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| 題名 | 利用集成學習預測台灣加權股價指數漲跌 Applying Ensemble Learning to Enhance TAIEX Trend Prediction |
| 作者 | 陳羿妘 Chen, Yi-Yun |
| 貢獻者 | 黃泓智 Huang, Hong-Chih 陳羿妘 Chen, Yi-Yun |
| 關鍵詞 | 集成學習 羅吉斯迴歸 隨機森林 支持向量機 台灣加權股價指數 股價趨勢預測 Ensemble learning Logistic regression Random forest Support vector machine TAIEX Stock trend prediction |
| 日期 | 2021 |
| 上傳時間 | 4-八月-2021 14:55:15 (UTC+8) |
| 摘要 | 本文旨在利用台灣加權股價指數TAIEX衍生之技術指標預測未來市場漲跌趨勢，藉由集成學習方法提升整體機器學習預測效果，結合羅吉斯迴歸、隨機森林、支持向量機三個異質演算法，增加模型間之差異性，並依據個別模型的特性，採用不同變數挑選方式，以提升資料品質，最終以單一模型作為標竿模型比較預測成效。整體而言，集成學習後之預測結果較單一模型具有更高的準確度，特別針對預測漲的部分，集成學習的效果較顯著，此外在長天期的趨勢預測中，集成學習的效果也更加明顯。 This study aims to enhance prediction of trends on TAIEX with ensemble learning. As the input, several technical indicators are selected to train the model. To increase diversity of ensemble model, we used three heterogeneous models (logistic regression, random forest, support vector machine) instead of homogeneous models as component learners. Besides, depends on characteristic of component learners, different methods of feature selection are applied to increase the quality of data. To evaluate performance of ensemble models, we used single classifier models as benchmark models, and we found that accuracy of ensemble models is higher than single models. Especially in long-term case, the improvement of ensemble learning is more significant. |
| 參考文獻 | 1. Ballings, M., Van den Poel, D., Hespeels, N., & Gryp, R. (2015). Evaluating multiple classifiers for stock price direction prediction. Expert systems with Applications, 42(20), 7046-7056. 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. Di, X. (2014). Stock trend prediction with technical indicators using SVM. Independent Work Report, Stanford Univ. 4. Dutta, A., Bandopadhyay, G., & Sengupta, S. (2012). Prediction of stock performance in the Indian stock market using logistic regression. International Journal of Business and Information, 7(1), 105. 5. Jiang, M., Liu, J., Zhang, L., & Liu, C. (2020). An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms. Physica A: Statistical Mechanics and its Applications, 541, 122272. 6. Kursa, M. B., & Rudnicki, W. R. (2010). Feature selection with the Boruta package. J Stat Softw, 36(11), 1-13. 7. Li, H., Yang, Z., & Li, T. (2014). Algorithmic trading strategy based on massive data mining. Stanford University Stanford. 8. Larsen, J. I. (2010). Predicting stock prices using technical analysis and machine learning (Master`s thesis, Institutt for datateknikk og informasjonsvitenskap). 9. Moews, B., Herrmann, J. M., & Ibikunle, G. (2019). Lagged correlation-based deep learning for directional trend change prediction in financial time series. Expert Systems with Applications, 120, 197-206. 10. Mierswa, I., & Morik, K. (2005). Automatic feature extraction for classifying audio data. Machine learning, 58(2), 127-149. 11. Naik, N., & Mohan, B. R. (2019, May). Stock price movements classification using machine and deep learning techniques-the case study of indian stock market. In International Conference on Engineering Applications of Neural Networks (pp. 445-452). Springer, Cham. 12. Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert systems with applications, 42(1), 259-268. 13. Vapnik, V. N. (1995). The nature of statistical learning. Theory. 14. Żbikowski, K. (2015). Using volume weighted support vector machines with walk forward testing and feature selection for the purpose of creating stock trading strategy. Expert Systems with Applications, 42(4), 1797-1805. |
| 描述 | 碩士 國立政治大學 風險管理與保險學系 108358008 |
| 資料來源 | http://thesis.lib.nccu.edu.tw/record/#G0108358008 |
| 資料類型 | thesis |
| dc.contributor.advisor | 黃泓智 | zh_TW |
| dc.contributor.advisor | Huang, Hong-Chih | en_US |
| dc.contributor.author (作者) | 陳羿妘 | zh_TW |
| dc.contributor.author (作者) | Chen, Yi-Yun | en_US |
| dc.creator (作者) | 陳羿妘 | zh_TW |
| dc.creator (作者) | Chen, Yi-Yun | en_US |
| dc.date (日期) | 2021 | en_US |
| dc.date.accessioned | 4-八月-2021 14:55:15 (UTC+8) | - |
| dc.date.available | 4-八月-2021 14:55:15 (UTC+8) | - |
| dc.date.issued (上傳時間) | 4-八月-2021 14:55:15 (UTC+8) | - |
| dc.identifier (其他 識別碼) | G0108358008 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/136378 | - |
| 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) | 108358008 | zh_TW |
