< PreviousA PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES 20 MAY | JUNE 2020 t h e v a l u e e x a m i n e r IntroGrowthMatureShakeoutDecline Predicted Sign12345678 Operating Cash Flow-++-++-- Investing Cash Flow----++++ Financing Cash Flow++-++-+- Srivastava, 20187) and capital investment (Demere, 20178) are functions of firm life cycle. Further, compensation structure (Drake and Martin, 2016),9 disclosure policy/ information environment (Lu and Tucker, 201210; Chang et al., 2016;11 Hamers et al., 2016a;12 Hamers et al., 2016b13), and corporate governance structure (Demere, 2017) are also impacted by differences across life cycle. Finally, firm life cycle is an important control variable for explaining book-to- tax differences (Drake, 201514; McGuire et al., 201415), audit misstatements (Omer et al., 2011),16 and audit fee/client acceptance decisions (Brown and Knechel, 2016;17 Donohoe and Knechel, 201418). 7 Luminita Enache and Anup Srivastavaache, “Should Intangible Investments Be Reported Separately or Commingled with Operating Expenses? New Evidence,” Management Science 64, no. 7 (July 2018): 2973–3468. 8 Bryant William Demere, “Institutional Ownership and Long-Term Investments Along the Corporate Life Cycle” (PhD diss., Michigan State University, 2017). 9 Katharine Drake and Melissa Martin, “Implementing Relative Performance Evaluation: The Role of Life Cycle Peers,” Journal of Management Accounting Research, forthcoming. 10 Hung-Yuan Richard Lu and Jennifer Wu Tucker, “Nonearnings Corporate Guidance,” Financial Management 41, no. 4 (Winter 2012): 947–977. 11 Hye Sun Chang, Michael Donohoe, and Theodore Sougiannis, “Do analysts understand the economic and reporting complexities of derivatives?,” Journal of Accounting and Economics 61, no. 2–3 (April 1, 2016): 584–604. 12 Lars Hamers, Annelies Renders, and Patrick Vorst, “Firm Life Cycle and Analyst Forecast Behavior” (working paper, Maastricht University, 2016). 13 Lars Hamers, Annelies Renders, and Patrick Vorst, “Firm Life Cycle and Stock Price Crash Risk” (working paper, Maastricht University, 2016). 14 Katharine Drake, “Does Firm Life Cycle Inform the Relation between Book-Tax Differences and Earnings Persistence?” (working paper, University of Arizona, 2015). 15 Sean T. McGuire, Thomas C. Omer, and Jaron H. Wilde, “Investment Opportunity Sets, Operating Uncertainty, and Capital Market Pressure: Determinants of Investments in Tax Shelter Activities,” The Journal of the American Taxation Association 36, no. 1 (Spring 2014): 1–26. 16 Thomas C. Omer, Marjorie K. Shelley, and Anne Thompson, “Income Statement and Balance Sheet Effects of Variation in Misstatement Quantification” (working paper, 2011), 17 Stephen V. Brown and W. Robert Knechel, “Auditor-Client Compatibility and Audit Firm Selection,” Journal of Accounting Research 54, no. 3 (June 2016): 725–775. 18 Michael P. Donohoe and W. Robert Knechel, “Does Corporate Tax Aggressiveness Influence Audit Pricing?,” Contemporary Accounting Research 31, no. 1 (Spring 2014): 284–308. Identification of Firm Life Cycle Using Cash Flow Patterns Dickinson (2011) captures firm life cycle based on the patterns (determined by the sign) of annual operating, investing, and financing cash flows, and connects the mappings of the cash flow signs to economic theory. Specifically, the LCS classification is obtained by matching the sign of each cash flow activity, in a given fiscal year, to the corresponding life cycle stage as shown in Figure 1. Dickinson (2011) states, “there are three cash flow types (operating, investing, and financing) and each can take a positive or negative sign which results in 23 = 8 possible combinations. The eight patterns are collapsed into five stages.” 19 (See Figure 1.) We use the categories Introduction, Growth, Mature, and Decline for this study. The Shakeout category is defined by exception, and the firm years in that category lack the homogeneity to facilitate meaningful analysis. The prior study also demonstrates that the life cycle classification based on cash flow patterns is superior to other life cycle proxies used in prior literature, such as age, sales growth, dividend payout, and capital expenditures (Anthony and Ramesh, 1992;20 Black, 199821). The previous proxies were based on single-variable sorts, which impose an assumption of equal distribution across sample years, whereas economic theory does not indicate that a uniform distribution across all four LCSs is descriptive (i.e., there is no reason to expect 25 percent of the observations to be classified in each life cycle category). 19 Victoria Dickinson, “Cash flow patterns as a proxy for firm life cycle” (see n. 1), 1974. 20 Joseph H. Anthony and K. Ramesh, “Association between accounting performance measures and stock prices: A test of the life cycle hypothesis,” Journal of Accounting and Economics 15, no. 2–3 (June–September 1992): 203–227. 