{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T04:14:26Z","timestamp":1768796066316,"version":"3.49.0"},"reference-count":9,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2018,9,28]],"date-time":"2018-09-28T00:00:00Z","timestamp":1538092800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,12,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Deep learning is an effective approach to solving image recognition problems. People draw intuitive conclusions from trading charts. This study uses the characteristics of deep learning to train computers in imitating this kind of intuition in the context of trading charts. The main goal of our approach is combining the time-series modeling and convolutional neural networks (CNNs) to build a trading model. We propose three steps to build the trading model. First, we preprocess the input data from quantitative data to images. Second, we use a CNN, which is a type of deep learning, to train our trading model. Third, we evaluate the model\u2019s performance in terms of the accuracy of classification. The experimental results show that if the strategy is clear enough to make the images obviously distinguishable the CNN model can predict the prices of a financial asset. Hence, our approach can help devise trading strategies and help clients automatically obtain personalized trading strategies.<\/jats:p>","DOI":"10.1515\/jisys-2018-0074","type":"journal-article","created":{"date-parts":[[2018,9,28]],"date-time":"2018-09-28T05:04:58Z","timestamp":1538111098000},"page":"941-958","source":"Crossref","is-referenced-by-count":9,"title":["Predict Forex Trend via Convolutional Neural Networks"],"prefix":"10.1515","volume":"29","author":[{"given":"Yun-Cheng","family":"Tsai","sequence":"first","affiliation":[{"name":"Center for General Education, National Taiwan University , Taipei , Taiwan"}]},{"given":"Jun-Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering , National Taiwan University , Taipei , Taiwan"}]},{"given":"Jun-Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering , National Taipei University , New Taipei City , Taiwan"}]}],"member":"374","published-online":{"date-parts":[[2018,9,28]]},"reference":[{"key":"2025120523362769268_j_jisys-2018-0074_ref_001","unstructured":"M. Binkowski, G. Marti and P. Donnat, Autoregressive convolutional neural networks for asynchronous time series, (2017). arXiv preprint arXiv:1703.04122."},{"key":"2025120523362769268_j_jisys-2018-0074_ref_002","unstructured":"A. Borovykh, S. Bohte and C. W. Oosterlee, Conditional time series forecasting with convolutional neural networks, (2017). arXiv preprint arXiv:1703.04691."},{"key":"2025120523362769268_j_jisys-2018-0074_ref_003","doi-asserted-by":"crossref","unstructured":"S. Browne, Optimal investment policies for a firm with a random risk process: exponential utility and minimizing the probability of ruin, Math. Oper. Res. 20 (1995), 937\u2013958.","DOI":"10.1287\/moor.20.4.937"},{"key":"2025120523362769268_j_jisys-2018-0074_ref_004","unstructured":"L. Di Persio and O. Honchar, Artificial neural networks approach to the forecast of stock market price movements, Int. J. Econ. Manag. 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Shreve, Stochastic Calculus for Finance II: Continuous-Time Models, Springer, New York, 2004.","DOI":"10.1007\/978-1-4757-4296-1"},{"key":"2025120523362769268_j_jisys-2018-0074_ref_009","unstructured":"H. Wang, B. Raj and E. P. Xing, On the origin of deep learning, (2017). arXiv preprint arXiv:1702.07800."}],"container-title":["Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/view\/journals\/jisys\/29\/1\/article-p941.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2018-0074\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2018-0074\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T23:38:19Z","timestamp":1764977899000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2018-0074\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,28]]},"references-count":9,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,4,25]]},"published-print":{"date-parts":[[2019,12,18]]}},"alternative-id":["10.1515\/jisys-2018-0074"],"URL":"https:\/\/doi.org\/10.1515\/jisys-2018-0074","relation":{},"ISSN":["2191-026X","0334-1860"],"issn-type":[{"value":"2191-026X","type":"electronic"},{"value":"0334-1860","type":"print"}],"subject":[],"published":{"date-parts":[[2018,9,28]]}}}