INTANGIBLE ASSETS
Measurement, Drivers, Usefulness
By
Feng Gu and Baruch Lev*
Please note: The various intangibles value metrics discussed here were designed by Baruch Lev who retains exclusive rights to the measures, and has a patent pending for them. The measures should not be used or reproduced without a written permission from Baruch Lev.
April 2001
*
Boston University and New York University, respectively
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INTANGIBLE ASSETS
1. How are The Intangibles Metrics Computed?
It is widely accepted that intangible (knowledge or intellectual) assets are the major drivers of corporate value and growth in most economic sectors, but the measurement of these assets has eluded so far managers, accountants, and financial analysts valuing investment projects. Why measure intangible assets? Evaluating profitability and performance of business enterprise, by say, return on investment, assets or equity (ROA, ROE) is seriously flawed since the value of the firm’s major asset— intangible capital—is missing from the denominator of these indicators. Measures of price relatives (e.g., price-to-book ratio) are similarly misleading, absent the value of intangible assets from accounting book values. Valuations for the purpose of mergers and acquisitions are incomplete without an estimate of intellectual capital. Resource allocation decisions within corporations require values of intangible capital. These and other uses create the need for valuing intangible assets, in practically all economic sectors. Intangible (knowledge) assets, such as new discoveries (drugs, software products, etc.), brands or unique organizational designs (e.g., Internet-based supply chains) are by and large not traded in organized markets, and the property rights over these assets are not fully secured by the company, except for intellectual properties, such as patents and trademarks. The risk of these assets (e.g., drugs or software programs under development not making it to the market) is generally higher than that of physical assets.1 Accordingly, many, particularly accountants and
corporate executives, are reluctant to recognize intangible, or intellectual capital as assets in
1
See, Baruch lev, Intangibles: Management, Measurement and Reporting, forthcoming from Brookings Institution Press, June 2001, for elaboration on the unique attributes of intangibles.
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financial reports, on par with physical and financial assets. While such attitude concerning balance sheets may be understandable, it does not satisfy the need to seek information about and value of intangible assets. Some have attempted to gauge the value of intangible assets from the difference between the company’s capital market value and its book value (the balance sheet value of net physical and financial assets). This approach is unsatisfactory because it is based on two flawed assumptions: (a) that there is no mispricing in capital markets (tell this to investors who bought Internet stocks in 1999 and saw them plummet in 2000), and (b) that balance sheet historical values of assets reflect their current values. The market-minus-book approach to valuing intangibles is also unsatisfactory because it is circulatory. One searches for measures of intangibles value in order to provide new information to managers and investors. What is the use of a measure (market-minus-book) that is derived from what investors already know (market and book values)? There is obviously a need for a different approach to estimating the value of intangible assets. 1. Preliminaries: Baruch Lev’s methodology for measuring the value of intangible assets is based on the economic concept of ―production function,‖ where the firm’s economic performance is stipulated to be generated by the three major classes of inputs: Physical, financial, and knowledge assets. Thus: Economic Performance = α(Physical Assets) + β(Financial Assets) + δ(Intangible Assets) α , β and δ represent the contributions of a unit of asset to the enterprise performance. A key ingredient in this approach is the definition of an enterprise economic performance as an aggregate of past core earnings (earnings excluding unusual and extraordinary items), and
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future earnings, or growth potential. A performance measure which is strictly based on past earnings or cash flows, or a modification of earnings (e.g., the various value added measures), misses a major part of what intangible assets are all about—creating future growth (e.g., by investment in R&D, Internet activities, or employee training). Having thus defined enterprise performance, the next step is the measurement of the performance drivers—the three major asset groups. The values of physical and financial (stocks, bonds, financial instruments) assets are obtained from the firm’s balance sheet and footnotes (with proper adjustments, such as converting accounting historical costs to current values). The derivation of the value of the third performance driver— intangible capital—is, in a sense, the solution to the above production function for the one unknown (intangible capital). This is done by estimating the ―normal rates of return‖ on physical and financial assets—the α and β coefficients in the above production function—and subtracting from the estimated economic performance of the enterprise the contributions of physical and financial assets, namely the normal asset returns multiplied by the values of physical and financial assets. What remains from this subtraction is the contribution of intangible assets to the enterprise performance, which I define as ―intangibles-driven earnings.‖ Capitalizing the expected stream of these earnings yields an estimate of ―intangible capital.‖ The intangibles value measurement procedure is demonstrated graphically in Figure 1.
