政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/118244

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/118244

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政大機構典藏 > 商學院 > 金融學系 > 學位論文 > Item 140.119/118244

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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:	2018-07-03 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.
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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
DOI:	10.6814/THE.NCCU.MB.005.2018.F06
顯示於類別:	[金融學系] 學位論文

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