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題名 建構輔以機器學習的技術交易動能策略
Constructing Technical Trading Momentum Strategies Using Machine Learning作者 郭汶靖
Kuo, Wen-Ching貢獻者 江彌修
Chiang, Mi-Hsiu
郭汶靖
Kuo, Wen-Ching關鍵詞 機器學習
邏輯斯回歸演算法
技術交易
投資人情緒指標
動能策略
交易訊號
市場擇時
Machine Learning
Logistic regression algorithm
Technical trading
Investor sentiment indicator
Momentum strategy
Trading signal
Market timing日期 2020 上傳時間 2-Sep-2020 11:50:09 (UTC+8) 摘要 金融時間序列的非定態特性使得預測未來股價非常困難。但精準預測股價並不是投資獲利的唯一方法。股價趨勢相對容易掌握，即交易訊號的預測較易被實踐。只要投資人可以精準擇時，在正確的時間點進行買賣交易，皆可因此而獲利。本研究以邏輯斯回歸演算法加入技術交易與投資人情緒指標建構機器學習模型產生交易訊號，希望藉由不同特徵值提升模型之市場擇時能力，以正確捕捉股市動能。另外，本研究亦針對空頭市場時各策略之表現以及策略是否有降低投資風險的效果進行討論，並針對捕捉不同天期之動能探討策略績效。
The non-stationary feature of financial time series makes the prediction of future stock price harder. However, predicting stock price correctly is not the only way to get return from investing. The trend of stock price is easier to control, which means predicting trading signals is likely to put to practice. As long as investor is able to time the market perfectly and make the right trading decision, making profit is no longer difficult. In our study, we apply technical trading indicators and investors sentiment indicator to logistic regression algorithm to build a machine learning model in order to predict trading signals. We intend to improve the model ability of timing market via importing different features. Furthermore, we discuss about the performance of different strategies and if they lower down the investment risk when facing bear market. We also talk about the performance of different strategies by capturing momentum of different time horizons in further discussion.參考文獻 1. Barberis, Nicholas, Andrei Shleifer, and Robert Vishny. 1998, A Model of Investor Sentiment, Journal of Financial Economics 49 (3): 307-3432. Daniel, K. D., D. Hirshleifer, and A. Subrahmanyam, 1998, Investor psychology and security market under- and over-reactions, Journal of Finance 53, 1839–1886.3. Fama, Eugene F., 1970, Efficient Capital Markets：A Review of Theory and Empirical Work, Journal of Finance 25,383-417.4. Fama, Eugene F., 1965, The behavior of stock-market prices, The Journal of Business 38, 34-105.5. Gerwin A. W. Griffioen, 2003, Technical Analysis in Financial Markets, University of Amsterdam - Faculty of Economics and Business (FEB),pp. 3226. Jegadeesh, Narasimhan, and Sheridan Titman, 1993, Returns to buying winners and selling losers: Implications for stock market efficiency, Journal of Finance 48, 65-91.7. Jegadeesh, Narasimhan, and Sheridan Titman, 2001, Profitability of momentum strategies: An evaluation of alternative explanations, Journal of Finance 56, 699-720.8. K. J. Hong, S. Satchell, 2015, Time Series Momentum Trading Strategy and Autocorrelation Amplification, Quantitative Finance, volume 15, issue 9, p.1471 - 14879. Lee A. Smales, 2016, Risk-On/Risk-Off: Financial Market Response to Investor Fear, Finance Research Letters, Vol. 17, 201610. Olivier C., Blaise Pascal, 2007, Neural network modeling for stock movement prediction, state of art, Journal of Computer Engineering and Technology, 10(3), pp. 20-3011. Ramon Lawrence, 1997, Using Neural Networks to Forecast Stock Market Prices, Course Project, University of Manitoba12. Simon Fong, Jackie Tai, Yain Whar Si , 2011,Trend Following Algorithms for Technical Trading in Stock Market, Journal of emerging technologies in web intelligence, vol.3, no.213. Smales, Lee A., 2017. The importance of fear: investor sentiment and stock market returns, Applied Economics, 49 (34): pp. 3395-342114. Subhadra Kompella and Kalyana Chakravarthy Chilukuri Chakravarthy Chilukuri, 2019, Stock Market Prediction Using Machine Learning Methods, International15. T. Kimoto, K. Asakawa, M. Yoda, M. Takeoka,1990, Stock market prediction system with modular neural networks, IJCNN International Joint Conference on Neural Networks16. White, H., K. Hornik and M. Stinchcombe, 1992, Artificial Neural Networks, Blackwell Publishers 描述 碩士
國立政治大學
金融學系
107352025資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107352025 資料類型 thesis dc.contributor.advisor 江彌修 zh_TW dc.contributor.advisor Chiang, Mi-Hsiu en_US dc.contributor.author (Authors) 郭汶靖 zh_TW dc.contributor.author (Authors) Kuo, Wen-Ching en_US dc.creator (作者) 郭汶靖 zh_TW dc.creator (作者) Kuo, Wen-Ching en_US dc.date (日期) 2020 en_US dc.date.accessioned 2-Sep-2020 11:50:09 (UTC+8) - dc.date.available 2-Sep-2020 11:50:09 (UTC+8) - dc.date.issued (上傳時間) 2-Sep-2020 11:50:09 (UTC+8) - dc.identifier (Other Identifiers) G0107352025 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131510 - 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) 107352025 zh_TW dc.description.abstract (摘要) 金融時間序列的非定態特性使得預測未來股價非常困難。但精準預測股價並不是投資獲利的唯一方法。