Logo PTI Logo rice

Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering

Annals of Computer Science and Information Systems, Volume 33

Comparative Study of Deep Learning Models for Predicting Stock Prices

, , , ,

DOI: http://dx.doi.org/10.15439/2022R02

Citation: Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 103–108 ()

Full text

Abstract. The stock market is volatile,dynamic, and nonlinear. Hence, predicting the stock prices has been a challenging task for any researcher in time seriesforecasting. Accurately predicting stock prices has been a hot topic for both financial and technical researchers. In this paper, we deploy six deep learning models (i.e., MLP, CNN, RNN, LSTM, GRU, and AE) to predict the closing price, one day ahead, of 20 different companies (i.e. 5 groups of 4) in the S\\&P 500 markets over the 7-years range (Jan 2015 - August 2022). The experimental results do not provide interesting insights, but also help us to deepen our understanding of how to use deep learning models in financial markets.

References

  1. J. Hur, M. Raj, and Y. E. Riyanto, “Finance and trade: A cross-country empirical analysis on the impact of financial development and asset tangibility on international trade,” World Development, vol. 34, no. 10, pp. 1728–1741, 2006. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0305750X06001148
  2. L. Li, Y. Wu, Y. Ou, Q. Li, Y. Zhou, and D. Chen, “Research on machine learning algorithms and feature extraction for time series,” in 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017, pp. 1–5.
  3. “Forecasting directional movements of stock prices for intraday trading using lstm and random forests,” Finance Research Letters, vol. 46, p. 102280, 2022.
  4. R. Xiong, E. P. Nichols, and Y. Shen, “Deep learning stock volatility with google domestic trends,” 2015. [Online]. Available: https://arxiv.org/abs/1512.04916
  5. R. Engle, “Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation,” Econometrica, vol. 50, no. 4, pp. 987–1007, 1982. [Online]. Available: https://EconPapers.repec.org/RePEc:ecm:emetrp:v:50:y:1982:i:4:p:987-1007
  6. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, p. 1735–1780, nov 1997. [Online]. Available: https://doi.org/10.1162/neco.1997.9.8.1735
  7. T. Fischer and C. Krauss, “Deep learning with long short-term memory networks for financial market predictions,” European Journal of Operational Research, vol. 270, no. 2, pp. 654–669, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0377221717310652
  8. P. Yu and X. Yan, “Stock price prediction based on deep neural networks,” Neural Comput. Appl., vol. 32, no. 6, pp. 1609–1628, 2020. [Online]. Available: https://doi.org/10.1007/s00521-019-04212-x
  9. D. Karmiani, R. Kazi, A. Nambisan, A. Shah, and V. Kamble, “Comparison of predictive algorithms: Backpropagation, svm, lstm and kalman filter for stock market,” in 2019 Amity International Conference on Artificial Intelligence (AICAI), 2019, pp. 228–234.
  10. P. Gao, R. Zhang, and X. Yang, “The application of stock index price prediction with neural network,” Mathematical and Computational Applications, vol. 25, no. 3, 2020. [Online]. Available: https://www.mdpi.com/2297-8747/25/3/53
  11. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, F. Pereira, C. Burges, L. Bottou, and K. Weinberger, Eds. Curran Associates, Inc.
  12. J. Schmidhuber, “Deep learning in neural networks: An overview,” CoRR, vol. abs/1404.7828, 2014. [Online]. Available: http://arxiv.org/abs/1404.7828
  13. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning Repre sentations by Back-Propagating Errors. Cambridge, MA, USA: MIT Press, 1988, p. 696–699.
  14. I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” CoRR, vol. abs/1409.3215, 2014. [Online]. Available: http://arxiv.org/abs/1409.3215
  15. A. Graves and J. Schmidhuber, “Framewise phoneme classification with bidirectional lstm and other neural network architectures,” Neural Networks, vol. 18, no. 5, pp. 602–610, 2005, iJCNN 2005. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0893608005001206
  16. P. Malhotra, L. Vig, G. M. Shroff, and P. Agarwal, “Long short term memory networks for anomaly detection in time series,” in ESANN, 2015.
  17. H. Hewamalage, C. Bergmeir, and K. Bandara, “Recurrent neural networks for time series forecasting: Current status and future directions,” International Journal of Forecasting, vol. 37, no. 1, pp. 388–427, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0169207020300996
  18. A. Graves, “Supervised sequence labelling with recurrent neural networks,” Ph.D. dissertation, Technical University Munich, 2008. [Online]. Available: https://d-nb.info/99115827X
  19. K. Cho, B. van Merrienboer, D. Bahdanau, and Y. Bengio, “On the properties of neural machine translation: Encoder-decoder approaches,” CoRR, vol. abs/1409.1259, 2014. [Online]. Available: http://arxiv.org/abs/1409.1259
  20. J. Chung, Ç. Gülçehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” CoRR, vol. abs/1412.3555, 2014. [Online]. Available: http://arxiv.org/abs/1412.3555
  21. M. A. Kramer, “Nonlinear principal component analysis using autoas sociative neural networks,” AIChE journal, vol. 37, no. 2, pp. 233–243, 1991.