{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:47:46Z","timestamp":1767340066915,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T00:00:00Z","timestamp":1605657600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Traditional portfolio theory divides stocks into different categories using indicators such as industry, market value, and liquidity, and then selects representative stocks according to them. In this paper, we propose a novel portfolio learning approach based on deep learning and apply it to China\u2019s stock market. Specifically, this method is based on the similarity of deep features extracted from candlestick charts. First, we obtained whole stock information from Tushare, a professional financial data interface. These raw time series data are then plotted into candlestick charts to make an image dataset for studying the stock market. Next, the method extracts high-dimensional features from candlestick charts through an autoencoder. After that, K-means is used to cluster these high-dimensional features. Finally, we choose one stock from each category according to the Sharpe ratio and a low-risk, high-return portfolio is obtained. Extensive experiments are conducted on stocks in the Chinese stock market for evaluation. The results demonstrate that the proposed portfolio outperforms the market\u2019s leading funds and the Shanghai Stock Exchange Composite Index (SSE Index) in a number of metrics.<\/jats:p>","DOI":"10.3390\/fi12110202","type":"journal-article","created":{"date-parts":[[2020,11,19]],"date-time":"2020-11-19T06:23:52Z","timestamp":1605767032000},"page":"202","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Portfolio Learning Based on Deep Learning"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4825-5988","authenticated-orcid":false,"given":"Wei","family":"Pan","sequence":"first","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"}]},{"given":"Jide","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7243-2783","authenticated-orcid":false,"given":"Xiaoqiang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,18]]},"reference":[{"key":"ref_1","unstructured":"Chan, E. (2009). Quantitative Trading: How to Build Your Own Algorithmic Trading Business, John Wiley & Sons."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/S0927-538X(03)00048-9","article-title":"Behavioral finance","volume":"11","author":"Ritter","year":"2003","journal-title":"Pac. Basin Financ. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"49","DOI":"10.3905\/jpm.1994.409501","article-title":"The sharpe ratio","volume":"21","author":"Sharpe","year":"1994","journal-title":"J. Portf. Manag."},{"key":"ref_4","first-page":"77","article-title":"Portfolio selection","volume":"7","author":"Markowitz","year":"1952","journal-title":"J. Financ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Masci, J., Meier, U., Cire\u015fan, D., and Schmidhuber, J. (2011, January 14\u201317). Stacked convolutional auto-encoders for hierarchical feature extraction. Proceedings of the International Conference on Artificial Neural Networks, Espoo, Finland.","DOI":"10.1007\/978-3-642-21735-7_7"},{"key":"ref_6","unstructured":"Donahue, J., Kr\u00e4henb\u00fchl, P., and Darrell, T. (2016). Adversarial feature learning. arXiv."},{"key":"ref_7","unstructured":"Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Cheng, Q., and Chen, G. (2016, January 19\u201324). Deep speech 2: End-to-end speech recognition in english and mandarin. Proceedings of the International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.ejor.2017.11.054","article-title":"Deep learning with long short-term memory networks for financial market predictions","volume":"270","author":"Fischer","year":"2018","journal-title":"Eur. J. Oper. Res."},{"key":"ref_10","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":"Jegadeesh","year":"1993","journal-title":"J. Financ."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"Akita, R., Yoshihara, A., Matsubara, T., and Uehara, K. (2016, January 26\u201329). Deep learning for stock prediction using numerical and textual information. Proceedings of the 2016 IEEE\/ACIS 15th International Conference on Computer and Information Science (ICIS), Okayama, Japan.","DOI":"10.1109\/ICIS.2016.7550882"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Loreggia, A., Malitsky, Y., Samulowitz, H., and Saraswat, V. (2016, January 12\u201317). Deep learning for algorithm portfolios. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.10170"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1145\/274440.274442","article-title":"Competitive solutions for online financial problems","volume":"30","year":"1998","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Borodin, A., El-Yaniv, R., and Gogan, V. (2000). On the competitive theory and practice of portfolio selection. Latin American Symposium on Theoretical Informatics, Springer.","DOI":"10.1007\/10719839_19"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eswa.2018.02.032","article-title":"Trading financial indices with reinforcement learning agents","volume":"103","author":"Pendharkar","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, Y., Tang, K., Wu, J., and Xiong, Z. (2019, January 4\u20138). Alphastock: A buying-winners-and-selling-losers investment strategy using interpretable deep reinforcement attention networks. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330647"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1587\/transinf.2016IIP0016","article-title":"Stock price prediction by deep neural generative model of news articles","volume":"101","author":"Matsubara","year":"2018","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3905\/jfds.2019.1.015","article-title":"Enhancing time-series momentum strategies using deep neural networks","volume":"1","author":"Lim","year":"2019","journal-title":"J. Financ. Data Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nevmyvaka, Y., Feng, Y., and Kearns, M. (2006, January 25\u201329). Reinforcement learning for optimized trade execution. Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA.","DOI":"10.1145\/1143844.1143929"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vargas, M.R., De Lima, B.S., and Evsukoff, A.G. (2017, January 26\u201328). Deep learning for stock market prediction from financial news articles. Proceedings of the 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), Annecy, France.","DOI":"10.1109\/CIVEMSA.2017.7995302"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Huang, Y., Capretz, L.F., and Ho, D. (2019, January 5\u20138). Neural Network Models for Stock Selection Based on Fundamental Analysis. Proceedings of the 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), Edmonton, AB, Canada.","DOI":"10.1109\/CCECE.2019.8861550"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"18569","DOI":"10.1007\/s11042-016-4159-7","article-title":"Stock prediction using deep learning","volume":"76","author":"Singh","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.eswa.2017.04.030","article-title":"Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies","volume":"83","author":"Chong","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_25","unstructured":"MacQueen, J. (1967, January 1). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA."},{"key":"ref_26","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_27","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201323). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/12\/11\/202\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:34:03Z","timestamp":1760178843000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/12\/11\/202"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,18]]},"references-count":30,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["fi12110202"],"URL":"https:\/\/doi.org\/10.3390\/fi12110202","relation":{},"ISSN":["1999-5903"],"issn-type":[{"type":"electronic","value":"1999-5903"}],"subject":[],"published":{"date-parts":[[2020,11,18]]}}}