More Web Proxy on the site http://driver.im/

research-article

Public Access

A Tensor-Based Information Framework for Predicting the Stock Market

Authors:

Hsinchun ChenAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 34, Issue 2

Article No.: 11, Pages 1 - 30

https://doi.org/10.1145/2838731

Published: 08 February 2016 Publication History

Abstract

To study the influence of information on the behavior of stock markets, a common strategy in previous studies has been to concatenate the features of various information sources into one compound feature vector, a procedure that makes it more difficult to distinguish the effects of different information sources. We maintain that capturing the intrinsic relations among multiple information sources is important for predicting stock trends. The challenge lies in modeling the complex space of various sources and types of information and studying the effects of this information on stock market behavior. For this purpose, we introduce a tensor-based information framework to predict stock movements. Specifically, our framework models the complex investor information environment with tensors. A global dimensionality-reduction algorithm is used to capture the links among various information sources in a tensor, and a sequence of tensors is used to represent information gathered over time. Finally, a tensor-based predictive model to forecast stock movements, which is in essence a high-order tensor regression learning problem, is presented. Experiments performed on an entire year of data for China Securities Index stocks demonstrate that a trading system based on our framework outperforms the classic Top-N trading strategy and two state-of-the-art media-aware trading algorithms.

References

[1]

Christopher Avery and Peter Zemsky. 1998. Multidimensional uncertainty and herd behavior in financial markets. American Economic Review (1998), 724--748.

[2]

Ricardo Baeza-Yates and Berthier Ribeiro-Neto. 1999. Modern information retrieval. Addison Wesley Longman Publisher (1999), 41--44.

Digital Library

[3]

Richard Bellman. 1956. Dynamic programming and lagrange multipliers. In Proceedings of the National Academy of Sciences of the United States of America, Vol. 42. National Academy of Sciences, 767.

[4]

Richard Ernest Bellman and Stuart E. Dreyfus. 1962. Applied Dynamic Programming. Rand Corporation.

[5]

Johan Bollen, Huina Mao, and Xiaojun Zeng. 2011. Twitter mood predicts the stock market. Journal of Computational Science 2, 1 (2011), 1--8.

[6]

Deng Cai, Xiaofei He, Ji-Rong Wen, Jiawei Han, and Wei-Ying Ma. 2006. Support tensor machines for text categorization. Report No. UIUCDCS-R-2006-2714 (2006).

[7]

Bryan Catanzaro, Narayanan Sundaram, and Kurt Keutzer. 2008. Fast support vector machine training and classification on graphics processors. In Proceedings of the 25th International Conference on Machine Learning. ACM, 104--111.

Digital Library

[8]

Wesley S. Chan. 2003. Stock price reaction to news and no-news: Drift and reversal after headlines. Journal of Financial Economics 70, 2 (2003), 223--260.

[9]

Yin-Wong Cheung and Lilian K. Ng. 1992. Stock price dynamics and firm size: An empirical investigation. Journal of Finance 47, 5 (1992), 1985--1997.

[10]

Fan R. K. Chung. 1997. Spectral Graph Theory. Vol. 92. American Mathematical Society.

Digital Library

[11]

Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Machine Learning 20, 3 (1995), 273--297.

[12]

Rita Cucchiara, Costantino Grana, Massimo Piccardi, and Andrea Prati. 2003. Detecting moving objects, ghosts, and shadows in video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 10 (2003), 1337--1342.

Digital Library

[13]

Patricia M. Dechow. 1994. Accounting earnings and cash flows as measures of firm performance: The role of accounting accruals. Journal of Accounting and Economics 18, 1 (1994), 3--42.

[14]

Robert D. Edwards, John Magee, and W. H. C. Bassetti. 2012. Technical Analysis of Stock Trends. CRC Press.

[15]

Eugene F. Fama. 1965. The behavior of stock-market prices. Journal of Business 38, 1 (1965), 34--105.

[16]

Eugene F. Fama and Kenneth R. French. 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, 1 (1993), 3--56.

[17]

Anthony Fleury, Michel Vacher, and Norbert Noury. 2010. SVM-based multimodal classification of activities of daily living in health smart homes: Sensors, algorithms, and first experimental results. IEEE Transactions on Information Technology in Biomedicine 14, 2 (2010), 274--283.