21 Ervin L. Black, “Life-Cycle Impacts on the Incremental Value-Relevance of Earnings and Cash Flow Measures,” Journal of Financial Statement Analysis 4, no. 1 (Fall 1998): 40–56. Figure 1: Mapping of Cash Flow Patterns and Life Cycle StagesA PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES t h e v a l u e e x a m i n e r MAY | JUNE 2020 21 AllIntroGrowthMatureDecline N firm years 199,59739,59965,38974,95819,651 % of observations100%19.8%32.8%37.6%9.8% MV ($ millions) 232.9042.44390.16570.8831.05 Assets ($ millions)331.3234.72627.86651.7734.37 Profit Margin2.8%-40.6%5.7%5.1%-45.2% Asset Turnover0.76 0.63 0.63 0.98 0.49 Leverage 54.3%54.7%57.2%52.2%54.3% ROA 1.7%-19.7%2.6%4.5%-22.4% ROE7.1%-14.7%8.3%10.3%-13.8% OCF/Revenue7.4%-27.3%13.6%12.0%-40.2% ICF/Revenue-7.8%-10.7%-25.4%-5.1%14.3% FCF/Revenue 1.6%47.6%11.8%-4.5%3.7% %NI<037.2%77.7%21.9%18.7%79.6% Table 1: Descriptive Statistics In contrast, the cash flow pattern proxy used in this paper allows an organic LCS assignment based on the transactions that give rise to the cash flow activities (i.e., based on the actual operations of the firm). Additionally, prior measures often relied on a time series of data to determine the LCS, whereas the measure adopted in this paper allows for a unique life cycle assignment using only current-year data. While some LCS assignments are relatively stable (especially in the mature stage), the cash flow measure allows for a more dynamic examination of changes in LCS over time. Finally, both this study and Dickinson (2011) confirm that the full range of life cycle classifications are present in every industry division, providing assurance that firm life cycle, as measured by cash flow patterns, is not simply capturing industry effects. Next, we use the life cycle classification to study the differential value relevance of revenue, net income, and operating cash flows, in general, and by industry division. Data To investigate the influence of LCS on several firm attributes, we obtained a sample of publicly traded firms with positive revenues for the years 1988 through 2019 from COMPUSTAT. The year 1988 is the first year for which we were able to obtain statement of cash flow data. Table 1 provides descriptive statistics for our sample. The main takeaway from this table is that firms differ substantially across the different LCSs in several key metrics. As expected, Intro and Decline firms are smaller than Growth and Mature firms in terms of market value and book assets. Moreover, the negative profit margin, ROA, and ROE for Intro and Decline firms indicate that these firms still tend to report losses on their income statements. Intro firms are less likely to be leveraged than Growth or Mature firms. For Intro and Decline firms, more than 75 percent of observations have negative net income. In contrast, for Growth and Mature firms, net income is negative for approximately 20 percent of the observations. Value Relevance by Life Cycle Stage The concept of value relevance is well established in the academic literature. Specifically, value relevance research assesses how well a given metric reflects information that is captured by stock prices. A commonly used approach to quantify a metric’s value relevance is based on its explanatory power (R2) with respect to the cross section of stock prices for publicly traded firms (e.g., Barth, Beaver, and Landsman, 2001).22 22 Mary E. Barth, William H. Beaver, and Wayne R. Landsman, “The relevance of the value relevance literature for financial accounting standard setting: another view,” Journal of Accounting and Economics 31, no. 1–3 (September 2001): 77–104. This table shows time-series averages of annually estimated medians for each variable by life cycle stage. N is the number of firm years. The number of observations across Intro, Growth, Mature, and Decline does not equal the total in All because observations in the Shakeout LCS are excluded in this analysis. MV is the firm’s market value (price per share × number of shares outstanding) at the end of the calendar year; Assets is the book value of a firm’s total assets; Asset Turnover equals revenues/assets; Profit Margin equals net income/revenues; Leverage equals liabilities/assets; ROA equals net income/assets; ROE equals net income/equity; OCF, ICF, and FCF refer to operating, investing, and financing cash flow, respectively; %NI<0 shows the time-series average of the annually measured percentage of times net income is negative. A PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES 22 MAY | JUNE 2020 t h e v a l u e e x a m i n e r 30% 40% 50% 60% 70% 80% 90% IntroGrowthMatureDecline Figure 2: Value Relevance by Life Cycle Stage REV NI OCF For Intro, Growth, and Mature firms, NI has the highest value relevance out of the three performance metrics considered. In contrast, for Decline firms, REV