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Figure 1
INTANGIBLE ASSETS
Past Earnings + Future Earnings
Normalized Earnings Subtract: Subtract: Equal: Return on Physical Assets Return on Financial Assets Intangibles-Driven Earnings
Capitalize:
Intangible Assets
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2.
Specifics: The measurement procedure outlined in Figure 1 starts with the estimation of annual ―normalized earnings,‖ referred to earlier as the performance of the enterprise, which are based on an average of several (generally 3-5) historical years of reported core earnings (net earnings adjusted for extraordinary and other ―one time‖ items), and same number of expected years earnings. For public companies, I use two alternative approaches to estimate expected earnings: consensus earnings forecasts by financial analysts, and an earnings forecast based on the pattern of the firm’s sales. In firm-specific applications, I use various public and proprietary sources to estimate growth potential. Normalized earnings is thus an annual weighted average of 6-10 earnings numbers, giving a heavier weight to expected earnings. Based on various economic studies and analyses, I estimate the average contributions of physical and financial assets, the α and β in the production function above. For public rankings of companies (Fortune, CFO magazine), I use after tax rates of 7% for physical assets and 4.5% for financial assets, reflecting economy-wide averages. For companyspecific applications, I estimate specific rates of return on assets. These rates will change, of course, with market and company conditions. I then subtract from normalized earnings (defined above), 7% of the value of physical assets and 4.5% of the value of financial assets. What remains of normalized earnings after these subtractions is the contribution of intangible assets to the enterprise performance, which I define as ―intangibles-driven earnings‖ (IDE).
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Lastly, I forecast the series of intangibles-driven earnings over three future periods (a 3stage valuation model): Future years 1-5, using financial analysts’ long-term growth forecasts (or a sales-based forecast); years 6-10, linearly converging the forecasts to the long-term growth of the economy—3%; and years 11 to infinity, where IDE are assumed to grow annually by 3%—the expected long-term growth rate of the economy. The discounted value of expected IDE series, using a discount rate which reflects the above-average riskiness of these earnings, yields the estimated of ―intangible assets.‖
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2.
How do They Look? Tables 1 and 2 present the 1999 intangibles measures computed for the five leading
companies in 22 nonfinancial industries, followed by the industry median measures. These data constitute the CFO 2001 ranking. 2 The metrics include the firms’ intangible capital (noted as knowledge cpital in the tables), as of August 2000; their 1999 intangibles-driven earnings; (noted knowledge earnings) and the new value measure—―market-to-comprehensive value‖ (third column from right). This measure modifies the well-known market-to-book ratio (market value of corporations divided by their book value—net assets on the balance sheet), by adding to the denominator of the ratio the estimated value of the firm’s intangible capital. Thus, the balance sheet value of physical and financial assets (book value), plus the value of intangibles missing from the balance sheet, comprises the ―comprehensive value.‖ Table 2 indicates, among other things, that many, so called ―old economy‖ industries, are reach in intangibles: aerospace and defence, food and beverages (particularly brands), home products, industrial, oil and gas, retail. Figure 2, based on about 2000 companies for the period 1990-1999, provides a similar message. We will see below the results of extensive tests demonstrating the unique usefulness of these measures reflecting intangibles assets.
2
This work was done in cooperation with Marc Bothwell, vice president and portfolio manager at Credit Suisse Asset Management.
