IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v57y2021i1d10.1007_s10614-020-10060-y.html
   My bibliography  Save this article

Predicting Stock Price Falls Using News Data: Evidence from the Brazilian Market

Author

Listed:
  • Juvenal José Duarte

    (Federal University of São Carlos - UFSCar Sorocaba)

  • Sahudy Montenegro González

    (Federal University of São Carlos - UFSCar Sorocaba)

  • José César Cruz

    (Federal University of São Carlos - UFSCar Sorocaba)

Abstract

Market participants use a wide set of information before they decide to invest in risk assets, such as stocks. Investors often follow the news to collect the information that will help them decide which strategy to follow. In this study, we analyze how public news and historical prices can be used together to anticipate and prevent financial losses on the Brazilian stock market. We include an extensive set of 64 securities in our analysis, which represent various sectors of the Brazilian economy. Our analysis compares the traditional Buy & Hold and the moving average strategies to several experiments designed with 11 machine learning algorithms. We explore daily, weekly and monthly time horizons for both publication and return windows. With this approach we were able to assess the most relevant set of news for investor’s decision, and to determine for how long the information remains relevant to the market. We found a strong relationship between news publications and stock price changes in Brazil, suggesting even short-term arbitrage opportunities. The study shows that it is possible to predict stock price falls using a set of news in Portuguese, and that text mining-based approaches can overcome traditional strategies when forecasting losses.

Suggested Citation

  • Juvenal José Duarte & Sahudy Montenegro González & José César Cruz, 2021. "Predicting Stock Price Falls Using News Data: Evidence from the Brazilian Market," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 311-340, January.
    • Handle: RePEc:kap:compec:v:57:y:2021:i:1:d:10.1007_s10614-020-10060-y
    DOI: 10.1007/s10614-020-10060-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-020-10060-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-020-10060-y?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. Del Negro, Marco & Giannone, Domenico & Giannoni, Marc P. & Tambalotti, Andrea, 2019. "Global trends in interest rates," Journal of International Economics, Elsevier, vol. 118(C), pages 248-262.
    2. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    3. Ben S. Bernanke & Vincent R. Reinhart, 2004. "Conducting Monetary Policy at Very Low Short-Term Interest Rates," American Economic Review, American Economic Association, vol. 94(2), pages 85-90, May.
    4. Yu, Hao & Nartea, Gilbert V. & Gan, Christopher & Yao, Lee J., 2013. "Predictive ability and profitability of simple technical trading rules: Recent evidence from Southeast Asian stock markets," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 356-371.
    5. Amos Tversky & Daniel Kahneman, 1991. "Loss Aversion in Riskless Choice: A Reference-Dependent Model," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 106(4), pages 1039-1061.
    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. Aparna Gupta & Vipula Rawte & Mohammed J. Zaki, 2024. "Predicting Firm Financial Performance from SEC Filing Changes Using Automatically Generated Dictionary," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 307-334, July.
    2. Ahmed R. M. Alsayed, 2023. "Turkish Stock Market from Pandemic to Russian Invasion, Evidence from Developed Machine Learning Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 1107-1123, October.
    3. Chhaya Dubey & Dharmendra Kumar & Ashutosh Kumar Singh & Vijay Kumar Dwivedi, 2024. "Applying machine learning models on blockchain platform selection," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(8), pages 3643-3656, August.
    4. Joao Vitor Matos Goncalves & Michel Alexandre & Gilberto Tadeu Lima, 2023. "ARIMA and LSTM: A Comparative Analysis of Financial Time Series Forecasting," Working Papers, Department of Economics 2023_13, University of São Paulo (FEA-USP).
    5. Arvind Kumar Sinha & Pradeep Shende, 2024. "Uncertainty Optimization Based Feature Selection Model for Stock Marketing," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 357-389, January.