has the highest value relevance. Figure 2: Value Relevance by Life Cycle Stage Following this approach, we estimate value relevance using the following cross- sectional regression, which we estimate by year and LCS: MVi = β0 + β1 Xi + εi (1) where X equals revenue (REV), net income (NI), or operating cash flow (OCF). For a firm-year to be included in the regression analysis, we need non-missing data for all required variables. Since it is difficult to use a negative number to explain a positive market valuation, firm-years included in the regression sample need positive values for REV and NI. Although this restriction leads to a substantial reduction in sample size (approximately 35 percent), it provides for a more meaningful comparison of the explanatory power across the different performance metrics. Given that OCF is, by definition, negative for Intro and Decline firms, we cannot impose the requirement of having positive OCF for these LCSs. To mitigate the influence of regression outliers, we first remove observations with a studentized residual above four for regression (1). A studentized residual is obtained by dividing the residual by an estimate of its standard error. Since our sample period covers 32 years (1988 through 2019), we obtain up to 32 annually estimated R2 for each performance metric. Table 1 shows the time- series averages of the R2 for each performance metric, by LCS. We use two-sided t-tests based on the time-series distribution of these 32 annually estimated R2 to determine the statistical significance of any differences across R2 and across LCSs. Figure 2 shows the value relevance of REV, NI, and OCF, measured as the time- series average R2 for each performance metric, by LCS. For example, an R2 of 83 percent for NI indicates that 83 percent of the cross-sectional variation in market values is explained by NI. For Intro, Growth, and Mature firms, NI has the highest value relevance out of the three performance metrics considered. In contrast, for Decline firms, REV has the highest value relevance. A PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES t h e v a l u e e x a m i n e r MAY | JUNE 2020 23 IntroGrowthMatureDecline REV 64.8%57.8%61.6%66.4% NI 75.4%74.4%83.0%64.4% OCF 40.5%62.8%71.5%41.8% Table 2 shows the value relevance of REV, NI, and OCF, measured as the time-series average R2 for each performance metric, by LCS. For Intro, Growth, and Mature firms, NI is the most value-relevant performance metric (differences statistically significant at p<0.05 or higher). Note: For the sample used in the cross-sectional regressions, only firm-years with positive values for REV and NI are included. For Decline firms, OCF is the least value-relevant metric. The R2 for REV and NI are significantly different from the R2 for OCF (p<0.01), but they are not significantly different from each other. Given that Decline firms by definition have negative OCFs, one would expect the R2 for OCF to be lower. Table 3: Percentage of Observations by Life Cycle and Division AllIntroGrowthMatureDecline All 100%37.6%32.8%9.8%19.8% Agriculture, Forestry, and Fishing 0.4%0.4%0.3%0.4%0.5% Mining 7.7%7.1%10.3%6.2%5.8% Construction 1.1%1.5%0.7%1.0%1.5% Manufacturing 38.7% 46.3%29.6%41.0%44.9% Transport., Communication, Electric, Gas & Sanitary 9.5%5.8%11.0%11.6%4.5% Wholesale Trade 3.2% 4.0%2.5%3.7%2.5% Retail Trade 5.6%4.1%5.4%7.4%2.7% Finance, Insurance, Real Estate 15.7%8.4%23.1%12.9%16.4% Services 16.9%20.5%16.4%15.0%18.5% Nonclassificable 1.2%2.0%0.7%0.7%2.7% A comparison of the R2 for a given metric across the different LCSs reveals substantial variation across LCSs. REV seems to have the highest R2 for Decline firms. However, the differences in the R2 for REV are not statistically significant across LCSs. NI has the highest R2 for Mature firms (difference statistically significant at p<0.05 or better). Since Intro and Decline firms have negative OCFs, by definition, one expects the R2 based on OCF to be low. OCF generates the highest R2 for Mature firms, followed by Growth firms. Value Relevance by Life Cycle Stage and Industry Division Table 3 shows the composition of each LCS in terms of divisions. For instance, for the Intro LCS, a relatively high proportion of firm-years is drawn from the Manufacturing and Services divisions. Table 2: Value Relevance by Life Cycle Stage Notes: This table shows time-series averages of annually estimated R2 from regression (1), estimated by LCS-year. Bold font indicates highest value relevance within the LCS-stage. LCS is measured based on the statement of cash flows as explained above. The sample used covered a total of 122,206 firm-years out of which 8,630; 50,398; 59,657; and 3,521 are in Intro, Growth, Mature, and Decline, respectively. REV, NI, and OCF designate revenues, net income before extraordinary items, and operating cash flow, respectively. The percentages