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Table 1
The Scope of Intangibles
Name HON LMT BA NOC RTN.B DAL AMR LUV U AMGN MEDI BGEN CHIR DD DOW PPG APD ROH IBM DELL HWP EMC SUNW MSFT ORCL CA VRTS SEBL AES DUK SO FPL D EMR ROK CBE APCC KO PEP HNZ UN CPB KMB IP GP WY HONEYWELL INTL INC COM LOCKHEED MARTIN CORP COM BOEING CO COM NORTHROP GRUMMAN CORP COM RAYTHEON CO CL B DELTA AIR LINES INC DEL COM AMR CORP COM SOUTHWEST AIRLS CO COM US AIRWAYS GROUP INC COM AMGEN INC COM MEDIMMUNE INC COM BIOGEN INC COM CHIRON CORP COM DU PONT E I DE NEMOURS & CO COM DOW CHEM CO COM PPG INDS INC COM AIR PRODS & CHEMS INC COM ROHM & HAAS CO COM INTERNATIONAL BUSINESS MACHS COM DELL COMPUTER CORP COM HEWLETT PACKARD CO COM E M C CORP MASS COM SUN MICROSYSTEMS INC COM MICROSOFT CORP COM ORACLE CORP COM COMPUTER ASSOC INTL INC COM VERITAS SOFTWARE CO COM SIEBEL SYS INC COM AES CORP COM DUKE ENERGY CORP COM SOUTHERN CO COM FPL GROUP INC COM DOMINION RES INC VA NEW COM EMERSON ELEC CO COM ROCKWELL INTL CORP NEW COM COOPER INDS INC COM Industry Aerospace & Defense Aerospace & Defense Aerospace & Defense Aerospace & Defense Aerospace & Defense Airlines Airlines Airlines Airlines Biotech Biotech Biotech Biotech Chemical Chemical Chemical Chemical Chemical Computer Hardware Computer Hardware Computer Hardware Computer Hardware Computer Hardware Computer Software Computer Software Computer Software Computer Software Computer Software Electric Utilities Electric Utilities Electric Utilities Electric Utilities Electric Utilities Electrical Electrical Electrical Knowledge Capital 8/31/2000 33,839 27,358 23,447 15,901 8,356 10,792 9,230 6,668 3,420 20,876 4,409 4,377 1,508 49,085 29,091 9,948 6,245 4,656 128,186 83,519 49,857 45,958 44,560 188,787 54,304 38,908 16,988 6,180 28,486 15,380 10,351 5,385 3,358 24,717 9,431 5,950 4,311 67,165 50,480 18,565 18,390 13,022 25,308 11,369 8,884 5,762 Change in Knowledge Market Value / Knowledge Knowledge Capital / Book Market Value / Comprehensive Earnings 1999 Earnings '99-'98 Value Book Value Value 2,157 1,417 1,590 894 800 709 425 374 251 1,041 124 219 80 2,543 1,844 632 379 280 6,597 2,490 2,598 1,569 1,849 8,526 2,314 1,782 176 176 691 934 847 391 418 1,426 536 363 199 3,484 2,334 1,064 1,306 835 1,579 1,103 854 572 235 -333 614 65 -595 -15 -174 68 60 NM 136 36 44 17 23 748 63 42 -29 212 547 -340 389 470 2,406 904 279 143 53 197 211 177 67 77 130 16 27 32 394 67 85 36 47 201 841 369 285 6.0 6.1 4.4 0.8 3.7 3.2 3.1 2.4 1.3 6.7 12.9 3.4 6.9 6.1 4.6 8.4 5.7 5.3 6.9 7.1 1.6 1.1 0.9 0.5 3.9 3.5 3.3 