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Published in: Review of Quantitative Finance and Accounting 3/2020

03-04-2019 | Original Research

How do industry peers influence individual firms’ voluntary disclosure strategies?

Authors: Ling Tuo, Ji Yu, Yu Zhang

Published in: Review of Quantitative Finance and Accounting | Issue 3/2020

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Abstract

This paper investigates how industry peer firms influence the voluntary disclosure strategies of individual firms. Our 2SLS regressions on an empirical sample of management earnings forecasts show that the disclosure strategies of individual firms are significantly influenced by their peer firms’ disclosure behaviors. Specifically, the increased disclosure frequency and disclosure horizon of their industry peers encourage individual firms to increase their disclosure frequency and disclosure horizon. Moreover, firms with S&P credit ratings, higher profitability, larger size, and/or a higher market-to-book ratio tend to be more sensitive to their peer firms’ voluntary disclosure frequency, and react more strongly to peer firms that are of dissimilar size or profitability. Finally, we find that the leader–follower relation does not influence the effects of peer firms’ disclosure strategies. Additional tests suggest that signaling theory and litigation risk provide stronger explanations of why firms mimic their peers than herding theory and free rider theory. This paper contributes to the accounting literature by providing new evidence on the effects of voluntary disclosure. Our findings are also of relevance to industry practitioners, and they shed light on the recently proposed voluntary disclosure regulations.

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Appendix
Available only for authorised users
Footnotes
1
Some researchers, practitioners, and regulators have expressed concern that management earnings guidance has become an earnings game because managers are forced to provide earnings guidance to meet the market expectations, which thus encourages managerial myopia. See, for example, Fuller and Jensen (2010), “Coke, Quarterly Estimation and ‘The Number Game,’” and “Numbers Game,” a speech delivered by former SEC Chairman Arthur Levitt in 1998.
 
2
Because the text-based network classification method generates a distinct peer for each specific firm in each year, we do not need to average over the peer variables.
 
3
To resolve the measurement errors caused by “bundled” MEFs suggested by Rogers and Buskirk (2013), we exclude the “bundled” MEFs that are issued concurrently with the earnings announcements. Then we remove the forecast observations with announcement dates more than 30 days after the associated firm-quarter fiscal period end date. We also exclude observations for which a MEF occurs within three days of either the analyst survey date or the announcement date of realized earnings. Finally, following Kothari et al. (2009), we exclude the extreme one percent of MEFs relative to analyst expectations and the extreme one percent of MEF forecast errors relative to realized EPS to mitigate the potential effects of miscoded data.
 
4
The non-disclosing firms are excluded from the sample when we use Horizon to measure the firms’ MEF behaviors.
 
5
The text-based network classification of industry data can be obtained from the Hoberg and Phillips data library online.
 
6
Table 2 presents the Pearson correlation results of the sample using the text-based network classification method to construct the peer firms. Untabulated results suggest similar Pearson correlations results are obtained for the sample using the 3-digit SIC code method to construct the peer firms.
 
7
In Sects. 7.1, 7.2, and 7.4, the tables present the results based on the sample using the text-based network classification method to construct the industry peers because this method generates slightly larger R-squares in our regressions. Untabulated results suggest similar conclusions based on the sample using the 3-digit SIC code method. In Sect. 7.3, the table presents the results based on the sample using the 3-digit SIC industry code method to construct the industry peers. Our sample size decreases significantly when determining the leader and follower under the text-based network classification method because there is only one distinct peer for each individual firm each year.
 
8
The tabulated results are based on the characteristics of the disclosure strategy. We also test the interaction terms by using the changes in disclosure strategy as independent/dependent variables and implement first different tests for robustness purposes. The untabulated results suggest that our conclusions are consistent.
 
9
In Table 7, we test the moderating effect on the association between a firm’s disclosure frequency/horizon and its peer firms’ after controlling the individual firm’s one-year lagged disclosure frequency/horizon. We also conduct robustness tests using either first difference tests or examining the moderating effect on the association between the change in a firm’s disclosure frequency/horizon and the change in that of its peer firms. Untabulated results suggest that our findings are consistent.
 
10
Kim and Skinner (2012) suggest that after controlling the firm characteristics, the industry-based measure can better measure a firm’s litigation risk. The industry-based measure is a dummy variable that equals 1 if the firm is in the biotech (SIC codes 2833-2836 and 8731-8734), computer (3570-3577 and 7370-7374), electronics (3600-3674), or retail (5200-5961) industry, and 0 otherwise.
 
11
We omit the coefficients and t-statistics of the control variables in Table 7 to make the table concise.
 
12
The manager’s ability and firm efficiency data are available on the author’s website. A detailed description of the measurement is introduced in Demerjian et al. (2012).
 
13
Although prior studies do not support that CEO reputation directly influences the mimicry of MEF behaviors, CEO reputation is commonly discussed in studies of peer effects in the management area. Therefore, this paper briefly investigates whether CEO reputation plays a moderating role in the interaction between a specific firm’s MEF behaviors and its peer firms’ MEF behaviors. Baik et al. (2011) suggest that earnings guidance contains useful information about the CEO’s ability. Moreover, prior research finds that the firm’s MEF performance influences managers’ job safety because the quality of MEF is positively correlated with CEO turnover (Peterson 2010; Lee et al. 2012). Hence, CEOs may carefully determine their management earnings forecast strategies to maintain their career and reputation. Because the relative performance evaluation is a popular method to determine executive compensation (Albuquerque 2009), managers may respond sensitively to peer firms’ MEF behaviors and may mimic the MEF strategies chosen by the industry leader.
 
14
We omit the coefficients and t-statistics of the control variables in Table 8 to make the table appear more concise.
 
15
We conduct robustness tests by either using first difference tests or examining the effect of leaders/followers’ disclosure strategies on an individual firm’s disclosure strategies. Untabulated results suggest our findings are consistent.
 
16
The largest 3000 U.S. companies cover the companies from S&P 500 Index, Domini 400 Social Index, 1000 Largest U.S. Companies, Large Cap Social Index, 2000 Small Cap U.S. Companies, and Broad Market Social Index. The approximate total number of companies covered per year is around 3100.
 
17
We define a firm as similar when the percent distances between the sample firm and a randomly chosen firm from another industry in terms of firm size, profitability, and MTB ratio are smaller than 0.1, and when the difference in the S&P credit rating between the specific firm and the randomly chosen firm from another industry is within the range of three continuous ratings.
 
18
We follow Campbell and Taksler (2003) in measuring the idiosyncratic stock return volatility.
 
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Metadata
Title
How do industry peers influence individual firms’ voluntary disclosure strategies?
Authors
Ling Tuo
Ji Yu
Yu Zhang
Publication date
03-04-2019
Publisher
Springer US
Published in
Review of Quantitative Finance and Accounting / Issue 3/2020
Print ISSN: 0924-865X
Electronic ISSN: 1573-7179
DOI
https://doi.org/10.1007/s11156-019-00811-w

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