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| Title: | 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 |
| Authors: | 陳思奇
Chen, Si-Qi |
| Contributors: | 廖四郎
Liao, Szu-Lang
陳思奇
Chen, Si-Qi |
| Keywords: | 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 |
| Date: | 2020 |
| Issue Date: | 2020-09-02 11:50:40 (UTC+8) |
| Abstract: | 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. |
| Reference: | [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 |
| Description: | 碩士
國立政治大學
金融學系
107352041 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0107352041 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202001114 |
| Appears in Collections: | [金融學系] 學位論文
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