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Predicting Firm Financial Performance from SEC Filing Changes Using Automatically Generated Dictionary

Author

Listed:
  • Aparna Gupta

    (Rensselaer Polytechnic Institute)

  • Vipula Rawte

    (Rensselaer Polytechnic Institute)

  • Mohammed J. Zaki

    (Rensselaer Polytechnic Institute)

Abstract

Textual data are increasingly used to predict firm performance, however extracting useful signals towards serving this goal with a continuously growing repository of financial reports and documents is challenging, even by the state-of-the-art machine learning and natural language processing (NLP) techniques. We propose a novel approach to automatically create a word list from SEC filings (10-K and 8-K reports) using advanced deep learning and NLP techniques and compare their performance against the widely used Loughran–McDonald sentiment dictionaries. We additionally analyze a corpus of 8-K and 10-K documents to evaluate their relative informativeness for firm performance prediction. Since 8-K filings provide corporate updates along a fiscal year, we compare their content against changes in 10-Ks between consecutive years to assess the incremental value of information provided in these regulatory filings. Information effectiveness is examined by predicting six key financial indicators for a set of US banks using ridge regression. Our results positively support sentiment dictionaries expansion by automatically extracting meaning from text and highlight the benefits obtainable from utilizing update filings.

Suggested Citation

  • 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.
  • Handle: RePEc:kap:compec:v:64:y:2024:i:1:d:10.1007_s10614-023-10443-x
    DOI: 10.1007/s10614-023-10443-x
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    References listed on IDEAS

    as
    1. Paul C. Tetlock & Maytal Saar‐Tsechansky & Sofus Macskassy, 2008. "More Than Words: Quantifying Language to Measure Firms' Fundamentals," Journal of Finance, American Finance Association, vol. 63(3), pages 1437-1467, June.
    2. Faisal Khalil & Gordon Pipa, 2022. "Is Deep-Learning and Natural Language Processing Transcending the Financial Forecasting? Investigation Through Lens of News Analytic Process," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 147-171, June.
    3. 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.
    4. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    5. Tim Loughran & Bill Mcdonald, 2016. "Textual Analysis in Accounting and Finance: A Survey," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 1187-1230, September.
    6. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
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    More about this item

    Keywords

    Regulatory filings; Text analytics; Bank risk; Performance; Prediction; Attention score;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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