Autor: |
Koelbl, Marina, Laschinger, Ralf, Steininger, Bertram I., Schaefers, Wolfgang |
Předmět: |
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Zdroj: |
European Journal of Finance; Nov(Late) 2024, Vol. 30 Issue 17, p2032-2058, 27p |
Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
Databáze: |
Complementary Index |
Externí odkaz: |
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