in italics in the All column/row show the percentage of observations by division (LCS). All other percentages show the proportion of observations covered by division in a given LCS. A PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES 24 MAY | JUNE 2020 t h e v a l u e e x a m i n e r Table 4: Value Relevance by Life Cycle Stage and Division Agriculture, Forestry and FishingWholesale Trade IntroGrowthMatureDeclineIntroGrowthMatureDecline REV 90.7%63.0%82.7%80.3% REV 61.5%61.8%52.0%77.7% NI 80.1%67.7%77.4%81.6% NI 76.8%87.9%84.8%78.4% OCF 85.7%58.3%86.1%80.9% OCF 39.6%79.2%74.4%62.2% MiningRetail Trade IntroGrowthMatureDeclineIntroGrowthMatureDecline REV 52.0%62.9%68.6%44.2% REV 69.3%66.9%67.9%74.6% NI 52.5%73.9%76.6%48.3% NI 73.0%85.4%90.0%78.7% OCF 55.6%81.9%87.3%44.7% OCF 48.6%80.3%87.8%72.5% ConstructionFinance, Insurance, Real Estate IntroGrowthMatureDeclineIntroGrowthMatureDecline REV 71.1%76.8%72.6%81.7% REV 73w.6%69.7%64.6%71.4% NI 80.0%78.5%78.3%75.4% NI 90.7%84.0%82.3%74.2% OCF 50.7%62.6%70.5%72.1% OCF 63.8%75.1%69.4%58.0% ManufacturingServices IntroGrowthMatureDeclineIntroGrowthMatureDecline REV 43.6%63.6%70.7%52.3% REV 53.1%54.1%63.8%60.9% NI 46.4%74.2%87.6%45.3% NI 58.1%70.6%87.9%57.7% OCF 35.4%76.2%87.9%44.4% OCF 45.7%71.3%87.0%53.5% Transport., Comm., Electric, Gas & SanitaryNonclassifiable IntroGrowthMatureDeclineIntroGrowthMatureDecline REV 64.6%69.2%77.8%71.9% REV 64.8%95.4%83.2%83.5% NI 71.1%72.1%83.5%79.4% NI 76.6%97.8%88.5%69.6% OCF 55.4%86.4%89.2%73.5% OCF 63.1%93.8%87.3%67.2% Casey et al. (2015)23 have shown that a firm’s industry division affects the relationship between performance metrics—such as REV, NI, and OCF—and price. In light of this finding, we repeat our analysis after not only decomposing our sample by LCS, but also by industry division (two-digit SIC code). Table 4 provides an interesting nuance about the interaction of LCS and division membership. If the goal is to identify which performance metric has the highest explanatory power with respect to market value, then one needs to consider LCS as well as division. For instance, for Intro firms in the Agriculture, Forestry, and Fishing division, REV generates an impressive R2 of 90.7 percent. In contrast, 23 Ryan Casey, Haimanot Kassa, and Philipp D. Schaberl, “Publicly Traded Firms: Accounting Metrics’ Relationship with Price, The Value Examiner (May/ June 2015): 10–16. Notes: This table shows time-series averages of annually estimated R2 from regression (1), estimated by division and LCS-year. LCS is measured based on the cash flow patterns as explained above. The sample used includes a total of 122,206 firm-years out of which 8,630; 50,398; 59,657; and 3,521 are in Intro, Growth, Mature, and Decline, respectively. REV, NI, and OCF stand for revenues, net income before extraordinary items, and operating cash flow, respectively. A PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES t h e v a l u e e x a m i n e r MAY | JUNE 2020 25 for Intro firms in the Manufacturing division, REV only explains 43.6 percent of the cross-sectional variation of market values. With an R2 of 90 percent, NI displays high explanatory power for Mature firms in the Retail division. In contrast, for Intro firms in the Manufacturing division, NI only achieves an R2 of 46.4 percent. Interestingly, for Growth and Mature firms in the Transportation, Communication, Gas, and Sanitary Services division, OCF beats REV and NI in terms of R2 (differences are statistically significant, p<0.01). Conclusion Firm life cycle stage is an important valuation metric and we have demonstrated in this study a classification methodology that is effective and straightforward in its application. By understanding how value drivers change conditional on firm life cycle, models can be constructed by LCS to enhance the forecasting and valuation functions. Firm LCS is an information variable that captures a firm’s strategy and investment choices, while also summarizing information about the firm’s industry and competitive environment. This study also points to interesting questions for future research. For example, can LCS be used in a DuPont decomposition framework to exploit differences in firm strategy? How does LCS interact with common valuation multiples; and can such insights be used to improve comparability and forecasting across firms? Finally, how does LCS interact with the relative importance of real options (i.e., abandonment or continuance)?24 Davit Adut, PhD, is an assistant professor of accounting at the Albers School of Business & Economics, Seattle University. He received his PhD in accounting from Texas A&M University and has held research appointments at the American University and University of Cincinnati. His research focuses on the effect of accounting information on executive compensation, analysts’ forecasts, and corporate governance. His