學術產出-Theses
Article View/Open
Publication Export
-
題名 卷積神經網路預測時間序列能力分析
Analysis of the predictive ability of time series using convolutional neural network作者 賴嘉蔚
Lai, Chia-Wei貢獻者 廖四郎
Liao, Szu-Lang
賴嘉蔚
Lai, Chia-Wei關鍵詞 深度學習
卷積神經網路
二維化
時間序列預測
演算法交易
Deep learning
Convolutional neural network
Visualization
Prediction of time series
Algorithmic trading日期 2018 上傳時間 3-Jul-2018 17:27:08 (UTC+8) 摘要 金融發展與科技一直以來都是密切相關的。整體來看，金融歷經了電子化、網路化等過程。而現階段，金融則是正處於自動化和智能化的階段。在本研究中，我們試圖將深度學習的概念與技術，應用於金融商品價格走勢的預測。主要概念是將金融商品一段時間的開盤價、最高價、最低價、收盤價一維時間序列資料二維化，從過去傳統一維測度的角度研究時間序列走勢，到本研究將視角提升到二維的測度。接著利用在圖像辨識有著卓越表現的卷積神經網路(CNN)進行特徵的萃取，進行金融商品未來漲跌的分類，藉此達到預測走勢的效果，進而建構一套可以穩定擊敗大盤的交易策略。實證發現，透過將時間序列二維化的方法，模型能比單純輸入時間序列數值學習到更多的資訊，績效也更穩定。而在預測金融商品價格走勢之外，我們一樣可以透過利用人工智慧的技術，創新金融商品和服務的模式，改善客戶體驗、提高服務效率。因此，在台灣開始發展金融科技之際，以期本研究有助於往後的研究者、金融機構和監理機關研發相關的技術。
Financial development and technology are always closely related. On the whole, finance has gone through the process of electronicization and networking. At the present stage, finance is in the process of automatization and intelligentization. In this paper, we try to apply the concepts and techniques of deep learning to the forecast of price trend of financial products. The main concept is that we transform one-dimensional time-series data of opening price, highest price, lowest price, closing price into two-dimensional planes. From the past, most researchers used one-dimensional measure to study time-series. Now we use two-dimensional measure to study time-series. Then, we use the convolution neural network (CNN), which has excellent performance in image recognition to capture features and make the classification of price trend, so as to achieve the effect of forecasting price trend and construct a trading strategy which can stably beat the market. The empirical result show that deep learning models can learn better by using the method of visualization than simply inputting time series values and the performance is more stable. Therefore, as Taiwan begins to develop FinTech, it is hoped that this paper will help future researchers, financial institutions, and supervision agencies develop related technologies.參考文獻 [1] Asness, C. S., T. J. Moskowitz and L. H. Pedersen, (2013). Value and Momentum Everywhere. Journal of Finance, Volume 68, Issue 3, Pages 929-985.[2] Banz, R. W.(1981). The relationship between return and market value of common stocks. Journal of Financial Economics, Volume 9, Issue 1, Pages 3-18.[3] Basu, S., (1997). The conservatism principle and the asymmetric timeliness of earnings. Journal of Accounting and Economics, Volume 24, Issue 1, Pages 3-37.[4] Bengio, Y., V.P. Lauzon, and R. Ducharme, (2001). Experiments on the application of IOHMMs to model financial returns series. Neural Networks, IEEE Transactions on, 12(1), 113-123.[5] Campbell, J. Y., and R.J. Shiller, (1987). Stock Prices, Earnings, and Expected Dividends. The Journal of Finance Vol. 43, No. 3, Papers and Proceedings of the Forty-Seventh Annual Meeting of the American Finance Association, Chicago, Illinois, December 28-30, pp. 661-676.[6] Chapados, N., and Y. Bengio, (2001). Cost functions and model combination for VaR-based asset allocation using neural networks. Neural Networks, IEEE Transactions on, 12(4), 890-906.[7] Chen, S. M., and P.Y, Kao, (2013). TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machine. information Sciences, 247, 62-71.[8] Duchi, J.,E. Hazan, and Y. Singer, (2011) Adaptive Subgradient Methods for Stochastic Optimization. The Journal of Machine Learning Research, Volume 12, Pages 2121-2159.[9] Fama, E. F., (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, Vol. 25, No. 2, Papers and Proceedings of the Twenty-Eighth Annual Meeting of the American Finance Association New York, N.Y., pp. 383-417.[10] Fama, E.F., and K.R. French, (1988). Dividend Yield and Expected Stock Return. Journal of Financial Economics, Volume 22, Issue 1, Pages 3-25.[11] Fama, E. F., and K.R. French, (1992). The Cross-Section of Expected Stock Returns. The Journal of Finance, Vol. 47, No. 2, pp. 427-465.