Revealing the risk perception of investors using machine learning

Marina Koelbl, Ralf Laschinger, Bertram I. Steininger and Wolfgang Schaefers

The European Journal of Finance, 2024, vol. 30, issue 17, 2032-2058

Abstract: Corporate disclosures convey crucial information to financial market participants. While machine learning algorithms are commonly used to extract this information, they often overlook the use of idiosyncratic terminology and industry-specific vocabulary within documents. This study uses an unsupervised machine learning algorithm, the Structural Topic Model, to overcome these issues. Our findings illustrate the link between machine-extracted risk factors discussed in corporate disclosures (10-Ks) and the corresponding pricing behavior by investors, focusing on a previously unexplored US REIT sample from 2005 to 2019. Surprisingly, when disclosed, most risk factors counterintuitively lead to a decrease in return volatility. This resolution of uncertainties surrounding known risk factors or the provision of additional facts about these factors contributes valuable insights to the financial market.

Date: 2024
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DOI: 10.1080/1351847X.2024.2364831

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