| dc.description.abstract (摘要) | 本文旨在利用台灣加權股價指數TAIEX衍生之技術指標預測未來市場漲跌趨勢，藉由集成學習方法提升整體機器學習預測效果，結合羅吉斯迴歸、隨機森林、支持向量機三個異質演算法，增加模型間之差異性，並依據個別模型的特性，採用不同變數挑選方式，以提升資料品質，最終以單一模型作為標竿模型比較預測成效。整體而言，集成學習後之預測結果較單一模型具有更高的準確度，特別針對預測漲的部分，集成學習的效果較顯著，此外在長天期的趨勢預測中，集成學習的效果也更加明顯。 | zh_TW |
| dc.description.abstract (摘要) | This study aims to enhance prediction of trends on TAIEX with ensemble learning. As the input, several technical indicators are selected to train the model. To increase diversity of ensemble model, we used three heterogeneous models (logistic regression, random forest, support vector machine) instead of homogeneous models as component learners. Besides, depends on characteristic of component learners, different methods of feature selection are applied to increase the quality of data. To evaluate performance of ensemble models, we used single classifier models as benchmark models, and we found that accuracy of ensemble models is higher than single models. Especially in long-term case, the improvement of ensemble learning is more significant. | en_US |
| dc.description.tableofcontents | 第一章 緒論 1 第一節 研究動機與背景 1 第二節 研究目的 6 第三節 研究流程 6 第二章 文獻探討 8 第一節 資料預處理 8 第二節 特徵值挑選 9 第三節 機器學習方法 9 第三章 研究方法 12 第一節 研究架構 12 第二節 資料預處理 12 第三節 特徵值挑選 23 第四節 個別模型架構 24 第五節 集成學習方法與建模流程 31 第四章 實證結果 34 第五章 結論與建議 42 參考文獻 45 附錄 48 | zh_TW |
| dc.format.extent | 1034344 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0108358008 | en_US |
| dc.subject (關鍵詞) | 集成學習 | zh_TW |
| dc.subject (關鍵詞) | 羅吉斯迴歸 | zh_TW |
| dc.subject (關鍵詞) | 隨機森林 | zh_TW |
| dc.subject (關鍵詞) | 支持向量機 | zh_TW |
| dc.subject (關鍵詞) | 台灣加權股價指數 | zh_TW |
| dc.subject (關鍵詞) | 股價趨勢預測 | zh_TW |
| dc.subject (關鍵詞) | Ensemble learning | en_US |
| dc.subject (關鍵詞) | Logistic regression | en_US |
| dc.subject (關鍵詞) | Random forest | en_US |
| dc.subject (關鍵詞) | Support vector machine | en_US |
| dc.subject (關鍵詞) | TAIEX | en_US |
| dc.subject (關鍵詞) | Stock trend prediction | en_US |
| dc.title (題名) | 利用集成學習預測台灣加權股價指數漲跌 | zh_TW |
| dc.title (題名) | Applying Ensemble Learning to Enhance TAIEX Trend Prediction | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | 1. Ballings, M., Van den Poel, D., Hespeels, N., & Gryp, R. (2015). Evaluating multiple classifiers for stock price direction prediction. Expert systems with Applications, 42(20), 7046-7056. 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. Di, X. (2014). Stock trend prediction with technical indicators using SVM. Independent Work Report, Stanford Univ. 4. Dutta, A., Bandopadhyay, G., & Sengupta, S. (2012). Prediction of stock performance in the Indian stock market using logistic regression. International Journal of Business and Information, 7(1), 105. 5. Jiang, M., Liu, J., Zhang, L., & Liu, C. (2020). An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms. Physica A: Statistical Mechanics and its Applications, 541, 122272. 6. Kursa, M. B., & Rudnicki, W. R. (2010). Feature selection with the Boruta package. J Stat Softw, 36(11), 1-13. 7. Li, H., Yang, Z., & Li, T. (2014). Algorithmic trading strategy based on massive data mining. Stanford University Stanford. 8. Larsen, J. I. (2010). Predicting stock prices using technical analysis and machine learning (Master`s thesis, Institutt for datateknikk og informasjonsvitenskap). 9. Moews, B., Herrmann, J. M., & Ibikunle, G. (2019). Lagged correlation-based deep learning for directional trend change prediction in financial time series. Expert Systems with Applications, 120, 197-206. 10. Mierswa, I., & Morik, K. (2005). Automatic feature extraction for classifying audio data. Machine learning, 58(2), 127-149. 11. Naik, N., & Mohan, B. R. (2019, May). Stock price movements classification using machine and deep learning techniques-the case study of indian stock market. In International Conference on Engineering Applications of Neural Networks (pp. 445-452). Springer, Cham. 12. Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert systems with applications, 42(1), 259-268. 13. Vapnik, V. N. (1995). The nature of statistical learning. Theory. 14. Żbikowski, K. (2015). Using volume weighted support vector machines with walk forward testing and feature selection for the purpose of creating stock trading strategy. Expert Systems with Applications, 42(4), 1797-1805. | zh_TW |
| dc.identifier.doi (DOI) | 10.6814/NCCU202100893 | en_US |