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    题名: 利用深度學習圖形辨識技術建置最適投資策略-以台灣股票市場為例
    Applying the Stock Chart Pattern Recognition with Deep Learning to Construct the Optimal Investment Strategy in Taiwan
    作者: 陳暐文
    Chen, Wei-Wen
    贡献者: 黃泓智
    Huang, Hong-Chih
    陳暐文
    Chen, Wei-Wen
    关键词: 人工智慧
    深度學習
    自動編碼器
    多層感知機
    股票線圖
    台股
    Artificial Intelligence
    Deep Learning
    AutoEncoder
    Stock charts
    Multiple Layer Perception
    日期: 2019
    上传时间: 2019-09-05 15:48:19 (UTC+8)
    摘要: 近年來，隨著電腦技術的革新，人工智慧在各領域皆有所突破。其中，圖像辨識可說是人工智慧運用的相當廣泛的一個領域，因此，本研究希望透過深度學習中圖像辨識相關技術，來預測股票線圖在未來的走勢，進一步選出預期報酬較高之股票作為投資組合。
    本研究針對股票線圖一共進行兩階段處裡，第一階段採用自動編碼器（Autoencoder）技術，訓練出可將股票蠟燭圖、成交量圖降維之模型；第二階段則使用多層感知機(Multiple Perception Layer)模型對降為後資料進行學習，預測未來股票報酬率，建置投資組合。
    最後，本文透過實證分析，回測模型績效，回測期間從2012至2019共8年，回測結果平均年化報酬率達22.69%，平均年化夏普比為1.49，明顯優於台灣加權指數表現。
    In recent years, with the innovation of computer technology, artificial intelligence has made lots of breakthroughs in various fields. Among them, image recognition can be said to be a really successful one. Therefore, this paper hopes to predict the trend of stock charts through the image recognition skill in deep learning in order to construct the optimal portfolio.
    This paper applies two models to predict stock charts. First, an AutoEncoder is used to reduce the candlesticks charts and volume charts from three dimensions to one dimension. We then take these 1D data as input to our second model - Multiple Layer Perception（MLP, supervised learning）. We apply MLP model to predict stocks’ future returns, thereby constructing the portfolio.
    Finally, this paper evaluates the investment strategy through the empirical analysis. In conclusion, the strategy deliver an average annualized return of 22.69% and an average annualized Sharpe Ratio of 1.49, which all outperform than Taiwan Capitalization Weighted Stock Index（TAIEX）.
    參考文獻: [1] Chen, T. and Chen, F. (2016). An intelligent pattern recognition model for supporting investment decisions in stock market. Information Sciences, 346-347, 261-274.
    [2] Ding, X., Zhang, Y., Liu T. and Duan J. (2015). Deep Learning for Event-Driven Stock Prediction. IJCAI`15 Proceedings of the 24th International Conference on Artificial Intelligece, 2327-2333.
    [3] Fama, E. F. (1998). Market efficiency, long-term returns, and behavioral finance. Journal of financial economics, 49(3), 283-306.
    [4] Fischer, T. and Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669.
    [5] He, K., Zhang, X., Ren, S. and Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
    [6] Hinton, G. E., Osindero, S. and Yee, W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554.
    [7] Hu, G., Hu, Y., Yang, K., Yu, Z., Sung, F., Zhang, Z., …Miemie, Q. (2018). Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
    [8] Hubel, D. H. and Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat`s visual cortex. Journal of Physiology, 160(1), 106-154.
    [9] Krizhevsky, A., Sutskever, I. and Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in neural information processing systems, 25(2), 1097-1105.
    [10] Lecun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
    [11] Masci, J., Meier U., Cireşan, D. and Schmidhuber, J. (2011). Stacked convolutional auto-encoders for hierarchical feature extraction. Artificial Neural Networks and Machine Learning - ICANN. 52-59.
    [12] Ranjan R., Patel V. M. and Chellappa R. (2017). Hyperface: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. The IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(1), 121-135.
    [13] Simonyan, K. and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv 1409.1556.
    [14] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed S., Anguelov, D., Erhan, D., Vanhoucke, V. and Rabinovich, A. (2015). Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
    [15] Takeuchi, L. and Lee, Y. (2013). Applying Deep Learning to Enhance Momentum Trading Strategies in Stocks. Stanford Technology Report.
    描述: 碩士
    國立政治大學
    風險管理與保險學系
    106358011
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0106358011
    数据类型: thesis
    DOI: 10.6814/NCCU201901080
    显示于类别:[風險管理與保險學系] 學位論文

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