{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:25:15Z","timestamp":1777695915205,"version":"3.51.4"},"reference-count":10,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2020,9,30]]},"abstract":"<jats:p>Researches on using deep learning models to predict prices usually take magnitude-based error measurements (such as R2) to measure the quality of learning models. Whether the forecasted prices for the models with the lowest error measurement can produce the most profit in actual trading is an issue with little research. In this study, we first find the parameter sets of LSTM and TCN models with low magnitude-based error and then use program trading to find out their profitability. The relationships between these profitability and error measurements are analyzed and studied on three commodities: gold, soybean, and crude oil (from GLOBEX). Our findings are: with given parameter sets, if merchandise (gold and soybean) is of low averaged magnitude error, then its profitability is more stable. If it is of a more significant magnitude error (crude oil), then its profitability is unstable. A high positive correlation does not exist between the profitability and error measurement, and TCN outperforms LSTM in almost all our examples. Our research indicates that, in assessing the performance of deep learning, how to use the predicted values in applications and the application results could also be part of the quality measurement for the model assessment in the learning.<\/jats:p>","DOI":"10.3233\/ida-194739","type":"journal-article","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T13:34:24Z","timestamp":1601645664000},"page":"1161-1173","source":"Crossref","is-referenced-by-count":0,"title":["On the profitability and errors of predicted prices from deep learning via program trading"],"prefix":"10.1177","volume":"24","author":[{"given":"Lichen","family":"Tai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chihcheng","family":"Hsu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-194739_ref2","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/0925-2312(90)90013-H","article-title":"Stock price prediction using neural networks: A project report","volume":"2","author":"Schoneburg","year":"1990","journal-title":"Neurocomputing"},{"key":"10.3233\/IDA-194739_ref3","first-page":"15","article-title":"Stupid data miner tricks: Overfitting the S&P 500","volume":"16","author":"Leinweber","year":"2007","journal-title":"The Journal of Investing Spring 2007"},{"key":"10.3233\/IDA-194739_ref4","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Proceedings of Nature"},{"key":"10.3233\/IDA-194739_ref5","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: A search space odyssey","volume":"28","author":"Greff","year":"2017","journal-title":"Proceedings of IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.3233\/IDA-194739_ref6","unstructured":"H. Li, Y. Shen and Y. Zhu, Stock Price Prediction Using Attention-based Multi-Input LSTM, in: Proceedings of The 10th Asian Conference on Machine Learning (PMLR 95), 2018, pp. 454\u2013469."},{"key":"10.3233\/IDA-194739_ref7","doi-asserted-by":"crossref","unstructured":"J. Walter, H. Ritter and K. Schulten, Non-linear Prediction with Self-organizing Maps, in: IJCNN International Joint Conference on Neural Networks, 1990, pp. 17\u201321.","DOI":"10.1109\/IJCNN.1990.137632"},{"key":"10.3233\/IDA-194739_ref10","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural nerworks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Networks"},{"key":"10.3233\/IDA-194739_ref11","unstructured":"A. Krizhevsky, I. Sutskever and G.E. Hinton, ImageNet Classification with Deep Convolutional Neural Network, in: Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS\u201912), Vol. 1, 2012, pp.\u00a01097\u20131105."},{"key":"10.3233\/IDA-194739_ref12","unstructured":"S. Hochreiter, G. Klambauer, T. Unterthiner and A. Mayr, Self-normalizing neural networks, Proceedings of NIPS\u201917 Advances in Neural Information Processing Systems 30 (2017)."},{"issue":"4","key":"10.3233\/IDA-194739_ref14","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.ijforecast.2006.03.001","article-title":"Another look at measures of forecast accuracy","volume":"22","author":"Hyndman","year":"2006","journal-title":"Proceedings of International Journal of Forecasting"}],"container-title":["Intelligent Data Analysis"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDA-194739","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:18:47Z","timestamp":1777454327000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDA-194739"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,30]]},"references-count":10,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.3233\/ida-194739","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"value":"1088-467X","type":"print"},{"value":"1571-4128","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,30]]}}}