Koelbl, Marina and Laschinger, Ralf and Steininger, Bertram I. and Schaefers, Wolfgang (2024) Revealing the risk perception of investors using machine learning. EUROPEAN JOURNAL OF FINANCE, 30 (17). pp. 2032-2058. ISSN 1351-847X, 1466-4364
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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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | INFORMATION-CONTENT; R PACKAGE; STOCK; REIT; EARNINGS; SENTIMENT; VOLUME; PRICE; DISCLOSURES; READABILITY; Risk; textual analysis; machine learning; structural topic model; 10-K filing; C45; C80; G14; G18; M41; R30 |
| Subjects: | 300 Social sciences > 330 Economics |
| Divisions: | Business, Economics and Information Systems > Institut für Immobilienenwirtschaft / IRE|BS > Lehrstuhl für Immobilienmanagement (Prof. Dr. Wolfgang Schäfers) Business, Economics and Information Systems > Institut für Betriebswirtschaftslehre > Lehrstuhl für Finanzierung (Prof. Dr. Gregor Dorfleitner) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 14 Jan 2026 07:17 |
| Last Modified: | 14 Jan 2026 07:17 |
| URI: | https://pred.uni-regensburg.de/id/eprint/64066 |
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