4.3 7.3 7.5 11.4 3.0 95.1 4.5 0.9 2.2 0.8 3.6 4.2 1.9 4.5 0.8 2.1 1.4 2.2 NM 22.4 24.3 10.2 5.4 3.5 2.0 2.2 2.9 1.8 12.1 17.5 8.2 32.2 27.7 8.9 39.4 2.7 15.1 45.6 7.3 2.9 2.1 1.7 1.8 4.5 2.8 1.8 4.6 14.2 9.1 8.1 4.4 81.3 5.6 1.2 1.1 1.5 3.3 1.8 3.8 1.5 0.9 1.2 0.7 3.7 0.71 0.34 1.30 0.28 0.50 0.38 0.31 1.17 0.72 3.20 3.44 1.90 2.95 0.75 0.47 0.53 0.87 0.77 1.58 1.26 1.85 4.06 3.91 1.60 4.19 0.41 2.40 5.76 0.90 1.10 0.99 0.85 1.22 0.91 0.62 0.43 0.87 1.71 1.08 0.65 1.10 0.85 1.02 0.63 0.35 0.81 Market Value 8/31/2000 30,891 11,407 46,270 5,440 9,457 6,071 4,920 11,280 2,280 77,958 17,651 10,229 9,863 46,779 17,761 7,045 7,746 6,356 232,413 113,251 119,385 213,677 202,719 368,819 254,509 18,763 48,465 40,715 29,119 27,531 19,418 9,488 12,604 28,273 7,534 3,292 4,629 130,326 61,593 13,223 27,007 11,140 31,514 15,361 4,568 10,322 Return 8/31/2000 2/28/2001 22% 32% 17% 21% 21% -15% 1% 23% 21% -5% -48% 4% -13% -1% 28% 28% 13% 29% -24% -50% -52% -58% -69% -15% -58% -2% -46% -61% -15% 10% 6% 24% 26% 2% 15% 25% -49% 1% 9% 14% 19% 20% 23% 20% 13% 18%
AMERICAN PWR CONVERSION CORP COM Electrical COCA COLA CO COM PEPSICO INC COM HEINZ H J CO COM UNILEVER N V N Y SHS NEW CAMPBELL SOUP CO COM KIMBERLY CLARK CORP COM INTL PAPER CO COM GEORGIA PAC CORP COM GA PAC GRP WEYERHAEUSER CO COM Food/Beverages Food/Beverages Food/Beverages Food/Beverages Food/Beverages Forest Products Forest Products Forest Products Forest Products
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WLL PG G CL CLX AVP TYC UTX CAT ITW IR DIS VIA.B CCU F GM DPH JCI PCAR GCI TRB NYT KRI DJ XOM RD CHV P UCL PFE MRK JNJ BMY PHA LLY WMT S TGT COST KSS INTC AMAT TXN BRCM HD LOW CVS WAG RSH VZ SBC T
WILLAMETTE INDS INC COM PROCTER & GAMBLE CO COM GILLETTE CO COM COLGATE PALMOLIVE CO COM CLOROX CO DEL COM AVON PRODS INC COM TYCO INTL LTD NEW COM UNITED TECHNOLOGIES CORP COM CATERPILLAR INC DEL COM ILLINOIS TOOL WKS INC COM INGERSOLL-RAND CO COM DISNEY WALT CO COM DISNEY VIACOM INC CL B
Forest Products Home Products Home Products Home Products Home Products Home Products Industrial Industrial Industrial Industrial Industrial Media Media
1,044 63,450 26,145 19,296 8,151 7,675 56,184 25,856 23,132 15,800 14,453 53,012 16,759 9,536 90,338 55,026 13,413 8,573 4,159 17,733 10,388 5,619 4,921 3,562 114,347 27,258 24,559 8,697 8,453 128,610 109,217 76,446 74,002 55,373 48,163 81,239 23,457 15,406 6,006 5,504 208,641 44,667 39,390 5,704 48,849 10,962 10,320 9,243 4,552 114,464 113,618 81,221
