Revealing the Risk Perception of Investors using Machine Learning

Text in corporate disclosures conveys important information to financial market participants. If incorporated in quantitative models, the intended meaning of a text is often hidden by the use of idiosyncratic terminology within an industry-specific vocabulary. This study uses an unsupervised machine learning algorithm, the Structural Topic Model, to overcome this issue. It illustrates the connection between machine-extracted risk factors discussed in corporate disclosures (10-Ks) and the corresponding pricing behavior of investors for a not yet investigated US REIT sample from 2005 to 2019. When disclosed, most risk factors counterintuitively decrease stock return volatility and are therefore beneficial for the pricing process on financial markets.

Keywords: Real Estate Investment Trust (REIT), risk, text analysis, machine learning, Latent Dirichlet Allocation, Structural Topic Model, 10-K, Item 1A, Item 7A

JEL Classification: C45, C80, G14, G18, K22, K40, M41, M48, R30

Suggested Citation: Suggested Citation

Koelbl, Marina and Laschinger, Ralf and Steininger, Bertram I. and Schäfers, Wolfgang, Revealing the Risk Perception of Investors using Machine Learning (February 6, 2022). Available at SSRN: https://ssrn.com/abstract=3686492 or http://dx.doi.org/10.2139/ssrn.3686492