    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. Day, Min-Yuh & Ni, Yensen, 2023. "The profitability of seasonal trading timing: Insights from energy-related markets," Energy Economics, Elsevier, vol. 128(C).
    2. Pick-Soon Ling & Ruzita Abdul-Rahim, 2017. "Market Efficiency Based on Unconventional Technical Trading Strategies in Malaysian Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 7(3), pages 88-96.
    3. Daniel, Kent & Hirshleifer, David & Teoh, Siew Hong, 2002. "Investor psychology in capital markets: evidence and policy implications," Journal of Monetary Economics, Elsevier, vol. 49(1), pages 139-209, January.
    4. Ahmad H. Juma’h & Yazan Alnsour, 2018. "Using Social Media Analytics: The Effect of President Trump’s Tweets On Companies’ Performance," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 17(1), pages 100-121, March.
    5. Aumeboonsuke, Vesarach & Dryver, Arthur L., 2014. "The importance of using a test of weak-form market efficiency that does not require investigating the data first," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 350-357.
    6. Osman, Khairul Nizam & Masih, Mansur, 2018. "Granger-causality of selective Dow Jones islamic and sustainability regional equity indices," MPRA Paper 104185, University Library of Munich, Germany.
    7. Konstantina Kappou & Ioannis Oikonomou, 2016. "Is There a Gold Social Seal? The Financial Effects of Additions to and Deletions from Social Stock Indices," Journal of Business Ethics, Springer, vol. 133(3), pages 533-552, February.
    8. Fang Wang & Marko Gacesa, 2024. "Semi-strong Efficient Market of Bitcoin and Twitter: an Analysis of Semantic Vector Spaces of Extracted Keywords and Light Gradient Boosting Machine Models," Papers 2409.15988, arXiv.org.
    9. Bashir Ahmad Joo & Kokab Durri, 2015. "Comprehensive Review of Literature on Behavioural Finance," Indian Journal of Commerce and Management Studies, Educational Research Multimedia & Publications,India, vol. 6(2), pages 11-19, May.
    10. Alhashel, Bader S. & Almudhaf, Fahad W. & Hansz, J. Andrew, 2018. "Can technical analysis generate superior returns in securitized property markets? Evidence from East Asia markets," Pacific-Basin Finance Journal, Elsevier, vol. 47(C), pages 92-108.
    11. Lee, Chien-Chiang & Zeng, Jhih-Hong, 2011. "Revisiting the relationship between spot and futures oil prices: Evidence from quantile cointegrating regression," Energy Economics, Elsevier, vol. 33(5), pages 924-935, September.
    12. Michael Nofer & Oliver Hinz, 2015. "Using Twitter to Predict the Stock Market," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 57(4), pages 229-242, August.
    13. Etse Nkukpornu & Prince Gyimah & Linda Sakyiwaa, 2020. "Behavioural Finance and Investment Decisions: Does Behavioral Bias Matter?," International Business Research, Canadian Center of Science and Education, vol. 13(11), pages 1-65, November.
    14. Bogna Gawronska-Nowak & Wojciech Grabowski, 2016. "Using Genetic Algorithm In Dynamic Model Of Speculative Attack," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 11(2), pages 287-306, June.
    15. Yensen Ni & Min-Yuh Day & Yirung Cheng & Paoyu Huang, 2022. "Can investors profit by utilizing technical trading strategies? Evidence from the Korean and Chinese stock markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    16. Eduard Marinov, 2017. "The 2017 Nobel Prize in Economics," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 6, pages 117-159.
    17. Committee, Nobel Prize, 2017. "Richard H. Thaler: Integrating Economics with Psychology," Nobel Prize in Economics documents 2017-1, Nobel Prize Committee.
    18. Zhengyang Bao & Andreas Leibbrandt & ple391, 2019. "Thar she resurges: The case of assets that lack positive fundamental value," Monash Economics Working Papers 12-19, Monash University, Department of Economics.
    19. Jying‐Nan Wang & Hung‐Chun Liu & Jiangze Du & Yuan‐Teng Hsu, 2019. "Economic benefits of technical analysis in portfolio management: Evidence from global stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(2), pages 890-902, April.
    20. Joyita Banerji & Kaushik Kundu & Parveen Ahmed Alam, 2023. "The Impact of Behavioral Biases on Individuals’ Financial Choices under Uncertainty: An Empirical Approach," Business Perspectives and Research, , vol. 11(3), pages 401-424, September.

    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:kap:compec:v:57:y:2021:i:1:d:10.1007_s10614-020-10060-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.