publications appear in The Accounting Review, Journal of Accounting and Public Policy, and Advances in Accounting. He also serves as the senior editor for the Journal of International Accounting, Auditing and Taxation. His interests include horseback riding, soccer, and swimming. Victoria Dickinson, PhD, CPA, is an associate professor of accounting at the Patterson School of Accountancy, University of Mississippi. She received her PhD in accounting from the University of Wisconsin—Madison. Her research examines the effect of industrial organization on firm performance and valuation through the lens of financial statement analysis. Her work has been published in The Accounting Review, Review of Accounting Studies, Management Science, Accounting Horizons, Journal of Business Finance & Accounting, Advances in Accounting, and Issues in Accounting Education. She enjoys cooking, reading, camping, and her family (husband Tom, two grown children, three dogs, and three cats). Email: vdickins@ olemiss.edu. Philipp Schaberl, PhD, is an assistant professor of accounting at the Monfort College of Business at the University of Northern Colorado. He earned his MS and PhD in accounting from the University of Cincinnati and his MS in business and economics from the Johannes Kepler University in Linz, Austria. He has over a decade of experience teaching financial accounting to undergraduate, MACC, and MBA students in the U.S. and internationally. Dr. Schaberl has published his work on capital markets, valuation, and financial analysis in journals such as Financial Management, European Financial Management, Advances in Accounting, and The Value Examiner. He is associate editor for Advances in Accounting and an ad hoc reviewer for Review of Accounting Studies; Journal of Accounting, Auditing & Finance; and Managerial Finance. VE 24 The authors plan to address some or all of these issues, as well as a case study that demonstrates how this novel life cycle measure can be used in firm valuations, in future articles.Duff & Phelps Cost of Capital Navigator: U.S. Cost of Capital Module PRICING U.S. Cost of Capital Module Basic (two most recent years) Single User . . . . . . . . . . . . . . . . . . . . . $295 . . . . . . . . . +$129 per Basic additional user U.S. Cost of Capital Module Pro (all years, 1999 to present) Single User . . . . . . . . . . . . . . . . . . . . . $595 . . . . . . . . . . +$299 per Pro additional user *The U.S. Cost of Capital Module Basic subscription has EVERYTHING contained in the regular Basic U.S. Cost of Capital Module, but limits the number of cost of capital analyses/downloads based on your subscription level. The limits are three cost of capital analyses/downloads for Silver, seven for Gold, and ten for Platinum. Data is updated quarterly. 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To learn more, visit or call Member/Client Services at (800) 246-2488. NEW ENHANCED FEATURES NEW! Now Included in KeyValueData Bundles at no additional cost, OR BUY standalone! 1 1 4 5 2 3 2 3 4A PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES t h e v a l u e e x a m i n e r MAY | JUNE 2020 27 VALUATION /////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// /////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// Asset Impairment in Light of COVID-19 By Rizvana Zameeruddin, JD, LLM, MST, CPA As the uncertainty of the coronavirus (COVID-19) crisis continues to cause panic and confusion, one thing is clear: COVID-19 has undeniably transformed the way we conduct and value business. Government-mandated social distancing, shelter- in-place rules, and travel bans have changed consumer behavior and forced companies to scale back or cease operations entirely. Although the long-term effects of COVID-19 are yet to be realized, many American businesses have already experienced sharp, unprecedented declines in revenue and stock value. As the negative effects of COVID-19 intensify, companies are faced with the very real possibility of asset impairment. This article looks at the financial reporting and asset valuation considerations for businesses affected by COVID-19, specifically the issues surrounding asset impairment as outlined in Accounting Standards Codification (ASC) 350, Intangibles—Goodwill and Other, and in the guidance on impairment or disposal of long-lived assets in ASC 360, Property, Plant, and Equipment. To determine the extent to which COVID-19 has directly or indirectly impacted the value of its assets, an entity should assess whether disruptions to its operations result in a change of circumstances that trigger asset impairment. Shortages of raw materials, lower-than-anticipated earnings, business closures, dwindling revenues, news reports predicting industry-wide declines, and falling stock prices brought about by COVID-19 could all be viable indicators of asset impairment.1 If it appears that