This section contains a discussion and demonstration of the relative valuation ratio techniques:
(1) price/earnings ratios (P/E), (2) the price to book value ratios (P/BV); (3) the price to cash flow ratios (P/CF ), and (4) the price to sales ratios (P/S). Again, we will begin with the detailed demonstration of the P/E ratio approach, which provides a specific valuation and an estimated rate of return for the industry based upon its intrinsic value that equals an estimate of future earn- ings per share and an industry multiple.
The analysis of the other relative valuation ratios is also more meaningful because we can compare the industry valuation ratios to the market valuation ratios while considering what fac- tors affect the specific valuation ratios.
You will recall that the earnings multiple technique is a two-step process that involves (1) a detailed estimation of future earnings per share, and (2) an estimate of an appropriate earnings multiplier (P/E ratio) based upon a consideration of P/E determinants derived from the DDM.
Estimating Earnings per Share To estimate earnings per share, you must start by esti- mating sales per share. The first part of this section describes three techniques that provide help and insights for the sales estimate. Next, we derive an estimate of earnings per share, which implies a net profit margin for the industry. As in Chapter 13 where we estimated earnings per share for a stock market series, we begin with the operating profit margin, which leads to an esti- mate of operating profits. Then we subtract estimates of depreciation expense and interest expense and apply a tax rate to arrive at an estimate of earnings per share.
Forecasting Sales per Share Assuming an analyst has completed the macroanalysis of the industry that included (1) considering how the industry is impacted by the business cycle, (2) what structural changes have occurred within the industry, and (3) where the industry is in its life cycle, the analyst would have a strong start regarding a sales estimate for the industry. At this point, we would make suggestions regarding two minor estimation techniques (time series and input-output analysis) and one major technique that should be considered for almost all industries (a specific analysis of the industry-economy relationship).
The Earnings Multiple Technique
Time-Series Analysis A simple time-series plot of the sales for an industry versus time can be very informative regarding the pattern and the rate of growth for industry sales. Analyzing this series along with designations of business cycle periods (expansions and recessions) and nota- tions regarding major events will provide further insights. Finally, for many industries, it is pos- sible to extrapolate the time series to derive an estimate of sales. For industries that have expe- rienced consistent growth, this can be a very useful estimate, especially if it is a new industry that has not developed a history with the economy. If the sales growth has been at a constant rate, you should do the time-series plot on semi-log paper where the constant growth shows as a straight line.
Input-Output Analysis Input-output analysis is another way to gain insights regarding the out- look for an industry by separating industries that supply the input for a specific industry from those that get its output. In other words, we want to identify an industry’s suppliers and cus- tomers. This will help us identify (1) the future demand from customers and (2) the ability of suppliers to provide the goods and services required by the industry. The goal is to determine the long-run sales outlook for the industry’s suppliers and its major customers. To extend this analy- sis to global industries, we must include worldwide suppliers and customers.
Industry-Economy Relationships The most rigorous and useful analysis involves comparing sales for an industry with one or several aggregate economic series that are related to the goods and services produced by the industry. The specific question is, What economic variables influ- ence the demand for this industry? Notably, you should be thinking of numerous factors that will have an impact on industry sales, how these economic variables will impact demand, and how the factors might interact. In the following example, we will demonstrate this industry-economy technique for the retail drugstore industry (RDS).
Demonstrating a Sales Forecast The retail drugstore (RDS) industry includes retailers of basic necessities, including pharmaceuticals and medical supplies and many nonmedical prod- ucts, such as cosmetics, snacks, pop, and liquor. Therefore, we want a series that (1) reflects broad consumption expenditures and (2) gives weight to the impact of medical expenditures. The economic series we consider are personal consumption expenditures (PCE) and PCE medical care. Exhibit 14.12 contains the aggregate and per-capita values for the two series.
A casual analysis of these time series indicates that although personal consumption expendi- tures (PCE) have experienced reasonably steady growth of about 7.5 percent a year during this period, PCE medical care has grown at a faster rate of almost 10 percent. As a result, as shown in the exhibit’s last column, medical care expenditures as a percentage of all PCE have grown from 9.6 percent in 1977 to almost 15 percent in 2000. Still, you would be concerned that this percent has declined steadily since 1995. Obviously, as an analyst, you would be pleased because it appears that retail drugstore sales had benefited from this growth in medical expenditures, as shown by the annual growth of drugstore sales of nearly 13 percent.
