{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T17:38:07Z","timestamp":1772041087946,"version":"3.50.1"},"reference-count":31,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2018]]},"DOI":"10.1587\/transinf.2016iip0016","type":"journal-article","created":{"date-parts":[[2018,3,31]],"date-time":"2018-03-31T22:29:38Z","timestamp":1522535378000},"page":"901-908","source":"Crossref","is-referenced-by-count":50,"title":["Stock Price Prediction by Deep Neural Generative Model of News Articles"],"prefix":"10.1587","volume":"E101.D","author":[{"given":"Takashi","family":"MATSUBARA","sequence":"first","affiliation":[{"name":"Graduate School of System Informatics Science, Kobe University"}]},{"given":"Ryo","family":"AKITA","sequence":"additional","affiliation":[{"name":"Graduate School of System Informatics Science, Kobe University"}]},{"given":"Kuniaki","family":"UEHARA","sequence":"additional","affiliation":[{"name":"Graduate School of System Informatics Science, Kobe University"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] F. Black and M. Scholes, \u201cThe pricing of options and corporate liabilities,\u201d Journal of Political Economy, vol.81, no.3, pp.637-654, May 1973. 10.1086\/260062","DOI":"10.1086\/260062"},{"key":"2","unstructured":"[2] J.J. Murphy, Technical analysis of the financial markets: A comprehensive guide to trading methods and applications, Prentice Hall Press, 1999."},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] F.E.H. Tay and L. Cao, \u201cApplication of support vector machines in financial time series forecasting,\u201d Omega, vol.29, no.4, pp.309-317, 2001. 10.1016\/s0305-0483(01)00026-3","DOI":"10.1016\/S0305-0483(01)00026-3"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] K.-j. Kim, \u201cFinancial time series forecasting using support vector machines,\u201d Neurocomputing, vol.55, no.1-2, pp.307-319, 2003. 10.1016\/s0925-2312(03)00372-2","DOI":"10.1016\/S0925-2312(03)00372-2"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] M.R. Hassan, B. Nath, and M. Kirley, \u201cA fusion model of HMM, ANN and GA for stock market forecasting,\u201d Expert Systems with Applications, vol.33, no.1, pp.171-180, 2007. 10.1016\/j.eswa.2006.04.007","DOI":"10.1016\/j.eswa.2006.04.007"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] R.C. Cavalcante, R.C. Brasileiro, V.L.F. Souza, J.P. Nobrega, and A.L. Oliveira, \u201cComputational intelligence and financial markets: A survey and future directions,\u201d Expert Systems with Applications, vol.55, pp.194-211, 2016. 10.1016\/j.eswa.2016.02.006","DOI":"10.1016\/j.eswa.2016.02.006"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] E.F. Fama, \u201cThe behavior of stock-market prices,\u201d Journal of Business, vol.38, no.1, pp.34-105, 1965. 10.1086\/294743","DOI":"10.1086\/294743"},{"key":"8","unstructured":"[8] V. Lavrenko, M. Schmill, D. Lawrie, P. Ogilvie, D. Jensen, and J. Allan, \u201cMining of concurrent text and time series,\u201d KDD-2000 Workshop on Text Mining, pp.37-44, 2000."},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] R.P. Schumaker and H. Chen, \u201cTextual analysis of stock market prediction using financial news articles,\u201d 12th Americas Conference on Information Systems (AMCIS), vol.27, no.2, pp.1-29, 2006.","DOI":"10.1145\/1462198.1462204"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] R. Luss and A. D'Aspremont, \u201cPredicting abnormal returns from news using text classification,\u201d Quantitative Finance, vol.15, no.6, pp.999-1012, March 2012. 10.1080\/14697688.2012.672762","DOI":"10.1080\/14697688.2012.672762"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] A. Yoshihara, K. Fujikawa, K. Seki, and K. Uehara, \u201cPredicting stock market trends by recurrent deep neural networks,\u201d Proc. 13th Pacific Rim International Conference on Artificial Intelligence, pp.759-769, 2014. 10.1007\/978-3-319-13560-1_60","DOI":"10.1007\/978-3-319-13560-1_60"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] X. Ding, Y. Zhang, T. Liu, and J. Duan, \u201cUsing structured events to predict stock price movement: An empirical investigation,\u201d Proc. 2014 Conference on Empirical Methods in Natural Language Processing, pp.1415-1425, 2014. 