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題名 使用C-RNN神經網絡模型預測匯率變動—以中美日台為例
Using C-RNN Neural Network Model to Predict Exchange Rate Movements - A Case Study of China, America, Japan and Taiwan作者 陳思奇
Chen, Si-Qi貢獻者 廖四郎
Liao, Szu-Lang
陳思奇
Chen, Si-Qi關鍵詞 深度學習
卷積神經網絡
循環神經網絡
C-RNN
匯率
Deep learning
Convolutional neural network
Circular neural network
C-RNN
Exchange rate日期 2020 上傳時間 2-Sep-2020 11:50:40 (UTC+8) 摘要 本篇論文採用了將卷積神經網絡和循環神經網絡相結合的C-RNN模型來作為預測未來匯率價格的工具，希望藉由此工具能預判未來匯率的走勢與價格來作為參考。為此本研究選用了CNY/USD、CNY/ TWD、CNY/JPY等四種貨幣間的三種匯率價格作為分析資料，將未來5天的匯率作為預測目標。C-RNN是一種深度學習的模型，由於其將（CNN）卷積神經網絡和（RNN）循環神經網絡相結合，擁有著兩者的各自優勢，既能從資料中提取出空間特徵又能通過循環掌握時間特徵，因此可能在對匯率的預測上能取得良好成果。
This paper uses a C-RNN model that combines convolutional neural networks and recurrent neural networks as a tool to predict future exchange rate. It is hoped that this tool can predict future exchange rate trends and prices as a reference. For this reason, this study selected three exchange rates among four currencies such as CNY/USD, CNY/TWD, and CNY/JPY as analysis data, and the exchange rate for the next 5 days was used as the forecast target. C-RNN is a deep learning model. Because it combines (CNN) Convolutional Neural Network and (RNN) Recurrent Neural Network, it has their own advantages. It can extract spatial features and time characteristics from data at the same time, so it is possible to achieve good results in the forecast of exchange rates.參考文獻 [1] Chen, K., Zhou, Y., & Dai, F. (2015, October). A LSTM-based method for stock returns prediction: A case study of China stock market. In 2015 IEEE international conference on big data (big data) (pp. 2823-2824). IEEE.[2] Dunis, C. L., & Huang, X. (2002). Forecasting and trading currency volatility: An application of recurrent neural regression and model combination. Journal of forecasting, 21(5), 317-354.[3] Dunis, C. L., Laws, J., & Sermpinis, G. (2011). Higher order and recurrent neural architectures for trading the EUR/USD exchange rate. Quantitative Finance, 11(4), 615-629.[4] Guresen, E., Kayakutlu, G., & Daim, T. U. (2011). Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 38(8), 10389-10397.[5] Gao, S. E., Lin, B. S., & Wang, C. M. (2018, December). Share price trend prediction using CRNN with LSTM structure. In 2018 International Symposium on Computer, Consumer and Control (IS3C) (pp. 10-13). IEEE.[6] Takeuchi, L., & Lee, Y. Y. A. (2013). Applying deep learning to enhance momentum trading strategies in stocks. In Technical Report. Stanford University.[7] Tino, P., Schittenkopf, C., & Dorffner, G. (2001). Financial volatility trading using recurrent neural networks. IEEE Transactions on Neural Networks, 12(4), 865-874.[8] Yu, S. S., Chu, S. W., Chan, Y. K., & Wang, C. M. (2019). Share Price Trend Prediction Using CRNN with LSTM Structure. Smart Science, 7(3), 189-197.[9] 賴嘉蔚，（2018）。卷積神經網絡預測時間序列能力分析。國立政治大學金融學研究所碩士論文，台北市。取自https://hdl.handle.net/11296/y25ux2 描述 碩士
國立政治大學
金融學系
107352041資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107352041 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) 陳思奇 zh_TW dc.contributor.author (Authors) Chen, Si-Qi en_US dc.creator (作者) 陳思奇 zh_TW dc.creator (作者) Chen, Si-Qi en_US dc.date (日期) 2020 en_US dc.date.accessioned 2-Sep-2020 11:50:40 (UTC+8) - dc.date.available 2-Sep-2020 11:50:40 (UTC+8) - dc.date.issued (上傳時間) 2-Sep-2020 11:50:40 (UTC+8) - dc.identifier (Other Identifiers) G0107352041 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131512 - 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) 107352041 zh_TW dc.description.abstract (摘要) 本篇論文採用了將卷積神經網絡和循環神經網絡相結合的C-RNN模型來作為預測未來匯率價格的工具，希望藉由此工具能預判未來匯率的走勢與價格來作為參考。