{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:07:33Z","timestamp":1760710053594,"version":"build-2065373602"},"reference-count":77,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,11,20]],"date-time":"2019-11-20T00:00:00Z","timestamp":1574208000000},"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>Financial markets forecasting represents a challenging task for a series of reasons, such as the irregularity, high fluctuation, noise of the involved data, and the peculiar high unpredictability of the financial domain. Moreover, literature does not offer a proper methodology to systematically identify intrinsic and hyper-parameters, input features, and base algorithms of a forecasting strategy in order to automatically adapt itself to the chosen market. To tackle these issues, this paper introduces a fully automated optimized ensemble approach, where an optimized feature selection process has been combined with an automatic ensemble machine learning strategy, created by a set of classifiers with intrinsic and hyper-parameters learned in each marked under consideration. A series of experiments performed on different real-world futures markets demonstrate the effectiveness of such an approach with regard to both to the Buy and Hold baseline strategy and to several canonical state-of-the-art solutions.<\/jats:p>","DOI":"10.3390\/computation7040067","type":"journal-article","created":{"date-parts":[[2019,11,20]],"date-time":"2019-11-20T11:06:03Z","timestamp":1574247963000},"page":"67","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Holistic Auto-Configurable Ensemble Machine Learning Strategy for Financial Trading"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9481-511X","authenticated-orcid":false,"given":"Salvatore","family":"Carta","sequence":"first","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1916-8041","authenticated-orcid":false,"given":"Andrea","family":"Corriga","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2196-7232","authenticated-orcid":false,"given":"Anselmo","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8646-6183","authenticated-orcid":false,"given":"Diego Reforgiato","family":"Recupero","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1734-0437","authenticated-orcid":false,"given":"Roberto","family":"Saia","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.eswa.2016.02.006","article-title":"Computational Intelligence and Financial Markets: A Survey and Future Directions","volume":"55","author":"Cavalcante","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_2","unstructured":"Preethi, G., and Santhi, B. (2012). Stock market forecasting techniques: A survey. J. Theor. Appl. Inf. Tech., 46."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.eswa.2014.07.040","article-title":"Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques","volume":"42","author":"Patel","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","unstructured":"Nguyen, T.H., and Shirai, K. (2015, January 26\u201331). Topic modeling based sentiment analysis on social media for stock market prediction. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China.","DOI":"10.3115\/v1\/P15-1131"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jocs.2010.12.007","article-title":"Twitter mood predicts the stock market","volume":"2","author":"Bollen","year":"2011","journal-title":"J. Comput. Sci."},{"key":"ref_7","unstructured":"Rao, T., and Srivastava, S. (2012, January 26\u201329). Analyzing stock market movements using twitter sentiment analysis. Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), Istanbul, Turkey."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Carta, S., Corriga, A., Mulas, R., Recupero, D.R., and Saia, R. (2019, January 17\u201319). A Supervised Multi-class Multi-label Word Embeddings Approach for Toxic Comment Classification. Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Vienna, Austria.","DOI":"10.5220\/0008110901050112"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1002\/ijfe.145","article-title":"Importance of technical and fundamental analysis in the European foreign exchange market","volume":"6","author":"Oberlechner","year":"2001","journal-title":"Int. J. Finance Econ."},{"key":"ref_10","first-page":"1","article-title":"Stock-Market \u201cPatterns\u201d In addition, Financial Analysis: Methodological Suggestions","volume":"14","author":"Roberts","year":"1959","journal-title":"J. Finance"},{"key":"ref_11","unstructured":"Weigend, A.S. (1994). Time Series Prediction: Forecasting the Future and Understanding the Past, Addison-Wesley."},{"key":"ref_12","unstructured":"Chatterjee, S., and Hadi, A.S. (2015). Regression Analysis by Example, John Wiley & Sons."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Misra, P., and Siddharth, L. (2017, January 5\u20136). Machine learning and time series: Real world applications. Proceedings of the 2017 IEEE International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India.","DOI":"10.1109\/CCAA.2017.8229832"},{"key":"ref_14","first-page":"263","article-title":"A hybrid forecasting model for stock market prediction","volume":"51","author":"Ince","year":"2017","journal-title":"Econ. Comput. Econ. Cybernetics Stud. