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A machine learning approach to risk disclosure reporting

Author

Listed:
  • Max Resende

    (Federal University of Santa Catarina)

  • Alexandre Ferreira

    (Save Advisers LLC)

Abstract

It is widely recognized that corporate annual reports play a key role in financial markets. Given the debate on risk analysis, this paper applies a machine learning statistical technique called Latent Dirichlet Allocation (LDA) in order to classify companies risks reported on 10-k SEC Form from 2006 to 2017 and applies a predictive logit model to assess the idiosyncratic risks of individual firms and relate it to firm-specific characteristics, such as market equity, total assets, among others. Among several results, it was verified that non-diversifiable risks, such as tax, competition, insurance, intellectual property and government behave similarly throughout all the industries, whereas Financial Statements concerns appear to be temporary. Moreover, market equity, total assets and the firm's age are predictive of all risks and firms for which the risk is captured are smaller on average, present lower market equity and total assets besides been younger and slightly less profitable when compared to traditional firms.

Suggested Citation

  • Max Resende & Alexandre Ferreira, 2021. "A machine learning approach to risk disclosure reporting," Economics Bulletin, AccessEcon, vol. 41(2), pages 234-251.
    • Handle: RePEc:ebl:ecbull:eb-20-00810
    as

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    File URL: http://www.accessecon.com/Pubs/EB/2021/Volume41/EB-21-V41-I2-P22.pdf
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    More about this item

    Keywords

    Risk factors; Topic modeling; LDA; 10-k SEC Form;
    All these keywords.

    JEL classification:

    • G3 - Financial Economics - - Corporate Finance and Governance
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

    Statistics

    Access and download statistics

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