assets have been impaired, valuation analysts can utilize the information provided in assessing their clients’ final asset value. In general, temporary disruptions to business operations are not indicators of impairment; however, extended disruptions to business 1 See the box on page 29 for a more comprehensive list of factors that should be considered when determining if impairment testing is necessary. activities can bring about a change of circumstances that could devalue assets. In establishing asset impairment and value, particular attention should be paid to those events that affect an asset’s fair value. Business management should consider all relevant facts and circumstances, especially when revising accounting estimates and preparing forecasts. This can prove quite challenging in the current economic climate, as it requires the use of considerable judgment by all parties involved. An entity should disclose its use of estimates in a footnote to the financial statements when the resolution of matters could differ significantly from what is currently expected, it is reasonably possible that the estimates will change in the near future, and the effect of the change will be material.2 Even if a company does not believe that its assets are impaired, given the unpredictability of current circumstances and pending the efforts taken to curb the spread of COVID-19, it is likely that at least some assets or reporting units will become devalued in the future, and accounting estimates should be revised before enlisting the help of a valuation analyst. If estimates are revised, and those revisions are material, then business management must disclose their use in the notes to the financial statements. Impairment Testing Impairment testing is usually performed at a minimum on an annual basis, but if impairment indicators such as those potentially brought about by COVID-19 exist, interim testing is warranted. If events or changes in circumstances indicate a more-likely-than-not probability (greater than 50 percent) that the asset or reporting unit is impaired, then businesses should begin testing. The order in which assets are tested, and the frequency of testing, are important. As outlined in 2 ASC 275-10-50-4.A PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES 28 MAY | JUNE 2020 t h e v a l u e e x a m i n e r ASC 350 and ASC 360, testing should be conducted in the following order: 1 Indefinite-Lived Intangible Assets (ASC 350-30)* Test annually; interim testing permitted when impairment indicators exist. 2 Long-Lived Assets to Be Held and Used (ASC 360-10-35) Test only when impairment indicators exist. 3 Goodwill (ASC 350-20) Test annually; interim testing permitted when impairment indicators exist. *Note that other assets, such as inventory and equity method investments, should be tested simultaneously. 1. Indefinite-Lived Intangible Assets (ASC 350-30) Indefinite-lived intangible assets are required to be tested first for impairment. Intangible assets (excluding financial assets) have no physical properties, do not include goodwill, and are generally amortized over their useful lives. Examples include patents, trademarks, copyrights, and client lists. Indefinite-lived intangible assets have an unlimited or indefinite3 useful life; instead of being amortized, they are tested for impairment when indicators exist.4 Regardless of whether indefinite- lived intangible assets are developed or acquired, if they are fundamentally inseparable from one another, they are grouped together and tested collectively as a single unit of measure.5 ASC 350-30 provides guidance on whether an asset is deemed fundamentally inseparable, but generally speaking, business management should consider all relevant factors before making a decision. If, based on current facts and circumstances, management deems certain assets fundamentally inseparable from one another, it is not necessarily bound by that decision for future periods. Management can, in the future, determine that indefinite-lived intangible assets are not inseparable and should no longer be grouped together and tested collectively, based upon the future period’s facts and circumstances. If an entity decides that it should proceed with impairment testing based upon current facts and circumstances, it should clearly document the negative indicators that led to the decision to test, such as dwindling revenues, or falling stock prices. When it is more likely than not (greater than a 50 percent chance) that an indefinite-lived intangible asset or reporting unit is impaired, management should perform a quantitative impairment test as illustrated below. 