[12] Gençay, R., and R. Gibson, (2007). Model risk for European-style stock index options. Neural Networks, IEEE Transactions on, 18(1), 193-202.[13] Jegadeesh, N., and S. Titman, (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, Vol. 48, Issue 1, pp. 65-91.[14] Keim, D. B., and R. F. Stambaugh, (1986). Predicting Return in The Stock and Bond Markets. Journal of Financial Economics, Volume 17, Issue 2, Pages 357-390.[15] Kendall, M. G., and A. B. Hill, (1953). The Analytics of Economic Time Series Part 1: Prices. Journal of the Royal Statistical Society, Series A (General), Volumn 116, Issue 1, 11-34.[16] Kercheval, A. N., and Y. Zhang, (2013). Modeling high-frequency limit order book dynamics with support vector machines. Quantitative Finance Volume 15,Issue 8: Special Issue on High Frequency Data Modeling in Finance, Pages 1315-1329.[17] Kingma, D. P. and J. Ba, (2015). Adam: A Method for Stochastic Optimization. 3rd International Conference for Learning Representations, San Diego.[18] Krizhevsky, A., I. Sutskever, and G.E. Hinton, (2012). ImageNet Classification with Deep Convolutional Neural Networks. Proceeding NIPS`12 Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1,Pages 1097-1105.[19] Kwon, Y. K. and B. R. Moon, (2007). A hybrid neurogenetic approach for stock forecasting. Neural Networks, IEEE Transactions on, 18(3), 851-864.[20] LeCun, Y. and Y. Bengio, (1995). Convolutional networks for images, speech, and time series.The handbook of brain theory and neural networks. The handbook of brain theory and neural networks, Pages 255-258.[21] LeCun, Y., L. Bottou, Y. Bengio, and P. Haffner, (1998). Gradient-based learning applied to document recognition. Proc. IEEE, vol. 86, no. 11, pp. 2278-2324.[22] Lu, T. H. and Y. C. Chen, (2015). Trend definition or holding strategy: What determines the profitability of candlestick charting? Journal of Banking & Finance, Volume 61, Pages 172-183.[23] Moskowitz, T. J., Y. H. Ooi and L. H. Pedersn, (2012). Time series momentum. Journal of Financial Economics, Volume 104, Issue 2, Pages 228-250.[24] Platanios, E. and S. P. Chatzis, (2014). Gaussian Process-Mixture Conditional Heteroscedasticity. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 36(5), 888-900.[25] Rojas, R., (1996). The Backpropagation Algorithm. Neural Networks, A Systematic Introduction. Pages 151-184.[26] Sitte, R. and J. Sitte, (2000). Analysis of the predictive ability of time delay neural networks applied to the S&P 500 time series. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 30(4), 568-572.[27] Takeuchi, L. and Y.-Y. Lee, (2013). Applying Deep Learning to Enhance Momentum Trading Strategies in Stocks.[28] Tino, P., C. Schittenkopf, and G. Dorffner, (2001). Financial volatility trading using recurrent neural networks. Neural Networks, IEEE Transactions on,12(4), 865-874.[29] Wang, Z., T. Oates, (2015). Imaging time-series to improve classification and imputation.[30] Xu, Z., S. MacEachern and X. Xu, (2007). Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 37(2), 372-382.[31] Zheng, Y., Q. Liu, E. Chen, Y. Ge, J. L. Zhao, (2014). Time series classification using multi-channels deep convolutional neural Networks. Proceedings of the International Conference on Web-Age Information Management (WAIM), pp.298–310.[32] 黃君平，(2016)。基於深度學習技術之金融市場價格趨勢預測。 描述 碩士
國立政治大學
金融學系
1053520303資料來源 http://thesis.lib.nccu.edu.tw/record/#G1053520303 資料類型 thesis dc.contributor.advisor 廖四郎 zh_TW dc.contributor.advisor Liao, Szu-Lang en_US dc.contributor.author (Authors) 賴嘉蔚 zh_TW dc.contributor.author (Authors) Lai, Chia-Wei en_US dc.creator (作者) 賴嘉蔚 zh_TW dc.creator (作者) Lai, Chia-Wei en_US dc.date (日期) 2018 en_US dc.date.accessioned 3-Jul-2018 17:27:08 (UTC+8) - dc.date.available 3-Jul-2018 17:27:08 (UTC+8) - dc.date.issued (上傳時間) 3-Jul-2018 17:27:08 (UTC+8) - dc.identifier (Other Identifiers) G1053520303 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/118244 - 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) 1053520303 zh_TW dc.description.abstract (摘要) 金融發展與科技一直以來都是密切相關的。整體來看，金融歷經了電子化、網路化等過程。而現階段，金融則是正處於自動化和智能化的階段。