政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/118805

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/118805

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政大典藏 > College of Commerce > Department of Money and Banking > Theses > Item 140.119/118805

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/118805

Title:	以循環神經網路信號建構交易策略 Constructing the Trading Strategies with Signals in Recurrent Neural Network
Authors:	陳采駿 Chen, Tsai-Chun
Contributors:	廖四郎 Liao, Szu-Lang 陳采駿 Chen,Tsai-Chun
Keywords:	循環神經網路長短期記憶神經網路股價指數預測 Recurrent neural network Long short term memory Neural network Price predict
Date:	2018
Issue Date:	2018-07-23 16:50:47 (UTC+8)
Abstract:	過去傳統計量模型使用的模型時，需要先對變數之間的關係，有著深厚的經驗，了解不同變數之間的因果關係，並且傳統計量模型在預測的時候，大多會忽略時間序列中間隔很久的重要事件，只考慮到短天期的變數變動情形。為了可以充分利用多變量時間序列中有用的資訊，並且進一步提高對於預測台灣加權股價指數的準確度，本文使用深度學習中的循環神經網路，對股價指數進行多變量的預測，希望能從眾多變數中提取出有效的資訊，並且解決過去時間序列中忽略間隔較久的重要事件。台灣加權股價指數被選為本次研究的對象，並且選取循環神經網路以及向量自我迴歸共 2 個模型，分別使用這兩個模型對股價指數進行預測，並比較這兩者在預測指數上的表現。最後結果顯示，在我們過去歷史資料中，循環神經網路的準確度明顯優於傳統的向量自我迴歸模型。 In the past, the models used in traditional financial econometric models need to have a deep experience in the relationship between variables, and understand the causal relationship between different variables. In traditional financial econometric models, most of them ignore long time distance or delays in time series, just focus on the variable change in short period. In order to make the most information in multivariate time series and to further improve the accuracy of forecasting Taiwan`s weighted stock price index, this paper uses the recurrent neural network in deep learning to perform multivariate forecasting on the stock price index in hopes of being able to extracts valid information from variable and resolves important events that were ignored at long time distance in past time series. The Taiwanese weighted stock price index was selected as the subject of this study, and two models of recurrent neural network and vector autoregression were selected. These two models were used to predict the stock price index and compare the two in the forecast index. which performed. The final result shows that in our past historical data, the accuracy of the recurrent neural network is significantly better than the traditional Vector Autoregression model.
Reference:	1. 黃旭淳，國際原油價格對總體經濟變數的影響。2005 年，未出版之碩士論文，交通大學，經營管理研究所，國際原油價格對總體經濟變數的影響，新竹。 2. Christian L. Dunis, Xuehuan Huang (2002). Forecasting and trading currency volatility: an application of recurrent neural regression and model combination. Journal of Forecasting, August 2002, Vol.21(5), pp.317-354. 3. Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio(2015). Neural Machine Translation by Jointly Learning to Align and Translate. Cornell University. 4. Erkam Guresena, Gulgun Kayakutlua, Tugrul U.Daim(2011). Using artificial neural network models in stock market index prediction. Expert Systems With Applications, 2011, Vol.38(8), pp.10389-10397 5. Gary Grudnitski, Larry Osburn (1993). Forecasting S&P and gold futures prices: An application of neural networks. Journal of Futures Markets, September 1993, Vol.13(6), pp.631-643 6. Has¸im Sak, Andrew Senior, Franc¸oise Beaufays (2014). Long Short-Term Memory Recurrent Neural Network Architecturesfor Large Scale Acoustic Modeling. Google, USA. 7. John Alberg, Zachary C. Lipton (2017). Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals. Cornell University. 8. Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Cornell University. 42 9. Lilian M.de Menezes, Nikolay Y.Nikolaev (2006). Forecasting with genetically programmed polynomial neural networks. International Journal of Forecasting, April-June 2006, Vol.22(2), pp.249-265 10. Mark T.Leung,An-Sing Chen, HazemDaouk (2000). Forecasting exchange rates using general regression neural networks. Computers and Operations Research, 2000, Vol.27(11), pp.1093-1110 11. Melike Bildiricia,Ö zgür Ö merErsin (2009). Improving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul Stock Exchange. Expert Systems With Applications, May 2009, Vol.36(4), pp.7355-7362 12. Mona Ebadi Jalal, Monireh Hosseini and Stefan Karlsson (2016) . Forecasting incoming call volumes in call centers with recurrent Neural Networks. Journal of Business Research, November 2016, Vol.69(11), pp.4811-4814 13. Richard A. Feinberg, Ik‐Suk Kim, Leigh Hokama, Ko de Ruyter, Cherie Keen, (2000). Operational determinants of caller satisfaction in the call center. International Journal of Service Industry Management, 01 May 2000, Vol.11(2), pp.131-141 14. Yu, Tiffany Hui-Kuang & Huarng, Kun-Huang(2008). A bivariate fuzzy time series model to forecast the TAIEX. Expert Systems With Applications, 2008, Vol.34(4), pp.2945-2952.
Description:	碩士國立政治大學金融學系 105352021
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G1053520211
Data Type:	thesis
DOI:	10.6814/THE.NCCU.MB.019.2018.F06
Appears in Collections:	[Department of Money and Banking] Theses

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