The scatter plot in Exhibit 14.13 indicates a strong linear relationship between retail drugstore sales per share and PCE medical care prior to the decline in RDS sales in 2000. Although not shown, there also is a good relationship with PCE. Therefore, if you can accurately estimate changes in these economic series, you should normally be able to derive a good estimate of expected sales for the RDS industry.
As the industry being analyzed becomes more specialized, you need a more individualized economic series that reflects the demand for the industry’s product. The selection of an appro- priate economic series is one place where an analyst can demonstrate knowledge and innovation.
There also can be instances where industry sales are dependent on several components of the economy, in which case you should probably consider a multivariate model that would include two or more economic series. For example, if you were dealing with the tire industry, you might
want to consider new-car production, new-truck production, and a series that would reflect the replacement tire demand.
You also should consider per-capita personal consumption expenditures—medical care.
Although aggregate PCE medical care increases each year, there also is an increase in the aggre- gate population, so the increase in the PCE medical care per capita (the average PCE medical care for each adult and child) will be less than the increase in the aggregate series. As an exam- ple, during 2000, aggregate PCE medical care increased about 6.6 percent, but per-capita PCE medical care increased only 5.6 percent. Finally, an analysis of the relationship between changes in the economic variable and changes in industry sales will indicate how the two series move 512 CHAPTER 14 INDUSTRYANALYSIS
S&P RETAIL DRUGSTORE SALES AND VARIOUS ECONOMIC SERIES: 1977–2000
PER CAPITA
PERSONAL PERSONAL
RETAILDRUGSTORE CONSUMPTION PCE CONSUMPTION PCE MEDICALCARE
SALES EXPENDITURES MEDICALCARE EXPENDITURES MEDICALCARE AS APERCENTAGE
YEAR ($/SHARE) ($ BILLIONS) ($ BILLIONS) (DOLLARS) (DOLLARS) OFPCE
1977 43.99 1,278.40 122.60 5,804.6 556.7 9.59
1978 49.87 1,430.40 140.00 6,426.3 629.0 9.79
1979 73.39 1,596.30 158.10 7,092.9 702.5 9.90
1980 84.82 1,762.90 181.20 7,741.3 795.7 10.28
1981 95.50 1,944.20 213.00 8,454.3 926.2 10.96
1982 109.22 2,079.30 239.30 8,955.2 1,030.6 11.51
1983 118.85 2,286.40 267.90 9,758.1 1,143.4 11.72
1984 135.15 2,498.40 294.60 10,570.9 1,246.5 11.79
1985 153.30 2,712.60 322.50 11,375.2 1,352.4 11.89
1986 157.74 2,895.20 346.80 12,030.7 1,441.1 11.98
1987 191.72 3,105.30 381.80 12,789.3 1,572.5 12.30
1988 217.80 3,356.60 429.90 13,699.2 1,754.5 12.81
1989 239.68 3,596.70 479.20 14,541.4 1,937.4 13.32
1990 265.77 3,831.50 540.60 15,327.7 2,162.6 14.11
1991 283.50 3,971.20 591.00 15,717.3 2,339.1 14.88
1992 309.78 4,209.70 652.60 16,482.1 2,555.1 15.50
1993 329.20 4,454.70 700.60 17,258.3 2,714.3 15.73
1994 363.71 4,716.40 737.30 18,095.7 2,828.8 15.63
1995 413.52 4,969.00 780.70 18,887.6 2,967.5 15.71
1996 434.15 5,237.50 814.40 19,726.8 3,067.4 15.55
1997 549.51 5,529.30 854.60 20,628.0 3,188.2 15.46
1998 612.86 5,850.90 898.60 21,629.2 3,321.9 15.36
1999 706.21 6,268.70 943.60 22,966.9 3,457.1 15.05
2000 687.77 6,810.80 1,005.60 24,733.1 3,651.8 14.76
Mean Annual Growth 12.70% 7.54% 9.58% 6.50% 8.52% 1.89%
EXHIBIT 14.12
Source: Financial Analysts Handbook (New York: Standard & Poor’s, 2001); and Economic Report of the President (Washington, DC: U.S. Government Printing Office, 1999). Reprinted with permission.
together and highlight any changes in the relationship. Using annual percentage changes pro- vides the following regression model:
➤14.4 % ∆Industry Sales = αi+ βi(% ∆in Economic Series)
The size of the βicoefficient should indicate how closely the two series move together. Assum- ing the intercept (αi) is close to zero, a slope (βi) value of 1.00 would indicate relatively equal percentages of change (e.g., this would indicate that a 10 percent increase in PCE typically is associated with a 10 percent increase in industry sales). A βiof less than unity would imply that industry sales are not as volatile annually as the economy is. This analysis and the levels rela- tionship reflected in Exhibit 14.13 would help you find an economic series that closely reflects the demand for the industry’s products; it also would indicate the form of the relationship.
