Abstract
This research aims to create an investment recommendation system based on the extraction of buy/sell signals from the results of technical analysis and prediction. In this case it focuses on the Spanish continuous market. As part of this research, different techniques have been studied for data extraction and analysis. After having reviewed the work related to the initial idea of the research, it is shown the development carried out, together with the data extraction and the machine learning algorithms for prediction used. The calculation of technical analysis metrics is also included. The development of a visualization platform has been proposed for high-level interaction between the user and the recommendation system. The result is a platform that provides a user interface for both data visualization, analysis, prediction and investment recommendation. The platform’s objective is not only to be usable and intuitive, but also to enable any user, whether an expert or not in the stock market, to abstract their own conclusions from the data and evaluate the information analyzed by the system.
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References
Arora, N.: Financial Analysis: Stock Market Prediction Using Deep Learning Algorithms (2019)
Atsalakis, G.S., Valavanis, K.P.: Surveying stock market forecasting techniques–Part II: soft computing methods. Expert Syst. Appl. 36(3), 5932–5941 (2009)
Corchado, J.M., Lees, B.: A hybrid case-based model for forecasting. Appl. Artif. Intell. 15(2), 105–127 (2001)
Coria, J.A.G., Castellanos-Garzón, J.A., Corchado, J.M.: Intelligent business processes composition based on multi-agent systems. Expert Syst. Appl. 41(4), 1189–1205 (2014)
Dang, N.C., De la Prieta, F., Corchado, J.M., Moreno, M.N.: Framework for retrieving relevant contents related to fashion from online social network data. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 335–347. Springer, Cham, June 2016
Dash, R., Dash, P.K.: A hybrid stock trading framework integrating technical analysis with machine learning techniques. J. Fin. Data Sci. 2(1), 42–57 (2016)
Carneiro, D., Araújo, D., Pimenta, A., Novais, P.: Real time analytics for characterizing the computer user’s state. ADCAIJ: Adv. Distrib. Comput. Artif. Intell. J. 5(4), 1–18 (2016). ISSN 2255-2863
Edwards, R.D., Magee, J., Bassetti, W.C.: Technical Analysis of Stock Trends. CRC Press, Boca Raton (2018)
Fdez-Riverola, F., Iglesias, E.L., Díaz, F., Méndez, J.R., Corchado, J.M.: Applying lazy learning algorithms to tackle concept drift in spam filtering. Expert Syst. Appl. 33(1), 36–48 (2007)
Fernández-Riverola, F., Diaz, F., Corchado, J.M.: Reducing the memory size of a fuzzy case-based reasoning system applying rough set techniques. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 37(1), 138–146 (2006)
Frikha, M., Mhiri, M., Gargouri, F.: A semantic social recommender system using ontologies based approach for Tunisian tourism (2015)
Fyfe, C., Corchado, J.M.: Automating the construction of CBR systems using kernel methods. Int. J. Intell. Syst. 16(4), 571–586 (2001)
Glez-Bedia, M., Corchado, J.M., Corchado, E.S., Fyfe, C.: Analytical model for constructing deliberative agents. Eng. Intell. Syst. Electr. Eng. Commun. 10(3), 173–185 (2002)
Urbano, J., Cardoso, H.L., Rocha, A.P., Oliveira, E.: Trust and normative control in multi-agent systems. ADCAIJ: Adv. Distrib. Comput. Artif. Intell. J. 1(1) (2012). ISSN 2255-2863
Khaidem, L., Saha, S., Dey, S.R.: Predicting the direction of stock market prices using random forest. arXiv preprint arXiv:1605.00003 (2016)
Morente-Molinera, J.A., Kou, G., González-Crespo, R., Corchado, J.M., Herrera-Viedma, E.: Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods. Knowl. Based Syst. 137, 54–64 (2017)
Nair, B.B., Mohandas, V.P.: An intelligent recommender system for stock trading. Intell. Decis. Technol. 9(3), 243–269 (2015)
Nicoletti, B., Nicoletti, W.: Future of FinTech. Palgrave Macmillan, Basingstoke (2017)
Patel, J., Shah, S., Thakkar, P., Kotecha, K.: Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Syst. Appl. 42(1), 259–268 (2015)
Pawlewski, P., Golinska, P., Dossou, P.-E.: Application potential of agent based simulation and discrete event simulation in enterprise integration modelling concepts. ADCAIJ: Adv. Distrib. Comput. Artif. Intell. J. 1(1) (2012). ISSN 2255-2863
Pimprikar, R., Ramachandran, S., Senthilkumar, K.: Use of machine learning algorithms and twitter sentiment analysis for stock market prediction. Int. J. Pure Appl. Math. 115(6), 521–526 (2017)
Pudaruth, S., Moheeputh, S., Permessur, N., Chamroo, A.: Sentiment analysis from Facebook comments using automatic coding in NVivo 11. ADCAIJ: Adv. Distrib. Comput. Artif. Intell. J. 7(1), 41–48 (2018)
Soni, S.: Applications of ANNs in stock market prediction: a survey. Int. J. Comput. Sci. Eng. Technol. 2(3), 71–83 (2011)
Taghavi, M., Bakhtiyari, K., Scavino, E.: Agent-based computational investing recommender system. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 455–458, October 2013
Yoo, P.D., Kim, M.H., Jan, T.: Machine learning techniques and use of event information for stock market prediction: a survey and evaluation. In: International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC 2006), vol. 2, pp. 835–841. IEEE, November 2005
Zhou, F., Qun, Z., Sornette, D., Jiang, L.: Cascading Logistic Regression Onto Gradient Boosted Decision Trees to Predict Stock Market Changes Using Technical Analysis (2018)
Acknowledgments
This research has been supported by the project “Intelligent and sustainable mobility supported by multi-agent systems and edge computing (InEDGEMobility): Towards Sustainable Intelligent Mobility: Blockchain-based framework for IoT Security”, Reference: RTI2018-095390-B-C32, financed by the Spanish Ministry of Science, Innovation and Universities (MCIU), the State Research Agency (AEI) and the European Regional Development Fund (FEDER).
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Hernández-Nieves, E., Bartolomé del Canto, Á., Chamoso-Santos, P., de la Prieta-Pintado, F., Corchado-Rodríguez, J.M. (2021). A Machine Learning Platform for Stock Investment Recommendation Systems. In: Dong, Y., Herrera-Viedma, E., Matsui, K., Omatsu, S., González Briones, A., Rodríguez González, S. (eds) Distributed Computing and Artificial Intelligence, 17th International Conference. DCAI 2020. Advances in Intelligent Systems and Computing, vol 1237. Springer, Cham. https://doi.org/10.1007/978-3-030-53036-5_33
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