221 3,882 1,343 1,097 502 455 2,970 1,564 1,166 957 819 2,126 646 447 6,685 4,257 962 480 306 1,087 502 336 329 210 8,544 3,818 2,210 877 376 5,796 6,583 4,336 4,254 2,193 2,641 4,867 1,421 885 349 250 9,502 1,858 1,860 137 2,230 567 512 510 271 6,462 6,903 4,851
69 143 124 109 96 24 NM 640 438 54 113 77 59 188 119 1,680 282 97 74 -4 137 140 44 12 10 878 585 1,026 198 42 3,017 902 699 424 543 328 1,167 115 128 40 50 2,749 1,090 1,012 38 621 171 84 73 60 1,277 2,730 -222
0.5 5.2 11.0 11.8 4.5 NM 3.7 3.4 4.2 3.1 4.5 2.2 0.3 0.9 3.7 1.9 3.8 3.5 1.9 3.8 1.7 4.2 3.0 6.6 1.7 0.8 1.3 1.7 3.4 8.6 8.6 4.3 8.3 4.7 8.7 2.9 3.6 2.6 1.5 2.9 5.7 7.3 3.1 6.8 3.5 2.1 2.6 2.3 6.3 3.3 4.0 0.7
1.5 6.6 13.3 17.8 4.7
1.01 1.07 1.11 1.40 0.86 1.27
3,331 80,719 31,590 29,257 8,517 9,304 96,177 29,231 12,705 16,922 7,340 82,396 102,113 27,518 50,941 38,758 9,205 4,589 3,246 14,928 10,999 6,594 4,127 5,467 284,382 131,204 55,150 15,756 8,106 273,069 160,694 127,891 104,255 75,998 82,453 211,872 10,697 20,999 15,404 18,486 502,711 70,011 109,810 55,509 111,287 17,154 14,504 33,231 10,962 118,573 141,514 118,288
54% 15% 9% 17% 0% 9% -4% 26% 15% 9% -4% -20% -26% -21% 18% -25% -14% 26% 13% 18% 14% 13% 10% -1% 0% -5% 3% -13% 7% 5% 15% 7% 21% -11% 10% 6% 33% 68% 21% 18% -62% -51% -56% -80% -11% 25% 65% 35% -27% 15% 15% -26%
6.3 3.9 2.3 3.3 2.3 3.5 2.1 2.7 2.1 1.3 2.6 1.9 1.5 3.2 1.7 4.9 2.5 10.1 4.2 3.7 2.9 3.1 3.3 18.2 12.6 7.1 11.7 6.5 15.0 7.5 1.7 3.5 3.8 9.8 13.7 11.4 8.7 65.8 8.0 3.3 3.7 8.2 15.2 3.5 5.0 1.1
1.34 0.87 0.44 0.81 0.42 1.07 1.55 1.40 0.44 0.46 0.54 0.42 0.51 0.67 0.66 0.95 0.63 1.33 1.57 2.10 1.27 1.14 0.74 1.90 1.32 1.35 1.26 1.13 1.54 1.94 0.36 0.98 1.52 2.50 2.05 1.38 2.11 8.48 1.77 1.06 1.02 2.50 2.08 0.80 1.00 0.62
CLEAR CHANNEL COMMUNICATIONS COM Media FORD MTR CO DEL COM PAR $0.01 GENERAL MTRS CORP COM DELPHI AUTOMOTIVE SYS CORP COM JOHNSON CTLS INC COM PACCAR INC COM GANNETT INC COM TRIBUNE CO NEW COM NEW YORK TIMES CO CL A KNIGHT RIDDER INC COM DOW JONES & CO INC COM EXXON MOBIL CORP COM Motor Vehicles Motor Vehicles Motor Vehicles Motor Vehicles Motor Vehicles Newspapers Newspapers Newspapers Newspapers Newspapers Oil
ROYAL DUTCH PETE CO NY REG GLD1.25 Oil CHEVRON CORPORATION COM PHILLIPS PETE CO COM UNOCAL CORP COM PFIZER INC COM MERCK & CO INC COM JOHNSON & JOHNSON COM BRISTOL MYERS SQUIBB CO COM PHARMACIA CORP COM LILLY ELI & CO COM WAL MART STORES INC COM SEARS ROEBUCK & CO COM TARGET CORP COM COSTCO WHSL CORP NEW COM KOHLS CORP COM INTEL CORP COM APPLIED MATLS INC COM TEXAS INSTRS INC COM BROADCOM CORP CL A HOME DEPOT INC COM LOWES COS INC COM CVS CORP COM WALGREEN CO COM RADIOSHACK CORP COM VERIZON COMMUNICATIONS COM SBC COMMUNICATIONS INC COM AT&T CORP COM Oil Oil Oil Pharaceuticals Pharaceuticals Pharaceuticals Pharaceuticals Pharaceuticals Pharaceuticals Retail Retail Retail Retail Retail Semiconductors Semiconductors Semiconductors Semiconductors Specialty Retail Specialty Retail Specialty Retail Specialty Retail Specialty Retail Telecom Telecom Telecom
