IDEAS home Printed from https://ideas.repec.org/a/taf/quantf/v15y2015i6p999-1012.html
   My bibliography  Save this article

Predicting abnormal returns from news using text classification

Author

Listed:
  • Ronny Luss
  • Alexandre D'Aspremont

Abstract

We show how text from news articles can be used to predict intraday price movements of financial assets using support vector machines. Multiple kernel learning is used to combine equity returns with text as predictive features to increase classification performance and we develop an analytic center cutting plane method to solve the kernel learning problem efficiently. We observe that while the direction of returns is not predictable using either text or returns, their size is, with text features producing significantly better performance than historical returns alone.

Suggested Citation

  • Ronny Luss & Alexandre D'Aspremont, 2015. "Predicting abnormal returns from news using text classification," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 999-1012, June.
    • Handle: RePEc:taf:quantf:v:15:y:2015:i:6:p:999-1012
    DOI: 10.1080/14697688.2012.672762
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/14697688.2012.672762
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/14697688.2012.672762?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Valeriy Gavrishchaka & Supriya Banerjee, 2006. "Support Vector Machine as an Efficient Framework for Stock Market Volatility Forecasting," Computational Management Science, Springer, vol. 3(2), pages 147-160, April.
    2. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    3. Mitchell, Mark L & Mulherin, J Harold, 1994. "The Impact of Public Information on the Stock Market," Journal of Finance, American Finance Association, vol. 49(3), pages 923-950, July.
    4. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
    5. Ederington, Louis H & Lee, Jae Ha, 1993. "How Markets Process Information: News Releases and Volatility," Journal of Finance, American Finance Association, vol. 48(4), pages 1161-1191, September.
    6. M. A. H. dempster & C. M. Jones, 2001. "A real-time adaptive trading system using genetic programming," Quantitative Finance, Taylor & Francis Journals, vol. 1(4), pages 397-413.
    7. Taylor, Stephen J. & Xu, Xinzhong, 1997. "The incremental volatility information in one million foreign exchange quotations," Journal of Empirical Finance, Elsevier, vol. 4(4), pages 317-340, December.
    8. Mark Austin & Graham Bates & Michael Dempster & Vasco Leemans & Stacy Williams, 2004. "Adaptive systems for foreign exchange trading," Quantitative Finance, Taylor & Francis Journals, vol. 4(4), pages 37-45.
    9. Kalev, Petko S. & Liu, Wai-Man & Pham, Peter K. & Jarnecic, Elvis, 2004. "Public information arrival and volatility of intraday stock returns," Journal of Banking & Finance, Elsevier, vol. 28(6), pages 1441-1467, June.
    10. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Banerjee, Ameet Kumar & Akhtaruzzaman, Md & Dionisio, Andreia & Almeida, Dora & Sensoy, Ahmet, 2022. "Nonlinear nexus between cryptocurrency returns and COVID-19 news sentiment," Journal of Behavioral and Experimental Finance, Elsevier, vol. 36(C).
    2. Xi Zhang & Jiawei Shi & Di Wang & Binxing Fang, 2018. "Exploiting Investors Social Network for Stock Prediction in China's Market," Papers 1801.00597, arXiv.org.
    3. Banerjee, Ameet Kumar & Dionisio, Andreia & Pradhan, H.K. & Mahapatra, Biplab, 2021. "Hunting the quicksilver: Using textual news and causality analysis to predict market volatility," International Review of Financial Analysis, Elsevier, vol. 77(C).
    4. Ummara Mumtaz & Summaya Mumtaz, 2023. "Potential of ChatGPT in predicting stock market trends based on Twitter Sentiment Analysis," Papers 2311.06273, arXiv.org.
    5. Ramit Sawhney & Shivam Agarwal & Vivek Mittal & Paolo Rosso & Vikram Nanda & Sudheer Chava, 2022. "Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models," Papers 2206.06320, arXiv.org.
    6. Xi Zhang & Yunjia Zhang & Senzhang Wang & Yuntao Yao & Binxing Fang & Philip S. Yu, 2018. "Improving Stock Market Prediction via Heterogeneous Information Fusion," Papers 1801.00588, arXiv.org.
    7. Yang, Ann Shawing, 2020. "Misinformation corrections of corporate news: Corporate clarification announcements," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    8. Wai Khuen Cheng & Khean Thye Bea & Steven Mun Hong Leow & Jireh Yi-Le Chan & Zeng-Wei Hong & Yen-Lin Chen, 2022. "A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock Forecasting," Mathematics, MDPI, vol. 10(14), pages 1-20, July.
    9. Huicheng Liu, 2018. "Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network," Papers 1811.06173, arXiv.org.
    10. Liping Wang & Jiawei Li & Lifan Zhao & Zhizhuo Kou & Xiaohan Wang & Xinyi Zhu & Hao Wang & Yanyan Shen & Lei Chen, 2023. "Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey," Papers 2308.04947, arXiv.org.