股價趨勢相對容易掌握，即交易訊號的預測較易被實踐。只要投資人可以精準擇時，在正確的時間點進行買賣交易，皆可因此而獲利。本研究以邏輯斯回歸演算法加入技術交易與投資人情緒指標建構機器學習模型產生交易訊號，希望藉由不同特徵值提升模型之市場擇時能力，以正確捕捉股市動能。另外，本研究亦針對空頭市場時各策略之表現以及策略是否有降低投資風險的效果進行討論，並針對捕捉不同天期之動能探討策略績效。 zh_TW dc.description.abstract (摘要) The non-stationary feature of financial time series makes the prediction of future stock price harder. However, predicting stock price correctly is not the only way to get return from investing. The trend of stock price is easier to control, which means predicting trading signals is likely to put to practice. As long as investor is able to time the market perfectly and make the right trading decision, making profit is no longer difficult. In our study, we apply technical trading indicators and investors sentiment indicator to logistic regression algorithm to build a machine learning model in order to predict trading signals. We intend to improve the model ability of timing market via importing different features. Furthermore, we discuss about the performance of different strategies and if they lower down the investment risk when facing bear market. We also talk about the performance of different strategies by capturing momentum of different time horizons in further discussion. en_US dc.description.tableofcontents 第一章 緒論 8第一節 研究動機 8第二節 研究目的 9第二章 理論探討與文獻回顧 11第一節 行為財務學中的動能 11第二節 技術分析面下的動能 12第三節 演算法如何捕捉動能 14第三章 基本假設與模型設定 15第一節 機器學習(Machine Learning) 15第二節 邏輯斯回歸演算法 16第三節 資料與策略 19第四節 特徵值選取 20第五節 分類模型的績效評估 21第六節 交易訊號訓練規則及流程 24第四章 實證分析與結果 26第一節 模型參數與超參數之選取 26第二節 於不同指標下捕捉之股市動能 34第三節 空頭市場分析 43第四節 捕捉不同天期動能之策略績效 46第五章 結論與建議 49參考文獻 50附錄 52 zh_TW dc.format.extent 2370634 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107352025 en_US dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) 邏輯斯回歸演算法 zh_TW dc.subject (關鍵詞) 技術交易 zh_TW dc.subject (關鍵詞) 投資人情緒指標 zh_TW dc.subject (關鍵詞) 動能策略 zh_TW dc.subject (關鍵詞) 交易訊號 zh_TW dc.subject (關鍵詞) 市場擇時 zh_TW dc.subject (關鍵詞) Machine Learning en_US dc.subject (關鍵詞) Logistic regression algorithm en_US dc.subject (關鍵詞) Technical trading en_US dc.subject (關鍵詞) Investor sentiment indicator en_US dc.subject (關鍵詞) Momentum strategy en_US dc.subject (關鍵詞) Trading signal en_US dc.subject (關鍵詞) Market timing en_US dc.title (題名) 建構輔以機器學習的技術交易動能策略 zh_TW dc.title (題名) Constructing Technical Trading Momentum Strategies Using Machine Learning en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 1. Barberis, Nicholas, Andrei Shleifer, and Robert Vishny. 1998, A Model of Investor Sentiment, Journal of Financial Economics 49 (3): 307-3432. Daniel, K. D., D. Hirshleifer, and A. Subrahmanyam, 1998, Investor psychology and security market under- and over-reactions, Journal of Finance 53, 1839–1886.3. Fama, Eugene F., 1970, Efficient Capital Markets：A Review of Theory and Empirical Work, Journal of Finance 25,383-417.4. Fama, Eugene F., 1965, The behavior of stock-market prices, The Journal of Business 38, 34-105.5. Gerwin A. W. Griffioen, 2003, Technical Analysis in Financial Markets, University of Amsterdam - Faculty of Economics and Business (FEB),pp. 3226. Jegadeesh, Narasimhan, and Sheridan Titman, 1993, Returns to buying winners and selling losers: Implications for stock market efficiency, Journal of Finance 48, 65-91.7. Jegadeesh, Narasimhan, and Sheridan Titman, 2001, Profitability of momentum strategies: An evaluation of alternative explanations, Journal of Finance 56, 699-720.8. K. J. Hong, S. Satchell, 2015, Time Series Momentum Trading Strategy and Autocorrelation Amplification, Quantitative Finance, volume 15, issue 9, p.1471 - 14879. Lee A. Smales, 2016, Risk-On/Risk-Off: Financial Market Response to Investor Fear, Finance Research Letters, Vol. 17, 201610. Olivier C., Blaise Pascal, 2007, Neural network modeling for stock movement prediction, state of art, Journal of Computer Engineering and Technology, 10(3), pp. 20-3011. Ramon Lawrence, 1997, Using Neural Networks to Forecast Stock Market Prices, Course Project, University of Manitoba12. Simon Fong, Jackie Tai, Yain Whar Si , 2011,Trend Following Algorithms for Technical Trading in Stock Market, Journal of emerging technologies in web intelligence, vol.3, no.213. Smales, Lee A., 2017. The importance of fear: investor sentiment and stock market returns, Applied Economics, 49 (34): pp. 3395-342114. Subhadra Kompella and Kalyana Chakravarthy Chilukuri Chakravarthy Chilukuri, 2019, Stock Market Prediction Using Machine Learning Methods, International15. T. Kimoto, K. Asakawa, M. Yoda, M. Takeoka,1990, Stock market prediction system with modular neural networks, IJCNN International Joint Conference on Neural Networks16. White, H., K. Hornik and M. Stinchcombe, 1992, Artificial Neural Networks, Blackwell Publishers zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202001706 en_US
