政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/124719

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/124719

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政大典藏 > College of Commerce > Department of MIS > Theses > Item 140.119/124719

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/124719

Title:	應用卷積神經網路於基金漲跌之研究 The Study of Application of Convolutional Neural Networks to Mutual Fund Trend
Authors:	方羿茗 Fang, Yi-Ming
Contributors:	杜雨儒劉文卿 Tu, Yu-Ju Liu, Wen-Ching 方羿茗 Fang, Yi-Ming
Keywords:	深度學習卷積神經網路共同基金漲跌預測技術分析
Date:	2019
Issue Date:	2019-08-07 16:08:41 (UTC+8)
Abstract:	本研究使用卷積神經網路(Convolutional Neural Network，CNN)針對股票型基金於未來20天後的漲跌趨勢做出預測，輸入資料(Input Data)先使用技術分析建立資料矩陣後，再用Sliding Window的方式在上方滑動擷取出二維的image做為訓練資料，資料標籤(Label)則是觀察上漲強度與收益率之間的關係並加上時間權重來設計出漲跌門檻，模型設計是以LeNet-5為基礎進行延伸。當CNN預測出一群未來會上漲的基金後，我們再以高關聯篩選或機率門檻排序來挑出5支欲購買的基金，買入後固定持有20天後賣出。實驗結果顯示在資料標籤設計時若考慮時間權重所設計出的上漲強度加上高關聯篩選能讓我們有較好的實驗結果，其中在2016年以距離平方型加上高關聯篩選能獲得23.89%的年化報酬率，在2017年以距離型加上高關聯篩選能獲得32.69%的年化報酬率。
Reference:	[1] 吳哲緯 (2017)。使用深度學習卷積神經網路預測股票買賣策略之分類研究。國立中山大學資訊管理學系研究所，高雄市。 [2] Colby, R. W. (2003). The encyclopedia of technical market indicators(2nd ed.). New York: McGraw-Hill. [3] Di Persio, L., & Honchar, O. (2016). Artificial neural networks architectures for stock price prediction: Comparisons and applications. International journal of circuits, systems and signal processing, 10, 403-413. [4] Ding, X., Zhang, Y., Liu, T., & Duan, J. (2015). Deep learning for event-driven stock prediction. In Twenty-fourth international joint conference on artificial intelligence(ICJAI15), 2327-2333. [5] Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological cybernetics, 36(4), 193-202. [6] Heaton, J. B., Polson, N. G., & Witte, J. H. (2016). Deep learning in finance. arXiv preprint arXiv:1602.06561. [7] Hubel, D. H., & Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat`s visual cortex. The Journal of physiology, 160(1), 106-154. [8] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, 1097-1105. [9] Kwon, Y. K., & Moon, B. R. (2007). A hybrid neurogenetic approach for stock forecasting. IEEE transactions on neural networks, 18(3), 851-864. [10] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436. [11] LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4), 541-551. [12] LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. [13] Murugan, P. (2017). Feed forward and backward run in deep convolution neural network. arXiv preprint arXiv:1711.03278. [14] Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2017). Stock price prediction using LSTM, RNN and CNN-sliding window model. In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 1643-1647.
Description:	碩士國立政治大學資訊管理學系 106356038
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0106356038
Data Type:	thesis
DOI:	10.6814/NCCU201900554
Appears in Collections:	[Department of MIS] Theses

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