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6885","DOI":"10.1016\/j.eswa.2010.03.033","article-title":"A method for automatic stock trading combining technical analysis and nearest neighbor classification","volume":"37","author":"Teixeira","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Upadhyay, V.P., Panwar, S., Merugu, R., and Panchariya, R. (2016, January 12\u201313). Forecasting stock market movements using various kernel functions in support vector machine. Proceedings of the International Conference on Advances in Information Communication Technology & Computing, Bikaner, India.","DOI":"10.1145\/2979779.2979886"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8849","DOI":"10.1016\/j.eswa.2008.11.028","article-title":"Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network","volume":"36","author":"Zhang","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.asoc.2014.12.028","article-title":"A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price","volume":"29","author":"Hafezi","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"51","DOI":"10.4236\/jcc.2018.63004","article-title":"Short-Term Financial Time Series Forecasting Integrating Principal Component Analysis and Independent Component Analysis with Support Vector Regression","volume":"6","author":"Chowdhury","year":"2018","journal-title":"J. Comput. Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6668","DOI":"10.1016\/j.eswa.2008.08.019","article-title":"An empirical methodology for developing stockmarket trading systems using artificial neural networks","volume":"36","author":"Vanstone","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_21","first-page":"40","article-title":"Sortino ratio: A better measure of risk","volume":"1","author":"Rollinger","year":"2013","journal-title":"Futures Mag."},{"key":"ref_22","unstructured":"White, J., and Haghani, V. (2019, November 19). A Brief History of Sharpe Ratio, and Beyond. Available online: https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3077552."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.ribaf.2016.03.007","article-title":"Returns, volatility and investor sentiment: Evidence from European stock markets","volume":"38","author":"Frugier","year":"2016","journal-title":"Res. Int. Bus. Finance"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Saia, R., and Carta, S. (2017, January 24\u201326). Evaluating Credit Card Transactions in the Frequency Domain for a Proactive Fraud Detection Approach. Proceedings of the 14th International Joint Conference on e-Business and Telecommunications (ICETE 2017), Madrid, Spain.","DOI":"10.5220\/0006425803350342"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Saia, R., and Carta, S. (2017, January 24\u201326). A Frequency-domain-based Pattern Mining for Credit Card Fraud Detection. Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security, Porto, Portugal.","DOI":"10.5220\/0006361403860391"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Saia, R. (2017, January 21\u201323). A Discrete Wavelet Transform Approach to Fraud Detection. Proceedings of the 11th International Conference on Network and System Security, Helsinki, Finland.","DOI":"10.1007\/978-3-319-64701-2_34"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Weng, H., Li, Z., Ji, S., Chu, C., Lu, H., Du, T., and He, Q. (2018, January 16\u201319). Online e-commerce fraud: A large-scale detection and analysis. Proceedings of the 2018 IEEE 34th International Conference on Data Engineering (ICDE), Paris, France.","DOI":"10.1109\/ICDE.2018.00162"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.future.2018.10.016","article-title":"Evaluating the benefits of using proactive transformed-domain-based techniques in fraud detection tasks","volume":"93","author":"Saia","year":"2019","journal-title":"Future Generation Comp. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Saia, R., Boratto, L., and Carta, S. (2015, January 12\u201314). Multiple Behavioral Models: A Divide and Conquer Strategy to Fraud Detection in Financial Data Streams. Proceedings of the 2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K), Lisbon, Portugal.","DOI":"10.5220\/0005637104960503"},{"key":"ref_30","unstructured":"Chatfield, C. (2016). The Analysis of Time Series: An Introduction, CRC Press."},{"key":"ref_31","unstructured":"Trippi, R.R., and Turban, E. (1992). Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real World Performance, McGraw-Hill, Inc."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5311","DOI":"10.1016\/j.eswa.2010.10.027","article-title":"Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange","volume":"38","author":"Kara","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wu, Y., Mao, J., and Li, W. (2018, January 22\u201324). Predication of Futures Market by Using Boosting Algorithm. Proceedings of the 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India.","DOI":"10.1109\/WiSPNET.2018.8538586"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"17287","DOI":"10.1109\/ACCESS.2019.2895252","article-title":"A Prediction Approach for Stock Market Volatility Based on Time Series Data","volume":"7","author":"Idrees","year":"2019","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Carta, S., Medda, A., Pili, A., Reforgiato Recupero, D., and Saia, R. (2019). Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data. Future Internet, 11.","DOI":"10.3390\/fi11010005"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Weerathunga, H.P.S.D., and Silva, A.T.P. (2018, January 26\u201329). DRNN-ARIMA Approach to Short-term Trend Forecasting in Forex Market. Proceedings of the 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer), Colombo, Sri Lanka.","DOI":"10.1109\/ICTER.2018.8615580"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3132","DOI":"10.1109\/TII.2018.2794389","article-title":"Forward Forecast of Stock Price Using Sliding-Window Metaheuristic-Optimized Machine-Learning Regression","volume":"14","author":"Chou","year":"2018","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.eswa.2019.01.083","article-title":"Combining Principal Component Analysis, Discrete Wavelet Transform and XGBoost to trade in the financial markets","volume":"125","author":"Nobre","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Gupta, D., Pratama, M., Ma, Z., Li, J., and Prasad, M. (2019). Financial time series forecasting using twin support vector regression. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0211402"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1016\/j.neucom.2015.04.034","article-title":"A new data-driven neural fuzzy system with collaborative fuzzy clustering mechanism","volume":"167","author":"Prasad","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_41","unstructured":"Patel, O.P., Bharill, N., Tiwari, A., and Prasad, M. (2019). A Novel Quantum-inspired Fuzzy Based Neural Network for Data Classification. IEEE Trans. Emerg. Topics Comput., 1\u201314."},{"key":"ref_42","unstructured":"Klir, G.J., and Folger, T.A. (1987). Fuzzy Sets, Uncertainty, and Information, Prentice-Hall, Inc."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.infoecopol.2019.05.002","article-title":"Deep learning in exchange markets","volume":"47","author":"Ribeiro","year":"2019","journal-title":"Inf. Econ. Policy"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.eswa.2018.06.032","article-title":"Forecasting stock market crisis events using deep and statistical machine learning techniques","volume":"112","author":"Chatzis","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1109\/TNNLS.2016.2522401","article-title":"Deep Direct Reinforcement Learning for Financial Signal Representation and Trading","volume":"28","author":"Deng","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Dietterich, T.G. (2000, January 21\u201323). Ensemble Methods in Machine Learning. Proceedings of the International Workshop on Multiple Classifier Systems, Cagliari, Italy.","DOI":"10.1007\/3-540-45014-9_1"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zainal, A., Maarof, M.A., Shamsuddin, S.M.H., and Abraham, A. (2008, January 8\u201310). Ensemble of One-Class Classifiers for Network Intrusion Detection System. Proceedings of the Fourth International Conference on Information Assurance and Security (IAS), Napoli, Italy.","DOI":"10.1109\/IAS.2008.35"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Saia, R., Salvatore, C., and RECUPERO, R. (2018, January 18\u201320). A Probabilistic-driven Ensemble Approach to Perform Event Classification in Intrusion Detection System. Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Seville, Spain.","DOI":"10.5220\/0006893801410148"},{"key":"ref_49","first-page":"13","article-title":"Fraud detection for E-commerce transactions by employing a prudential Multiple Consensus model","volume":"46","author":"Carta","year":"2019","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_50","first-page":"e1249","article-title":"Ensemble learning: A survey","volume":"8","author":"Sagi","year":"2018","journal-title":"Wiley Interdiscip. Rew.: Data Min. Knowl. Discov."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.eneco.2017.12.030","article-title":"A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting","volume":"70","author":"Zhu","year":"2018","journal-title":"Energy Econ."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ratto, A.P., Merello, S., Oneto, L., Ma, Y., Malandri, L., and Cambria, E. (2018, January 18\u201321). Ensemble of Technical Analysis and Machine Learning for Market Trend Prediction. Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India.","DOI":"10.1109\/SSCI.2018.8628795"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.eneco.2018.10.015","article-title":"Interval decomposition ensemble approach for crude oil price forecasting","volume":"76","author":"Sun","year":"2018","journal-title":"Energy Econ."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Gan, K.S., Chin, K.O., Anthony, P., and Chang, S.V. (2018, January 8). Homogeneous Ensemble FeedForward Neural Network in CIMB Stock Price Forecasting. Proceedings of the International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Kota Kinabalu, Malaysia.","DOI":"10.1109\/IICAIET.2018.8638452"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.energy.2018.04.133","article-title":"A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting","volume":"154","author":"Ding","year":"2018","journal-title":"Energy"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Gomes, H.M., Barddal, J.P., Enembreck, F., and Bifet, A. (2017). A Survey on Ensemble Learning for Data Stream Classification. ACM Comput. Surv., 50.","DOI":"10.1145\/3054925"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Choi, S. (2015). Independent component analysis. Encyclopedia of Biometrics, Springer.","DOI":"10.1007\/978-1-4899-7488-4_305"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/S0893-6080(00)00026-5","article-title":"Independent component analysis: Algorithms and applications","volume":"13","author":"Oja","year":"2000","journal-title":"Neural Netw."