政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/131510

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政大機構典藏 > 商學院 > 金融學系 > 學位論文 > Item 140.119/131510

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/131510

Title:	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
Authors:	郭汶靖 Kuo, Wen-Ching
Contributors:	江彌修 Chiang, Mi-Hsiu 郭汶靖 Kuo, Wen-Ching
Keywords:	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
Date:	2020
Issue Date:	2020-09-02 11:50:09 (UTC+8)
Abstract:	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.
Reference:	1. Barberis, Nicholas, Andrei Shleifer, and Robert Vishny. 1998, A Model of Investor Sentiment, Journal of Financial Economics 49 (3): 307-343 2. 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. 322 6. 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 - 1487 9. Lee A. Smales, 2016, Risk-On/Risk-Off: Financial Market Response to Investor Fear, Finance Research Letters, Vol. 17, 2016 10. Olivier C., Blaise Pascal, 2007, Neural network modeling for stock movement prediction, state of art, Journal of Computer Engineering and Technology, 10(3), pp. 20-30 11. Ramon Lawrence, 1997, Using Neural Networks to Forecast Stock Market Prices, Course Project, University of Manitoba 12. 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.2 13. Smales, Lee A., 2017. The importance of fear: investor sentiment and stock market returns, Applied Economics, 49 (34): pp. 3395-3421 14. Subhadra Kompella and Kalyana Chakravarthy Chilukuri Chakravarthy Chilukuri, 2019, Stock Market Prediction Using Machine Learning Methods, International 15. T. Kimoto, K. Asakawa, M. Yoda, M. Takeoka,1990, Stock market prediction system with modular neural networks, IJCNN International Joint Conference on Neural Networks 16. White, H., K. Hornik and M. Stinchcombe, 1992, Artificial Neural Networks, Blackwell Publishers
Description:	碩士國立政治大學金融學系 107352025
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0107352025
Data Type:	thesis
DOI:	10.6814/NCCU202001706
Appears in Collections:	[金融學系] 學位論文

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