Stock Movement Prediction Using Price Factor and Deep Learning
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:80726", author = "Hy Dang and Bo Mei", title = "Stock Movement Prediction Using Price Factor and Deep Learning", 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.", volume = "16", number = "3", pages = "73-4", }