在本研究中，我們試圖將深度學習的概念與技術，應用於金融商品價格走勢的預測。主要概念是將金融商品一段時間的開盤價、最高價、最低價、收盤價一維時間序列資料二維化，從過去傳統一維測度的角度研究時間序列走勢，到本研究將視角提升到二維的測度。接著利用在圖像辨識有著卓越表現的卷積神經網路(CNN)進行特徵的萃取，進行金融商品未來漲跌的分類，藉此達到預測走勢的效果，進而建構一套可以穩定擊敗大盤的交易策略。實證發現，透過將時間序列二維化的方法，模型能比單純輸入時間序列數值學習到更多的資訊，績效也更穩定。而在預測金融商品價格走勢之外，我們一樣可以透過利用人工智慧的技術，創新金融商品和服務的模式，改善客戶體驗、提高服務效率。因此，在台灣開始發展金融科技之際，以期本研究有助於往後的研究者、金融機構和監理機關研發相關的技術。 zh_TW dc.description.abstract (摘要) Financial development and technology are always closely related. On the whole, finance has gone through the process of electronicization and networking. At the present stage, finance is in the process of automatization and intelligentization. In this paper, we try to apply the concepts and techniques of deep learning to the forecast of price trend of financial products. The main concept is that we transform one-dimensional time-series data of opening price, highest price, lowest price, closing price into two-dimensional planes. From the past, most researchers used one-dimensional measure to study time-series. Now we use two-dimensional measure to study time-series. Then, we use the convolution neural network (CNN), which has excellent performance in image recognition to capture features and make the classification of price trend, so as to achieve the effect of forecasting price trend and construct a trading strategy which can stably beat the market. The empirical result show that deep learning models can learn better by using the method of visualization than simply inputting time series values and the performance is more stable. Therefore, as Taiwan begins to develop FinTech, it is hoped that this paper will help future researchers, financial institutions, and supervision agencies develop related technologies. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究動機 1第二節 研究目的 2第三節 研究背景 3第四節 研究架構 5第二章 文獻回顧 6第一節 計量方法預測時間序列之相關文獻 6第二節 機器學習方法預測時間序列之相關文獻 6第三節 文獻回顧總結 7第三章 研究方法 8第一節 卷積神經網路 8第二節 卷積神經網路架構 8第三節 研究對象 17第四節 一維時間序列資料二維化 20第五節 實驗架構 22第四章 實證分析 26第一節 建構交易策略及績效評估因子 26第二節 實證結果 28一、 GASF搭配 (10k+15k+30k) 29二、 GADF搭配 (10k+15k+30k) 33三、 GASF搭配 (15k+30k+60k) 37四、 GADF搭配 (15k+30k+60k) 41五、 GASF搭配 (30k+60k+日k) 45六、 GADF搭配 (30k+60k+日k) 49七、 對照組Neural Network之結果比較 53第五章 結論與建議 57第一節 結論 57第二節 未來展望 58附錄 59參考文獻 66 zh_TW dc.format.extent 3194823 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G1053520303 en_US dc.subject (關鍵詞) 深度學習 zh_TW dc.subject (關鍵詞) 卷積神經網路 zh_TW dc.subject (關鍵詞) 二維化 zh_TW dc.subject (關鍵詞) 時間序列預測 zh_TW dc.subject (關鍵詞) 演算法交易 zh_TW dc.subject (關鍵詞) Deep learning en_US dc.subject (關鍵詞) Convolutional neural network en_US dc.subject (關鍵詞) Visualization en_US dc.subject (關鍵詞) Prediction of time series en_US dc.subject (關鍵詞) Algorithmic trading en_US dc.title (題名) 卷積神經網路預測時間序列能力分析 zh_TW dc.title (題名) Analysis of the predictive ability of time series using convolutional neural network en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] Asness, C. S., T. J. Moskowitz and L. H. Pedersen, (2013). Value and Momentum Everywhere. Journal of Finance, Volume 68, Issue 3, Pages 929-985.[2] Banz, R. W.(1981). The relationship between return and market value of common stocks. Journal of Financial Economics, Volume 9, Issue 1, Pages 3-18.[3] Basu, S., (1997). The conservatism principle and the asymmetric timeliness of earnings. Journal of Accounting and Economics, Volume 24, Issue 1, Pages 3-37.[4] Bengio, Y., V.P. Lauzon, and R. Ducharme, (2001). Experiments on the application of IOHMMs to model financial returns series. Neural Networks, IEEE Transactions on, 12(1), 113-123.[5] Campbell, J. Y., and R.J. Shiller, (1987). Stock Prices, Earnings, and Expected Dividends. The Journal of Finance Vol. 43, No. 3, Papers and Proceedings of the Forty-Seventh Annual Meeting of the American Finance Association, Chicago, Illinois, December 28-30, pp. 661-676.[6] Chapados, N., and Y. Bengio, (2001). Cost functions and model combination for VaR-based asset allocation using neural networks. Neural Networks, IEEE Transactions on, 12(4), 890-906.[7] Chen, S. M., and P.Y, Kao, (2013). TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machine. information Sciences, 247, 62-71.