning. 255-273.[7] Fama Eugene F., Kenneth R French. (1992). The cross-section of expected stock returns. The Journal of Political Economy.[8] Fan A., Palaniswami M. (2001). Stock selection using support vector machines. In proceedings of the international joint conference on Neural Networks.[9] Liaw A., Wiener M. (2002). Classification and regression by randomForest. R news. 18-22.[10] Ruder S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.[11] Sharpe, W.F. (1970). Portfolio theory and capital markets. The Journal of Finance, 1-18.[12] Sharpe, W.F. (1994). The sharpe ratio. The Journal of Portfolio Management, 49-58.[13] Van der Hart J., Slagter E., Van Dijk D. (2003). Stock selection strategies in emerging markets. The Journal of Empirical Finance. 105-132.[14] Yangming Z., Guoping Q. (2018). Random forest for label ranking. 99-109.[15] Zhu M., Philpotts D., Sparks R., Stevenson M J. (2011). A hybrid approach to combining CART and logistic regression for stock ranking. The Journal of Portfolio Management. 100-109.[16] Zhu M., Philpotts D., Stevenson M J. (2012). The benefits of tree-based models for stock selection. 437-448.[17] Zheng T., Ziqin Y., Guangwei Z. (2019). Stock selection with random forest: An exploitation of excess return in the Chinese stock market. Heliyon. | zh_TW |
| dc.identifier.doi (DOI) | 10.6814/NCCU202000702 | en_US |