As indicated in this analysis, the best relationship was between retail drugstore sales and PCE medical care. The specific regression result was
% ∆Retail Drugstore Sales =5.40 +0.55 (% ∆PCE Medical Care) (t values) (1.85) (1.98)
R2=0.22
The specific sales estimate procedure would begin with an estimate of aggregate PCE for the coming year. Given the importance of the PCE series, it should be relatively easy to find one or several estimates. The next step would be to estimate a change in the proportion of PCE spent on medical care. As noted, this proportion has grown from 9.6 percent to 14.76 percent, but it
800 700 600 500 400 300 200 100 0
800 700 600 500 400 300 200 100 0
RDS Sales per Share
122 222 322 422 522 622 722 822 922
PCE Medical Care ($ Bil.)
EXHIBIT 14.13 SCATTER PLOT OF RDS SALES PER SHARE AND PCE MEDICAL CARE: 1977–2000
Source: International Monetary Fund, International Financial Statistics.
has declined since 1995. Once you estimate this percentage spent on medical care, you apply it to the PCE estimate to arrive at an estimate of the percentage change in PCE medical care.
Finally, you would use this PCE medical care estimate in a regression equation to derive a sales estimate for the industry. This procedure will be demonstrated in a subsequent section.
Forecasting Earnings per Share After the sales forecast, it is necessary to estimate the industry’s profitability based on an analysis of the industry income statement. An analyst should also benefit from the prior macroanalysis that considered where the industry is in its life cycle, which impacts its profitability. How does this industry relate to the business cycle and what does this imply regarding profit margins at this point in the cycle? Most important, what did you con- clude regarding the competitive environment in the industry and what does this mean for pricing and profitability of sales?
514 CHAPTER 14 INDUSTRYANALYSIS
PROFIT MARGINS AND COMPONENT EXPENSES FOR THE S&P INDUSTRIALS INDEX AND THE RDS INDUSTRY INDEX: 1977–2000
EBITDA MARGIN DEPRECIATION INTEREST NET PROFIT
EBITDA ($) (%) EXPENSE ($) EXPENSE ($) TAX RATE (%) MARGIN (%)
YEAR S&P IND RDS S&P IND RDS S&P IND RDS S&P IND RDS S&P IND RDS S&P IND RDS
1977 32.20 3.94 14.36 8.96 8.53 0.41 3.22 0.13 48.90 48.60 5.15 4.07
1978 36.19 4.38 14.40 8.78 9.64 0.49 3.81 0.13 49.00 47.90 5.19 4.03
1979 42.01 5.31 14.37 7.40 10.82 0.71 4.58 0.23 46.00 45.70 5.57 3.47
1980 43.08 6.00 13.16 7.07 12.37 0.83 5.95 0.28 45.60 44.70 4.92 3.47
1981 44.50 6.85 12.92 7.17 13.82 1.02 7.49 0.39 43.30 44.20 4.86 3.45
1982 42.67 7.97 12.78 7.30 15.30 1.21 8.23 0.41 45.00 44.60 3.95 3.44
1983 45.57 9.48 13.64 7.98 15.67 1.40 7.62 0.43 44.60 45.00 4.42 3.79
1984 51.50 9.72 13.56 7.19 16.31 1.71 8.54 0.78 42.70 45.10 4.77 3.04
1985 53.23 10.73 13.36 7.00 18.19 2.02 9.24 0.73 46.70 45.90 3.84 2.96
1986 51.02 11.62 13.16 7.37 19.41 2.08 9.75 0.77 42.70 45.10 3.75 3.11
1987 58.89 13.57 13.68 7.08 20.21 2.63 10.14 1.17 40.50 43.90 4.77 2.88
1988 74.31 14.60 15.26 6.70 23.59 3.09 14.84 1.42 35.30 38.60 5.51 2.90
1989 79.52 16.08 14.69 6.71 24.21 3.39 18.79 1.65 37.00 37.80 5.01 2.79
1990 82.47 17.84 13.87 6.71 26.31 4.15 20.17 1.63 39.60 38.30 4.26 2.89
1991 75.10 18.49 12.80 6.52 27.50 4.03 18.74 1.32 41.19 36.70 2.97 2.91
1992 78.17 19.68 13.00 6.35 29.48 4.39 16.20 1.08 37.91 36.95 3.27 2.89
1993 82.16 20.56 13.61 6.25 28.72 4.75 14.66 0.80 37.27 39.29 3.73 2.15
1994 91.28 23.36 14.58 6.42 29.58 5.32 12.79 1.01 36.12 38.29 5.24 2.89
1995 104.67 26.93 15.47 6.51 33.06 6.02 14.21 1.57 36.62 38.62 5.24 2.84
1996 112.69 28.34 16.05 6.53 36.71 6.07 14.32 2.05 36.47 37.19 5.86 3.21
1997 123.15 38.81 16.40 7.06 39.77 8.45 14.84 3.08 35.62 45.11 5.61 1.96
1998 129.75 42.90 17.17 7.00 32.89 9.66 14.56 3.62 34.93 38.15 5.14 2.58
1999 135.72 48.56 16.71 6.88 41.70 10.25 15.50 3.55 35.01 38.92 6.27 2.70
2000 150.17 42.69 17.59 6.21 43.50 10.80 16.39 1.25 37.41 39.25 6.33 3.04
EXHIBIT 14.14
Source: Financial Analysts Handbook (New York: Standard & Poor’s, 2001). Reprinted with permission.
