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Review of Accounting Studies
Published in: Review of Accounting Studies 4/2019

25-08-2019

Key performance indicators as supplements to earnings: Incremental informativeness, demand factors, measurement issues, and properties of their forecasts

Authors: Dan Givoly, Yifan Li, Ben Lourie, Alexander Nekrasov

Published in: Review of Accounting Studies | Issue 4/2019

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Abstract

The documented decline in the information content of earnings numbers has paralleled the emergence of disclosures, mostly voluntary, of industry-specific key performance indicators (KPIs). We find that the incremental information content conveyed by KPI news is significant for many KPIs yet diminished when details about the computation of the KPI are absent or when the computation changes over time. Consistent with analysts responding to investor information demand, we find that analysts are more likely to produce forecasts for a KPI when that KPI has more information content and when earnings are less informative. We also analyze the properties of analysts’ KPI forecasts and find that KPI forecasts are more accurate than mechanical forecasts and their accuracy exceeds that of earnings forecasts. Our study contributes to the literature on the information content of KPIs as well as research on the properties of analysts’ forecasts. We provide evidence on whether and how to regulate voluntary disclosures.
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Appendix
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Footnotes
1
See, for example, Collins et al. (1997); Dichev and Tang (2008); Donelson et al. (2011); Francis and Schipper (1999); Givoly and Hayn (2000); Lev and Zarowin (1999); and Lev and Gu (2016).
 
2
For example, in its earnings announcement on January 25, 2018, American Airlines Group mentions available seat miles (ASM) 42 times.
 
3
A search on Amazon.​com yields close to 300 book titles dealing with or relating to “key performance indicators.” The popularity of the subject is apparently at such a high level that it warranted the publication of yet another book, Key Performance Indicators for Dummies (March 2015).
 
4
The measurement of some KPIs, particularly financial ones, are uniformly defined and measured. For example, KPIs such as “exploration expense” or “production expense” in the oil and gas industry are uniformly based on GAAP. The measurement of other KPIs may be determined by the regulator. For example, the value of “Capital Tier 1” is dictated by bank regulators, and the measurement of “proved reserves” in the oil and gas industry is prescribed in great detail by the SEC. The measurement of other KPIs may sometimes vary across firms and over time (e.g., same-store sales in the retail industry).
 
5
These four industries are the only nonfinancial industries with sufficient observations.
 
6
Industry-focused research has several advantages, including greater comparability of firms within the industry and ability to consider the economic context in which the performance measures are reported (Shevlin 1996).
 
7
See Clarkson and Matelis (2018). In June and August 2018, two companies that offer web hosting and online and email marketing products were the targets of SEC enforcement action for artificially inflating the rate of growth in subscribers (one of their important KPIs) by changing the definition of a “paying subscriber.” The case was eventually settled (see https://​www.​sec.​gov/​litigation/​admin/​2018/​33-10504.​pdf).
 
8
The survey covered more than 200 institutional investors, including portfolio managers, equity analysts, chief investment officers, and managing directors.
 
9
Similar guidance is offered by the EU Directive (2003) and by the IASB (see IASB 2010).
 
10
Our reading of comment letters suggests the following. While there seems to be general support for a principle-based approach that emphasizes materiality, the majority of respondents, including Big Four auditors, did not recommend prescriptive requirements for disclosure of specific KPIs. Their concerns included a potential reduction in the flexibility for the registrants to select variables that they consider most important and difficulties in identifying KPIs that apply to all firms in the industry.
 
11
Regulators abroad are equally concerned about the disclosure and standardization of KPIs, and these regulators either require or suggest adequate disclosures of them (e.g., IASB 2010; the EU Accounts Modernization Directive 2003; Section 417 of the Companies Act (2006) in the United Kingdom).
 
12
The KPI data were obtained directly from Thomson Reuters in February 2016.
 
13
I/B/E/S non-industry-specific KPIs relate to financial statement items (e.g., cost of goods sold, R&D expense, cash flow from operations), financial ratios (e.g., price-to-sales ratio, return on capital), and other variables not specific to any particular industry (e.g., free cash flow, number of shares outstanding). These “KPIs” are excluded because they do not represent information beyond that which is available or directly derived from the financial statements.
 
14
We exclude the financial industry because the majority of KPIs provided by I/B/E/S for that industry can be directly inferred from financial statements. For example, the three most forecasted KPIs in the financial industry are net interest income, loan loss provisions, and non-interest expense, all of which can be directly inferred from financial statements.
 