[8] Duchi, J.,E. Hazan, and Y. Singer, (2011) Adaptive Subgradient Methods for Stochastic Optimization. The Journal of Machine Learning Research, Volume 12, Pages 2121-2159.[9] Fama, E. F., (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, Vol. 25, No. 2, Papers and Proceedings of the Twenty-Eighth Annual Meeting of the American Finance Association New York, N.Y., pp. 383-417.[10] Fama, E.F., and K.R. French, (1988). Dividend Yield and Expected Stock Return. Journal of Financial Economics, Volume 22, Issue 1, Pages 3-25.[11] Fama, E. F., and K.R. French, (1992). The Cross-Section of Expected Stock Returns. The Journal of Finance, Vol. 47, No. 2, pp. 427-465.[12] Gençay, R., and R. Gibson, (2007). Model risk for European-style stock index options. Neural Networks, IEEE Transactions on, 18(1), 193-202.[13] Jegadeesh, N., and S. Titman, (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, Vol. 48, Issue 1, pp. 65-91.[14] Keim, D. B., and R. F. Stambaugh, (1986). Predicting Return in The Stock and Bond Markets. Journal of Financial Economics, Volume 17, Issue 2, Pages 357-390.[15] Kendall, M. G., and A. B. Hill, (1953). The Analytics of Economic Time Series Part 1: Prices. Journal of the Royal Statistical Society, Series A (General), Volumn 116, Issue 1, 11-34.[16] Kercheval, A. N., and Y. Zhang, (2013). Modeling high-frequency limit order book dynamics with support vector machines. Quantitative Finance Volume 15,Issue 8: Special Issue on High Frequency Data Modeling in Finance, Pages 1315-1329.[17] Kingma, D. P. and J. Ba, (2015). Adam: A Method for Stochastic Optimization. 3rd International Conference for Learning Representations, San Diego.[18] Krizhevsky, A., I. Sutskever, and G.E. Hinton, (2012). ImageNet Classification with Deep Convolutional Neural Networks. Proceeding NIPS`12 Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1,Pages 1097-1105.[19] Kwon, Y. K. and B. R. Moon, (2007). A hybrid neurogenetic approach for stock forecasting. Neural Networks, IEEE Transactions on, 18(3), 851-864.[20] LeCun, Y. and Y. Bengio, (1995). Convolutional networks for images, speech, and time series.The handbook of brain theory and neural networks. The handbook of brain theory and neural networks, Pages 255-258.[21] LeCun, Y., L. Bottou, Y. Bengio, and P. Haffner, (1998). Gradient-based learning applied to document recognition. Proc. IEEE, vol. 86, no. 11, pp. 2278-2324.[22] Lu, T. H. and Y. C. Chen, (2015). Trend definition or holding strategy: What determines the profitability of candlestick charting? Journal of Banking & Finance, Volume 61, Pages 172-183.[23] Moskowitz, T. J., Y. H. Ooi and L. H. Pedersn, (2012). Time series momentum. Journal of Financial Economics, Volume 104, Issue 2, Pages 228-250.[24] Platanios, E. and S. P. Chatzis, (2014). Gaussian Process-Mixture Conditional Heteroscedasticity. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 36(5), 888-900.[25] Rojas, R., (1996). The Backpropagation Algorithm. Neural Networks, A Systematic Introduction. Pages 151-184.[26] Sitte, R. and J. Sitte, (2000). Analysis of the predictive ability of time delay neural networks applied to the S&P 500 time series. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 30(4), 568-572.[27] Takeuchi, L. and Y.-Y. Lee, (2013). Applying Deep Learning to Enhance Momentum Trading Strategies in Stocks.[28] Tino, P., C. Schittenkopf, and G. Dorffner, (2001). Financial volatility trading using recurrent neural networks. Neural Networks, IEEE Transactions on,12(4), 865-874.[29] Wang, Z., T. Oates, (2015). Imaging time-series to improve classification and imputation.[30] Xu, Z., S. MacEachern and X. Xu, (2007). Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 37(2), 372-382.[31] Zheng, Y., Q. Liu, E. Chen, Y. Ge, J. L. Zhao, (2014). Time series classification using multi-channels deep convolutional neural Networks. Proceedings of the International Conference on Web-Age Information Management (WAIM), pp.298–310.[32] 黃君平，(2016)。基於深度學習技術之金融市場價格趨勢預測。 zh_TW dc.identifier.doi (DOI) 10.6814/THE.NCCU.MB.005.2018.F06 -