Industry Profit Margin Forecast Similar to the aggregate market, the net profit margin is the most volatile and the hardest margin to estimate directly. Alternatively, it is suggested that you begin with the operating profit margin (EBITDA/Sales) and then estimate depreciation expense, interest expense, and the tax rate.
The Industry’s Operating Profit Margin Recall that in the market analysis, we analyzed the factors that should influence the economy’s operating profit margin, including capacity uti- lization, unit labor cost, inflation, and net exports. The most important variables were capacity utilization and unit labor cost. We cannot do such an analysis for most industries because the rel- evant variables typically are not available for individual industries. As an alternative, we can assume that movements in these industry profit margin variables are related to movements in similar economic variables. For example, when an increase in capacity utilization for the aggre- gate economy exists, there is probably a comparable increase in utilization for the auto industry or the chemical industry. The same could be true for unit labor cost and exports. If there is a sta- ble relationship between these variables for the industry and the economy, you would expect a relationship to exist between the profit margins for the industry and the economy. Although it is not necessary that the relationship be completely linear, it is important for the relationship (what- ever it is) to be generally stable.
The operating profit margin (OPM) for the S&P Industrials Index and the retail drugstore (RDS) index is presented in Exhibit 14.14. The time-series plot in Exhibit 14.15 indicates that the S&P Industrials Index OPM experienced a decline during the 1980s and early 1990s but increased steadily during the rest of the 1990s and ended the period with a record margin of over 17.50 percent. In contrast, the RDS OPM experienced a fairly steady decline with the exception of a small increase in 1997. The analysis of the relationship between the OPM for the market and industry using regression analysis was not useful, so it is not discussed. These results indicate that the best estimate for the RDS industry can be derived from the OPM time-series plot using
18 16 14 12 10 8 6 4
18 16 14 12 10 8 6 4
OPM (Percent)
1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 Year
S&P Industrials Index RDS
EXHIBIT 14.15 TIME-SERIES PLOT OF OPERATING PROFIT MARGINS FOR THE S&P INDUSTRIALS INDEX AND THE RDS INDUSTRY: 1977–2000
what we know about the changing competitive environment and profit trends in the retail drugstore business. It is a matter of judgment for each specific industry whether you use regression analysis and/or the time-series analysis. The point is, any such mathematical analysis should be considered a supplement to the economic analysis of the competitive environment for the industry.
Either regression analysis or time-series techniques can be useful tools, but neither technique should be applied mechanically. You should be aware of any unique factors affecting the specific industry, such as price wars, contract negotiations, building plans, or foreign competition. An analysis of these unique events is critical when estimating the final gross profit margin or when estimating a range of industry profit margins (optimistic, pessimistic, most likely).
Beyond this discussion, which is primarily concerned with an estimate of the near-term OPM, it also is important to consider the long-term profitability of the industry based on the competi- tive structure of the industry as discussed previously.
Industry Depreciation The next step is estimating industry depreciation, which typically is easier because the series generally is increasing; the only question is by how much. As shown in Exhibit 14.14, except for 1993 and 1998, the depreciation series for RDS increased every year since 1977. The time-series plots in Exhibit 14.16 relate depreciation for the S&P Industrials Index and the RDS industry. To estimate depreciation expense, one can consider the two tech- niques used in the market analysis chapter (i.e., the time-series analysis and the specific estimate technique using the depreciation expense/PPE ratio) or an industry-market relationship.