15
The results are very similar when we do not delete stale forecasts.
 
16
The requirement eliminates approximately 2.7% of KPI-firm-quarter observations. The five most populated KPIs excluded from our analysis are revenue per passenger mile in the airline industry, capacity for refining crude oil (measured in barrels per day), upstream income, refining income, and downstream income in the oil and gas industry.
 
17
Excluding financial industries, I/B/E/S reports industry KPIs for five industries: airline, oil and gas, pharmaceutical, retail, and technology. I/B/E/S uses a proprietary industry classification to construct these five industries. The oil and gas industry includes integrated oil and gas, exploration and production, and refining and marketing. The retail industry includes retail stores and restaurants. None of KPIs in the technology industry have 100 firm-quarters with analyst forecasts; therefore we exclude them from our analyses.
 
18
Our inferences remain intact when we delete observations in 2010–2016 in the pharmaceutical industry or when we exclude the pharmaceutical industry from the sample.
 
19
Many KPI, such as available seat miles and oil production per day, are measured in unscaled nonmonetary numbers; others such as same store sales and passenger load factor are measured as a growth rate or a ratio; while others—such as Distributable Cash Flow—reflect dollar amounts. Given this heterogeneity, scaling by average absolute value of the actual and forecasted value makes more sense than scaling by share price as is typically done for earnings and revenue surprises.
 
20
We use terciles rather than deciles to ensure a sufficient number of sample observations in each KPI surprise group, as some KPI have a relatively small number of observations. The results are robust to using deciles or quintiles.
 
21
Aside from capturing the collective information content of the industry KPIs, using the average surprise has the advantage of alleviating the difficulty (created by the high correlation between the industry KPI surprises) of identifying the incremental information content of individual KPIs.
 
22
Higher maintenance capital expenditures and higher production tax may convey positive information to investors, so there might be some ambiguity about the expected signs for these KPIs.
 
23
We also explored the market reaction in the post-announcement window (over the interval [+2,+63]) and did not find a significant drift in the market response to KPI surprises, EPS surprises, or revenue surprises in our sample. The absence of a significant drift could be due to insufficient test power.
 
24
The significant KPIs are ASM, RPM, DCF, OPD, RPG, EBX, EXP, TPP, RZP, SAL, SSS, and RES.
 
25
As discussed in Section 2.2, these problems are common to other voluntary and nonfinancial disclosures, such as those pertaining to intangible assets or to corporate social responsibility.
 
26
When the explanatory variables in the regression are uncorrelated, the contribution of an individual explanatory variable, Xi, to the multiple regression R2 is the R2 of the regression of Y on Xi. Shapley values can be used to assess the contribution of the explanatory variables in the more common case when the explanatory variables are not independent of each other. A convenient feature of the Shapley values is that they sum up to the regression R2. For a good introduction to Shapley values, see Israeli (2007).
 
27
Similar results (untabulated) are obtained when we use the standardized error, computed as the difference above deflated by the standard deviation of the time series of the actual values.
 
28
One explanation for this finding could be that the I/B/E/S data on KPIs are incomplete, because they omit the better KPI forecasts issued by analysts who prefer to share them only with their preferred clients rather than contribute them to I/B/E/S. This explanation is not very compelling, however, given the improved coverage of I/B/E/S in recent years and the fact that these better KPI forecasters still contribute their earnings and revenue forecasts to I/B/E/S.
 
29
The use of a single KPI, SSS in this case, for the analysis has the advantage of allowing variability of the informativeness of the KPI (as gauged in the case of SSS by the number of its mentions) over firm-quarters to affect analysts’ decision on whether to forecast the KPI.
 
