{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T17:28:17Z","timestamp":1761845297257,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T00:00:00Z","timestamp":1604966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Strategic Research Council (SRC) at the Academy of Finland - Manufacturing 4.0 \u2013project","award":["313396"],"award-info":[{"award-number":["313396"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>This study investigated the performance of a trading agent based on a convolutional neural network model in portfolio management. The results showed that with real-world data the agent could produce relevant trading results, while the agent\u2019s behavior corresponded to that of a high-risk taker. The data used were wide in comparison with earlier reported research and was based on the full set of the S&amp;P 500 stock data for twenty-one years supplemented with selected financial ratios. The results presented are new in terms of the size of the data set used and with regards to the model used. The results provide direction and offer insight into how deep learning methods may be used in constructing automatic trading systems.<\/jats:p>","DOI":"10.3390\/axioms9040130","type":"journal-article","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T10:47:28Z","timestamp":1605005248000},"page":"130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Deep Reinforcement Learning Agent for S&amp;P 500 Stock Selection"],"prefix":"10.3390","volume":"9","author":[{"given":"Tommi","family":"Huotari","sequence":"first","affiliation":[{"name":"School of Business and Management, LUT University, 53850 Lappeenranta, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9721-642X","authenticated-orcid":false,"given":"Jyrki","family":"Savolainen","sequence":"additional","affiliation":[{"name":"School of Business and Management, LUT University, 53850 Lappeenranta, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2976-2829","authenticated-orcid":false,"given":"Mikael","family":"Collan","sequence":"additional","affiliation":[{"name":"School of Business and Management, LUT University, 53850 Lappeenranta, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,10]]},"reference":[{"key":"ref_1","first-page":"77","article-title":"Portfolio selection","volume":"7","author":"Markowitz","year":"1952","journal-title":"J. Financ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1086\/296468","article-title":"Mutual fund performance: An analysis of quarterly portfolio holdings","volume":"62","author":"Grinblatt","year":"1989","journal-title":"J. Bus."},{"key":"ref_3","first-page":"74","article-title":"Post-Modern Portfolio Theory","volume":"18","author":"Kasten","year":"2005","journal-title":"J. Financ. Plan."},{"key":"ref_4","first-page":"19","article-title":"Abnormal Returns to a Fundamental Analysis Strategy","volume":"73","author":"Abarbanell","year":"1998","journal-title":"Account. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1111\/j.1540-6261.1994.tb04424.x","article-title":"Market Statistics and Technical Analysis: The Role of Volume","volume":"49","author":"Blume","year":"1994","journal-title":"J. Financ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1111\/j.1540-6261.1993.tb04702.x","article-title":"Returns to buying winners and selling losers: Implications for stock market efficiency","volume":"48","author":"Titman","year":"1993","journal-title":"J. Financ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/0304-405X(81)90018-0","article-title":"The relationship between return and market value of common stocks","volume":"9","author":"Banz","year":"1981","journal-title":"J. Financ. Econ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/0304-405X(83)90031-4","article-title":"The Relationship between Earnings Yield, Market Value and Return for NYSE Common Stocks: Further Evidence","volume":"12","author":"Basu","year":"1983","journal-title":"J. Financ. Econ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/0304-405X(88)90020-7","article-title":"Dividend yields and expected stock returns","volume":"22","author":"Fama","year":"1988","journal-title":"J. Financ. Econ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Schwert, G.W.W. (2002). Anomalies and Market Efficiency. SSRN Electron. J.","DOI":"10.2139\/ssrn.338080"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1016\/j.eswa.2007.05.019","article-title":"Using neural network ensembles for bankruptcy prediction and credit scoring","volume":"34","author":"Tsai","year":"2008","journal-title":"Expert Syst. Appl."},{"key":"ref_12","unstructured":"Philip, K., and Chan, S.J.S. (2000, January 11\u201313). Toward scalable learning with non-uniform class and cost distributions: A case study in credit card fraud detection. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, Manchester, UK."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2019\/7816154","article-title":"An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy, Mathematical problems in engineering","volume":"2019","author":"Lv","year":"2019","journal-title":"Math. Probl. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ta, V.-D., Liu, C.-M., and Addis, D. (2018, January 6\u20137). Prediction and portfolio optimization in quantitative trading using machine learning techniques. Proceedings of the 9th International Symposium of Information and Communication Technology (SolICT), Da Nang, Vietnam.","DOI":"10.1145\/3287921.3287963"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.procs.2019.01.256","article-title":"Stock Market Prediction Based on Generative Adversarial Network","volume":"147","author":"Zhang","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/j.ejor.2016.10.031","article-title":"Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500","volume":"259","author":"Krauss","year":"2017","journal-title":"Eur. J. Oper. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1109\/72.935097","article-title":"Learning to Trade via Direct Reinforcement","volume":"12","author":"Moody","year":"2001","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1109\/TSMCA.2007.904825","article-title":"A multiagent approach to Q-learning for daily stock trading","volume":"37","author":"Lee","year":"2007","journal-title":"IEEE Trans. Syst. Man Cybern. Part A Syst. Hum."},{"key":"ref_19","unstructured":"Jiang, Z., Xu, D., and Liang, J. (2017). A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. arXiv."},{"key":"ref_20","unstructured":"Liang, Z., Hao, C., Junhao, Z., Kangkang, J., and Yanran, L. (2018). Adversial Deep Reinforcement Learning in Portfolio Management. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1080\/14697688.2019.1683599","article-title":"Index tracking through deep latent representation learning","volume":"20","author":"Kim","year":"2020","journal-title":"Quant. Financ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lee, J., and Kang, J. (2020). Effectively training neural networks for stock index prediction: Predicting the S&P 500 index without using its index data. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0230635"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C. (2018). Neural Networks and Deep Learning, Springer International Publishing.","DOI":"10.1007\/978-3-319-94463-0"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1002\/(SICI)1099-131X(1998090)17:5\/6<441::AID-FOR707>3.0.CO;2-#","article-title":"Performance functions and reinforcement learning for trading systems and portfolios","volume":"17","author":"Moody","year":"1998","journal-title":"J. Forecast."},{"key":"ref_25","unstructured":"Lu, D.W. (2017). Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.eswa.2017.06.023","article-title":"An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown","volume":"87","author":"Almahdi","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_27","unstructured":"Ding, X., Zhang, Y., Liu, T., and Duan, J. (2015, January 25\u201331). Deep learning for event-driven stock prediction. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"112891","DOI":"10.1016\/j.eswa.2019.112891","article-title":"Continuous control with stacked deep dynamic recurrent reinforcement learning for portfolio optimization","volume":"140","author":"Aboussalah","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_29","unstructured":"Pereira, F., Burges, C.J.C., Bottou, L., and Weinberger, K.Q. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25, Curran Associates, Inc."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kalchbrenner, N., Grefenstette, E., and Blunsom, P. (2014). A convolutional neural network for modelling sentences. arXiv.","DOI":"10.3115\/v1\/P14-1062"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A.-R., and Hinton, G. (2013, January 26\u201331). Speech recognition with deep recurrent neural networks. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Institute of Electrical and Electronics Engineers (IEEE), Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1023\/A:1010884214864","article-title":"Noisy Time Series Prediction using Recurrent Neural Networks and Grammatical Inference","volume":"44","author":"Giles","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_33","unstructured":"Sutton, R., and Barto, A. (2018). Reinforcement Learning: An Introduction, MIT Press."},{"key":"ref_34","unstructured":"Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013). Playing atari with deep reinforcement learning. arXiv."},{"key":"ref_35","unstructured":"Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D. (2015). Continuous control with deep reinforcement learning. arXiv."},{"key":"ref_36","first-page":"484","article-title":"Mastering the game of Go with deep neural networks and tree search","volume":"529","author":"Silver","year":"2016","journal-title":"Nat. Cell Biol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1090\/S0002-9904-1954-09848-8","article-title":"The Theory of Dynamic Programming","volume":"60","author":"Bellman","year":"1954","journal-title":"Bull. Am. Math. 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