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The impacts of product market competition on the quantity and quality of voluntary disclosures

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Abstract

This study examines how firms’ voluntary disclosure decisions are influenced by product market competition. Using separate measures to capture different dimensions of competition, I show that competition from potential entrants increases disclosure quantity while competition from existing rivals decreases disclosure quantity. I also find that competition enhances disclosure quality mainly through reducing the optimism in profit forecasts and reducing the pessimism in investment forecasts. Moreover, I find that the above association is less pronounced for industry leaders, consistent with industry leaders facing less competitive pressures than industry followers.

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Notes

  1. The only exception is Shin (2002), which differentiates capacity competition from price competition and shows that firms engaged in capacity competition disclose relatively more information than those engaged in price competition.

  2. The only exception is Rogers and Stocken (2005), who use Herfindahl–Hirschman Index as a proxy for industry concentration. However, as Karuna (2007) points out, industry concentration alone could be a poor proxy for competition due to endogeneity.

  3. To the best of my knowledge, Brown et al. (2006) and Jones and Cole (2008) are the only papers that study management forecasts on future capital expenditures.

  4. Dye (1985) and Jung and Kwon (1988) argue that partial disclosure could also be achieved if investors are unsure about the manager’s endowment of private information. However, all theoretical models discussed in this section assume that the manager is always endowed with private information and it is certain to investors that the manager has such information endowment.

  5. Using equally-weighted PP&E, capital expenditures, and R&D in the analysis does not change the results.

  6. For example, Sutton (1991) regards R&D outlays as endogenous sunk costs firms incur at stage 1 to enhance the demand for their products at stage 2. In contrast, setup costs are regarded as exogenous sunk costs at stage 1.

  7. Note that to compute the value of principal components, original variables are standardized. Therefore, principal components could have negative values.

  8. Ali et al. (2009) argue that Compustat-based industry concentration measures are subject to measurement error, as most of the private firms are not covered by Compustat and high Compustat-based concentration ratio is likely to be due to the declining of the industry, which is left with only a few large, public firms relative to private firms. Alternatively, they suggest that researchers should use concentration ratios from US Census data. In this paper, I choose to use Compustat concentration measures for the following reasons. First, the US Census measure of concentration is only available for the year 2002 of my sample period and only available for manufacturing industries. Hence, using US Census data would reduce the sample size, thereby contradicting the aim of this paper to provide large sample evidence. Second, using a Compustat-based concentration measure in this paper is a conservative approach. Previous literature suggests that firms with poor performance usually provide less voluntary disclosures (Miller 2002; Kothari et al. 2009). Therefore, if the Compustat-based concentration ratio is capturing the declining of the industry, using it will work against me finding the results for H1B that existing competition (industry concentration) is negatively (positively) associated disclosure quantity. Nevertheless, I further address the measurement error problem with Compustat-based competition measures in the robustness analysis by using exploratory factor analysis.

  9. Under Safe Harbor, it is more difficult to prove the defendant guilty, because plaintiffs must identify the specific statement or statements that are misleading when they file the lawsuit rather than undertaking a “fishing expedition” for supporting documentation during the discovery process (Johnson et al. 2001).

  10. The results are unchanged if total assets are used as the deflator.

  11. Management forecasts are mainly issued through conference calls, conferences, analyst meetings, shareholder presentations and press releases. To obtain information about management investment forecasts, I first use key words to search in Factiva and download all output articles. The search period starts 24 months before the forecasting fiscal year-end. Then, I use Perl to extract relevant information from downloaded articles. Finally, I manually read and code the extracted information.

  12. The purpose of this data requirement is to have meaningful industry-average measures. Nevertheless, the results are not sensitive to this data requirement (unreported).

  13. Two possible reasons may cause the data on management forecasts to be missing: (1) the firm did not issue any forecast or (2) the firm was outside the coverage of First Call database, which primarily covers only firms followed by analysts (Anilowski et al. 2007). I, therefore, limit the sample to firms that have data available on analyst estimates. In other words, a firm with missing data on management forecasts is regarded as a nonforecaster only if it has non-missing data on analyst estimates. Firm-years that are not covered by analysts are excluded from the sample.