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BLS WCOM CSCO LU MOT GLW QCOM
BELLSOUTH CORP COM WORLDCOM INC GA NEW COM CISCO SYS INC COM LUCENT TECHNOLOGIES INC COM MOTOROLA INC COM CORNING INC COM QUALCOMM INC COM
Telecom Telecom Telecom Equipment Telecom Equipment Telecom Equipment Telecom Equipment Telecom Equipment
53,812 23,277 162,218 62,824 26,947 24,786 19,317
3,568 1,772 4,910 3,220 1,684 867 672
660 30 2,434 315 1,016 210 192
3.3 0.4 6.1 2.4 1.3 3.3 3.3
4.3 1.9 18.5 5.3 3.7 12.6 7.7
1.00 1.35 2.60 1.57 1.62 2.97 1.78
70,185 104,734 489,845 139,633 78,639 96,184 44,610
13% -54% -65% -70% -58% -75% -8%
NM – Not Meaningful
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Table 2
Intangible Capital
23,447 7,949 4,393 9,948 49,857 38,908 10,351 7,690 18,565 8,884 19,296 23,132 16,759 13,413 5,619 24,559 75,224 15,406 42,029 10,320 81,221 26,947
Industry
Aerospace & Defense Airlines Biotech Chemical Computer Hardware Computer Software Electric Utilities Electrical Food/Beverages Forest Products Home Products Industrial Media Motor Vehicles Newspapers Oil Pharaceuticals Retail Semiconductors Specialty Retail Telecom Telecom Equipment
Industry Medians (of Companies in Table1) IntangiblesChange in Intangible Market Market Value/ Driven Intangibles Capital/ Book Value/ Book Comprehensive Market Earnings Earnings Value Value Value Value(8/31/2000)
1,417 399 171 632 2,490 1,782 691 450 1,306 854 1,097 1,166 646 962 336 2,210 4,295 885 1,859 512 4,851 1,684 65 22 40 42 389 279 177 29 67 285 109 113 119 97 44 585 621 115 1,051 84 660 315 3.58 2.12 5.18 3.08 6.69 5.68 1.11 3.70 7.48 0.87 8.10 3.65 0.94 3.50 3.77 1.71 8.44 2.89 6.23 2.62 3.26 3.25 1.77 0.96 16.29 2.18 17.53 15.15 2.09 3.63 9.13 1.48 6.57 3.30 2.72 1.87 3.18 3.30 12.16 3.75 12.57 8.01 3.47 7.73 0.50 0.55 3.07 0.75 1.85 2.40 0.99 0.75 1.08 0.81 1.11 0.81 1.40 0.46 0.67 1.27 1.34 1.52 2.08 1.77 1.00 1.78 11,407 5,496 13,940 7,746 202,719 48,465 19,418 6,081 27,007 10,322 29,257 16,922 82,396 9,205 6,594 55,150 116,073 18,486 89,911 17,154 118,288 96,184
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13
Figure 2
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3.