Digital Library

[18]

Jennifer Francis, J. Douglas Hanna, and Donna R. Philbrick. 1997. Management communications with securities analysts. Journal of Accounting and Economics 24, 3 (1997), 363--394.

[19]

Murray Z. Frank and Werner Antweiler. 2004. Is all that talk just noise? The information content of internet stock message boards. Journal of Finance 59, 3 (2004), 1259--1294.

[20]

Gyozo Gidofalvi. 2001. Using news articles to predict stock price movements. Department of Computer Science and Engineering, University of California, San Diego (2001).

[21]

Eric Gilbert and Karrie Karahalios. 2010. Widespread worry and the stock market. In Proceedings of the 4th International AAAI Conference on Weblogs and Social Media. 1--8.

[22]

Navendu Jain, Mike Dahlin, and Renu Tewari. 2005. Using bloom filters to refine web search results. In Proceedings of the 8th International Workshop on the Web and Databases (WebDB). ACM, 25--30.

[23]

Narasimhan Jegadeesh and Sheridan Titman. 1993. Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance 48, 1 (1993), 65--91.

[24]

Tamara G. Kolda and Brett W. Bader. 2009. Tensor decompositions and applications. SIAM Review 51, 3 (2009), 455--500.

Digital Library

[25]

Victor Lavrenko, Matt Schmill, Dawn Lawrie, Paul Ogilvie, David Jensen, and James Allan. 2000. Language models for financial news recommendation. In Proceedings of the 9th International Conference on Information and Knowledge Management (CIKM). ACM, 389--396.

Digital Library

[26]

Blake LeBaron, W. Brian Arthur, and Richard Palmer. 1999. Time series properties of an artificial stock market. Journal of Economic Dynamics & Control 23, 9--10 (1999), 1487--1516.

[27]

Qing Li, Jia Wang, Yuanzhu Peter Chen, and Zhangxi Lin. 2010. User comments for news recommendation in forum-based social media. Information Sciences 180 (2010), 4929--4939.

Digital Library

[28]

Qing Li, Tiejun Wang, Qixu Gong, Yuanzhu Chen, Zhangxi Lin, and Sa-kwang Song. 2014a. Media-aware quantitative trading based on public web information. Decision Support Systems 61 (2014), 93--105.

[29]

Qing Li, TieJun Wang, Ping Li, Ling Liu, Qixu Gong, and Yuanzhu Chen. 2014b. The effect of news and public mood on stock movements. Information Sciences 278 (2014), 826--840.

[30]

Andrew W. Lo and Archie Craig MacKinlay. 1988. Stock market prices do not follow random walks: Evidence from a simple specification test. Review of Financial Studies 1, 1 (1988), 41--66.

[31]

James Bradford De Long, Andrei Shleifer, Lawrence Henry Summers, and Robert James Waldmann. 1990. Noise trader risk in financial markets. Journal of Political Economy 98, 4 (1990), 703--738.

[32]

Xueming Luo, Jie Zhang, and Wenjing Duan. 2013. Social media and firm equity value. Information Systems Research 24, 1 (2013), 146--163.

Digital Library

[33]

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. In Workshop Proceedings of International Conference on Learning Representations (ICLR). https://sites.google.com/site/representationlearning2013/workshop-proceedings.

[34]

Gilad Mishne and Maarten De Rijke. 2006. Capturing global mood levels using blog posts. In Proceedings of AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs. AAAI, 145--152.

[35]

Marc-Andre Mittermayer and Gerhard F. Knolmayer. 2006. Newscats: A news categorization and trading system. In Proceedings of the 6th International Conference on Data Mining (ICDM). ACM, 1002--1007.

Digital Library

[36]

Kenta Oku, Shinsuke Nakajima, Jun Miyazaki, and Shunsuke Uemura. 2006. Context-aware SVM for context-dependent information recommendation. In Proceedings of the 7th International Conference on Mobile Data Management. IEEE, 109.

Digital Library

[37]

Michael Rechenthin and W. Nick Street. 2013. Using conditional probability to identify trends in intra-day high-frequency equity pricing. Physica A: Statistical Mechanics and Its Applications 392, 24 (2013), 6169--6188.

