research-article

Stock Movement Classification from Twitter via Mogrifier Based Memory Cells with Attention Mechanism

Author:

Tian QinAuthors Info & Claims

ICITEE '20: Proceedings of the 3rd International Conference on Information Technologies and Electrical Engineering

Pages 74 - 80

https://doi.org/10.1145/3452940.3452954

Published: 17 May 2021 Publication History

Abstract

A growing number of financial market participants use the deep learning models to predict the future market movement via exploiting the news and social media messages. Memory cells like long short-term memory (LSTM) and gated recurrent unit (GRU), though play indispensable roles in sequence prediction, are criticized for lacking the interaction between inputs and previous context. The Mogrifier LSTM cell is designed to enhance such relationship. We employ this novel cell in our hybrid attention prediction network. The new structure enriches the communication between the previous message embedding and the current signal, hence a better learning process representation compared with the neural network without such mechanism. Experiment on real-world stock market data shows the Mogrifier based cell can outperform the venerable ones across different market sectors while still achieves lower prediction volatility.

References

[1]

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383--417.

[2]

Jung, J., & Shiller, R. J. (2005). Samuelson's dictum and the stock market. Economic Inquiry, 43(2), 221--228.

[3]

A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing

[4]

Wu, Y., Zhang, S., Zhang, Y., Bengio, Y., & Salakhutdinov, R. R. (2016). On multiplicative integration with recurrent neural networks. In Advances in neural information processing systems (pp. 2856--2864).

[5]

Jia, R., & Liang, P. (2017). Adversarial examples for evaluating reading comprehension systems. arXiv preprint arXiv:1707.07328.

[6]

Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.

[7]

Kihoro, J., Otieno, R. O., & Wafula, C. (2004). Seasonal time series forecasting: A comparative study of ARIMA and ANN models.

[8]

Baillie, R. T. (1980). Predictions from ARMAX models. Journal of Econometrics, 12(3), 365--374.

[9]

Golmohammadi, K., Zaiane, O. R., & Díaz, D. (2014, November). Detecting stock market manipulation using supervised learning algorithms. In 2014 International Conference on Data Science and Advanced Analytics (DSAA) (pp. 435--441). IEEE.

[10]

Basak, S., Kar, S., Saha, S., Khaidem, L., & Dey, S. R. (2019). Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 47, 552--567.

[11]

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985). Learning internal representations by error propagation (No. ICS-8506). California Univ San Diego La Jolla Inst for Cognitive Science.

[12]

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735--1780.

[13]

Chung, J., Gulcehre, C, Cho, K., & Bengio, Y. (2015, June). Gated feedback recurrent neural networks. In International conference on machine learning (pp. 2067--2075).

[14]

Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2017, September). Stock price prediction using LSTM, RNN and CNN-sliding window model. In 2017 international conference on advances in computing, communications and informatics (icacci) (pp. 1643--1647). IEEE.

[15]

Sim, H. S., Kim, H. I, & Ahn, J. J. (2019). Is deep learning for image recognition applicable to stock market prediction?. Complexity, 2019.

[16]

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv: 1301.3781.

[17]

Pennington, J., Socher, R., & Manning, C. D. (2014, October). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532--1543).

[18]

Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.

[19]

Shih, S. Y., Sun, F. K., & Lee, H. Y. (2019). Temporal pattern attention for multivariate time series forecasting. Machine Learning, 108(8-9), 1421--1441.

Digital Library

[20]

Hu, Z., Liu, W., Bian, J., Liu, X., & Liu, T. Y. (2018, February). Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction. In Proceedings of the eleventh ACM international conference on web search and data mining (pp. 261--269).

Digital Library

[21]

Melis, G., Kočiský, T., & Blunsom, P. (2019). Mogrifier lstm. arXiv preprint arXiv: 1909 01792.

[22]

Xu, Y., & Cohen, S. B. (2018, July). 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) (pp. 1970--1979).

Cited By

Wang JWang SLv MJiang H(2024)Forecasting VaR and ES by using deep quantile regression, GANs-based scenario generation, and heterogeneous market hypothesisFinancial Innovation10.1186/s40854-023-00564-510:1Online publication date: 7-Jan-2024
https://doi.org/10.1186/s40854-023-00564-5
Kong PSun BYang HHuang X(2024)Nonferrous metal price forecasting based on signal decomposition and ensemble learningJournal of Process Control10.1016/j.jprocont.2023.103146133(103146)Online publication date: Jan-2024
https://doi.org/10.1016/j.jprocont.2023.103146

Index Terms

Stock Movement Classification from Twitter via Mogrifier Based Memory Cells with Attention Mechanism
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

Recommendations

Analysis of look back period for stock price prediction with RNN variants: A case study on banking sector of NEPSE
Abstract
Stock market prediction is an attempt of determining the future value of a stock traded on a stock exchange. Stock market investors try to predict the stock’s future price to make trading decisions such that optimum profit can be earned. Deep ...
A New Model for Stock Price Movements Prediction Using Deep Neural Network
SoICT '17: Proceedings of the 8th International Symposium on Information and Communication Technology

In this paper, we introduce a new prediction model depend on Bidirectional Gated Recurrent Unit (BGRU). Our predictive model relies on both online financial news and historical stock prices data to predict the stock movements in the future. Experimental ...
Forecasting multistep daily stock prices for long-term investment decisions: A study of deep learning models on global indices
Abstract
Deep machine learning algorithms play an important role in facilitating the development of predictive models for the stock market. However, most studies focus on predicting next-day stock prices or movements, limiting the usability of the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICITEE '20: Proceedings of the 3rd International Conference on Information Technologies and Electrical Engineering

December 2020

687 pages

ISBN:9781450388665

DOI:10.1145/3452940

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 May 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICITEE2020

ICITEE2020: The 3rd International Conference on Information Technologies and Electrical Engineering

December 3 - 5, 2020

Hunan, Changde City, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
53
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)0

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wang JWang SLv MJiang H(2024)Forecasting VaR and ES by using deep quantile regression, GANs-based scenario generation, and heterogeneous market hypothesisFinancial Innovation10.1186/s40854-023-00564-510:1Online publication date: 7-Jan-2024
https://doi.org/10.1186/s40854-023-00564-5
Kong PSun BYang HHuang X(2024)Nonferrous metal price forecasting based on signal decomposition and ensemble learningJournal of Process Control10.1016/j.jprocont.2023.103146133(103146)Online publication date: Jan-2024
https://doi.org/10.1016/j.jprocont.2023.103146

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten