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Earnings Conference Call Content and Stock Price: The Case of REITs

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Abstract

Using computer based content analysis, we quantify the linguistic tone of quarterly earnings conference calls for publicly traded Real Estate Investment Trusts (REITs). After controlling for the earnings announcement, we examine the relation between conference call tone and the contemporaneous stock price reaction. We find that the tone of the conference call dialogue has significant explanatory power for the abnormal returns at and immediately following quarterly earnings announcements. The question and answer portion of the conference calls dominates prepared managerial introductory remarks in explanatory significance. Furthermore, an overall positive tone in the conference call discussion between management and analysts is found to nearly offset the damaging effects of a negative earnings surprise.

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Notes

  1. REITs are required to hold individual properties for at least 4 years and can sell up to 10% of their asset bases in a given year. The intent of the rule is to keep REITs from buying property primarily for resale. Muhlhofer (2008) demonstrates that REITs would buy and sell assets more frequently if they were not subject to the holding period requirement. Still, there are no restrictions on the number of properties they can acquire in a given year. With new acquisitions and the potential to sell up to 10% of their portfolios, REITs are able to substantially alter their asset bases each year.

  2. By definition this includes all publicly traded REITs.

  3. Regulation Fair Disclosure, effective October 23, 2000, opened up conference calls to the public. The final SEC rule, widely known as “Regulation FD”, is available at http//www.sec.gov/rules/final/33-7881.htm.

  4. See Bernard (1992) for a thorough review of possible explanations for the anomalous evidence for stock price reactions to earnings announcements.

  5. This is also noted in Frankel et al. (1999).

  6. Sunder (2002) and Irani (2004) find that this holds both before and after passage of Regulation Fair Disclosure.

  7. This is also noted in Fama (1998).

  8. Content analysis techniques, developed over 40 years ago, were originally applied in journalism, psychology, communications, and other social sciences.

  9. See Krippendorf (2004) for a complete history of content analysis, including its development and application across disciplines.

  10. Internal Revenue Code Sect. 857(a)(1).

  11. A growing body of empirical research documents REIT transparency (Hardin et al. 2005; Devos et al. 2007; Hardin and Hill 2008; Blau et al. 2009; Dolvin and Pyles 2009). See Blau et al. (2009) for a review of this work.

  12. http://www.reit.com/InstitutionalInvestors/Dividends/tabid/101/Default.aspx

  13. Because the NAREIT index tracks all publicly traded tax qualified REITs, there is no index inclusion effect such as is seen when firms are included in, for example, the S&P 500 Index, which could to lead to increased exposure with higher levels of analyst coverage.

  14. http://www.djindexes.com/

  15. Differences between the FTSE NAREIT All REITs Index and the Dow Jones REIT Composite Index are minimal. The NAREIT index is set to 100 as of December 31, 1971, while DJRC index pegs the index at 100 on the base date of December 31, 1991. Additionally, the exact date a firm is added to, or removed from, index calculation may vary slightly between the two. On a monthly basis, returns between the NAREIT and DJRC indices have a correlation coefficient of 0.9988 during the sample period.

  16. See Tetlock et al. (2008) for a fairly detailed listing of work involving the application of content analysis in finance.

  17. See the Appendix for further detail on content analysis, a description of the categories, and word lists.

  18. Loughran and McDonald (2009) create a customized word list dealing with financial terms as well. However, we use the Henry (2008) word list because we are dealing specifically with earnings and earnings announcement related terminology, consistent with Davis et al. (2007), Henry (2008), Sadique et al. (2008).

  19. We run our analysis using an analyst EPS forecast based surprise measure as well. While results are unchanged, incorporating analyst forecasts causes substantial sample attrition which can potentially lead to a large firm bias. Furthermore, Ljungqvist et al. (2009) show analyst data sources to be unreliable and Graham et al. (2005) find that 85.1% of CFO’s consider earnings in the same quarter of the prior year to be the most important earnings benchmark. Accordingly, analyst forecast based measure results are untabulated.

  20. Although Downs and Guner (2006) find that analyst REIT FFO forecast quality is higher than for EPS, Baik et al. (2008) show that the usefulness of FFO is dependent on its reconciliation to GAAP EPS.

  21. Although not shown, the descriptive statistics for the various tone measures broken down by quarter show little variation through time.

  22. Again, results are identical for both H-TONE1 and H-TONE2 based abnormal return portfolios.

  23. Feldman et al. (2009) advocate using the change-in-tone from the most recent past rather than tone levels. They argue that change-in-tone helps mitigate potential problems associated with recurring word usage which may be unique to a particular industry or firm. Additionally, the unexpected component of tone may convey the most relevant information to investors. Davis et al. (2007) and Engelberg (2008) use change-in-tone in addition to tone levels as alternative tone measures and find their results remain qualitatively unchanged. Following Davis et al. (2007), we take the difference in conference call tone in the current and immediately preceding quarter as an alternative specification. Although not shown, we find similar results when the tone measures are calculated as the change-in-tone from the preceding quarter.

  24. While we find statistical significance, the proportion of unexplained variation in abnormal returns remains large as seen with the low R-squared measure. However, this is entirely consistent with the literature which generally finds low R-squared measures when working with CARs.

  25. Recalling the relation between earnings announcement and conference call dates displayed in Table 1, the two-day conference call period includes or immediately follows the earnings announcement date. Thus, the nine-day significantly negative coefficients on earnings surprise suggest correction to initial overreaction.

