Is the MD&A of US REITs informative? A textual sentiment study

Publication Date10 Apr 2020
AuthorMarina Koelbl
SubjectProperty management & built environment,Real estate & property,Property valuation & finance
Is the MD&A of US REITs
informative? A textual
sentiment study
Marina Koelbl
IREBS International Real Estate Business School, University of Regensburg,
Regensburg, Germany
Purpose This study examines whether language disclosed in the Management Discussion and Analysis
(MD&A) of US Real Estate Investment Trusts (REITs) provides signals regarding future firm performance
and thus generates a market response.
Design/methodology/approach This research conducts tex tual analysis on a sample of approxima tely
6,500 MD&As of US REITs filed by the SEC between 2003 and 2018. Specifically, the Loughran and
Mcdonald (2011) financ ial dictionary, and a cus tom dictionary for the rea l estate industry create d by
Ruscheinskyet al. (2018), are employed to determine the inherent sentiment, that is,the level of pessimistic or
optimistic language f or each filing. Thereaf ter, a panel fixed-effe cts regression enable s investigating the
relationship betwee n sentiment and future firm performanc e, as well as the marketsreaction.
Findings The empirical results suggest that higher levels of pessimistic (optimistic) language in the MD&A
predict lower (higher) future firm performance. Hereby, the use of a domain-specific real estate dictionary,
namely that developed by Ruscheinsky et al. (2018) leads to superior results. Corresponding to the notion that
the human psyche is affected more strongly by negative than positive news (Rozin and Royzman, 2001), the
market responds solely to pessimistic language in the MD&A.
Practical implications The results suggest that the market can benefit from textual analysis, as
investigating the language in the MD&A reduces information asymmetries between US REIT managers and
Originality/value This is the first study to analyze exclusively US REITs, whether language in the MD&A
is predictive of future firm performance and whether the market responds to textual sentiment.
Keywords US REITs, Sentiment, Textual analysis, Dictionary-based approach, MD&A, 10K, 10Q
Paper type Research paper
1. Introduction
In 1968, theUS Securities and ExchangeCommission (SEC) introducedreporting requirements
that mandate publicly traded firms to include a narrative disclosure, called Management
Discussion and Analysis (MD&A), in their annual and quarterly reports. The justification for
this requirementis as follows: The Commission has long recognized the need for a narrative
explanation of the financial statements, because a numerical presentation and brief
accompanying footnotes alone may be insufficient for an investor to judge the quality of
earningsand the likelihood thatpast performance is indicativeof future performance.MD&A is
intended to give the investor an opportunity to look at the company through the eyes of
managementby providing both a short andlong-term analysis of the businessof the company
(SEC, 1987).
Although the MD&A is clearly meant to inform investors, and specific SEC rules for the
MD&A as wellas subsequent SEC releasesgive detailed instructionsand interpretive guidance
to assist companiesin preparing their MD&A disclosures(Huefner, 2007), the informative ness
of the MD&A has frequently been criticized. For example, Pavaand Epstein (1993) show that
although most of the companies they study accurately describe historical events, very few
provide usefuland accurate forecastsin their MD&As. However, earlyresearch examining the
market implications of qualitative disclosure relied on human coders making item-by-item
subjective assessments of tone (e.g. Bryan, 1997;Barron et al.,1999;andCallahan and Smith,
MD&A of US
The current issue and full text archive of this journal is available on Emerald Insight at:
Received 8 December 2019
Revised 4 February 2020
Accepted 5 March 2020
Journal of Property Investment &
Vol. 38 No. 3, 2020
pp. 181-201
© Emerald Publishing Limited
DOI 10.1108/JPIF-12-2019-0149
2004). Recognizing the limitations of manual coding (e.g. small sample sizes, subjectivity),
whether the MD&A is truly informative remains an open empirical question.
With the rise of behavioral finance in the last decade, this discussion was resumed and
textual analysis has garnered increased attention, with the aim of assessing the
informativeness of corporate disclosures. Thereby, researchers most frequently relied on
textual tone analysis to examine firmsprospectuses (Feldman et al., 2010;Jegadeesh and Wu,
2013;Li, 2010;Loughran and Mcdonald, 2011,2015). Furthermore, most studies reviewed a
random sample of firms, restricted only by the availability of necessary data. However,
Callahan and Smith (2004) find evidence that the impact of language varies across industries.
Thispaper adds a new dimensionto the discussion by analyzingMD&As for a sampleof US
Real Estate Investment Trusts (REITs). In contrast to a sample randomly drawn from the
capital market, the US REIT market provides a number of beneficial characteristics. First,
equityREITs are fairly homogeneousregarding characteristics thatusually vary widely across
differentindustries (Hartzell et al.,2008).Second, US REITs are requiredto pay out a minimum
of 90% of taxable earnings to shareholders as dividends. Consequently, in order to take
advantage of growth opportunities, US REITs must turn to the capital markets. As such,US
REIT managers have an unusually strong incentive to be transparent and maintain investor
trust (Danielsen et al., 2009;Doran et al.,2012;Price et al., 2017).Third, the underlying assetsof
US REITs are realestate, which is an illiquid, slow-moving asset and thusmore compatible to
analysis over a relatively large time span(e.g. from one-quarter to the subsequentquarter).
These unique characteristics suggest that the MD&As of US REITs are particularly
informative. However, the asset classs peculiarities also indicate that results from previous
studies cannot automatically be extended to US REITs. Thus, we investigate the information
content of the MD&A for a US REIT sample by answering the following questions: Does
textual sentiment in the MD&A reveal managersexpectations regarding future firm
performance? If so, does the market process the information efficiently?
To extract sentiment from the MD&A, we rely on a dictionary-based approach.
Specifically, we employ the Loughran and Mcdonald (2011) financial dictionary and a custom
wordlist for the real estate industry created by Ruscheinsky et al. (2018) to determine the
overall sentiment inherent in each filing. Our findings suggest that higher levels of
pessimistic (optimistic) language in the MD&A are associated with lower (higher) future firm
performance. This holds even after controlling for the information released in other
concurrent disclosures that may be predictive of future performance. Hereby, the use of a
domain-specific real estate dictionary, namely the dictionary developed by Ruscheinsky et al.
(2018) leads to superior results. Moreover, we find a significant market response to
pessimistic language in the MD&A at the time of the SEC filing. However, corresponding to
the notion that individuals are affected more strongly by negative than positive news, we
cannot find a significant impact of optimistic language. Overall, to the best of our knowledge,
this is the first study providing evidence that the use of language in the MD&A reveals US
REIT managersexpectations regarding future firm performance and that the market
responds to this information. We demonstrate that the market can benefit from textual
analysis, as investigating the language in the MD&A should decrease information
asymmetries between US REIT managers and investors.
The remainder of the paper is organized as follows. Section 2 discusses related literature.
Section 3 introduces the data, that is, sample and variables. Section 4 defines the specific
sentiment measures and presents empirical methods for the analysis. Finally, Section 5
reports the empirical results, and Section 6 concludes.
2. Related literature and hypothesis development
Textual analysis has recently attracted increased attention to address many pivotal
questions in behavioral finance. Not least because in the current world, a huge amount of

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