  14. Note that the numbers for ACCURACY and SURPRS in this paper are multiplied by 100.

  15. The results are qualitatively similar but with lower statistical significance if the standard errors are clustered at industry-level, due to the small degrees of freedom for industry-level regressions.

  16. The results are qualitatively similar but with lower statistical significance if the standard errors are clustered at industry level, due to the small degrees of freedom for industry level regressions.

  17. The results are similar if the standard errors are clustered by both industry and calendar year.

  18. Although the medians are negative, the magnitudes are much smaller, indicating a long right tail. The reason why investment forecasts are pessimistic on average is unclear and outside the scope of this paper. The empirical evidence on the market reaction to investment announcements is mixed. For example, McConnell and Muscarella (1985) find that announcements of increases in capital expenditures lead to significant positive stock returns for industrial firms, but such association does not exist for public utility firms. Chung et al. (1998) find that share price reaction to a firm’s capital expenditure decisions depends critically on the capital market’s assessment of the quality of its investment opportunities.

  19. Note that competition measures based on common factors, such as POTENT-COMP, EXIST-COMP, POTENT-COMPEFA, and EXIST-COMPEFA, have been multiplied by −1, so that higher value indicates higher competition level. Therefore, the coefficients on some of the original variables, such as IND-PPE, IND-CPX, and IND-CON4, in this table have the opposite signs as those on POTENT-COMP and EXIST-COMP in Table 6.

  20. I thank the referee for pointing out this issue and suggesting the H-statistic.

  21. So far, this measure has only been empirically applied in banking industries.

  22. In unreported analysis, results from regressing POTENT-COMPj,t on POTENT-COMPj,t−1, POTENT-COMPj,t−2, FORECASTERj,t−1%, FORECASTERj,t−2%, and control variables indicate that disclosure does not increase potential competition in a Granger sense. Similarly, results from regressing EXIST-COMPj,t on EXIST-COMPj,t−1, EXIST-COMPj,t−2, FORECASTERj,t−1%, FORECASTERj,t−2%, and control variables indicate that disclosure does not decrease existing competition in a Granger sense.

  23. I thank the referee for pointing this out and suggesting the alternative measure. The results are qualitatively similar if I use the average forecasting accuracy from years t − 3, t − 2, and t − 1 as an alternative.

  24. For example, For example, Harris (1998) and Botosan and Stanford (2005) find that competition encourages disclosure, while other empirical studies generally find that competition discourages disclosure.

  25. SFAS No. 14 became effective in 1976. Therefore, 1977 is the first calendar year when segment data were available for all firms.

  26. This is consistent with the methodology that SIC uses to assign primary SIC code to each firm.

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Acknowledgments

I am grateful to my supervisor, Lakshmanan Shivakumar, for his support and guidance. I appreciate comments from the editor, Stephen Penman, two anonymous referees, the discussant, Christo Karuna, and other participants at the 2009 Review of Accounting Studies Conference. I also thank Maria Correia, Francesca Franco, Julian Franks, Emeric Henry, Oguzhan Karakaş, Ningzhong Li, Yun Lou, Ane Tamayo, İrem Tuna, Oktay Urcan, Florin Vasvari, Paolo Volpin, Li Zhang, and workshop participants at University of Minnesota, University of Pittsburgh, McGill University, University of Illinois at Chicago, University of Washington, University of Georgia, University of Southern California, Temple University, Ohio State University, Boston College, London School of Economics and Political Science, HEC Paris, ESSEC Business School, INSEAD, Lancaster University, Singapore Management University, National University of Singapore, and Nanyang Technological University.

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  1. London Business School, Regent’s Park, London, NW1 4SA, UK

    Xi Li

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Correspondence to Xi Li.

Appendices

Appendices

1.1 Appendix 1: Variable definitions

IND-PPE:

The weighted average of property, plant, and equipment of all firms in an industry. A firm’s market share, calculated as the ratio of its segment sales to industry aggregate sales, is used as its weight. A firm’s segment PP&E is allocated according to the ratio of the segment sales to the firm’s total sales.

IND-R&D:

The weighted average of research and development of all firms in an industry. A firm’s market share, calculated as the ratio of its segment sales to industry aggregate sales, is used as its weight. If a firm’s segment R&D is missing, it is replaced by the firm’s total R&D multiplied with the ratio of the segment sales to the firm’s total sales.

