政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/150259

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/150259

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文筆數/總筆數 : 113648/144635 (79%)
造訪人次 : 51600745 線上人數 : 846

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜尋範圍

查詢小技巧：

您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果

若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料

進階搜尋

主頁 ‧ 登入 ‧ 上傳 ‧ 說明 ‧ 關於政大典藏 ‧ 管理

到手機版

政大機構典藏 > 資訊學院 > 資訊科學系碩士在職專班 > 學位論文 > Item 140.119/150259

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/150259

題名:	使用多頭注意力機制的多重輸入 LSTM 股價預測模型 A Multi-input LSTM model for Stock Price Prediction using Multi-head Self-Attention
作者:	葉國毅 Yeh, Kuo-Yi
貢獻者:	彭彥璁 Peng, Yan-Tsung 葉國毅 Yeh, Kuo-Yi
關鍵詞:	股價預測深度學習長短期記憶注意力機制 Stock Price Prediction Deep Learning LSTM Attention
日期:	2024
上傳時間:	2024-03-01 14:11:40 (UTC+8)
摘要:	隨著機器學習在不同領域的成功應用，機器學習在金融市場的應用越來越蓬勃發展，預測股價在這當中也是一個受到關注的研究領域。透過機器學習的技術和方法，我們可以利用大量的市場數據、技術指標和其他多元資料來建立預測模型，如此不僅能夠識別和分析股價的趨勢和模式，同時能夠考慮到多種相關因素的影響，例如財務數據、市場情緒、新聞事件等，這種綜合考慮的方式使得預測模型能夠更準確地捕捉市場的變化，並提供投資者有價值的信息和建議，以幫助投資者做出更明智的股票交易決策。本研究以台灣50指數的成分股作為訓練資料，透過皮爾森相關係數找出當日與標的股價相關的證券以及基準指數作為多重輸入特徵，結合注意力機制與LSTM建構股價預測模型，並應用模型預測值作為交易訊號進行交易績效回測。研究結果顯示多重的輸入資料以及注意力機制相對於其他基礎模型有更好的預測效果，在交易績效回測上相對於持有到期的方法，更能夠規避風險，穩定獲得正報酬。 Machine learning has been successfully applied in various fields, including financial markets. Among them, stock price prediction has become a prominent research field. By utilizing machine learning techniques, such as analyzing large amounts of market data, technical indicators and other diverse information, we can build accurate predictive models.These models consider features such as financial data, market sentiment, and news events to capture market changes.Providing investors with valuable insights that enable them to make informed trading decisions. This study uses multiple input data of Taiwan 50 Index constituent stocks for training. We use the Pearson correlation coefficient to find relationships between stocks, benchmark indexes, and target prices as input features. We combines an attention mechanism with LSTM to predict stock prices and uses these predictions as trading signals for backtesting. The results show that using multiple inputs and attention mechanism outperforms other baseline models. In trading backtesting, the model achieved more positive returns than a simple buy-and-hold strategy.
參考文獻:	[1] J. J. Murphy, “Technical analysis of the financial markets: A comprehensive guide to trading methods and applications.” New York: New York Institute of Finance., 1999. [2] M. Lam, “Neural network techniques for financial performance prediction: integrating fundamental and technical analysis,” Decision Support Systems, 2004. [3] S. S. Groth and J. Muntermann, “An intraday market risk management approach based on textual analysis,” Decision Support Systems, 2011. [4] X. Liang, R.-C. Chen, Y. He, and Y. Chen, “Associating stock prices with web financial information time series based on support vector regression,” Neurocomputing, 2013. [5] A. Nikfarjam, E. Emadzadeh, and S. Muthaiyah, “Text mining approaches for stock market prediction,” in 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), 2010. [6] E. J. Ruiz, V. Hristidis, C. Castillo, A. Gionis, and A. Jaimes, “Correlating financial time series with micro-blogging activity,” in WSDM ’12, 2012. [7] B. Wang, H. Huang, and X. Wang, “A novel text mining approach to financial time series forecasting,” Neurocomputing, 2012. [8] L. A. Teixeira and A. L. I. de Oliveira, “A method for automatic stock trading combining technical analysis and nearest neighbor classification,” Expert Systems with Applications, 2010. [9] Y.-H. Lui and D. Mole, “The use of fundamental and technical analyses by foreign exchange dealers: Hong kong evidence,” Journal of International Money and Finance, 1998. [10] P. Ravi Kumar and V. Ravi, “Bankruptcy prediction in banks and firms via statistical and intelligent techniques–a review,” European Journal of Operational Research, 2007. [11] Z. C. Lipton, J. Berkowitz, and C. Elkan, “A critical review of recurrent neural networks for sequence learning,” 2015. [12] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, 1997. [13] H. Li, Y. Shen, and Y. Zhu, “Stock price prediction using attention-based multi-input lstm,” in Proceedings of The 10th Asian Conference on Machine Learning, 2018. [14] Y.