What Drives Intangible Capital? Intangible (intellectual) capital is driven by diverse factors: innovation, human capital,
organizational processes, customer and supplier relations, to name some major ones. For most of these drivers (e.g., customer satisfaction), there are no standardized, public information available. I, therefore, limit the analysis here to the several intangibles drivers which are publicly available: R&D, advertising (brand support), capital expenditures, information systems, technology acquisition. Table 3, based on data for about 2000 companies, spanning the period 1989-1999, identifies five major drivers of intangibles-driven earnings (IDE): R&D, advertising (brand enhancement), capital expenditure (intangibles embedded in physical assets), information technology, and technology acquisitions. It is clear from the table that these are indeed driverstheir intensity is positively correlated with the ratio of IDE to sales.3 In current work (conducted with Towers Perrin and Feng Gu of Boston University), we find that various measures reflecting human resource practices (e.g., extent of incentive-based compensation, termed LPCT in Table 4, employee training, etc.), are also strongly correlated with intangibles earnings and capital. This is reflected in Table 4. This is just the beginning of a detailed identification and quantification of the drivers of intangible capital, and in turn, corporate value. Business and investment decisions are predicated on the understanding and quantification of the major drivers of corporate value and growth.
3
This work is conducted with Feng Gu of Boston University.
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Table 3
17
Table 4
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4. Do They Work? Given the proliferation of new measures and indicators, proposed to managers and investors, it’s incumbent on the proponents of such measures not only to argue that they are needed and useful, but to prove empirically that they indeed are doing the job. Such a proof is unfortunately missing from most of the proposed measures and analytical techniques. Below, are comprehensive statistical tests indicating the superiority of the intangibles metrics as indicators of enterprise performance over conventional ones. A frequently used methodology in finance and accounting research to gauge relevance of information and data is to correlate the proposed information with the consequences of investors’ decisions, such as reflected in stock price changes. A weak correlation indicates that the decision makers (e.g., investors) did not find the information very useful, and vice versa for a strong correlation. Following this approach, I correlated (with Feng Gu) annual stock returns (stock price changes adjusted for dividends), reflecting investors’ decisions, with the annual growth in firms’ intangibles-driven earnings, over the period 1989-1999 (about 2,000 companies in each year). For comparison purposes, I did the same for the annual growth in reported cash flows (from operations) and earnings, two of the most widely used corporate performance measures. Figure 3 shows the clear superiority of intangibles-driven earnings (IDE), over accounting earnings and cash flows. Specifically, while the correlations between stock returns and reported cash flows or earnings are 0.11 and 0.29, respectively (so much for ―cash is king‖), the correlations between returns and IDE (based on sales’ growth) is 0.40, and between returns and IDE (based on analysts’ forecasts) is 0.53. Thus, both version of intangibles-driven earnings, with and without analysts’ forecasts, beat earnings and cash flows in the ―return correlation race.‖
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The conclusion: the earnings stream generated by intangible assets (IDE) provide substantially more relevant information to investors than reported earnings and cash flows. The reason: while total earnings, or cash flows reflect the performance of all assets, some of which (e.g., various kinds of physical assets) don’t contribute to growth, IDE focuses on the contribution of intangibles—the major growth contributes. Also, while earnings and cash flows are strictly historic (backward-looking) measures, IDE explicitly reflect growth expectations.4 While I cannot perform similar statistical analyses on managerial decisions, analogous to the capital market analysis reported here, it stands to reason that the intangibles metrics reported here will also provide new and useful information for corporate managers. The reason: most corporate decisions are guided by accounting metrics, such as earnings and return on investment measure, which appear inferior to the intangibles metrics.
4
For those interested in a regression analysis, supplementing the univariate correlations of Figure 3, Table 5 provides the appropriate estimates, where annual stock returns are regressed on reported earnings (level and change) and various configurations of the intangibles metrics.
