FINANCIAL MANAGEMENT

CASE 5

Business Performance Evaluation: Approaches for Thoughtful Forecasting

Every day, fortunes are won and lost on the backs of business performance assessments and forecasts. Because of the uncertainty surrounding business performance, the man-ager should appreciate that forecasting is not the same as fortune-telling; unanticipated events have a way of making certain that specific forecasts are never exactly correct. This note purports, however, that thoughtful forecasts greatly aid managers in under-standing the implications of various outcomes (including the most probable outcome) and identify the key bets associated with a forecast. Such forecasts provide the manager with an appreciation of the odds of business success. This note examines principles in the art and science of thoughtful financial fore-casting for the business manager. In particular, it reviews the importance of (1) under-standing the financial relationships of a business enterprise, (2) grounding business forecasts in the reality of the industry and macroenvironment, (3) modeling a forecast that embeds the implications of business strategy, and (4) recognizing the potential for cognitive bias in the forecasting process. The note closes with a detailed example of financial forecasting based on the example of the Swiss food and nutrition com-pany Nestle.

Understanding the Financial Relationships of the Business Enterprise

Financial statements provide information on the financial activities of an enterprise. Much like the performance statistics from an athletic contest, financial statements provide an array of identifying data on various historical strengths and weaknesses

This technical note was prepared by Professor Michael J. Schill. Special thanks go to Vladimir Kolcin for data-collection assistance and to Lee Ann Long-Tyler and Ray Nedzel for technical assistance. Copyright © 2015 by the University of Virginia Darden School Foundation, Charlottesville, VA. All rights reserved. To order copies, send an e-mail to [email protected]. No part of this publication may be reproduced, stored in a retrieval system, used in a spreadsheet, or transmitted in any form or by any means—electronic, mechanical, photocopying, recording, or otherwise—without the permission of the Darden School Foundation.

Part Two Financial Analysis and Forecasting

across a broad spectrum of business activities. The income statement (also known as the profit-and-loss statement) measures flows of costs, revenue, and profits over a defined period of time, such as a year. The balance sheet provides a snapshot of business invest-ment and financing at a particular point in time, such as the end of a year. Both state-ments combine to provide a rich picture of a business’s financial performance. The analysis of financial statements is one important way of understanding the mechanics of the systems that make up business operations. Interpreting Financial Ratios Financial ratios provide a useful way to identify and compare relationships across finan-cial statement line items.1 Trends in the relationships captured by financial ratios are particularly helpful in modeling a financial forecast. The comparison of ratios across time or with similar firms provides diagnostic tools for assessing the health of the vari-ous systems in the enterprise. These tools and the assessments obtained with them provide the foundation for financial forecasting. We review common financial ratios for examining business operating performance. It is worth noting that there is wide variation in the definition of financial ratios. A mea-sure such as return on assets is computed many different ways in the business world. Although the precise definitions may vary, there is greater consensus on the interpreta-tion and implication of each ratio. This note presents one such definition and reviews the interpretation. Growth rates: Growth rates capture the year-on-year percentage change in a particu-lar line item. For example, if total revenue for a business increases from $1.8 million to $2.0 million, the total revenue growth for the business is said to be 11.1% [(2.0 − 1.8)/1.8]. Total revenue growth can be further decomposed into two other growth measures: unit growth (the growth in revenue due to an increase in units sold) and price growth (the growth in revenue due to an increase in the price of each unit). In the above example, if unit growth for the business is 5.0%, the remaining 6.1% of total growth can be attributed to increases in prices or price growth. Margins: Margin ratios capture the percentage of revenue that flows into profit or, alternatively, the percentage of revenue not consumed by business costs. Business profits can be defined in many ways. Gross profit reports the gains to revenue after subtracting the direct expenses. Operating profit reports the gains to revenue after subtracting all associated operating expenses. Operating profit is also commonly re-ferred to as earnings before interest and taxes (EBIT). Net profit reports the gains to revenue after subtracting all associated expenses, including financing expenses and taxes. Each of these measures of profits have an associated margin. For example, if operating profit is $0.2 million and total revenue is $2.0 milli