為此本研究選用了CNY/USD、CNY/ TWD、CNY/JPY等四種貨幣間的三種匯率價格作為分析資料，將未來5天的匯率作為預測目標。C-RNN是一種深度學習的模型，由於其將（CNN）卷積神經網絡和（RNN）循環神經網絡相結合，擁有著兩者的各自優勢，既能從資料中提取出空間特徵又能通過循環掌握時間特徵，因此可能在對匯率的預測上能取得良好成果。 zh_TW dc.description.abstract (摘要) This paper uses a C-RNN model that combines convolutional neural networks and recurrent neural networks as a tool to predict future exchange rate. It is hoped that this tool can predict future exchange rate trends and prices as a reference. For this reason, this study selected three exchange rates among four currencies such as CNY/USD, CNY/TWD, and CNY/JPY as analysis data, and the exchange rate for the next 5 days was used as the forecast target. C-RNN is a deep learning model. Because it combines (CNN) Convolutional Neural Network and (RNN) Recurrent Neural Network, it has their own advantages. It can extract spatial features and time characteristics from data at the same time, so it is possible to achieve good results in the forecast of exchange rates. 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 2第二章 文獻探討 5第一節 神經網絡模型應用之相關文獻 5第二節 文獻回顧總結 5第三章 研究方法 6第一節 卷積神經網絡 6第二節 循環神經網絡 10第三節 卷積-循環神經網絡 13第四節 研究對象 14第五節 一維時間序列資料二維化 14第四章 實證分析 16第一節 實驗架構設計 16第二節 實證結果 21第五章 結論與建議 27第一節 結論 27第二節 未來展望 27參考文獻 28 zh_TW dc.format.extent 1180088 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107352041 en_US dc.subject (關鍵詞) 深度學習 zh_TW dc.subject (關鍵詞) 卷積神經網絡 zh_TW dc.subject (關鍵詞) 循環神經網絡 zh_TW dc.subject (關鍵詞) C-RNN zh_TW dc.subject (關鍵詞) 匯率 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) Circular neural network en_US dc.subject (關鍵詞) C-RNN en_US dc.subject (關鍵詞) Exchange rate en_US dc.title (題名) 使用C-RNN神經網絡模型預測匯率變動—以中美日台為例 zh_TW dc.title (題名) Using C-RNN Neural Network Model to Predict Exchange Rate Movements - A Case Study of China, America, Japan and Taiwan en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] Chen, K., Zhou, Y., & Dai, F. (2015, October). A LSTM-based method for stock returns prediction: A case study of China stock market. In 2015 IEEE international conference on big data (big data) (pp. 2823-2824). IEEE.[2] Dunis, C. L., & Huang, X. (2002). Forecasting and trading currency volatility: An application of recurrent neural regression and model combination. Journal of forecasting, 21(5), 317-354.[3] Dunis, C. L., Laws, J., & Sermpinis, G. (2011). Higher order and recurrent neural architectures for trading the EUR/USD exchange rate. Quantitative Finance, 11(4), 615-629.[4] Guresen, E., Kayakutlu, G., & Daim, T. U. (2011). Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 38(8), 10389-10397.[5] Gao, S. E., Lin, B. S., & Wang, C. M. (2018, December). Share price trend prediction using CRNN with LSTM structure. In 2018 International Symposium on Computer, Consumer and Control (IS3C) (pp. 10-13). IEEE.[6] Takeuchi, L., & Lee, Y. Y. A. (2013). Applying deep learning to enhance momentum trading strategies in stocks. In Technical Report. Stanford University.[7] Tino, P., Schittenkopf, C., & Dorffner, G. (2001). Financial volatility trading using recurrent neural networks. IEEE Transactions on Neural Networks, 12(4), 865-874.[8] Yu, S. S., Chu, S. W., Chan, Y. K., & Wang, C. M. (2019). Share Price Trend Prediction Using CRNN with LSTM Structure. Smart Science, 7(3), 189-197.[9] 賴嘉蔚，（2018）。卷積神經網絡預測時間序列能力分析。國立政治大學金融學研究所碩士論文，台北市。取自https://hdl.handle.net/11296/y25ux2 zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202001114 en_US
