Abstract
This paper examines the effect of risk disclosure on firm operational efficiency using a unique database of nonfinancial, and non-utility French firms belonging to the SBF 120 index over the period 2007–2015. In a first step, we use a data envelopment analysis output-oriented variable returns to scale model to determine firm operational efficiency scores based on one output (i.e., sales revenue) and three inputs (i.e., net property, plant, and equipment; cost of goods sold; and selling, general, and administrative costs). These scores are used in a second step to estimate the effect of risk disclosure on operational efficiency after controlling for a set of other factors. The empirical results show a statistically significant positive relation between risk disclosure and operational efficiency, suggesting that firms tend to be relatively more efficient when they disclose more about their risk exposure. Overall, we provide evidence that firms with greater risk disclosure are seen by stakeholders as more credible and trustworthy, leading them to conduct better transactions and, consequently, to improve their operational efficiency. This result is consistent with the notion that stakeholders perceive transparent firms positively, particularly those revealing bad news.
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09 March 2020
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Notes
US firms are required to file their annual report in a standardized form (10-K form) that includes Section Item 1A for describing significant risk factors.
See Articles L.225-100, L. 225-100-1, and L. 225-100-2 of the French Commercial Code.
This management report should be accompanied by the chairperson’s report on internal control and risk management procedures.
DOC-2009-16 AMF (Autorité des Marchés Financiers) Position/Recommendation (Guide to preparing registration documents) provides details on information that should be reported in the “Risk Factors” section of the annual report.
Proprietary costs theory suggests that firms disclosing private information incur proprietary costs, including those related to competitive advantage, reputation, or litigation costs, because many parties (e.g., competitors, regulatory bodies, the government, pressure groups) can use public information in ways that are harmful to the firms’ interests (Verrecchia 1983).
For example, information on the risk of losing a major customer could help competitors in their benchmarking and revision of their commercial strategy in target markets. Such information would, however, reduce information asymmetry with the market, thus helping the assessment of future sales and of revenue variability (Fee et al. 2006).
The authors contend that, even though risk disclosures are of negative tone, it is possible that these disclosures will be positively perceived by stakeholders when firms convey less negative information than expected, that is the overall news from the disclosure is positive.
In the same line of reasoning, Sirmon et al. (2007, p. 275) argue that “uncertainty in the industry or in potential competitors’ actions affects the type and amount of resources needed in the resource portfolio, the capabilities necessary to outperform rivals, and the leveraging strategies required to gain and maintain a competitive advantage.”
We conduct our main analysis using these three inputs due to the large amount of data missing for other additional inputs that we use in robustness checks (Sect. 5.2.4).
The DEA CSR model is used in robustness checks (Sect. 5.2.1).
Although sample homogeneity is a main assumption for running a DEA model, we notice that using a sample of firms belonging to different industries would not significantly affect our main results. Indeed, we have already controlled for possible variation of operational efficiency among industries by including industry dummies in the second-stage model regressions. Moreover, results from Panel B in Table 2 show the existence of a small variation in the mean operational efficiency across industries—ranging from 0.7630 (petroleum) to 0.5159 (Services)— which means that the heterogeneity of our sample does not seem to bias our results. In this respect, Demerjian et al. (2012) contend that operational efficiency scores are, in large part, a function of the number of observations available to estimate. Consistent with this view, Leverty and Qian (2011) confirm that a considerable industry operational efficiency variation is not attributable to sample heterogeneity but simply to the fact that, for industry with too few firms, a large percentage of these firms will be on the frontier resulting in a high average firm efficiency score. Demerjian et al. (2012), for example, require at least 100 observations to estimate DEA. In our case, only three industries among the 11 formed industry groups have at least 100 observations over the study period, which makes it more relevant to run DEA model regressions over the whole sample following a number of studies such as Tsolas (2015) and Weill (2003).