Case 5 Business Performance Evaluation: Approaches for Thoughtful Forecasting 91 margin is 10% (0.2/2.0). Thus, for each revenue dollar, an operating profit of $0.10 is generated and $0.90 is consumed by operating expenses. The margin provides the analyst with a sense of the cost structure of the business. Common definitions of mar-gin include the following: Gross margin = Gross profit/Total revenue where gross profit equals total revenue less the cost of goods sold. Operating margin = Operating profit/Total revenue where operating profit equals total revenue less all operating expenses (EBIT). NOPAT margin = Net operating profit after tax (NOPAT)/Total revenue where NOPAT equals EBIT multiplied by (1 − t), where t is the prevailing marginal income tax rate. NOPAT measures the operating profits on an after-tax basis without accounting for tax effects associated with business financing. Net profit margin = Net income/Total revenue where net income or net profit equals total revenue less all expenses for the period. A business that has a high gross margin and low operating margin has a cost structure that maintains high indirect operating expenses such as the costs associated advertising or with property, plant, or equipment (PPE). Turnover: Turnover ratios measure the productivity, or efficiency, of business assets. The turnover ratio is constructed by dividing a measure of volume from the in-come statement (i.e., total revenue) by a related measure of investment from the balance sheet (i.e., total assets). Turnover provides a measure of how much business flow is generated per unit of investment. Productive or efficient assets produce high levels of asset turnover. For example, if total revenue is $2.0 million and total assets are $2.5 million, the asset-turnover measure is 0.8 times (2.0/2.5). Thus, each dollar of total asset investment is producing $0.80 in revenue or, alternatively, total assets are turning over 0.8 times a year through the operations of the business. Common mea-sures of turnover include the following: Accounts receivable turnover = Total revenue/Accounts receivable Accounts receivable turnover measures how quickly sales on credit are collected. Busi-nesses that take a long time to collect their bills have low receivable turnover because of their large receivable levels. Inventory turnover = Cost of goods sold/Inventory Inventory turnover measures how inventory is working in the business, and whether the business is generating its revenue on large levels or small levels of inventory. For inven-tory turnover (as well as payable turnover) it is customary to use cost of sales as the volume measure because inventory and purchases are on the books at cost rather than at the expected selling price. PPE turnover = Total revenue/Net PPE

Part Two Financial Analysis and Forecasting

PPE turnover measures the operating efficiency of the fixed assets of the business. Businesses with high PPE turnover are able to generate large amounts of revenue on relatively small amounts of PPE, suggesting high productivity or asset efficiency.

Asset turnover = Total revenue/Total assets

Total capital turnover = Total revenue/Total capital

Total capital is the amount of capital that investors have put into the business and is defined as total debt plus total equity. Since investors require a return on the total capital they have invested, total capital turnover provides a good measure of the productivity of that investment. Accounts payable turnover = Cost of goods sold/Accounts payable Accounts payable turnover measures how quickly purchases on credit are paid. Businesses that are able to take a long time to pay their bills have low payable turnover because of their large payables levels. An alternative and equally informative measure of asset productivity is a “days” measure, which is computed as the investment amount divided by the volume amount multiplied by 365 days. This measure captures the average number of days in a year that an investment item is held by the business. For example, if total revenue is $2.0 million and accounts receivable is $0.22 million, the accounts receivable days measure is calcu-lated as 40.2 days (0.22/2.0 × 365). The days measure can be interpreted as that the average receivable is held by the business for 40.2 days before being collected. The lower the days measure, the more efficient is the investment item. If the accounts receiv-able balance equals the total revenue for the year, the accounts receivable days measure is equal to 365 days as the business has 365 days of receivables on their books. This means it takes the business 365 days, on average, to collect their accounts receivable. While the days measure does not actually provide any information that is not already contained in the respective turnover ratio (as it is simply the inverse of the turnover measure multiplied by 365 days), many managers find the days measure to be more in-tuitive than the turnover measure. Common days measures include the following: Accounts receivable days = Accounts receivable/Total revenue × 365 days Inventory days = Inventory/Cost of goods sold × 365 days Accounts payable days = Accounts payable/Cost of goods sold × 365 days Return on investment: Return on investment captures the profit generated per dollar of investment. For example, if operating profit is $0.2 million and total assets are $2.5 million, pretax return on assets is calculated as operating profit divided by total assets (0.2/2.5), or 8%. Thus, the total dollars invested in business assets are generating pretax operating-profit returns of 8%. Common measures of return on investment in-clude the following: Return on equity (ROE) = Net income/Shareholders’ equity where shareholders’ equity is the amount of money that shareholders have put into the business. Since net income is the money that is available to be distributed back to equity