An analysis of the graph, as well as regression analysis of levels and annual percentage changes, indicates that the relationship between this industry and the market is not good enough to use for an estimate. Alternatively, the depreciation expense series has been increasing at a fairly steady rate between 8 and 10 percent a year, so a time-series estimate could provide a viable estimate.
516 CHAPTER 14 INDUSTRYANALYSIS
45 40 35 30 25 20 15 10 5 0
45 40 35 30 25 20 15 10 5 0
OPM (Percent)
1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 Year
S&P Industrials Index RDS
EXHIBIT 14.16 TIME-SERIES PLOT OF DEPRECIATION FOR THE S&P INDUSTRIALS INDEX AND THE RDS INDUSTRY: 1977–2000
Exhibit 14.17 contains the components needed to derive a specific depreciation expense esti- mate similar to what we did for the S&P Industrials Index using the following four steps:9
1. Calculate the annual PPE turnover for the RDS industry.
2. Based upon your sales estimate and your expected PPE turnover ratio, estimate the expected PPE for next year.
3. Calculate the annual depreciation expense as a percent of PPE for the RDS industry.
4. Estimate depreciation expense as follows:
(Estimated ) Estimated Depreciation Expense Ratio
PPE × PPE
COMPONENTS FOR DERIVING SPECIFIC ESTIMATES FOR DEPRECIATION EXPENSE AND INTEREST EXPENSE FOR THE RDS INDUSTRY
PPE DEPREC. TOTAL T. ASSET INT. EXP.
YEAR NETSALES NETPPE TURNOVER EXP. PPE ASSETS TURNOVER L-T DEBT T. ASSETS INT. EXP. L-T. DEBT
1977 43.99 3.32 13.25 0.41 0.12 15.51 2.84 1.25 0.08 0.13 0.10
1978 49.87 4.00 12.47 0.49 0.12 17.73 2.81 1.29 0.07 0.13 0.10
1979 73.39 6.18 11.88 0.71 0.11 24.49 3.00 2.69 0.11 0.23 0.09
1980 84.82 7.84 10.82 0.83 0.11 27.89 3.04 2.84 0.10 0.28 0.10
1981 95.50 9.68 9.87 1.02 0.11 31.53 3.03 2.48 0.08 0.39 0.16
1982 109.22 11.27 9.69 1.21 0.11 36.76 2.97 2.04 0.06 0.41 0.20
1983 118.85 12.90 9.21 1.40 0.11 41.61 2.86 1.82 0.04 0.43 0.24
1984 135.15 15.66 8.63 0.71 0.05 50.92 2.65 4.05 0.08 0.78 0.19
1985 153.30 18.56 8.26 2.02 0.11 56.74 2.70 8.25 0.15 1.14 0.14
1986 157.74 22.81 6.92 2.08 0.09 58.12 2.71 7.02 0.12 0.77 0.11
1987 191.72 26.45 7.25 2.63 0.10 68.54 2.80 8.76 0.13 1.17 0.13
1988 217.80 28.31 7.69 3.09 0.11 77.30 2.82 9.41 0.12 1.42 0.15
1989 239.68 38.80 7.78 3.39 0.11 85.50 2.80 16.41 0.19 1.65 0.10
1990 265.77 34.66 7.67 4.15 0.12 94.39 2.82 17.24 0.18 1.63 0.09
1991 283.50 37.55 7.55 4.03 0.11 99.34 2.85 12.84 0.13 1.32 0.10
1992 309.78 40.23 7.70 4.39 0.11 109.39 2.83 11.81 0.11 1.08 0.09
1993 329.20 43.63 7.55 4.75 0.11 118.19 2.79 14.52 0.12 0.80 0.06
1994 363.71 50.39 7.22 5.32 0.11 138.33 2.63 18.45 0.13 1.01 0.05
1995 413.52 58.69 7.05 6.02 0.10 155.38 2.66 22.61 0.15 1.57 0.07
1996 434.15 68.31 6.36 6.07 0.09 215.05 2.02 42.75 0.20 2.05 0.05
1997 549.51 73.01 7.53 8.45 0.12 246.75 2.23 39.01 0.16 3.08 0.08
1998 612.86 88.83 6.90 9.66 0.11 300.00 2.04 46.89 0.16 3.62 0.08
1999 706.21 97.40 7.25 10.25 0.11 337.42 2.09 50.35 0.15 3.55 0.07
2000 687.77 74.97 9.17 10.80 0.10 235.48 2.92 9.68 0.04 1.25 0.13
EXHIBIT 14.17
Source: Financial Analysts Handbook (New York: Standard & Poor’s, 2001). Reprinted with permission.