IND-CPX:

The weighted average of capital expenditures of all firms in an industry. A firm’s market share, calculated as the ratio of its segment sales to industry aggregate sales, is used as its weight. If a firm’s segment capital expenditures are missing, they are replaced by the firm’s total capital expenditures multiplied with the ratio of the segment sales to the firm’s total sales.

IND-MKTS:

Product market size, measured as the natural log of industry aggregate sales.

IND-CON4:

Four-firm concentration ratio, measured as the sum of market shares of the four largest firms in an industry.

IND-HHI:

Herfindahl–Hirschman Index, measured as the sum of squared market shares of all firms in an industry.

IND-NUM:

Total number of firms in the industry.

IND-MGN:

Price-cost margin, measured as industry aggregate sales divided by industry aggregate operating costs. If a firm’s segment operating cost is missing, it is replaced by the segment sales divided by the firm’s price-cost margin.

IND-ROA:

Return on assets, measured as industry aggregate operating profit before depreciation divided by industry aggregate total assets. If a firm’s segment operating profit before depreciation is missing, it is replaced by the segment assets multiplied by the firm’s ROA. If a firm’s segment total assets is missing, it is replaced by segment operating profit before depreciation divided by the firm’s ROA. If both segment operating profit before depreciation and segment total assets are missing, they are replaced by the firm’s total operating profit before depreciation multiplied by the ratio of the segment sales to the firm’s total sales and the firm’s total assets multiplied by the ratio of the segment sales to the firm’s total sales, respectively.

POTENT-COMP:

The negative of PC2 from principal component analysis of nine competition variables. It measures competition from potential entrants.

EXIST-COMP:

The negative of PC1 from principal component analysis of nine competition variables. It measures competition from existing rivals.

IND-PROFIT:

PC3 from principal component analysis of nine competition variables. It measures industry profitability.

FORECASTER%:

The ratio of forecasters to the total number of firms in an industry. A firm-year is identified as a forecaster if it issues at least one forecast for the subsequent fiscal year-end.

NUM-FOR:

Total number of forecasts issued by a firm in a certain year.

ACCURACY:

Forecasting accuracy, defined as the negative of the absolute difference between actual earnings per share and management earnings forecast deflated by stock price two trading days before management forecast date. For investment forecasts, it is defined as the negative of the absolute difference between actual capital expenditures and management capital expenditures forecast deflated by market value of equity at fiscal year-end.

ERROR:

Forecast error, defined as the difference between actual earnings per share and management earnings forecast deflated by stock price two trading days before management forecast date. For investment forecasts, it is defined as the difference between actual capital expenditures and management capital expenditures forecast deflated by market value of equity at fiscal year-end.

SIZE:

Firm size, measured as natural log of a firm’s market value of equity (Item prcc_f × csho) at fiscal year-end.

MTB:

Market-to-book ratio, measured as market value of equity plus book value of liability (Item lt) divided by book value of total assets (Item at).

LEV:

Leverage ratio, measured as total liability (Item lt) minus deferred taxes (Item txdb) divided by total assets (Item at).

STDEV:

Earnings or capital expenditures volatility, measured as the standard deviation of earnings before extraordinary items or the standard deviation of capital expenditures scaled by total assets over the past five years. At least three years’ observations are required.

ANALYST:

The number of analysts following. Data are obtained from I/B/E/S database.

SHRINST:

The percentages of shares owned by institutional investors. Data are obtained from Thomson-Reuters Institutional Holdings (13F) Database.

ABSCH:

Absolute value of actual earnings change scaled by market value of equity; absolute value of actual capital expenditures change scaled by total assets.

DCH:

Dummy variable indicating that the actual earnings/capital expenditures during forecasting period are higher than the previous year.

OPTM:

Analyst optimism, measured as the difference between analyst consensus estimation at the beginning of the fiscal year and the actual earnings per share, scaled by the absolute value of actual earnings per share.

SURPRS:

Management forecasting surprise, defined as the difference between management earnings forecast and the latest consensus analyst estimation deflated by stock price two trading days before management forecast date. For investment forecasts, previous year’s actual capital expenditures are used as the proxy for market expectation and the market value of equity at fiscal year-end is used as the scalar.