Case 5 Business Performance Evaluation: Approaches for Thoughtful Forecasting 93 investors, ROE provides a measure of the return the business is generating for the equity investors. Return on assets (ROA) = NOPAT/Total assets where NOPAT equals EBIT × (1 − t), EBIT is the earnings before interest and taxes, and t is the prevailing marginal income tax rate. Like many of these ratios, there are many other common definitions. One common alternative definition of ROA is the following: Return on assets (ROA) = Net income/Total assets and, lastly, Return on capital (ROC) = NOPAT/Total capital Since NOPAT is the money that can be distributed back to both debt and equity inves-tors and total capital measures the amount of capital invested by both debt and equity investors, ROC provides a measure of the return the business is generating for all investors (both debt and equity). It is important to observe that return on investment can be decomposed into a margin effect and a turnover effect. That relationship means that the same level of business profitability can be attained by a business with high margins and low turnover, such as Nordstrom, as by a business with low margins and high turnover, such as Wal-Mart. This decomposition can be shown algebraically for the ROC: ROC = NOPAT margin × Total capital turnover NOPAT NOPAT = Total capital Total revenue × Total revenue Total capital Notice that the equality holds because the quantity for total revenue cancels out across the two right-hand ratios. ROE can be decomposed into three components: ROC = Net profit margin × Total capital turnover × Total capital leverage Net income Net income = Shareholders’ equity Total revenue × Total revenue Total capital Total capital × Shareholders’ equity This decomposition shows that changes in ROE can be achieved in three ways: changes in net profit margin, changes in total capital productivity, and changes in total capital leverage. This last measure is not an operating mechanism but rather a financing mech-anism. Businesses financed with less equity and more debt generate higher ROE but also have higher financial risk. Using Financial Ratios in Financial Models Financial ratios provide the foundation for forecasting financial statements because fi-nancial ratios capture relationships across financial statement line items that tend to be preserved over time. For example, one could forecast the dollar amount of gross profit for next year through an explicit independent forecast. However, a better approach is to forecast two ratios: a revenue growth rate and a gross margin. Using these two ratios in Part Two Financial Analysis and Forecasting combination one can apply the growth rate to the current year’s revenue, and then use the gross margin rate to yield an implicit dollar forecast for gross profit. As an example, if we estimate revenue growth at 5% and operating margin at 24%, we can apply those ratios to last year’s total revenue of $2.0 million to derive an implicit gross profit fore-cast of $0.5 million [2.0 × (1 + 0.05) × 0.24]. Given some familiarity with the financial ratios of a business, the ratios are generally easier to forecast with accuracy than are the expected dollar values. The approach to forecasting is thus to model future financial statements based on assumptions about future financial ratios. Financial models based on financial ratios can be helpful in identifying the impact of particular assumptions on the forecast. For example, models can easily allow one to see the financial impact on dollar profits of a difference of one percentage point in op-erating margin. To facilitate such a scenario analysis, financial models are commonly built in electronic spreadsheet packages such as Microsoft Excel. Good financial fore-cast models make the forecast assumptions highly transparent. To achieve transparency, assumption cells for the forecast should be prominently displayed in the spreadsheet (e.g., total revenue growth rate assumption cell, operating margin assumption cell), and then those cells should be referenced in the generation of the forecast. In this way, it becomes easy not only to vary the assumptions for different forecast scenarios, but also to scrutinize the forecast assumptions. Grounding Business Forecasts in the Reality of the Industry and Macroenvironment Good financial forecasts recognize the impact of the business environment on the per-formance of the business. Financial forecasting should be grounded in an appreciation for industry-and economy-wide pressures. Because business performance tends to be correlated across the economy, information regarding macroeconomic business trends should be incorporated into a business’s financial forecast. If, for example, price in-creases for a business are highly correlated with economy-wide inflation trends, the financial forecast should incorporate price growth assumptions that capture the avail-able information on expected inflation. If the economy is in a recession, then the fore-cast should be consistent with that economic reality. Thoughtful forecasts should also recognize the industry reality. Business prospects are dependent on the structure of the industry in which the business operates. Some in-dustries tend to be more profitable than others. Microeconomic theory provides some explanations for the variation in industry profitability. Profitability within an industry is likely to be greater if (1) barriers to entry discourage industry entrants, (2) ease of indus-try exit facilitates redeployment of assets for unprofitable players, (3) industry partici-pants exert bargaining power over buyers and suppliers, or (4) industry consolidation reduces price competition.2 Table 5.1 shows the five most and the five least profitable industries in the United States based on median pretax ROAs for all public firms from

Case 5 Business Performance Evaluation: Approaches for Thoughtful Forecasting 97 financial forecast. The forecast should recognize, however, that business strategy does not play out in isolation. Competitors do not stand still. A good forecast recognizes that business strategy also begets competitive response. All modeling of the effects of busi-ness strategy should be tempered with an appreciation for the effects of aggressive competition. One helpful way of tempering the modeling of business strategy’s effects is to complement the traditional bottom-up approach to financial forecasting with a top-down approach. The top-down approach starts with a forecast of industry sales and then works back to the particular business of interest. The forecaster models firm sales by modeling market share within the industry. Such a forecast makes more explicit the challenge that sales growth must come from either overall industry growth or market share gain. A forecast that explicitly demands a market share gain of, say, 20% to 24%, is easier to scrutinize from a competitive perspective than a forecast that simply projects sales growth without any context (e.g., at an 8% rate). Another helpful forecasting technique is to articulate business perspectives into a coherent qualitative view on business performance. This performance view encourages the forecaster to ground the forecast in a qualitative vision of how the future will play out. In blending qualitative and quantitative analyses into a coherent story, the fore-caster develops a richer understanding of the relationships between the financial fore-cast and the qualitative trends and developments in the enterprise and its industry. Forecasters can better understand their models by identifying the forecast’s value drivers, which are those assumptions that strongly affect the overall outcome. For example, in some businesses the operating margin assumption may have a dramatic impact on overall business profitability, whereas the assumption for inventory turnover may make little difference. For other businesses, the inventory turnover may have a tremendous impact and thus becomes a value driver. In varying the assumptions, the forecaster can better appreciate which assumptions matter and thus channel resources to improve the forecast’s precision by shoring up a particular assumption or altering the business strategy to improve the performance of a particular line item. Lastly, good forecasters understand that it is more useful to think of forecasts as ranges of possible outcomes rather than as precise predictions. A common term in fore-casting is the “base-case forecast.” A base-case forecast represents the best guess out-come or the expected value of the forecast’s line items. In generating forecasts, it is also important to have an unbiased appreciation for the range of possible outcomes, which is commonly done by estimating a high-side and a low-side scenario. In this way, the forecaster can bound the forecast with a relevant range of outcomes and can best appreciate the key bets of the financial forecast.