    11. Qinkai Chen, 2021. "Stock Movement Prediction with Financial News using Contextualized Embedding from BERT," Papers 2107.08721, arXiv.org.
    12. Audrino, Francesco & Tetereva, Anastasija, 2019. "Sentiment spillover effects for US and European companies," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 542-567.
    13. Farnoush Ronaghi & Mohammad Salimibeni & Farnoosh Naderkhani & Arash Mohammadi, 2021. "COVID19-HPSMP: COVID-19 Adopted Hybrid and Parallel Deep Information Fusion Framework for Stock Price Movement Prediction," Papers 2101.02287, arXiv.org, revised Jul 2021.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Deniz Erdemlioglu & Sébastien Laurent & Christopher J. Neely, 2013. "Econometric modeling of exchange rate volatility and jumps," Chapters, in: Adrian R. Bell & Chris Brooks & Marcel Prokopczuk (ed.), Handbook of Research Methods and Applications in Empirical Finance, chapter 16, pages 373-427, Edward Elgar Publishing.
    2. Andersen, Torben G. & Bollerslev, Tim & Cai, Jun, 2000. "Intraday and interday volatility in the Japanese stock market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 10(2), pages 107-130, June.
    3. Charles M. Jones & Owen Lamont & Robin Lumsdaine, 1996. "Public Information and the Persistence of Bond Market Volatility," NBER Working Papers 5446, National Bureau of Economic Research, Inc.
    4. Karmakar, Madhusudan & Paul, Samit, 2019. "Intraday portfolio risk management using VaR and CVaR:A CGARCH-EVT-Copula approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 699-709.
    5. Chang, Yuanchen & Taylor, Stephen J., 2003. "Information arrivals and intraday exchange rate volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 13(2), pages 85-112, April.
    6. Simonsen, Ola, 2006. "The Impact of News Releases on Trade Durations in Stocks -Empirical Evidence from Sweden," Umeå Economic Studies 688, Umeå University, Department of Economics.
    7. Min Liu & Chien‐Chiang Lee & Wei‐Chong Choo, 2021. "An empirical study on the role of trading volume and data frequency in volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 792-816, August.
    8. Simonsen, Ola, 2006. "Stock Data, Trade Durations, And Limit Order Book Information," Umeå Economic Studies 689, Umeå University, Department of Economics.
    9. Bertelsen, Kristoffer Pons & Borup, Daniel & Jakobsen, Johan Stax, 2021. "Stock market volatility and public information flow: A non-linear perspective," Economics Letters, Elsevier, vol. 204(C).
    10. Robert F. Engle & Martin Klint Hansen & Asger Lunde, 2012. "And Now, The Rest of the News: Volatility and Firm Specific News Arrival," CREATES Research Papers 2012-56, Department of Economics and Business Economics, Aarhus University.
    11. Abderrazak Ben Maatoug & Rim Lamouchi & Russell Davidson & Ibrahim Fatnassi, 2018. "Modelling Foreign Exchange Realized Volatility Using High Frequency Data: Long Memory versus Structural Breaks," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 10(1), pages 1-25, March.
    12. Karmakar, Madhusudan & Paul, Samit, 2016. "Intraday risk management in International stock markets: A conditional EVT approach," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 34-55.
    13. Giot, Pierre & Laurent, Sebastien, 2004. "Modelling daily Value-at-Risk using realized volatility and ARCH type models," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 379-398, June.
    14. Laakkonen, Helinä, 2004. "The impact of macroeconomic news on exchange rate volatility," Bank of Finland Research Discussion Papers 24/2004, Bank of Finland.
    15. Mauro Bernardi & Leopoldo Catania & Lea Petrella, 2014. "Are news important to predict large losses?," Papers 1410.6898, arXiv.org, revised Oct 2014.
    16. Sévi, Benoît, 2014. "Forecasting the volatility of crude oil futures using intraday data," European Journal of Operational Research, Elsevier, vol. 235(3), pages 643-659.
    17. van Mierlo, J.G.A., 2001. "Over de verhouding tussen overheid, marktwerking en privatisering. Een economische meta-analyse," Research Memorandum 014, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    18. repec:zbw:bofrdp:2004_024 is not listed on IDEAS
    19. Jones, Charles M. & Lamont, Owen & Lumsdaine, Robin L., 1998. "Macroeconomic news and bond market volatility," Journal of Financial Economics, Elsevier, vol. 47(3), pages 315-337, March.
    20. Melvin, Michael & Yin, Xixi, 2000. "Public Information Arrival, Exchange Rate Volatility, and Quote Frequency," Economic Journal, Royal Economic Society, vol. 110(465), pages 644-661, July.
    21. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2009. "Quantifying high-frequency market reactions to real-time news sentiment announcements," CFS Working Paper Series 2009/31, Center for Financial Studies (CFS).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:quantf:v:15:y:2015:i:6:p:999-1012. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RQUF20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.