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Do Investors Infer Vocal Cues from CEOs During Quarterly REIT Conference Calls?

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Abstract

We examine the investor reaction to emotionally charged information. Using audio files of quarterly earnings conference calls and specialized Layered Voice Analysis software, we isolate the emotional content of managers’ vocal cues. With results that are both statistically and economically significant, we find that executive emotion is positively related to investors’ initial reaction. Moreover, this strong investor reaction to emotional signals by REIT managers appears to be justified, suggesting that credible, value-relevant information is contained in the emotion related signals. However, we also find some limited evidence of a partial reversal in subsequent trading windows, suggesting that investors may second guess themselves or fear they overreacted.

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Notes

  1. Comments made in a speech at Fordham Law School in New York as reported in a Wall Street Journal blog by Emily Chasan, “SEC Chairman: New Rules Could Lead to ‘Information Overload’”, 10/4/13 12:39 pm, accessed 1/28/14 2:30 pm.

    http://blogs.wsj.com/cfo/2013/10/04/sec-chairman-new-rules-could-lead-to-information-overload/

  2. See, for example, the Wall Street Journal article “App Tells You How You Feel” by Amir Mizroch, 3/10/14 7:17 pm, accessed 3/11/14 9:27 am.

    http://www.wsj.com/articles/SB10001424052702303824204579421242295627138?alg=y

  3. A more detailed discussion of this important feature follows in the background section below.

  4. Emily Chasan, “Mary Jo White Wants SEC to ‘Rethink’ Corporate Disclosures”, 1/27/14 12:55 pm, accessed 1/28/14 2:11 pm.

    http://blogs.wsj.com/cfo/2014/01/27/mary-jo-white-wants-sec-to-rethink-corporate-disclosures/

  5. Max Colchester, “Banks Listen In to Trader Talk”, 12/5/13 7:19 am, accessed 12/5/13 11:01 am.

    http://online.wsj.com/news/articles/SB10001424052702304096104579239564055355146

  6. To increase uniformity of the REIT sample as much as possible, in addition to implementing standard controls (e.g., firm size, book-to-market equity, and so forth) we also control for property sector using data from SNL Financial since firms within the REIT industry have become more focused on single asset types over time.

  7. There is an intermediate step where REITs use their limited cash holdings and short-term lines of credit to fund initial acquisitions on a timely basis. However, in the long-run they are subject to an exogenous capital constraint and must turn to the capital markets for continued growth. For example, Ott et al. (2005) find that 84 % of REIT investment is financed by equity and long-term debt. See Hardin and Hill (2008), Hardin et al. (2009), and An et al. (2012) for discussions of REIT cash holdings and revolving debt utilization.

  8. In contrast to Mayew and Venkatachalam (2012a) who parse the first five minutes of each call due to the labor-intensive nature of the process, we parse all speakers for the entire call. In later analysis, we find that our results are concentrated in the vocal cues of the CEO. This is not surprising given the CEO’s role as corporate figurehead. Mayew and Venkatachalam (2012a) also find CEO speech to matter more than CFO speech and limit their reporting and discussion to CEO driven results. For brevity, we report variables which stem from the LVA software and the corresponding results for the CEO only.

  9. Mayew and Venkatachalam (2012a) accomplish a similar check using the earnings surprise, where they argue that a negative earnings surprise will increase investor scrutiny. We contend that managers may be challenged on a call even when the earnings surprise is positive and the Cognitive variable allows us to tease out such a condition. Nonetheless, we repeat our analysis following the Mayew and Venkatachalam approach for separating high and low scrutiny using positive and negative earnings surprises and obtain consistent results.

  10. While descriptive statistics for all variables are in Table 1, a complete correlation table is included in the Appendix.

  11. The LVAi 7.7 output can potentially range from 1 through 8, however there are no observations in the sample with values of 1 or 2.

  12. For a partial list of studies which use Diction for word recognition and categorization see the company’s website, http://www.dictionsoftware.com/published-studies/#peerarticles. A non-exhaustive list of financially oriented research using Diction includes Rogers et al. (2011), Davis et al. (2012), and Davis and Tama-Sweet (2012). Li (2010) provides an early review of textual analysis in financially oriented disclosure research.

  13. A tone measure of 0 would indicate a call that is tone neutral, 1 would indicate a call devoid of negative words, and −1 would indicate the complete absence of positive words.

  14. http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

  15. We also separately run our analysis using four additional benchmarks for computing abnormal returns including the daily returns to the equity REIT index from NAREIT, the S&P 500 index, the CRSP value weighted index, and the CRSP equal weighted index. We further check our results using a market model for abnormal return estimation. The untabulated results are robust to model and benchmark variation.

  16. Following Mayew and Venkatachalam (2012a), we now report p-values using one-tailed tests for our variables of interest (Emotion and Cognitive) given that we now have a more definitive expectation with respect to the direction of the signs based on the unconditional correlations from Table 1 and the portfolio level sorts in Table 2, Panel A.

  17. We obtain consistent results (untabulated) when isolating abnormal returns to just the call date itself (trading-day t = 0). Moreover, when we isolate the week running up to the call date (trading-days t = −5 through t = −1) our variables of interest (Emotion/Cognitive) are not significant (untabulated). Thus, the reaction to the emotionally charged signals cannot be misconstrued as a reaction to pre-call information leakage.

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Acknowledgments

The authors wish to thank Amir Liberman, CEO of Nemesysco, for providing access to the proprietary Layered Voice Analysis software. This paper benefited greatly from multiple conversations with, and LVA assistance from, Bill Mayew. We also acknowledge valuable feedback from Stephen Dempsey, Mike Highfield, Matt Hill, Masaki Mori, two anonymous reviewers, and seminar participants at the University of Connecticut and the 2014 AREUEA International Conference. We are grateful to Brad Case and NAREIT for providing index data. All remaining errors are our own.

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Authors and Affiliations

  1. Perella Department of Finance, Lehigh University, Bethlehem, PA, 18015, USA

    S. McKay Price

  2. Mason School of Business, College of William & Mary, Williamsburg, VA, 23187, USA

    Michael J. Seiler

  3. College of Arts & Sciences, Regent University, 1000 Regent University Drive, Virginia Beach, VA, 23464, USA

    Jiancheng Shen

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  1. S. McKay Price
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  2. Michael J. Seiler
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  3. Jiancheng Shen
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Correspondence to S. McKay Price.

Appendix

Appendix

(Table 10).

Table 10 Unconditional correlations
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Price, S.M., Seiler, M.J. & Shen, J. Do Investors Infer Vocal Cues from CEOs During Quarterly REIT Conference Calls?. J Real Estate Finan Econ 54, 515–557 (2017). https://doi.org/10.1007/s11146-016-9557-0

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