Recognizing the Potential for Cognitive Bias in the Forecasting Process

A substantial amount of research suggests that human decision making can be system-atically biased. Bias in financial forecasts creates systematic problems in managing and investing in the business. Two elements of cognitive bias that play a role in financial forecasting are optimism bias and overconfidence bias. This note defines optimism bias

Case 5 Business Performance Evaluation: Approaches for Thoughtful Forecasting 99 One approach to forecasting the financial statements for 2014 is to forecast each line item from the income statement and balance sheet independently. Such an ap-proach, however, ignores the important relationships among the different line items (e.g., costs and revenues tend to grow together). To gain an appreciation for those relationships, we calculate a variety of ratios (Exhibit 5.1). In calculating the ratios, we notice some interesting patterns. First, sales growth declined sharply in 2013, from 7.4% to 2.7%. The sales decline was also accompanied by much smaller decline in profitability margins; operating margin declined from 14.9% to 14.1%. Meanwhile, the asset ratios showed modest improvement; total asset turnover improved only slightly, from 0.7× to 0.8×. Asset efficiency improved across the various classes of assets (e.g., accounts receivable days improved in 2013, from 53.0 days to 48.2 days; PPE turnover also improved, from 2.8× to 3.0×). Overall in 2013 Nestle’s declines in sales growth and margins were counteracted with improvements in asset efficiency such that return on assets improved from 6.9% to 7.1%. Because return on assets com-prises both a margin effect and an asset-productivity effect, we can attribute the 2013 improvement in return on assets to a denominator effect—Nestle’s asset efficiency improvement. The historical ratio analysis gives us some sense of the trends in busi-ness performance. A common way to begin a financial forecast is to extrapolate current ratios into the future. For example, a simple starting point would be to assume that the 2013 financial ratios hold in 2014. If we make that simplifying assumption, we generate the financial forecast presented in Exhibit 5.2. We recognize this forecast as naïve, but it provides a straw-man forecast through which the relationships captured in the financial ratios can be scrutinized. In generating the forecast, all the line-item figures are built on the ratios used in the forecast. The financial line-item forecasts are com-puted as referenced to the right of each figure based on the ratios below. Such a forecast is known as a financial model. The design of the model is thoughtful. By linking the dollar figures with the financial ratios, the model preserves the existing relationships across line items and can be easily adjusted to accommodate different ratio assumptions. Based on the naïve model, we can now augment the model with qualitative and quantitative research on the company, its industry, and the overall economy. In early 2014, Nestle was engaged in important efforts to expand the company product line in foods with all-natural ingredients as well the company presence in the Pacific Asian region. These initiatives required investment in new facilities. It was hoped that the initiatives would make up for ongoing declines in some of Nestle’s important prod-uct offerings, particularly prepared dishes. Nestle was made up of seven major busi-ness units: powdered and liquid beverages (22% of total sales), water (8%), milk products and ice cream (18%), nutrition and health science (14%), prepared dishes and cooking aids (15%), confectionary (11%), and pet care (12%). The food process-ing industry had recently seen a substantial decline in demand for its products in the developing world. Important macroeconomic factors had led to sizable declines in demand from this part of the world. The softening of growth had led to increased competitive pressures within the industry that included such food giants as Mondelez, Tyson, and Unilever.