-H. Chou, S.-Y. Kuo, and Y.-C. Jiang, “A novel portfolio optimization model based on trend ratio and evolutionary computation,” IEEE Transactions on Emerging Topics in Computational Intelligence, 2019. [15] X. Fu, J. Du, Y. Guo, M. Liu, T. Dong, and X. Duan, “A Machine Learning Framework for Stock Selection,” arXiv e-prints, 2018. [16] P. J. Coen, E. D. Gomme, and M. G. Kendall, “Lagged relationships in economic forecasting,” Journal of the Royal Statistical Society. Series A (General), 1969. [17] Y. Chen, W. Lin, and J. Z. Wang, “A dual-attention-based stock price trend prediction model with dual features,” IEEE Access, 2019. [18] W. Lu, J. Li, J. Wang, and L. Qin, “A cnn-bilstm-am method for stock price prediction,” Neural Computing and Applications, 2020. [19] W. Yeoh, Y. Jhang, S. Kuo, and Y. Chou, “Automatic stock trading system combined with short selling using moving average and gqts algorithm,” in 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018. [20] H. Yang, X. Liu, and Q. Wu, “A practical machine learning approach for dynamic stock recommendation,” in 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), 2018. [21] Z. Xiong, X. Liu, S. Zhong, H. Yang, and A. Walid, “Practical deep reinforcement learning approach for stock trading,” CoRR, 2018. [22] Y.-Y. Song and L. Ying, “Decision tree methods: applications for classification and prediction,” Shanghai archives of psychiatry, 2015. [23] L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, “Classification and regression trees,” in Routledge, 1984. [24] L. Breiman, “Random forests,” Mach. Learn., 2001. [25] T. K. Ho, “Random decision forests,” in Proceedings of 3rd international conference on document analysis and recognition, 1995. [26] L. Breiman, “Bagging predictors,” Machine Learning, 2004. [27] K. Kirasich, T. Smith, and B. Sadler, “Random forest vs logistic regression: Binary classification for heterogeneous datasets,” SMU Data Science Review, 2018. [28] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, 2015. [29] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, 2012. [30] J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities.” Proceedings of the National Academy of Sciences, 1982. [31] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, 1997. [32] F. A. Gers, J. Schmidhuber, and F. Cummins, “Learning to Forget: Continual Prediction with LSTM,” Neural Computation, 2000. [33] A. Graves, A. Mohamed, and G. E. Hinton, “Speech recognition with deep recurrent neural networks,” CoRR, 2013. [34] Vaswani, Ashish, Shazeer, Noam, Parmar, Niki, Uszkoreit, Jakob, L. Jones, Gomez, A. N, Kaiser, L. ukasz, Polosukhin, and Illia, “Attention is all you need,” in Advances in Neural Information Processing Systems, 2017. [35] T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. Le Scao, S. Gugger, M. Drame, Q. Lhoest, and A. Rush, “Transformers: State-of-theart natural language processing,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 2020. [36] K. L. Jacob Devlin, Ming-Wei Chang and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,” CoRR, 2018. [37] T. Gneiting, “Making and evaluating point forecasts,” Journal of the American Statistical Association, 2011. [38] A. Graves, A. rahman Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” 2013. [39] K. Cho, B. van Merrienboer, Ç. Gülçehre, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” CoRR, 2014.
描述:	碩士國立政治大學資訊科學系碩士在職專班 108971025
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0108971025
資料類型:	thesis
顯示於類別:	[資訊科學系碩士在職專班] 學位論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
102501.pdf		3048Kb	Adobe PDF	5	檢視/開啟

在政大典藏中所有的資料項目都受到原著作權保護.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回饋