Can Risks be Good News? Revealing Risk Perception of Real Estate Investors using Machine Learning

55 Pages Posted: 9 Sep 2020 Last revised: 2 Mar 2021

See all articles by Marina Koelbl

Marina Koelbl

University of Regensburg - International Real Estate Business School (IREBS)

Ralf Laschinger

University of Regensburg - Department of Finance

Bertram I. Steininger

Royal Institute of Technology (KTH)

Wolfgang Schäfers

University of Regensburg - International Real Estate Business School (IREBS)

Date Written: February 28, 2021

Abstract

The SEC mandates firms to inform investors about their assessment of future contingencies in their 10 Ks. However lengthy and complex disclosures – mostly for dozens of firms in an investor’s portfolio – can barely be processed by a human being. To cope with the flood of information, we exploit an unsupervised machine learning algorithm, the Structural Topic Model, to identify the risk factors discussed in 10-Ks. We apply this algorithm to a US REIT sample between 2005 and 2019 to assess whether the probability of appearance of the extracted risk factors helps to explain the perceived risk on the stock market. We find that the majority of risk factors is significantly associated with volatility indicating that our machine-assisted modeling presents a valid approach to quantify risk disclosures in textual form. Furthermore, we investigate in which direction individual topics affect investor risk perception. Even if all kinds of directions exist, uninformative topics with no impact, increasing risk-perception topics, and decreasing risk-perception topics, the latter is clearly predominant. The predominance of the risk-reducing effect indicates that risk disclosures can indeed be considered good news as long as they clarify the implications of already known risk.

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

Koelbl, Marina and Laschinger, Ralf and Steininger, Bertram I. and Schäfers, Wolfgang, Can Risks be Good News? Revealing Risk Perception of Real Estate Investors using Machine Learning (February 28, 2021). Available at SSRN: https://ssrn.com/abstract=3686492 or http://dx.doi.org/10.2139/ssrn.3686492

Marina Koelbl

University of Regensburg - International Real Estate Business School (IREBS) ( email )

Universitaetsstrasse 31
Regenburg, Bavaria 93040
Germany

Ralf Laschinger

University of Regensburg - Department of Finance ( email )

Regensburg, 93040
Germany

Bertram I. Steininger (Contact Author)

Royal Institute of Technology (KTH) ( email )

Stockholm
Sweden

Wolfgang Schäfers

University of Regensburg - International Real Estate Business School (IREBS) ( email )

Universitaetsstrasse 31
Regenburg, Bavaria 93040
Germany

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