Market Movement Prediction Algorithm Selection by Metalearning

• A. V. P. M. Bandeira Universidade de São Paulo
• G. M. Ferracioli Universidade de São Paulo
• M. R. dos Santos Universidade de São Paulo
• A. C. P. L. F. de Carvalho Universidade de São Paulo

DOI: https://doi.org/10.5753/kdmile.2022.227947

Resumo

The prediction of market price movement is an essential tool for decision-making in trading scenarios. However, there are several candidate methods for this task. Metalearning can be an important ally for the automatic selection of methods, which can be machine learning algorithms for classification tasks, named here classification algorithms. In this work, we present an empirical evaluation of the metalearning application for the classification algorithms selection in the market movement prediction task. Different setups and metrics were evaluated for the meta-target selection. Cumulative return was the metric that achieved the best meta and base-level results. According to the experimental results, metalearning was a competitive selection strategy for predicting market price movement.

Referências

Alcobaça, E., Siqueira, F., Rivolli, A., Garcia, L. P. F., Oliva, J. T., and de Carvalho, A. C. P. L. F.Mfe: Towards reproducible meta-feature extraction. Journal of Machine Learning Research 21 (111): 1–5, 2020.

Brazdil, P., Carrier, C. G., Soares, C., and Vilalta, R. Metalearning: Applications to data mining. SpringerScience & Business Media, 2008.

Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. Classification and regression trees. Routledge,2017.

Charkha, P. R. Stock price prediction and trend prediction using neural networks. In 2008 First InternationalConference on Emerging Trends in Engineering and Technology. IEEE, pp. 592–594, 2008.

Chen, T. and Guestrin, C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkddinternational conference on knowledge discovery and data mining. pp. 785–794, 2016.

Cover, T. and Hart, P. Nearest neighbor pattern classification. IEEE transactions on information theory 13 (1):21–27, 1967.

Cox, D. R. The regression analysis of binary sequences. Journal of the Royal Statistical Society: Series B (Method-ological) 20 (2): 215–232, 1958.

Dong, S., Wang, J., Luo, H., Wang, H., and Wu, F.-X. A dynamic predictor selection algorithm for predictingstock market movement. Expert Systems with Applications vol. 186, pp. 115836, 2021.

Dunn, O. J. Multiple comparisons among means. Journal of the American statistical association 56 (293): 52–64,1961.

Freund, Y. and Schapire, R. E. A decision-theoretic generalization of on-line learning and an application to boosting.Journal of computer and system sciences 55 (1): 119–139, 1997.

Haugen, R. A. and Baker, N. L. The efficient market inefficiency of capitalization–weighted stock portfolios. Thejournal of portfolio management 17 (3): 35–40, 1991.

Ho, T. K. Random decision forests. In Proceedings of 3rd international conference on document analysis andrecognition. Vol. 1. IEEE, pp. 278–282, 1995.

Huang, C.-J., Yang, D.-X., and Chuang, Y.-T. Application of wrapper approach and composite classifier to thestock trend prediction. Expert Systems with Applications 34 (4): 2870–2878, 2008.

Jordan, M. I. and Mitchell, T. M. Machine learning: Trends, perspectives, and prospects. Science 349 (6245):255–260, 2015.

Lee, M.-C. Using support vector machine with a hybrid feature selection method to the stock trend prediction. ExpertSystems with Applications 36 (8): 10896–10904, 2009.

Lin, Y., Guo, H., and Hu, J. An svm-based approach for stock market trend prediction. In The 2013 internationaljoint conference on neural networks (IJCNN). IEEE, pp. 1–7, 2013.

Lubba, C. H., Sethi, S. S., Knaute, P., Schultz, S. R., Fulcher, B. D., and Jones, N. S. catch22: Canonicaltime-series characteristics. Data Mining and Knowledge Discovery 33 (6): 1821–1852, 2019.

Mann, H. B. and Whitney, D. R. On a test of whether one of two random variables is stochastically larger thanthe other. The annals of mathematical statistics, 1947.

McCallum, A., Nigam, K., et al. A comparison of event models for naive bayes text classification. In AAAI-98workshop on learning for text categorization. Vol. 752. Citeseer, pp. 41–48, 1998.

Ni, L.-P., Ni, Z.-W., and Gao, Y.-Z. Stock trend prediction based on fractal feature selection and support vectormachine. Expert Systems with Applications 38 (5): 5569–5576, 2011.

Pillai, K. S. Some new test criteria in multivariate analysis. The Annals of Mathematical Statistics, 1955.

Prudêncio, R. B. and Ludermir, T. B. Meta-learning approaches to selecting time series models. Neurocomput-ing vol. 61, pp. 121–137, 2004.

Tsaih, R., Hsu, Y., and Lai, C. C. Forecasting s&p 500 stock index futures with a hybrid ai system. Decisionsupport systems 23 (2): 161–174, 1998.

Vapnik, V. Statistical Learning Theory. NY: Wiley, 1998.

Wen, M., Li, P., Zhang, L., and Chen, Y. Stock market trend prediction using high-order information of timeseries. Ieee Access vol. 7, pp. 28299–28308, 2019.

Zhang, J., Cui, S., Xu, Y., Li, Q., and Li, T. A novel data-driven stock price trend prediction system. ExpertSystems with Applications vol. 97, pp. 60–69, 2018.