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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Notes
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.
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.
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.
The non-disclosing firms are excluded from the sample when we use Horizon to measure the firms’ MEF behaviors.
The text-based network classification of industry data can be obtained from the Hoberg and Phillips data library online.
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.
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.
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.
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.
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.
We omit the coefficients and t-statistics of the control variables in Table 7 to make the table concise.
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).
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.
We omit the coefficients and t-statistics of the control variables in Table 8 to make the table appear more concise.
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.
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.
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.
We follow Campbell and Taksler (2003) in measuring the idiosyncratic stock return volatility.
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Appendix: Variable definition
Appendix: Variable definition
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Freq = the number of quarterly earnings forecasts released by the sample firm in the sample year.
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Lag_Freq = the number of quarterly earnings forecasts released by the sample firm in year t−1.
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Freq_SH = the frequency of short-horizon MEFs issued during the sample year. The short-horizon MEFs are the earnings forecasts released within 90 days prior to the forecast period.
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Freq_LH = the frequency of long-horizon MEFs issued during the sample year. The long-horizon MEFs are the earnings forecasts released more than 90 days prior to the forecast period.
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Horizon = one plus the natural log of the difference between fiscal end date and the forecast date.
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Lag_Horizon = one plus the natural log of the difference between fiscal end date and the forecast date in year t−1.
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Inst_Owner = the percentage of firm’s shares owned by the institutional investors at the period end.
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No_Analyst = the number of financial analysts following the sample firm.
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Ret_Vol = the standard deviation of monthly raw return over the 36 months prior to the sample period.
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MEF_Cost = the voluntary disclosure cost, inversely measured by the industry level weighted average entry costs to measure firms’ competency to face the threat of new entrants.
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Earn_Predict = the logarithm transformation of R-square from regressing return-on-assets for the period t on return-on-assets for period t−4 over a rolling window of 16 quarters prior to period t.
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ERC = regressing 3-day cumulative market adjusted stock returns on unexpected earnings over 36 months prior to the period t.
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Nonsynch = the earnings non-synchronicity which is the residual from the model which pair-wise regresses the specific firm i’s return-on-asset (ROA) on its peer firms’ (within the same two-digit SIC code, excluding firm i) ROA over the 16 quarters prior to quarter t, following Gong et al. (2013).
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Peer_Nonsynch = the mean of peer firms’ earnings non-synchronicity excluding the specific firm.
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SEO = a dummy variable which equals 1 if the firm issues new equity in the period t + 1 and zero otherwise.
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Growth = the difference between present total assets and previous year total assets scaled by previous year total assets.
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Litigation = the dummy variable which equals 1 if the firm is in the high-risk industry (SICs 2833-2836, 3570-3577, 7370-7374, 3600-3674 and 5200-5961), zero otherwise.
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\(\Delta EPS\) = the changes in earnings from the previous year.
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SIZE = natural logarithm of firm total assets.
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BTM = book-to-market ratio.
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LOSS = the dummy variable which equals 1if the firm has a negative income before extraordinary items.
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Fasset = Fixed assets scaled by total assets.
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ROA = return on assets.
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Distress = 1 if sample year is 2008 or 2009.
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Rating_Firm = the dummy variable which equals 1 if the firm has the S&P credit rating, zero otherwise.
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Profit_Firm = the dummy variable which equals 1 if the firm’s profitability is within the top 25 percentile within each industry and equals 0 if the firm’s profitability is within the bottom 25 percentile within each industry
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Large_Firm = the dummy variable which equals 1 if the firm’s size is within the top 25 percentile within each industry and equals 0 if the firm’s size is within the bottom 25 percentile within each industry.
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MTB_Firm = the dummy variable which equals 1 if the firm’s market-to-book ratio is within the top 25 percentile within each industry and equals 0 if the firm’s market-to-book ratio is within the bottom 25 percentile within each industry.
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CEO_Reputation = alternatively measured by the accuracy of management earnings forecasts in the year t−1 and the CEO’s age in year t.
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Litigation_Risk = alternatively measured by the industry membership (a dummy variable which 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) and the percentage of contingent guarantee liability scaled by total assets in the year t.
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Profession = alternatively measured by manager’s ability and firm efficiency developed by Demerjian et al. (2012).
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Profit_Dissimilarity = absolute value of percent difference between the profitability of a specific firm and the average profitability of its peer firms.
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Size_Dissimilarity = absolute value of percent difference between the size of a specific firm and the average size of its peer firm.
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We define a firm is 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 sample firm and the randomly chosen firm from another industry is within the range of three continuous ratings. We use this method to define similar (dissimilar) firms for following variable:
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Similar_Firm_Freq = the disclosure frequency of a similar firm from a different industry.
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Similar_Firm_Horizon = the disclosure horizon of a similar firm from a different industry.
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Similar_Firm_Freq_LH = the disclosure frequency of long-horizon MEFs of a similar firm from a different industry.
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Similar_Firm_Freq_SH = the disclosure frequency of short-horizon MEFs of a similar firm from a different industry.
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We calculate the absolute value of the percent difference between an individual firm and its peer firms in terms of firm size or profitability and compare the value to the average distance between the size (profitability) of the individual firm and all of its peer firms. If a peer firm’s distance to the individual firm is smaller (larger) than the average distance, it is defined as a similar (dissimilar) peer firm. We use this method to define similar (dissimilar) peer firms for following variables:
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Similar_Size_Peer_Freq = disclosure frequency of peer firms with similar size compared to a specific firm.
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Dissimilar_Size_Peer_Freq = disclosure frequency of peer firms with dissimilar size compared to a specific firm.
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Similar_Profit_Peer_Freq = disclosure frequency of peer firms with similar profitability compared to a specific firm.
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Dissimilar_Profit_Peer_Freq = disclosure frequency of peer firms with dissimilar profitability compared to a specific firm.
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Tuo, L., Yu, J. & Zhang, Y. How do industry peers influence individual firms’ voluntary disclosure strategies?. Rev Quant Finan Acc 54, 911–956 (2020). https://doi.org/10.1007/s11156-019-00811-w
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DOI: https://doi.org/10.1007/s11156-019-00811-w
Keywords
- Management earnings forecasts
- Industry peer firms
- Peer effects
- Voluntary disclosure
- Managerial incentive
- Information asymmetry