30
For example, GAP Inc. reports SSSM for its three segments: Gap Global, Banana Republic Global, and Old Navy Global.
 
Literature
American Accounting Association (AAA), Financial Accounting Standards Committee. (2002). Recommendations on Disclosure of Nonfinancial Performance Measures. Accounting Horizons, 16, 353–362.
American Institute Of Certified Public Accountants (AICPA). (1994). Improving business reporting—A customer focus: A comprehensive report of the special committee on financial reporting. New York: AICPA.
Amir, E., & Lev, B. (1996). Value-Relevance of Nonfinancial Information: The Wireless Communications Industry. Journal of Accounting and Economics, 22, 3–30.
Barron, O., Kile, C., & O’Keefe, T. (1999). MD&A Quality as Measured by the SEC and Analysts’ Earnings Forecasts. Contemporary Accounting Research, 16, 75–109.
Bartov, E., Givoly, D., & Hayn, C. (2002). The Rewards to Meeting or Beating Earnings Expectations. Journal of Accounting and Economics, 33, 173–204.
Bowen, R., Davis, A., & Matsumoto, D. (2002). Do Conference Calls Affect Analysts’ Forecasts? The Accounting Review, 77(2), 285–316.
Bradshaw, M., 2011. Analysts’ Forecasts: What Do We Know After Decades of Work? Working paper, Boston College.
Bradshaw, M., Drake, M., Myers, J., & Myers, L. (2012). A Re-Examination of Analysts’ Superiority over Time-Series Forecasts of Annual Earnings. Review of Accounting Studies, 17(4), 944–968.
Bradshaw, M., Lee, L., & Peterson, K. (2016). The Interactive Role of Difficulty and Incentives in Explaining the Annual Earnings Forecast Walkdown. The Accounting Review, 91, 995–1021.
Brown, L. (2001). A Temporal Analysis of Earnings Surprises: Profits versus Losses. Journal of Accounting Research, 393, 221–241.
Call, A., Chen, S., & Tong, Y. (2009). Are Analysts’ Earnings Forecasts More Accurate when Accompanied by Cash Flow Forecasts? Review of Accounting Studies, 14, 358–391.
Chapman, K., & Green, J. (2015). Analysts’ Influence on Firm’s Voluntary Disclosure. Working paper.
Collins, D., Maydew, E., & Weiss, I. (1997). Changes in the Value-Relevance of Earnings and Book Values over the Past Forty Years. Journal of Accounting and Economics, 24, 39–68.
Curtis, A., Lundholm, R., & McVay, S. (2014). Forecasting Sales: A Model and Some Evidence from the Retail Industry. Contemporary Accounting Research, 31, 581–608.
DeFond, M., & Hung, M. (2003). An Empirical Analysis of Analysts’ Cash Flow Forecasts. Journal of Accounting and Economics, 35, 73–100.
Dichev, I., & Tang, V. (2008). Matching and the Changing Properties of Accounting Earnings over the Last 40 years. The Accounting Review, 83, 1425–1460.
Donelson, D., Jennings, R., & McInnis, J. (2011). Changes over Time in the Revenue-Expense Relation: Accounting or Economics? The Accounting Review, 86, 945–974.
Easterwood, J. C., & Nutt, S. R. (1999). Inefficiency in Analysts’ Earnings Forecasts: Systematic Miscreaction or Systematic Optimism? Journal of Finance, (5), 1777–1797.
Ehinger, A., Lee, J. A., Stomberg, B., & Towery, E.. (2017). Let’s Talk About Tax: The Determinants and Consequences of Income Tax Mentions During Conference Call. Working paper.
Elzahar, H., Hussainey, K., Mazzi, F., & Tsalavoutas, I. (2015). Economic Consequences of Key Performance Indicators’ Disclosure Quality. International Review of Financial Analysis, 39, 96–112.
Ertimur, Y., Livnat, J., & Martikainen, M. (2003). Differential Market Reactions to Revenue and Expense Surprises. Review of Accounting Studies, 8, 185–211.
Ertimur, Y., Mayew, W., & Stubben, S. (2011). Analyst Reputation and the Issuance of Disaggregated Earnings Forecasts to I/B/E/S. Review of Accounting Studies, 16, 29–58.
Financial Accounting Standards Board. (2001). Business and Financial Reporting: Challenges from the New Economy.
Francis, J., & Schipper, K. (1999). Have Financial Statements Lost Their Relevance? Journal of Accounting Research, 37, 319–352.
Francis, J., Schipper, K., & Vincent, L. (2003). The Relative and Incremental Information Content of Alternative (to Earnings) Performance Measures. Contemporary Accounting Research, 20, 121–164.
Fried, D., & Givoly, D. (1982). Financial Analysts’ Forecasts of Earnings: A Better Surrogate for Market Expectations. Journal of Accounting and Economics, 4(2), 85–107.