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1142\/S012906570400208X","article-title":"Advances in blind source separation (BSS) and independent component analysis (ICA) for nonlinear mixtures","volume":"14","author":"Jutten","year":"2004","journal-title":"Int. J. Neural Syst."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0165-1684(91)90079-X","article-title":"Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture","volume":"24","author":"Jutten","year":"1991","journal-title":"Signal Process."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2513","DOI":"10.1016\/j.cor.2004.03.016","article-title":"Forecasting stock market movement direction with support vector machine","volume":"32","author":"Huang","year":"2005","journal-title":"Comput. Operat. Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/j.ijforecast.2017.02.003","article-title":"Predicting recessions with boosted regression trees","volume":"33","author":"Fritsche","year":"2017","journal-title":"Int. J. Forecast."},{"key":"ref_63","unstructured":"Kirkpatrick, C.D., and Dahlquist, J.R. (2010). Technical Analysis: The Complete Resource for Financial Market Technicians, FT Press Science."},{"key":"ref_64","unstructured":"Tomasini, E., and Jaekle, U. (2011). Trading Systems, Harriman House Limited."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1080\/095400996116785","article-title":"On combining artificial neural nets","volume":"8","author":"Sharkey","year":"1996","journal-title":"Connect. Sci."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.inffus.2004.04.003","article-title":"Diversity in search strategies for ensemble feature selection","volume":"6","author":"Tsymbal","year":"2005","journal-title":"Inf. Fusion"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/j.ejor.2006.05.029","article-title":"Improved customer choice predictions using ensemble methods","volume":"181","author":"Potharst","year":"2007","journal-title":"Eur. J. Operat. Res."},{"key":"ref_68","first-page":"99","article-title":"Maximum drawdown","volume":"17","author":"Atiya","year":"2004","journal-title":"Risk Mag."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"299","DOI":"10.2307\/2491479","article-title":"The impact of trading commission incentives on analysts\u2019 stock coverage decisions and earnings forecasts","volume":"36","author":"Hayes","year":"1998","journal-title":"J. Account. Res."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3233\/WEB-170349","article-title":"Directional prediction of stock prices using breaking news on Twitter","volume":"15","author":"Alostad","year":"2017","journal-title":"Web Intell."},{"key":"ref_71","first-page":"84","article-title":"The P\/E Effect on the Croatian Stock Market","volume":"10","author":"Alajbeg","year":"2016","journal-title":"J. Int. Sci. Publ. Econ. Bus."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/0304-405X(86)90053-X","article-title":"A comparison of equity carve-outs and seasoned equity offerings: Share price effects and corporate restructuring","volume":"15","author":"Schipper","year":"1986","journal-title":"J. Financial Econ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.procs.2011.08.038","article-title":"Stock market prediction with multiple regression, fuzzy type-2 clustering and neural networks","volume":"6","author":"Enke","year":"2011","journal-title":"Procedia Comput. Sci."},{"key":"ref_74","unstructured":"Klassen, M. (2005, January 27\u201329). Investigation of Some Technical Indexes in Stock Forecasting Using Neural Networks. Proceedings of the Third World Enformatika Conference, Istanbul, Turkey."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1111\/j.1540-6261.2007.01232.x","article-title":"Giving content to investor sentiment: The role of media in the stock market","volume":"62","author":"Tetlock","year":"2007","journal-title":"J. Finance"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1145\/1327452.1327492","article-title":"MapReduce: Simplified data processing on large clusters","volume":"51","author":"Dean","year":"2008","journal-title":"Commun. ACM"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s11192-016-1945-y","article-title":"MapReduce: Review and open challenges","volume":"109","author":"Hashem","year":"2016","journal-title":"Scientometrics"}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/7\/4\/67\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:36:03Z","timestamp":1760189763000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/7\/4\/67"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,20]]},"references-count":77,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["computation7040067"],"URL":"https:\/\/doi.org\/10.3390\/computation7040067","relation":{},"ISSN":["2079-3197"],"issn-type":[{"type":"electronic","value":"2079-3197"}],"subject":[],"published":{"date-parts":[[2019,11,20]]}}}