[38]

Robert P. Schumaker and Hsinchun Chen. 2009a. A quantitative stock prediction system based on financial news. Information Processing & Management 45, 5 (2009), 571--583.

Digital Library

[39]

Robert P. Schumaker and Hsinchun Chen. 2009b. Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems 27, 2 (2009), 12:1--12:19.

Digital Library

[40]

Robert P. Schumaker, Yulei Zhang, Chun-Neng Huang, and Hsinchun Chen. 2012. Evaluating sentiment in financial news articles. Decision Support Systems 53, 3 (2012), 458--464.

Digital Library

[41]

Andrei Shleifer and Robert W. Vishny. 1997. The limits of arbitrage. Journal of Finance 52, 1 (1997), 35--55.

[42]

Alex J. Smola and Bernhard Schölkopf. 2004. A tutorial on support vector regression. Statistics and Computing 14, 3 (2004), 199--222.

Digital Library

[43]

Gabor Szabo and Bernardo A. Huberman. 2010. Predicting the popularity of online content. Communications of the ACM 53, 8 (2010), 80--88.

Digital Library

[44]

Paul C. Tetlock. 2007. Giving content to investor sentiment: The role of media in the stock market. Journal of Finance 62, 3 (2007), 1139--1168.

[45]

Paul C. Tetlock. 2010. Does public financial news resolve asymmetric information? Review of Financial Studies 23, 9 (2010), 3520--3557.

[46]

Paul C. Tetlock, Maytal Saar-Tsechansky, and Sofus Macskassy. 2008. More than words: Quantifying language to measure firms’ fundamentals. Journal of Finance 63, 3 (2008), 1437--1467.

[47]

Baohua Wang, Hejiao Huang, and Xiaolong Wang. 2012. A novel text mining approach to financial time series forecasting. Neurocomputing 83 (2012), 136--145.

Digital Library

[48]

Beat Wüthrich, Vincent Cho, Steven Leung, D. Permunetilleke, K. Sankaran, J. Zhang, and W. Lam. 1998. Daily stock market forecast from textual web data. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. IEEE, 2720--2725.

[49]

Sean Xin Xu and Xiaoquan Michael Zhang. 2013. Impact of Wikipedia on market information environment: Evidence on management disclosure and investor reaction. MIS Quarterly 37, 4 (2013), 1043--1068.

Digital Library

[50]

Yang Yu, Wenjing Duan, and Qing Cao. 2013. The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support Systems 55 (2013), 919--926.

Digital Library

[51]

Ilya Zheludev, Robert Smith, and Tomaso Aste. 2014. When can social media lead financial markets? Scientific Reports 4 (2014).

Cited By

Zhang QZhang YBao FNing YZhang CLiu P(2025)Graph-based stock prediction with multisource information and relational data fusionInformation Sciences10.1016/j.ins.2024.121561690(121561)Online publication date: Feb-2025
https://doi.org/10.1016/j.ins.2024.121561
Suleymanov EGubadli MYagubov U(2024)Test of Volatile Behaviors with the Asymmetric Stochastic Volatility Model: An Implementation on Nasdaq-100Risks10.3390/risks1205007612:5(76)Online publication date: 3-May-2024
https://doi.org/10.3390/risks12050076
Fu KZhang Y(2024)Incorporating Multi-Source Market Sentiment and Price Data for Stock Price PredictionMathematics10.3390/math1210157212:10(1572)Online publication date: 17-May-2024
https://doi.org/10.3390/math12101572
Show More Cited By

Index Terms

A Tensor-Based Information Framework for Predicting the Stock Market
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications

Recommendations

The Value of Public Information from Securities Company for Stock Returns: Empirical Evidence in Chinese Stock Market
ICSEB '20: Proceedings of the 2020 4th International Conference on Software and e-Business

Will investors earn expected stock returns when they completely rely on the public information released by companies (i.e., the company's annual report or the securities company's recommendation)? The statement may be valid under the effective market ...
Information and Behavior Creditability of Underwriter and Mutual Funds for Retail Investors: Empirical Evidence in Chinese Stock and Mutual Fund Market
ICSEB '21: Proceedings of the 2021 5th International Conference on Software and e-Business

