Stock Trading Classifier with Multichannel Convolutional Neural Network

  • Davi Nascimento Centro Universitário FEI
  • Anna Costa Universidade de São Paulo
  • Reinaldo Bianchi Centro Universitario FEI

Resumo


Stock market forecasting has been a quite popular challenge in machine learning research. Recently, studies have been using deep learning techniques, such as Convolutional Neural Networks (CNN), to perform regression on the prices or classification on trading signal as an operation indication. However, they did not reach a satisfactory financial result. In this work we aim to design a financially profitable stock market method by proposing a novel approach called Multichannel CNN Trading Classifier (MCNN-TC). The model was evaluated using data from the Brazilian stock market. The results indicate a satisfactory financial trading performance compared to the Buy and Hold strategy and good classification metrics.

Palavras-chave: convolutional neural networks, deep learning, stock market

Referências

Bao, W., Yue, J., and Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one, 12(7):e0180944.

Boureau, Y.-L., Ponce, J., and LeCun, Y. (2010). A theoretical analysis of feature pooling in visual recognition. In Proceedings of the 27th International Conference on Machine Learning (ICML-10), pages 111–118.

Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P., and Oliveira, A. L. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55:194–211.

Colby, R. W. and Meyers, T. A. (1988). The encyclopedia of technical market indicators. Dow Jones-Irwin Homewood, IL.

Fischer, T. and Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2):654–669.

Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press.

Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580.

Hoseinzade, E. and Haratizadeh, S. (2019). Cnnpred: Cnn-based stock market prediction using a diverse set of variables. Expert Systems with Applications, 129:273–285.

Jain, S., Gupta, R., and Moghe, A. A. (2018). Stock price prediction on daily stock data using deep neural networks. In 2018 International Conference on Advanced Computation and Telecommunication (ICACAT), pages 1–13. IEEE.

Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.

Krauss, C., Do, X. A., and Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the s&p 500. European Journal of Operational Research, 259(2):689–702.

LeCun, Y., Boser, B. E., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W. E., and Jackel, L. D. (1990). Handwritten digit recognition with a back-propagation network. In Advances in Neural Information Processing Systems, pages 396–404.

Malkiel, B. G. and Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2):383–417.

Mittal, A. and Goel, A. (2012). Stock prediction using twitter sentiment analysis. Standford University, CS229 (http://cs229.stanford.edu/proj2011/GoelMittalStockMarketPredictionUsingTwitterSentimentAnalysis.pdf).

Selvin, S., Vinayakumar, R., Gopalakrishnan, E., Menon, V. K., and Soman, K. (2017). Stock price prediction using lstm, rnn and cnn-sliding window model. In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pages 1643–1647. IEEE.

Sezer, O. B. and Ozbayoglu, A. M. (2018). Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing, 70:525–538.

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1):1929–1958.

Tsai, Y.-C., Chen, J.-H., and Wang, C.-C. (2019). Encoding candlesticks as images for patterns classification using convolutional neural networks. arXiv preprint arXiv:1901.05237.

Vargas, M. R., De Lima, B. S., and Evsukoff, A. G. (2017). Deep learning for stock market prediction from financial news articles. In 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pages 60–65. IEEE.
Publicado
20/10/2020
Como Citar

Selecione um Formato
NASCIMENTO, Davi; COSTA, Anna; BIANCHI, Reinaldo. Stock Trading Classifier with Multichannel Convolutional Neural Network. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 282-293. DOI: https://doi.org/10.5753/eniac.2020.12136.