政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/131512

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/131512

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文笔数/总笔数 : 113822/144841 (79%)
造访人次 : 51777494 在线人数 : 555

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜寻范围

查询小技巧：

您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果

若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据

进阶搜寻

主页 ‧ 登入 ‧ 上传 ‧ 说明 ‧ 关于政大典藏 ‧ 管理

到手机版

政大機構典藏 > 商學院 > 金融學系 > 學位論文 > Item 140.119/131512

请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/131512

题名:	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
贡献者:	廖四郎 Liao, Szu-Lang 陳思奇 Chen, Si-Qi
关键词:	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
上传时间:	2020-09-02 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
数据类型:	thesis
DOI:	10.6814/NCCU202001114
显示于类别:	[金融學系] 學位論文

文件中的档案:

档案	大小	格式	浏览次数
204101.pdf	1152Kb	Adobe PDF2	741	检视/开启

在政大典藏中所有的数据项都受到原著作权保护.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回馈