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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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. 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.
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    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. 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.

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