{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T06:01:45Z","timestamp":1771567305569,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,1,3]],"date-time":"2019-01-03T00:00:00Z","timestamp":1546473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Stock market prediction and trading has attracted the effort of many researchers in several scientific areas because it is a challenging task due to the high complexity of the market. More investors put their effort to the development of a systematic approach, i.e., the so called \u201cTrading System (TS)\u201d for stocks pricing and trend prediction. The introduction of the Trading On-Line (TOL) has significantly improved the overall number of daily transactions on the stock market with the consequent increasing of the market complexity and liquidity. One of the most main consequence of the TOL is the \u201cautomatic trading\u201d, i.e., an ad-hoc algorithmic robot able to automatically analyze a lot of financial data with target to open\/close several trading operations in such reduced time for increasing the profitability of the trading system. When the number of such automatic operations increase significantly, the trading approach is known as High Frequency Trading (HFT). In this context, recently, the usage of machine learning has improved the robustness of the trading systems including HFT sector. The authors propose an innovative approach based on usage of ad-hoc machine learning approach, starting from historical data analysis, is able to perform careful stock price prediction. The stock price prediction accuracy is further improved by using adaptive correction based on the hypothesis that stock price formation is regulated by Markov stochastic propriety. The validation results applied to such shares and financial instruments confirms the robustness and effectiveness of the proposed automatic trading algorithm.<\/jats:p>","DOI":"10.3390\/computation7010004","type":"journal-article","created":{"date-parts":[[2019,1,3]],"date-time":"2019-01-03T11:11:56Z","timestamp":1546513916000},"page":"4","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Advanced Markov-Based Machine Learning Framework for Making Adaptive Trading System"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1766-3065","authenticated-orcid":false,"given":"Francesco","family":"Rundo","sequence":"first","affiliation":[{"name":"STMicroelectronics ADG\u2014Central R&amp;D, 95121 Catania, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2524-3837","authenticated-orcid":false,"given":"Francesca","family":"Trenta","sequence":"additional","affiliation":[{"name":"IPLAB\u2014Department of Mathematics and Computer Science, University of Catania, 95121 Catania, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Agatino Luigi","family":"Di Stallo","sequence":"additional","affiliation":[{"name":"GIURIMATICA Lab, Department of Applied Mathematics and LawTech; 97100 Ragusa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6127-2470","authenticated-orcid":false,"given":"Sebastiano","family":"Battiato","sequence":"additional","affiliation":[{"name":"IPLAB\u2014Department of Mathematics and Computer Science, University of Catania, 95121 Catania, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/S0957-4174(00)00027-0","article-title":"Genetic Algorithms Approach to Feature Discretization in Artificial Neural Networks for the prediction of Stock Price Index","volume":"19","author":"Kim","year":"2000","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kimoto, T., Asakawa, K., and Yoda, M. (1990, January 17\u201321). Stock Market Prediction System with Modular Neural Networks. Proceedings of the 1990 IJCNN International Joint Conference on Neural Networks, San Diego, CA, USA.","DOI":"10.1109\/IJCNN.1990.137535"},{"key":"ref_3","unstructured":"Wang, J.-H., and Leu, J.-Y. (1996, January 3\u20136). Stock market trend prediction using ARIMA-based neural networks. Proceedings of the International Conference on Neural Networks (ICNN\u201996), Washington, DC, USA."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/2251-712X-9-1","article-title":"Forecasting s&p 500 index using artificial neural networks and design of experiments","volume":"9","author":"Niaki","year":"2013","journal-title":"J. Ind. Eng. Int."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Nelson, D.M.Q., Pereira, A.C.M., and de Oliveira, R.A. (2017, January 14\u201319). Stock market\u2019s price movement prediction with LSTM neural networks. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966019"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Shen, Y., Zhang, G., Song, Y., and Zhu, Y. (2017, January 24\u201326). Short-term Prediction for Opening Price of Stock Market Based on Self-adapting Variant PSO-Elman Neural Network. Proceedings of the 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China.","DOI":"10.1109\/ICSESS.2017.8342901"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kumar, I., Dogra, K., Utreja, C., and Yadav, P. (2018, January 20\u201321). A Comparative study of supervised machine learning algorithms for stock market trend prediction. Proceedings of the 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India.","DOI":"10.1109\/ICICCT.2018.8473214"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1109\/MC.2011.323","article-title":"Twitter mood as a stock market predictor","volume":"44","author":"Bollen","year":"2011","journal-title":"Computer"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1109\/72.963769","article-title":"LSTM Recurrent Neural Networks Learn Simple Context Free and Context Sensitive Languages","volume":"12","author":"Gers","year":"2001","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_10","unstructured":"Diebold, F.X. (2007). Elements of Forecasting, Thomson Learning. [4th ed.]."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1111\/j.2517-6161.1976.tb01585.x","article-title":"Forecasting Transformed Series","volume":"38","author":"Granger","year":"2001","journal-title":"J. R. Stat. Soc."},{"key":"ref_12","first-page":"115","article-title":"Learning precise timing with LSTM recurrent networks","volume":"3","author":"Gers","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Savvopoulos, A., Kanavos, A., Mylonas, P., and Sioutas, S. (2018). LSTM Accelerator for Convolutional Object Identification. Algorithms, 11.","DOI":"10.3390\/a11100157"},{"key":"ref_14","unstructured":"(2018, October 24). Yahoo Finance. Available online: https:\/\/it.finance.yahoo.com\/."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Somani, P., Talele, S., and Sawant, S. (2014, January 20\u201321). Stock Market Prediction Using Hidden Markov Model. Proceedings of the 7th Joint International Information Technology and Artificial Intelligence Conference, Chongqing, China.","DOI":"10.1109\/ITAIC.2014.7065011"},{"key":"ref_16","unstructured":"(2018, October 24). Keras Documentation. Available online: https:\/\/keras.io\/."},{"key":"ref_17","unstructured":"(2018, October 24). Tensorflow.org. Available online: https:\/\/www.tensorflow.org\/."},{"key":"ref_18","unstructured":"Pang, X., Zhou, Y., Wang, P., Lin, W., and Chang, V. (2018). An innovative neural network approach for stock market prediction. J. Supercomput., 1\u201321."},{"key":"ref_19","unstructured":"(2018, December 17). Investopedia. Available online: https:\/\/www.investopedia.com\/terms\/d\/drawdown.asp."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Reid, D., Hussain, A.J., and Tawfik, H. (2013, January 4\u20139). Spiking Neural Networks for Financial Data Prediction. Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, USA.","DOI":"10.1109\/IJCNN.2013.6707140"},{"key":"ref_21","unstructured":"(2018, December 17). Artificial Intelligence (AI)\u2014STMicroelectronics. Available online: https:\/\/www.st.com\/content\/st_com\/en\/about\/innovation---technology\/artificial-intelligence.html."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rundo, F., Conoci, S., Ortis, A., and Battiato, S. (2018). An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment. Sensors, 18.","DOI":"10.3390\/s18020405"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rundo, F., Ortis, A., Battiato, S., and Conoci, S. (2018). Advanced Bio-Inspired System for Noninvasive Cuff-Less Blood Pressure Estimation from Physiological Signal Analysis. Computation, 6.","DOI":"10.3390\/computation6030046"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1049\/iet-cvi.2018.5195","article-title":"Evaluation of Levenberg\u2013Marquardt neural networks and stacked autoencoders clustering for skin lesion analysis, screening and follow-up","volume":"12","author":"Rundo","year":"2018","journal-title":"IET Comput. Vis."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ortis, A., Farinella, G.M., Torrisi, G., and Battiato, S. (2018, January 4\u20136). Visual Sentiment Analysis Based on Objective Text Description of Images. Proceedings of the International Conference on Content-Based Multimedia Indexing (CBMI), La Rochelle, France.","DOI":"10.1109\/CBMI.2018.8516481"}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/7\/1\/4\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:23:21Z","timestamp":1760185401000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/7\/1\/4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,3]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["computation7010004"],"URL":"https:\/\/doi.org\/10.3390\/computation7010004","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,3]]}}}