  26. The abnormal return pattern in Fig. 1 reflects the combined influences of the conference call and the earnings release.

  27. Sorts based on H-TONE1 and H-TONE2, in addition to sorts using medians, are untabulated since the results are qualitatively the same as those presented. Furthermore, results for H-TONE1 and H-TONE2 are identical to one another since the sort breakpoints are the same for the two tone constructs.

  28. Across both tables, 12 out of 12 two-day CARs in the low earnings surprise tercile are negative, indicating that these earnings releases tend to be negative surprises.

  29. Regression results are consistent across all tone measures and choice of benchmark model used to estimate abnormal returns. For brevity, results using the TONE1 and H-TONE1 tone measures, as well as the benchmark model that incorporates both the Dow Jones REIT Composite Index returns and the CRSP Value Weighted returns as explanatory variables in abnormal returns estimation, are not tabulated. Additionally, untabulated descriptive statistics show that the tone of the introductory portion of the calls is slightly more positive than discussion portion in every quarter of the sample, with little difference in both relative and absolute magnitudes of the different tone measures over time.

  30. We thank an anonymous referee for suggesting this addition. The additional variables are selected following related work by Davis et al. (2007), Tetlock (2007), Tetlock et al. (2008), Engelberg (2008), and Frankel et al. (2010), which control for both the disclosure of additional information and other factors that are known to affect returns.

  31. The literature shows that REIT stocks and industrial equities behave in a similar manner in some cases and differently in others. For example, when analyzing REIT IPOs, Hartzell et al. (2005) and Buttimer et al. (2005) find that, in contrast to typical equities, REITs do not experience short term underpricing and long run underperformance. However, other studies find that REITs tend to behave like other stocks following structural changes to the industry in the early 1990s. Chan et al. (2005) find that REITs exhibit the weekend effect similar to typical stocks. In a similar fashion, Redman et al. (1997) show that the January effect is present in REIT returns like other equities.

  32. Furthermore, in untabulated results we find that our inferences remain unchanged when the tone measures are calculated as the change in tone from the previous quarter.

  33. We thank an anonymous referee for suggesting this robustness check.

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Acknowledgements

The authors acknowledge the helpful comments and suggestions of Thomas Barkley, Bruce Billings, Dean Gatzlaff, David Geltner, Elaine Henry, James Kau, Bill Mayew, Rick Morton, Milena Petrova, CF Sirmans, Stacy Sirmans, an anonymous referee, and workshop participants at MIT, the ARUEUA 2009 Mid-Year Conference, and the 2009 FMA conference. We thank Brad Case for providing FTSE NAREIT All REITs Index firm data and Aleksander Wawer for General Inquirer technical support. All remaining errors are our own.

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Authors and Affiliations

  1. Department of Finance, College of Business, Florida State University, 821 Academic Way, P.O. Box 3061110, Tallahassee, FL, 32306–1110, USA

    James S. Doran & David R. Peterson

  2. Perella Department of Finance, Lehigh University, 621 Taylor Street, Bethlehem, PA, 18015, USA

    S. McKay Price

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  1. James S. Doran
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  2. David R. Peterson
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  3. S. McKay Price
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Correspondence to S. McKay Price.

Appendix

Appendix

The term content analysis appears in the Webster’s Dictionary of the English Language defined as, “analysis of the manifest and latent content of a body of communicated material through classification, tabulation, and evaluation of its key symbols and themes in order to ascertain its meaning and probable effect.” This study uses the General Inquirer (GI), a computer based application that identifies, classifies and tabulates the words in a written document. Developed by Harvard researcher, Philip J. Stone, this package has been widely used across various disciplines for over 40 years. Its application can be found in psychology, communication, journalism, and other social sciences (Krippendorf 2004).

The GI applies over 6,300 disambiguation rules in order to properly identify words within a body of text. For example, the program is able to identify Accomplished, Accomplishes, and Accomplishing as having the same common root, Accomplish, which it then classifies according to the definitions or categorizations of the dictionary applied to the analysis. The GI will recognize Accomplishments, as a separate word with the root Accomplishment, which then receives its own categorization based on the dictionary.

The default dictionary is the Harvard IV-4 Psychosocial Dictionary, a broad collection of roughly 12,000 words that fall within one or more of 77 separate categories. The categories include not only positive and negative words, but cover diverse areas such as political, religious, academic, gender specific, or even race related words. We identified eight relevant categories for the purpose of establishing an overall measure of positive and negative tone. Category descriptions, the number of root words, and a corresponding description of the type of words found in each category are in Table 9. The eight categories form four opposing pairs. Ultimately, it doesn’t matter how positive a body of text is considered in isolation since the positivity can be offset by an equal or greater amount of negativity. To account for this we use ratios in our analysis to create a relative measure.

Table 9 Harvard dictionary categories, description, and word counts
Full size table

Henry (2008) argues that the Harvard IV-4 Psychosocial Dictionary is too broad to be properly applied within the realm of finance and related disciplines. As such she creates her own list of “domain relevant” words that connote either a positive or negative outlook. The list is shown in Table 10. Of the collection of 189 words in the Henry list, all are found within the Harvard dictionary categorized under one of the eight categories listed in Table 9, except four words–Deteriorate, Downturn, Hurdle, and Hurdles.

Table 10 Henry word list
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Doran, J.S., Peterson, D.R. & Price, S.M. Earnings Conference Call Content and Stock Price: The Case of REITs. J Real Estate Finan Econ 45, 402–434 (2012). https://doi.org/10.1007/s11146-010-9266-z

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