Part Two Financial Analysis and Forecasting Based on this simple business and industry assessment, we take the view that Nestle will maintain its position in a deteriorating industry. We can adjust the naïve 2014 forecast based on that assessment (Exhibit 5.3). We suspect that the softening of demand in developing markets and the prepared dishes line will lead to zero sales growth for Nestle in 2014. We also expect the increased competition within the industry will increase amount spent on operating expenses to an operating expense-to-sales ratio of 35%. Those assumptions give us an operating margin estimate of 12.9%. We expect the increased competition to reduce Nestle’s ability to work its inventory such that inventory turnover returns to the average between 2012 and 2013 of 5.53. We project PPE turnover to decline to 2.8× with the increased investment in new facilities that are not yet operational. Those assumptions lead to an implied financial forecast. The result-ing projected after-tax ROA is 6.3%. The forecast is thoughtful. It captures a coherent view of Nestle based on the company’s historical financial relationships, a grounding in the macroeconomic and industry reality, and the incorporation of Nestle’s specific busi-ness strategy. We recognize that we cannot anticipate all the events of 2014. Our forecast will inevitably be wrong. Nevertheless, we suspect that, by being thoughtful in our analysis, our forecast will provide a reasonable, unbiased expectation of future performance. Exhibit 5.4 gives the actual 2014 results for Nestle. The big surprise was that the effect of competition was worse than anticipated. Nestle’s realized sales growth was actually negative, and its operating margin dropped from 14.9% and 14.1% in 2012 and 2013, respectively, to 11.9% in 2014. Our asset assumptions were fairly close to the outcome, although the inventory turnover and PPE turnover were a little worse than we had expected. Overall, the ROA for Nestle dropped from 7.1% in 2013 to 5.3% in 2014. Although we did not complete a high-side and a low-side scenario in this simple example, we can hope that, had we done so, we could have appropriately assessed the sources and level of uncertainty of our forecast. Appendix To test for forecasting bias among business school forecasters, an experiment was per-formed in 2005 with the 300 first-year MBA students at the Darden School of Business at the University of Virginia. Each student was randomly assigned to both a U.S. public company and a year between 1980 and 2000.3 Some students were assigned the same company, but no students were assigned the same company and the same year. The students were asked to forecast sales growth and operating margin for their assigned company for the subsequent three years. The students based their forecasts on the 3More precisely, the population of sample firms was all U.S. firms followed by Compustat and the Value Line Investment Survey. To ensure meaningful industry forecast data, we required that each firm belong to a meaningful industry, which is to say that multiform, industrial services, and diversified industries were not considered). We also required that Value Line report operating profit for each firm. To maintain consistency in the representation of firms over time, the sample began with a random identification of 25 firms per year. The forecast data were based on Value Line forecasts during the summer of the first year of the forecast. All historical financial data were from Compustat.

CASE In the Bruner text, carefully read Case 10: Best Practices” in Estimating the Cost of Capital: An Update on pages 145 to 171.

Part Three

Estimating the Cost of Capital

Over the years, theoretical developments in finance converged into compelling recom-mendations about the cost of capital to a corporation. By the early 1990s, a consensus had emerged prompting descriptions such as “traditional . . . textbook . . . appropriate,” “theoretically correct,” “a useful rule of thumb” and a “good vehicle.” In prior work with Bob Bruner, we reached out to highly regarded firms and financial advisors to see how they dealt with the many issues of implementation.1 Fifteen years have passed since our first study. We revisit the issues and see what now constitutes best practice and what has changed in both academic recommendations and in practice. We present evidence on how some of the most financially sophisticated compa-nies and financial advisors estimate capital costs. This evidence is valuable in several respects. First, it identifies the most important ambiguities in the application of cost of capital theory, setting the stage for productive debate and research on their resolu-tion. Second, it helps interested companies to benchmark their cost of capital estima-tion practices against best-practice peers. Third, the evidence sheds light on the accuracy with which capital costs can be reasonably estimated, enabling executives to use the estimates more wisely in their decision-making. Fourth, it enables teachers to answer the inevitable question, “But how do companies really estimate their cost of capital?” The paper is part of a lengthy tradition of surveys of industry practice. For in-stance, Burns and Walker (2009) examine a large set of surveys conducted over the last quarter century into how U.S. companies make capital budgeting decisions. They find that estimating the weighted average cost of capital is the primary approach to select-ing hurdle rates. More recently, Jacobs and Shivdasani (2012) report on a large-scale survey of how financial practitioners implement cost of capital estimation. Our approach differs from most papers in several important respects. Typically studies are based on written, closed-end surveys sent electronically to a large sample of firms, often covering a wide array of topics, and commonly using multiple choice or fill-in-the-blank ques-tions. Such an approach typically yields low response rates and provides limited op-portunity to explore subtleties of the topic. For instance, Jacobs and Shivdasani (2012) provide useful insights based on the Association for Finance Professionals (AFP) cost of capital survey. While the survey had 309 respondents, AFP (2011, page 18) reports this was a response rate of about 7% based on its membership companies. In contrast, we report the result of personal telephone interviews with practitioners from a care-fully chosen group of leading corporations and financial advisors. Another important difference is that many existing papers focus on how well accepted modern financial techniques are among practitioners, while we are interested in those areas of cost of capital estimation where finance theory is silent or ambiguous and practitioners are left to their own devices. The following section gives a brief overview of the weighted-average cost of capi-tal. The research approach and sample selection are discussed in Section II. Section III reports the general survey results. Key points of disparity are reviewed in Section IV.

Section V discusses further survey results on risk adjustment to a baseline cost of capi-tal, and Section VI highlights some institutional and market forces affecting cost of capital estimation. Section VII offers conclusions and implications for the financial practitioner.