Following the argument of Campbell et al. (2014), since our interest is in risk factor section where the tone is, by nature, negative, and not in disclosures regarding the firm’s overall Management Discussion and Analysis (MD & A), we have accounted for the number of key words, sentences and factors relating to risk. We acknowledge that we do not explicitly control for the risk factor section’s net tone, that is the residual of positive over negative tone. Indeed, it is not easy to do as it requires software-reading technologies that are not particularly accurate (Kothari et al. 2009). Thus, we follow the same range of studies examining the tone of pure risk factor disclosures (e.g., Linsley and Shrives 2006; Dobler et al. 2011; Kravet and Muslu 2013; Abraham and Shrives 2014; Campbell et al. 2014) and count the number of words, sentences and factors that relate to risk as well as the number of total sentences, assuming that the context of these elements/units is negative/pessimistic. Moreover, Loughran, (2018) suggests to use only negative tone of risk disclosures (that is, the number of risk words, risk-related sentences or risk factors) because investors often discount positive language used by managers, such that positive words would probably not be included in the calculation of overall tone.
For example, a percentile rank of 75 means that firm operational efficiency is the same as or better than 75% of the normed comparison sample.
Scaling free cash flow by total sales does not affect our results.
Or that an average (median) firm can reduce its inputs by 40.08% (36%) and still produce the same level of output (input-oriented view).
Using the variables Risk words, Total sentences, or Risk factors instead of Risk sentences does not qualitatively alter the results of this sensitivity analysis.
See footnote 12.
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Appendix: Variable descriptions
Appendix: Variable descriptions
Variable | Description |
|---|---|
Dependent variable | |
Operational efficiency | The efficiency of a firm’s operations. In a first step, operational efficiency scores are measured based on a DEA output-oriented VRS model using sales revenue as output and three inputs, namely, net property, plant, and equipment, the cost of goods sold; and selling, general, and administrative expenses. In a second step, we use the standardized percentile ranks of firm operational efficiency scores for our regression analysis |
Risk disclosure variables | |
Risk words | The natural logarithm of the number of risk words in the section on risk factors of the annual report. The risk word list is from Kravet and Muslu (2013) and consists of the following words (as well as their derivatives, if any): against, catastrophe, challenge, chance, decline, decrease, differ, diversify, fail, fluctuate, gain, high increase, less, loss, low, peak, probable, reverse, risk, significant, shortage, threat, unable, uncertain, and viable |
Risk sentences | The natural logarithm of the number of sentences in the section on risk factors of the annual report containing at least one risk word appearing in the above list |
Total sentences | The natural logarithm of the number of total sentences in the section on risk factors of the annual report |
Risk factors | The number of risk factors reported in the section on risk factors of the annual report |
Risk disclosure index | The common factor extracted from the main risk disclosure variables (i.e., Risk sentences, Risk words, Total sentences, and Risk factors) using principal component analysis |
General risk factors | The natural logarithm of the risk factors that are not specific to the firm or to the industry to which it belongs |
Specific risk factors | The natural logarithm of the risk factors that are specific to the firm or to the industry to which it belongs |
Firm characteristics | |
Size | The natural logarithm of total sales |
Age | The natural logarithm of the number of years since the firm’s incorporation |
Free cash flow | The ratio of earnings before depreciation and amortization minus the change in working capital and capital expenditure to total assets |
Foreign operations | A dummy variable that equals one if the firm reports a nonzero value for foreign currency adjustments and zero otherwise |
Market share | The ratio of the firm’s sales to the total sales of all French listed firms in the industry to which the firm belongs and determined according to Campbell’s (1996) industry classification |
Segment concentration | The Herfindahl–Hirschman index for business segment concentration, i.e., the sum of the squares of the ratio of sales of each segment in which the firm operates to the firm’s total sales |
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Derouiche, I., Manita, R. & Muessig, A. Risk disclosure and firm operational efficiency. Ann Oper Res 297, 115–145 (2021). https://doi.org/10.1007/s10479-020-03520-z
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DOI: https://doi.org/10.1007/s10479-020-03520-z