10.3115\/v1\/d14-1148","DOI":"10.3115\/v1\/D14-1148"},{"key":"13","unstructured":"[13] X. Ding, Y. Zhang, T. Liu, and J. Duan, \u201cDeep learning for event-driven stock prediction,\u201d Proc. 25th International Joint Conference on Artificial Intelligence, pp.2327-2333, 2015."},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] R. Akita, A. Yoshihara, T. Matsubara, and K. Uehara, \u201cDeep learning for stock prediction using numerical and textual information,\u201d Proc. IEEE\/ACIS 15th International Conference on Computer and Information Science, 2016. 10.1109\/icis.2016.7550882","DOI":"10.1109\/ICIS.2016.7550882"},{"key":"15","unstructured":"[15] D.P. Kingma and M. Welling, \u201cAuto-encoding variational Bayes,\u201d International Conference on Learning Representations, pp.1-14, 2014."},{"key":"16","unstructured":"[16] D.P. Kingma, D.J. Rezende, and M. Welling, \u201cSemi-supervised learning with deep generative models,\u201d Advances in Neural Information Processing Systems, pp.1-9, 2014."},{"key":"17","unstructured":"[17] K. Sohn, H. Lee, and X. Yan, \u201cLearning structured output representation using deep conditional generative models,\u201d Advances in Neural Information Processing Systems, pp.3483-3491, 2015."},{"key":"18","unstructured":"[18] T. Mikolov, G. Corrado, K. Chen, and J. Dean, \u201cEfficient estimation of word representations in vector space,\u201d Proc. International Conference on Learning Representations, pp.1-12, 2013."},{"key":"19","unstructured":"[19] T. Mikolov, K. Chen, G. Corrado, and J. Dean, \u201cDistributed representations of words and phrases and their compositionality,\u201d Advances in Neural Information Processing Systems, vol.1, pp.3111-3119, Oct. 2013."},{"key":"20","unstructured":"[20] J.L. Ba, J.R. Kiros, and G.E. Hinton, \u201cLayer normalization,\u201d arXiv, 2016."},{"key":"21","unstructured":"[21] V. Nair and G.E. Hinton, \u201cRectified linear units improve restricted Boltzmann machines,\u201d Proc. 27th International Conference on Machine Learning, no.3, pp.807-814, 2010."},{"key":"22","unstructured":"[22] Y. Burda, R. Grosse, and R. Salakhutdinov, \u201cImportance weighted autoencoders,\u201d International Conference on Learning Representations, pp.1-12, 2015."},{"key":"23","unstructured":"[23] D. Kingma and J. Ba, \u201cAdam: A method for stochastic optimization,\u201d International Conference on Learning Representations, Dec. 2015."},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] K. Radinsky, S. Davidovich, and S. Markovitch, \u201cLearning causality for news events prediction,\u201d Proc. 21st International Conference on World Wide Web (WWW2012), pp.909-918, 2012. 10.1145\/2187836.2187958","DOI":"10.1145\/2187836.2187958"},{"key":"25","unstructured":"[25] T. Kudo, \u201cMeCab: Yet another part-of-speech and morphological analyzer,\u201d url: http:\/\/mecab.sourceforge.net\/"},{"key":"26","unstructured":"[26] S&P 500 dataset, url: http:\/\/ir.hit.edu.cn\/xding\/index_english.htm"},{"key":"27","unstructured":"[27] Reuters, url: http:\/\/www.reuters.com\/"},{"key":"28","unstructured":"[28] Bloomberg, url: http:\/\/www.bloomberg.com"},{"key":"29","unstructured":"[29] J.E. Moody and M. Saffell, \u201cReinforcement learning for trading,\u201d Advances in Neural Information Processing Systems 11, no.1998, pp.917-923, 1999."},{"key":"30","doi-asserted-by":"publisher","unstructured":"[30] Y. Deng, F. Bao, Y. Kong, Z. Ren, and Q. Dai, \u201cDeep direct reinforcement learning for financial signal representation and trading,\u201d IEEE Trans. Neural Netw. Learn. Syst., vol.28, no.3, pp.653-664, 2017. 10.1109\/tnnls.2016.2522401","DOI":"10.1109\/TNNLS.2016.2522401"},{"key":"31","unstructured":"[31] D.M. Blei and J.D. McAuliffe, \u201cSupervised topic models,\u201d Advances in Neural Information Processing Systems 20, vol.21, no.1, pp.121-128, 2008."}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E101.D\/4\/E101.D_2016IIP0016\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,13]],"date-time":"2019-10-13T21:33:29Z","timestamp":1571002409000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E101.D\/4\/E101.D_2016IIP0016\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":31,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2018]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2016iip0016","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]}}}