I. The Weighted-Average Cost of Capital

A key insight from finance theory is that any use of capital imposes an opportunity cost on investors; namely, funds are diverted from earning a return on the next best equal-risk investment. Since investors have access to a host of financial market opportunities, corporate uses of capital must be benchmarked against these capital market alternatives. The cost of capital provides this benchmark. Unless a firm can earn in excess of its cost of capital on an average-risk investment, it will not create economic profit or value for investors. A standard means of expressing a company’s cost of capital is the weighted-average of the cost of individual sources of capital employed. In symbols, a company’s weighted-average cost of capital (or WACC) is:

WACC = (Wdebt(1 − t)Kdebt) + (WequityKequity),

where:

K = component cost of capital.

W = weight of each component as percent of total capital.

t = marginal corporate tax rate.

For simplicity, this formula includes only two sources of capital; it can be easily ex-panded to include other sources as well. Finance theory offers several important observations when estimating a company’s WACC. First, the capital costs appearing in the equation should be current costs reflect-ing current financial market conditions, not historical, sunk costs. In essence, the costs should equal the investors’ anticipated internal rate of return on future cash flows as-sociated with each form of capital. Second, the weights appearing in the equation should be market weights, not historical weights based on often arbitrary, out-of-date book values. Third, the cost of debt should be after corporate tax, reflecting the benefits of the tax deductibility of interest. Despite the guidance provided by finance theory, use of the weighted-average expression to estimate a company’s cost of capital still confronts the practitioner with a number of difficult choices.2 As our survey results demonstrate, the most nettlesome component of WACC estimation is the cost of equity capital; for unlike readily avail-

Part Three Estimating the Cost of Capital able yields in bond markets, no observable counterpart exists for equities. This forces practitioners to rely on more abstract and indirect methods to estimate the cost of equity capital. II. Sample Selection This paper describes the results of conversations with leading practitioners. Believ-ing that the complexity of the subject does not lend itself to a written questionnaire, we wanted to solicit an explanation of each firm’s approach told in the practitioner’s own words. Though our telephone interviews were guided by a series of questions, the conversations were sufficiently open-ended to reveal many subtle differences in practice. Since our focus is on the gaps between theory and application rather than on aver-age or typical practice, we aimed to sample practitioners who were leaders in the field. We began by searching for a sample of corporations (rather than investors or financial advisors) in the belief that they had ample motivation to compute WACC carefully and to resolve many of the estimation issues themselves. Several publications offer lists of firms that are well-regarded in finance; of these, we chose Fortune’s 2012 listing of Most Admired Companies.3 Working with the Hay Group, Fortune creates what it terms “the definitive report card on corporate reputations.” Hay provided us with a listing of companies ranked by the criterion “wise use of assets” within industry. To create our sample we only used companies ranked first or second in their industry. We could not obtain raw scores that would allow comparisons across industries. The 2012 Fortune rankings are based on a survey of 698 companies, each of which is among the largest in its industry. For each of 58 industry lists, Hay asks executives, directors, and analysts to rate companies in their own industry on a set of criteria. Start-ing with the top two ranked firms in each industry, we eliminated companies headquar-tered outside North America (eight excluded).4 We also eliminated the one firm classified as a regulated utility (on the grounds that regulatory mandates create unique issues for capital budgeting and cost of capital estimation) and the seven firms in finan-cial services (inclusive of insurance, banking, securities and real estate). Forty-seven companies satisfied our screens. Of these, 19 firms agreed to be interviewed and are included in the sample given in Table I. Despite multiple concerted attempts we made to contact appropriate personnel at each company, our response rate is lower than Bruner, Eades, Harris, and Higgins (1998) but still much higher than typical cost of capital surveys. We suspect that increases in the number of surveys and in the demands on executives’ time influence response rates now versus the late 1990s.

Part Three Estimating the Cost of Capital pressures regarding cost of capital. When an advisor is representing the sell side of an M&A deal, the client wants a high valuation but the reverse may be true when an advisor is acting on the buy side. In addition, banks may be engaged by either side of the deal to provide a Fairness Opinion about the transaction. We wondered whether the pressures of these various roles might result in financial advisors using assumptions and methodologies that result in different cost of capital estimates than those made by operating companies. This proved not to be the case. ∙ Textbooks and Trade books. In parallel with our prior study, we focus on a handful of widely-used books. From a leading textbook publisher we obtained names of the four best-selling, graduate-level textbooks in corporate finance in 2011. In addition, we consulted two popular trade books that discuss estimation of the cost of capital in detail. III. Survey Findings Table II summarizes responses to our questions and shows that the estimation ap-proaches are broadly similar across the three samples in several dimensions: ∙ Discounted Cash Flow (DCF) is the dominant investment evaluation technique. ∙ WACC is the dominant discount rate used in DCF analyses. ∙ Weights are based on market not book value mixes of debt and equity.6 ∙ The after-tax cost of debt is predominantly based on marginal pretax costs, and marginal tax rates.7 ∙ The Capital Asset Pricing Model (CAPM) is the dominant model for estimating the cost of equity. Despite shortcomings of the CAPM, our companies and finan-cial advisors adopt this approach. In fact, across both companies and financial advisors, only one respondent did not use the CAPM.8 These practices parallel many of the findings from our earlier survey. First, the “best practice” firms show considerable alignment on many elements of practice. Sec-ond, they base their practice on financial economic models rather than on rules of thumb or arbitrary decision rules. Third, the financial frameworks offered by leading texts and trade books are fundamentally unchanged from our earlier survey.

