Stock Movement Prediction Using Price Factor and Deep Learning

Authors: Hy Dang, Bo Mei

Abstract:

The development of machine learning methods and techniques has opened doors for investigation in many areas such as medicines, economics, finance, etc. One active research area involving machine learning is stock market prediction. This research paper tries to consider multiple techniques and methods for stock movement prediction using historical price or price factors. The paper explores the effectiveness of some deep learning frameworks for forecasting stock. Moreover, an architecture (TimeStock) is proposed which takes the representation of time into account apart from the price information itself. Our model achieves a promising result that shows a potential approach for the stock movement prediction problem.

Keywords: Classification, machine learning, time representation, stock prediction.

References:

[1] E. F. Fama, “Efficient capital markets: A review of theory and empirical work,” The journal of Finance, vol. 25, no. 2, pp. 383–417, 1970.
[2] B. G. Malkiel, A random walk down Wall Street: including a life-cycle guide to personal investing. WW Norton & Company, 1999.
[3] I. R. Renu and P. Christie, “Fundamental analysis versus technical analysis–a comparative review,” International Journal of Recent Scientific Research, vol. 9, no. 1, pp. 23 009–23 013, 2018.
[4] Y. Xu and S. B. Cohen, “Stock movement prediction from tweets and historical prices,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018, pp. 1970–1979.
[5] S. M. Kazemi, R. Goel, S. Eghbali, J. Ramanan, J. Sahota, S. Thakur, S. Wu, C. Smyth, P. Poupart, and M. Brubaker, “Time2vec: Learning a vector representation of time,” arXiv preprint arXiv:1907.05321, 2019.
[6] W. N. Goetzmann, S. J. Brown, M. J. Gruber, and E. J. Elton, “Modern portfolio theory and investment analysis,” John Wiley & Sons, vol. 237, 2014.
[7] F. Black and M. Scholes, “The pricing of options and corporate liabilities,” in World Scientific Reference on Contingent Claims Analysis in Corporate Finance: Volume 1: Foundations of CCA and Equity Valuation. World Scientific, 2019, pp. 3–21.
[8] M. Waqar, H. Dawood, P. Guo, M. B. Shahnawaz, and M. A. Ghazanfar, “Prediction of stock market by principal component analysis,” in 2017 13th International Conference on Computational Intelligence and Security (CIS). IEEE, 2017, pp. 599–602.
[9] G. P. Zhang, “Time series forecasting using a hybrid arima and neural network model,” Neurocomputing, vol. 50, pp. 159–175, 2003.
[10] P. Ghosh, A. Neufeld, and J. K. Sahoo, “Forecasting directional movements of stock prices for intraday trading using lstm and random forests,” arXiv preprint arXiv:2004.10178, 2020.
[11] J. Qiu, B. Wang, and C. Zhou, “Forecasting stock prices with long-short term memory neural network based on attention mechanism,” PloS one, vol. 15, no. 1, p. e0227222, 2020.
[12] W. Lu, J. Li, Y. Li, A. Sun, and J. Wang, “A cnn-lstm-based model to forecast stock prices,” Complexity, vol. 2020, 2020.
[13] J. Kalyani, P. Bharathi, P. Jyothi et al., “Stock trend prediction using news sentiment analysis,” arXiv preprint arXiv:1607.01958, 2016.
[14] R. Sawhney, S. Agarwal, A. Wadhwa, and R. R. Shah, “Deep attentive learning for stock movement prediction from social media text and company correlations,” 2020.
[15] B. Xie, R. Passonneau, L. Wu, and G. G. Creamer, “Semantic frames to predict stock price movement,” in Proceedings of the 51st annual meeting of the association for computational linguistics, 2013, pp. 873–883.
[16] F. Gers, “Long short-term memory in recurrent neural networks,” Ph.D. dissertation, Verlag nicht ermittelbar, 2001.
[17] K. Cho, B. Van Merri¨enboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078, 2014.
[18] D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473, 2014.
[19] R. G. Brown, Smoothing, forecasting and prediction of discrete time series. Courier Corporation, 2004.
[20] T. H. Nguyen and K. Shirai, “Topic modeling based sentiment analysis on social media for stock market prediction,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2015, pp. 1354–1364.