1 Fair Value Calculation Is the fair value of the indefinite-lived intangible asset less than its carrying value? If Yes: go to 2 If No: stop, no impairment exists 2 Impairment Loss The amount by which the carrying value of the asset or reporting unit exceeds the fair value of the asset or reporting unit is equal to the impairment loss. 3 See ASC 350-30-35-4. The useful life of an intangible asset is indefinite if that life extends beyond the foreseeable horizon; that is, there is no foreseeable limit on the period of time over which it is expected to contribute to the cash flows of the reporting entity. 4 ASC 350-30. 5 ASC 350-30-35-21.A PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES t h e v a l u e e x a m i n e r MAY | JUNE 2020 29 • Unit carrying value is lower than its fair value Example: As a result of a loss of market share due to COVID-19, the fair value of a marketing firm subsidiary’s stock declines from $10 per share to $1 per share. The par value of the stock is $5 per share, with 10,000 shares outstanding, thus the reporting unit’s fair value is calculated as $100,000 before COVID-19 and $10,000 after COVID-19, which is lower than the legal capital amount of $50,000. This indicates that the carrying value of the unit is less than its fair value. • Business closures Example: An automobile manufacturer or its competitors are required to close due to government mandates. Note that whether the closure is temporary or permanent is not relevant. • Negative news reports Example: News channels are reporting a decline in the restaurant industry overall. • The competition has experienced a decrease in revenue Example: A general retailer’s competitors have recently experienced a substantial decrease in revenue. • Labor shortages Example: A meat processing plant or its competition are unable to maintain workforce due to government-mandated closures or lack of demand for their products, whether temporary or permanent. • Decreased demand Example: A cross-fit gym is unable to meet its contractual obligations due to decreased demand for its services as a result of obligatory closures. • Shortage of raw materials Example: An automobile manufacturer that imports steel from Japan is unable to obtain the steel due to COVID-19- related import restrictions. • Earnings decline or plateau Example: A department store’s revenue declines or stays below forecasted levels. • Forecasted earnings fall short Example: An entity that sells golf equipment predicted next quarter’s sales to be $100,000, but based upon COVID-19- related loss of business, next quarter’s earnings are now anticipated to be $1,000. • Market indicators Example: A home health company that is prevented from visiting and billing patients due to COVID-19 has advised the market that revenue goals will not be met. • Operating losses Example: A concert promoter is forced to refund ticket sales and cash flows are negatively affected, resulting in an operating loss. • Continued losses Example: A concert promoter is forced to cancel promoted concerts and refund ticket sales until the end of the year and cash flows are continuously negatively affected, resulting in a carryover of losses from one quarter to the next. • Stock price slumps Example: A movie theater’s stock price continues, for at least two consecutive periods, to be substantially below the pre- COVID-19 fair value amount. • Reporting unit disposition Example: A parent company of a chain of banquet halls is forced to permanently close one or more of its reporting units and anticipates the disposal of one or more of those units. • Recovery period revised Example: The recovery period of a movie theater’s goodwill is revised downward due to bleak industry forecasts. • Subsidiary goodwill impairment Example: A restaurant that also owns and operates a grocery store next door to its facility anticipates that the grocery store will operate at a loss. Source: ASC 350-30-35-18B (Indefinite-Lived Intangible Assets) and ASC 350-20-35-3C (Goodwill). Asset Impairment Testing Indicators Fair value calculation. When valuing assets in the COVID-19 era, valuation analysts should refer to ASC 820, which outlines three key valuation approaches that are most typically used by valuation experts: the cost, income, and market approaches. The cost approach uses the principle of substitution, where historical cost is the actual cost of the asset, and replacement cost is the current cost of a similar asset. The income approach converts future earnings into a single discounted amount (present value), and the method is usually reserved for income-producing intangible assets. A key concern of the income approach is separating cash flows uniquely related to the intangible asset from the cash flows related to the entire business unit. Under the market approach, a comparison is made between transactions of substantially similar intangible assets recently exchanged in arm’s-length transactions. This approach is most often used by valuation analysts when valuing assets for a public company, as data for public companies is more readily available. Next >