Part Three Estimating the Cost of Capital On the other hand, disagreements exist within and among groups on matters of application, especially when it comes to using the CAPM to estimate the cost of equity. The CAPM states that the required return (K) on any asset can be expressed as: K = Rf + β(Rm − Rf), where: Rf = interest rate available on a risk-free asset. Rm = return required to attract investors to hold the broad market portfolio of risky assets. β = the relative risk of the particular asset. According to CAPM then, the cost of equity, Kequity, for a company depends on three components: returns on riskfree assets (Rf), the stock’s equity “beta” which mea-sures risk of the company’s stock relative to other risky assets (β = 1.0 is average risk), and the market risk premium (Rm − Rf) necessary to entice investors to hold risky assets generally versus risk-free instruments. In theory, each of these components must be a forward-looking estimate. Our survey results show substantial disagreements, espe-cially in terms of estimating the market risk premium. A. The Risk-Free Rate of Return As originally derived, the CAPM is a single period model, so the question of which in-terest rate best represents the risk-free rate never arises. In a multi period world typi-cally characterized by upward-sloping yield curves, the practitioner must choose. The difference between realized returns on short-term U.S. Treasury-bills and long-term T-bonds has averaged about 150 basis points over the longrun; so choice of a risk-free rate can have a material effect on the cost of equity and WACC.9 Treasury bill yields are more consistent with the CAPM as originally derived and reflect risk-free returns in the sense that T-bill investors avoid material loss in value from interest rate movements. However, long-term bond yields more closely reflect the default-free holding period returns available on long-lived investments and thus more closely mirror the types of investments made by companies. Our survey results reveal a strong preference on the part of practitioners for long-term bond yields. As shown in Table II (Question 9), all the corporations and financial advisors use Treasury bond yields for maturities of 10 years or greater, with the 10-year rate being the most popular choice. Many corporations said they matched the term of the risk-free rate to the tenor of the investment. In contrast, a third of the sample books sug-gested subtracting a term premium from long-term rates to approximate a shorter term yield. Half of the books recommended long-term rates but were not precise on the choice of maturity.

Case 10 “Best Practices” in Estimating the Cost of Capital: An Update 157 Because the yield curve is ordinarily relatively flat beyond ten years, the choice of which particular long-term yield to use often is not a critical one. However, at the time of our survey, Treasury markets did not display these “normal” conditions in the wake of the financial crisis and expansionary monetary policy. In the year we conducted our survey (2012), the spread between 10-and 30-year Treasury yields averaged 112 basis points.10 While the text and trade books do not directly address the question of how to deal with such markets, it is clear that some practitioners are looking for ways to “nor-malize” what they see as unusual circumstances in the government bond markets. For instance, 21% of the corporations and 36% of the financial advisors resort to some his-torical average of interest rates rather than the spot rate in the markets. Such an averag-ing practice is at odds with finance theory in which investors see the current market rate as the relevant opportunity. We return to this issue later in the paper. B. Beta Estimates Finance theory calls for a forward-looking beta, one reflecting investors’ uncertainty about the future cash flows to equity. Because forward-looking betas are unobservable, practitioners are forced to rely on proxies of various kinds. Often this involves using beta estimates derived from historical data. The usual methodology is to estimate beta as the slope coefficient of the market model of returns: Rit = αi + βi(Rmt), where: Rit = return on stock I in time period (e.g., day, week, month) t. Rmt = return on the market portfolio in period t. αi = regression constant for stock i. βi = beta for stock i. In addition to relying on historical data, use of this equation to estimate beta re-quires a number of practical compromises, each of which can materially affect the re-sults. For instance, increasing the number of time periods used in the estimation may improve the statistical reliability of the estimate but risks including stale, irrelevant in-formation. Similarly, shortening the observation period from monthly to weekly, or even daily, increases the size of the sample but may yield observations that are not normally distributed and may introduce unwanted random noise. A third compromise involves choice of the market index. Theory dictates that Rm is the return on the “market portfolio,” an unobservable portfolio consisting of all risky assets, including human