This paper discusses whether retail investors are likely to increase the probability of gain from stock investment if retail investors incorporate the investigation process for all recommended stocks from underwriters and behaviors from mutual funds. ...
Hedge Funds and Stock Market Efficiency

We measure misvaluation using the discounted residual income model. As shown in the literature, this measure of stocks' misvaluation significantly explains their future cross-sectional returns. We measure the market-level misvaluation market ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 34, Issue 2

April 2016

220 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/2891107

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

Issue’s Table of Contents

Copyright © 2016 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 ACM 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: 08 February 2016

Accepted: 01 October 2015

Revised: 01 October 2015

Received: 01 December 2014

Published in TOIS Volume 34, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Sichuan National Science Foundation for Distinguished Young Scholars
National Natural Science Foundation of China (NSFC)
U.S. National Science Foundation
Fundamental Research Funds for the Central Universities
University of Arizona and the China National 1000-Talent Program at the Tsinghua University

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

72
Total Citations
View Citations
2,385
Total Downloads

Downloads (Last 12 months)331
Downloads (Last 6 weeks)42

Reflects downloads up to 04 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhang QZhang YBao FNing YZhang CLiu P(2025)Graph-based stock prediction with multisource information and relational data fusionInformation Sciences10.1016/j.ins.2024.121561690(121561)Online publication date: Feb-2025
https://doi.org/10.1016/j.ins.2024.121561
Suleymanov EGubadli MYagubov U(2024)Test of Volatile Behaviors with the Asymmetric Stochastic Volatility Model: An Implementation on Nasdaq-100Risks10.3390/risks1205007612:5(76)Online publication date: 3-May-2024
https://doi.org/10.3390/risks12050076
Fu KZhang Y(2024)Incorporating Multi-Source Market Sentiment and Price Data for Stock Price PredictionMathematics10.3390/math1210157212:10(1572)Online publication date: 17-May-2024
https://doi.org/10.3390/math12101572
Niu YLu LDolphin RPoti VDong R(2024)Evaluating Financial Relational Graphs: Interpretation Before PredictionProceedings of the 5th ACM International Conference on AI in Finance10.1145/3677052.3698644(564-572)Online publication date: 14-Nov-2024
https://dl.acm.org/doi/10.1145/3677052.3698644
Cheng JHuang KZheng Z(2024)Can Perturbations Help Reduce Investment Risks? Risk-aware Stock Recommendation via Split Variational Adversarial TrainingACM Transactions on Information Systems10.1145/364313142:4(1-28)Online publication date: 25-Jan-2024
https://dl.acm.org/doi/10.1145/3643131
Anbaee Farimani SJahan MMilani Fard A(2024)An Adaptive Multimodal Learning Model for Financial Market Price PredictionIEEE Access10.1109/ACCESS.2024.344102912(121846-121863)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3441029
Ma YMao RLin QWu PCambria E(2024)Quantitative stock portfolio optimization by multi-task learning risk and returnInformation Fusion10.1016/j.inffus.2023.102165104:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.inffus.2023.102165
Tan JDeveci MLi JZhong K(2024)Asset pricing via fused deep learning with visual cluesInformation Fusion10.1016/j.inffus.2023.102049102(102049)Online publication date: Feb-2024
https://doi.org/10.1016/j.inffus.2023.102049
Zhang LBai JSun BGuo YChen X(2024)Kernel multi-granularity double-quantitative rough set based on ensemble empirical mode decomposition: Application to stock price trends predictionInternational Journal of Approximate Reasoning10.1016/j.ijar.2024.109217172(109217)Online publication date: Sep-2024
https://doi.org/10.1016/j.ijar.2024.109217
Praturi SRamakrishnan ADeepthi L(2024)Stock Price Prediction Using Sentiment Analysis on Financial NewsData Science and Applications10.1007/978-981-99-7817-5_40(551-567)Online publication date: 18-Jan-2024
https://doi.org/10.1007/978-981-99-7817-5_40
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

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 Article

Media

Figures

Other

Tables

View Issue’s Table of Contents