Givoly, D., & Hayn, C. (2000). The Changing Time-Series Properties of Earnings, Cash Flows and Accruals: Has Financial Reporting Become More Conservative? Journal of Accounting and Economics, 29, 287–320.
International Accounting Standards Board (IASB). (2010). IFRS Practice Statement. Management Commentary: A framework for presentation. London: International Accounting Standards Board Foundation.
Hand J., H. Laurion, A. Lawrence, and N. Martin. 2018. Analyst Forecast Data Feeds Are Not What They Used To Be. Working paper.
Hayn, C. (1995). The Information Content of Losses. The Accounting Review, 20(2), 125–153.
Healy, P., Hutton, A., & Palepu, K. (1999). Stock Performance and Intermediation Changes Surrounding Sustained Increases in Disclosure. Contemporary Accounting Research, 16(3), 485–520.
Israeli, O. (2007). A Shapley-Based Decomposition of the R-square of a Linear Regression. The Journal of Economic Inequality, 5, 199–212.
Ittner, C., & Larcker, D. (1998). Are Nonfinancial Measures Leading Indicators of Financial Performance? An Analysis of Customer Satisfaction. Journal of Accounting Research, 36, 1–35.
Kothari, S., So, E., & Verdi, R. (2016). Analysts’ Forecasts and Asset Pricing: A Survey. The Annual Review of Financial Economics, 8, 197–219.
Langer, M. (2006). Comparability of Sustainability Reports. A Comparative Content Analysis of Austrian Sustainability Reports. In S. Schaltegger, M. Bennet, & R. Burrit (Eds.), Sustainability Accounting and Reporting (pp. 581–602). Dordrecht: Springer.
Lev, B. (2001). Intangibles: Measurement, Management, and Reporting. Washington D.C: Brookings Institution.
Lev, B., & Gu, F. (2016). The End of Accounting and the Path Forward for Investors and Managers. Hoboken: John Wiley & Sons.
Lev, B., & Zarowin, P. (1999). The Boundaries of Financial Reporting and How to Extend Them. Journal of Accounting Research, 37, 353–386.
Lundholm, R. J., & Sloan, R. G. (2004). Equity Valuation and Analysis with eVal. New York: McGraw-Hill.
Matsumoto, D. (2002). Management’s Incentives to Avoid Negative Earnings Surprises. The Accounting Review, 77, 483–513.
O’Brian, P. C. (1988). Analysts’ Forecasts as Earnings Expectations. Journal of Accounting and Economics, 10, 53–83.
Pae, J., S. Wang, and C. Yoo. 2007. Do Analysts Who Issue Cash Flow Forecasts Predict More Accurate Earnings? Working paper.
Patatoukas, P., Sloan, R., & Zha, J. (2015). On the Pricing of Mandatory DCF Disclosures: Evidence from Oil and Gas Royalty Trusts. The Accounting Review, 90, 2449–2482.
Rajgopal, S., Shevlin, T., & Venkatachalam, M. (2003a). Does the Stock Market Fully Appreciate the Implications of Leading Indicators for Future Earnings? Evidence from Order Backlog. Review of Accounting Studies, 8(4), 461–492.
Rajgopal, S., Venkatachalam, M., & Kotha, S. (2003b). The Value Relevance of Network Advantages: The Case of E-Commerce Firms. Journal of Accounting Research, 41(1), 135–162.
Richardson, S., Teoh, S., & Wysocki, P. (2004). The Walk-Down to Beatable Analyst Forecasts: The Role of Equity Issuance and Insider Trading Incentives. Contemporary Accounting Research, 21, 885–924.
Schilit, H. M., & Perler, J. (2010). Financial Shenanigans, Third Edition. New York: McGraw-Hill.
Shapley, L. (1953). A Value for n-Person Games. Annals of Mathematical Studies, 28, 307–317.
Shevlin, T. (1996). The Value-Relevance of Nonfinancial Information: A Discussion. Journal of Accounting and Economics, 22, 31–42.
Trueman, B., Wong, F., & Zhang, X. J. (2001). The Eyeballs Have It: Searching for the Value in Internet Stocks. Journal of Accounting Research, 38, 137–162.
Wald, A. (1943). Tests of Statistical Hypotheses Concerning Several Parameters When the Number of Observations Is Large. Transactions of the American Mathematical Society, 54, 426–482.
Metadata
Title
Key performance indicators as supplements to earnings: Incremental informativeness, demand factors, measurement issues, and properties of their forecasts
Authors
Dan Givoly
Yifan Li
Ben Lourie
Alexander Nekrasov
Publication date
25-08-2019
Publisher
Springer US
Published in
Review of Accounting Studies / Issue 4/2019
Print ISSN: 1380-6653
Electronic ISSN: 1573-7136
DOI
https://doi.org/10.1007/s11142-019-09514-y

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