DIFFI:

Forecasting difficulty, measured as the standard deviation of analyst estimates prior to the corresponding management forecast.

HORIZ:

Forecasting horizon, measured as the number of days between forecast release date and forecasting fiscal year-end divided by 100.

ISSUE:

A dummy variable equal to 1 if the firm issues either public equity or public debt in a subsequent two-year period, and zero otherwise. Data are extracted from Thomson Deal (SDC) database.

LIT:

Proxy for litigation risk, measured as a dummy variable equal to 1 if the firm operates in an industry facing high litigation risk, namely industries with primary four-digit SIC code 2833−2836, 8731−8734 (bio-tech), 3570−3577 (computer hardware), 3600−3674 (electronics), 7371−7379 (computer software), 5200−5961 (retailing), 4812−4813, 4833, 4841, 4899 (communications), or 4911, 4922−4924, 4931, 4941 (utilities).

STDRET:

Standard deviation of stock returns over a 120-day period prior to the forecast release date.

DACCR:

Discretionary accruals, estimated using the cross-sectional modified Jones model.

ACCURACY, ERROR, SURPRISE, STDRET, and DIFFI are multiplied by 100 in descriptive statistics and regressions for expositional purpose.

1.2 Appendix 2: measuring product market competition

The data for computing product market competition variables are extracted from Compustat segments and fundamentals annual databases for the period from 1977 to 2007. Footnote 25 The data and sample selection process are described as follows:

  1. 1.

    I delete firms incorporated outside the United States, as those firms are likely to face a different product market.

  2. 2.

    Data on net sales (Item sale), operating profit (Item ops), operating income before depreciation (Item oibd), research and development (Item rd), capital expenditures (Item capx), and identifiable total assets (Item at) are obtained from Compustat segments. Only business segments with valid primary four-digit SIC code (Item ssic1) are retained. Segments with identical SIC codes under the same firm are merged into one, and all financial items are aggregated.

  3. 3.

    Merge segment data with Compustat fundamentals annual data. Firms without segment information are treated as having a single segment.

  4. 4.

    Calculate industry-wide variables: IND-PPE, IND-R&D, IND-CPX, IND-MKTS, IND-CON4, IND-HHI, IND-NUM, IND-MGN, and IND-ROA.

  5. 5.

    I require non-missing values for all competition variables to conduct Principal Component Analysis and Exploratory Factor Analysis. The final sample consists of 27,053 industry-years over the period from 1977 to 2007.

  6. 6.

    Classify firms into different industries according to their primary segment SIC code. If a firm has multiple business segments, the segment with the same four-digit SIC code as the firm is identified as the primary segment. If none of the segments have the same SIC code as the firm, the segment with the largest sales is treated as the primary segment. Footnote 26

1.3 Appendix 3: examples for investment forecasts

Management investment forecasts data used in this paper are hand-collected from Factiva. I use management forecasts on future capital expenditures as the proxy for investment forecasts. Examples for investment forecasts are illustrated below:

Q4 2003 ALLTEL Corp. Earnings Conference Call, Jan. 23, 2004:

Turning to 2004, as Scott mentioned, we are making organizational changes to improve service delivery to our customers. These organizational changes which include a reduction of approximately 400 to 600 employees will result in a one-time charge of roughly $15 million in the first quarter, an operating expense savings of approximately $20 million this year. For the year, we expect total revenue growth of 2% to 5%, capital expenditures of $1.2 billion to $1.3 billion, and earnings per share from current businesses of $3.10 to $3.30.

Q4 2003 AMETEK Inc. Earnings Conference Call, Jan. 28, 2004:

For 2004 we expect the capital expenditures will total approximately $23 million, while depreciation and amortization should be about $35 million. Operating cash flow for 2004 is expected to be up low to mid single digit percentage from the exceptional 2003 level, driven by higher income and less positive changes in the balance sheet.

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Li, X. The impacts of product market competition on the quantity and quality of voluntary disclosures. Rev Account Stud 15, 663–711 (2010). https://doi.org/10.1007/s11142-010-9129-0

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