Case 10 “Best Practices” in Estimating the Cost of Capital: An Update 165 We probed the extent to which respondents alter discount rates to reflect risk dif-ferences in questions about variations in project risk, strategic investments, terminal values, multidivisional companies, and synergies (Table II, Questions 13 and 17–20). Responses indicate that the great preponderance of financial advisors and text authors strongly favor varying the discount rate to capture differences in risk (Table II, Ques-tions 13, 19, and 20). Corporations, on the other hand, are more evenly split, with a sizeable minority electing not to adjust discount rates for risk differences among indi-vidual projects (Table II, Questions 13 and 17). Comparing these results with our earlier study, it is worth noting that while only about half of corporate respondents adjust dis-count rates for risk, this figure is more than double the percentage reported in 1998. Despite continuing hesitance, companies are apparently becoming more comfortable with explicit risk adjustments to discount rates. A closer look at specific responses suggests that respondents’ enthusiasm for risk-adjusting discount rates depends on the quality of the data available. Text authors live in a largely data-free world and thus have no qualms recommending risk adjustments whenever appropriate. Financial advisors are a bit more constrained. They regularly confront real-world data, but their mission is often to value companies or divisions where extensive market information is available about rates and prices. Correspond-ingly, virtually all advisors questioned value multi division businesses by parts when the divisions differed materially in size and risk, and over 90% are prepared to use sepa-rate division WACCs to reflect risk differences. Similarly, 82% of advisors value merger synergies and strategic opportunities separately from operating cash flows, and 73% are prepared to use different discount rates when necessary on the various cash flows. There is a long history of empirical research on how shareholder returns vary across firm size, leading some academics to suggest that a small cap premium should be added to the calculated cost of capital for such firms.17 Our study focuses on large public com-panies, so it is not surprising that firm responses do not reveal any such small cap adjust-ments. In contrast, financial advisors work with a wide spectrum of companies and are thus more likely to be sensitive to the issue—as indeed they are. Among financial advi-sors interviewed, 91% said they would at times increase the discount rate when evaluat-ing small companies. Of those who did make size adjustments, half mentioned using Ibbotson (2012) data which show differences in past returns among firms of different size. The adjustment process varied among advisors, as the following illustrative quotes suggest. “Adjustments are discretionary, but we tend to adjust for extreme size.” “We have used a small cap premium, but we don’t have a set policy to make adjustments. It is fairly subjective.” “We apply a small cap premium only for microcap companies.” “We use a small cap premium for $300 million and below.” In important ways corporate executives face a more complex task than financial advisors or academics. They must routinely evaluate investments in internal opportuni-ties, and new products and technologies, for which objective, third party information is

Part Three Estimating the Cost of Capital the influence of a wide array of stakeholders. For instance, a number of companies voiced that any change in estimation methods would raise red flags with auditors look-ing for process consistency in key items such as impairment estimates. Some advisors mentioned similar concerns, citing their work in venues where consistency and prece-dent were major considerations (e.g., fairness opinions, legal settings). Moreover, some companies noted that they “outsourced” substantial parts of their estimation to advisors or data providers. These items serve as a reminder that the art of cost of capital estima-tion and its use are part of a larger process of management—not simply an application of finance theory. The financial upheaval in 2008–2009 provided a natural test of respondents’ com-mitment to existing cost of capital estimation methodologies and applications. When confronted with a major external shock, did companies make wholesale changes or did they keep to existing practices? When we asked companies and advisors if financial market conditions in 2008–2009 caused them to change the way they estimate and use the cost of capital (Table II, Question 15), over three-fifths replied “No.” In the main, then, there was not a wholesale change in methods. That said, a number of respondents noted discomfort with cost of capital estimation in recent years. Some singled out high volatility in markets. Others pointed to the low interest rate environment resulting from Federal Reserve policies to stimulate the U.S. economy. Combining low interest rates and typical historical risk premiums created capital cost estimates that some practitio-ners viewed as “too low.” One company was so distrustful of market signals that it placed an arbitrary eight percent floor under any cost of capital estimate, noting that “since 2008, as rates have decreased so drastically, we don’t feel that [the estimate] represents a long-term cost of capital. Now we don’t report anything below 8% as a minimum [cost of capital].” Among the minority who did revise their estimation procedures to cope with these market forces, one change was to put more reliance on historical numbers when estimating interest rates as indicated in Table XI. This is in sharp contrast to both finance theory and what we found in our prior study. Such rejection of spot rates in favor of historical averages or arbitrary numbers is inconsistent with the academic view that historical data do not accurately reflect current attitudes in competitive markets. The academic challenge today is to better articulate the extent to which the superiority of spot rates still applies when markets are highly volatile and when gov-ernments are aggressively attempting to lower rates through such initiatives as quan-titative easing. Another change in estimation methods since our earlier study is reflected in the fact that more companies are using forward-looking risk premiums as we reported earlier and illustrated in Table VII. Since the forward-looking premiums cited by our respon-dents were higher than historical risk premiums, they mitigated or offset to some degree the impact of low interest rates on estimated capital costs.