9As noted previously, it is apparent that there has been an adjustment to this industry index in terms of makeup that has changed the data substantially in 2000. Therefore, the estimate will not use the data for this latest year.
DEPREC. EXP. L-T DEBT
For example, the PPE turnover had tended to decline during the recent period. A conservative estimate would be a PPE turnover of 7.40. This turnover value combined with a sales estimate for 2002 of $870 implies a PPE estimate of $117.57. In turn, the depreciation expense/PPE ratio was in the 10 percent range before 1996. Subsequently, it has been between 10 and 12 percent.
Using the recent five-year average indicates an estimate of depreciation expense/PPE of about 10.60 percent. Applying this estimated percent to the PPE estimate of $117.57 implies a depre- ciation expense estimate of $12.46 ($117.57 ×0.1060).
Subtracting an estimate of depreciation expense from the operating profit figure indicates the industry’s net income before interest and taxes (EBIT).
Industry Interest Expense An industry’s interest expense will be a function of its financial leverage and interest rates. As shown in Exhibit 14.18, interest expense for the RDS industry always has been relatively low when compared to the S&P Industrials Index and did not increase at the same rate during the 1980s. Therefore, looking for a relationship between the two interest expense series would not be fruitful. Your estimate for the future should be based on two sepa- rate estimates: (1) changes in the amount of debt outstanding for this industry during the year, and (2) an estimate of the level of interest rates (will they increase or decline?).
Estimating Interest Expense The historical data needed to derive a specific estimate of interest expense are also in Exhibit 14.17. Recall the following steps used in Chapter 13:
1. Calculate the annual total asset turnover (TAT) for the RDS industry.
2. Use your 2002 sales estimate and an estimate of TAT to estimate total assets next year.
3. Calculate the annual long-term (interest-bearing) debt as a percentage of total assets for the RDS industry.
4. Use your estimate of total assets and the ratio of long-term debt as a percentage of total assets to estimate long-term debt for the next year.
518 CHAPTER 14 INDUSTRYANALYSIS
25
20
15
10
5
0
25
20
15
10
5
0
OPM (Percent)
1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 Year
S&P Industrials Index RDS
EXHIBIT 14.18 TIME-SERIES PLOT OF INTEREST EXPENSE FOR THE S&P INDUSTRIALS INDEX AND THE RDS INDUSTRY: 1977–2000
5. Calculate the annual interest cost as a percentage of long-term debt and analyze the trend of this series.
6. Estimate next year’s interest cost of debt for this industry based upon your prior estimate of market yields.
7. Estimate interest expense based on the following:
(Estimated Interest Cost of Debt) ×(Estimated Long-Term Debt)
For example, our sales estimate of $870 and a TAT that has averaged about 2.15 over the most recent years imply total assets of $405 next year. Long-term, interest-bearing debt has averaged about 15 percent of total assets for the RDS industry, which implies long-term debt next year of about $61 ($405 ×0.15). In turn, interest expense as a percentage of long-term debt during the recent period has averaged about 6.85 percent for this industry. Based upon the expectation of a small increase in market interest rates during 2002, we would estimate this interest rate to be 7.00 percent in 2002. This interest rate estimate combined with our long-term debt estimate of
$61 implies interest expense of $4.27 (0.07 ×$61).
Industry Tax Rate As you might expect, tax rates differ between industries. An extreme example would be the oil industry where heavy depletion allowances cause lower taxes. In some instances, however, you can assume that tax law changes have similar impacts on all industries.
To see if this is valid, you need to examine the relationship of tax rates over time for your indus- try and the aggregate market to determine if you can use regression analysis in your estimation process. Alternatively, a time-series plot could provide a useful estimate.
As shown in Exhibit 14.19, except for 1997, the RDS tax rate has moved with the economy’s tax rate. Therefore, the time-series plot in this figure is fairly informative, although you still need to consider pending national legislation and unique industry tax factors. Once you have
51
48
45
42
39
36
33
51
48
45
42
39
36
33
OPM (Percent)
1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 Year
S&P Industrials Index RDS
EXHIBIT 14.19 TIME-SERIES PLOT OF THE TAX RATES FOR THE S&P INDUSTRIALS INDEX AND THE RDS INDUSTRY: 1977–2000