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Figure 3
21
Table 5
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5. Can They Predict? The statistical validation tests reported in Section 4 were contemporaneous; namely stock return correlated with same year growth in intangibles-driven earnings. Contemporaneous correlations indicate the relevance of an information item to investors. But if the item is widely available, despite it being relevant, you will not be able to use it to gain superior investment returns (the information is already priced). To test whether intangibles measures can be used to gain ―abnormal returns‖ one must use a multiperiod predictive test. Such a preliminary test is reported in Table 6. With Marc Bothwell of Credit Swiss Asset Management, I estimated for each of the 105 companies in Table 1, its market-to-comprehensive value (M/C) indicator for August 31, 2000. (Recall that the M/C ratio is a modified market-to-book ratio, where the value of intangible capital is added to the denominator). We then correlated the M/C values with the subsequent stock performance of these companies (during September 1, 2000 through December 31, 2000; a period of sharp stock price declines).5 We found a strong negative correlation, confirming that companies with above-average M/C values (i.e., overvalued by investors, according to the intangibles measures) were subsequently downgraded by investors, and vice versa for companies with below-average M/C value (undervalued companies). Table 6 indicates that the 53 companies with below-median M/C values (undervalued) gained, on average, 7% in the subsequent period, while the 52 stocks with above-median M/C (overvalued) lost, on average, 15.5% during September-December 2000. The market-to-comprehensive measure thus appears to distinguish between undervalued and overvalued stocks. With Feng Gu (Boston University) I derive even stronger results for a much longer period:1989-1999, and a larger sample of about 2,000 companies. The M/C
5
These numbers appear in the right, and third from right columns in Table 1.
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investment scheme is profitable during the three-years after portfolio formation and easily beats the widely used measure of Market-to-Book. Table 7 provides portfolio returns for three investment strategies: book-to-market (B/M), comprehensive-to-market based on analysts’ forecasts (C/M), and comprehensive-to-market based on a sales growth model (AC/M).6 In each case, the sample companies (about 2000 companies, over 1989-1999) are classified into five portfolios according to increasing size of B/M or C/M. The portfolio return data for one, two, and three years subsequent to portfolio formation indicate: (a) For each year and portfolio strategy, returns are monotonically increasing from the first to the fifth portfolios, a finding documented in finance literature for the B/M portfolios. (b) The increases are steeper for the C/M than for B/M portfolios, see the right column of ―Q5 – Q1 Difference‖ in the three panels of Table 7. (c) The total returns are also higher for the C/M strategy than for the B/M strategy (e.g., for portfolio Q5, the 36 months return is 71.8% for C/M vs. 62.1% for B/M). (d) There are no distinguishable differences in performance between the two versions of C/M; with and without analysts’ forecasts. This is graphically indicated by Figure 4. Tables 8-10 pit directly the B/M strategy against the C/M portfolio choice. In a series of 5x5 classifications, five by B/M and five by C/M, for 12 months ahead (Table 8) and 24 and 36 months subsequent to portfolio formation (Tables 9 and 10), one can observe the generation of returns for one strategy, when the other is held constant (movement across rows or columns). It is clear from each of the three tables that the significant returns are exhibited across rows, from low to high C/M portfolios (see returns on the right column: CM Q5-Q1). Once the C/M portfolios are accounted for, the B/M portfolio strategy does not
6
In empirical work, the inverse of the multiples (e.g., book-to-market) is preferred , to avoid negative values in the denominator.
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generate substantial returns (bottom row in Tables). Thus, the C/M strategy subsumes the well known B/M (―Value‖) strategy. Results for AC/M—the intangibles-based comprehensive value based on sales growth model (in contrast with the C/M which is based on analysts’ forecasts), are essentially identical to those using analysts’ forecasts presented in Tables 8-10. Finally, Tables 11-12 present tests of C/M (or AC/M) portfolio returns adjusted for various risk factors: beta, size, book-to-market, and the ―return momentum.‖ This is the well-known 4-factor model in finance research. The numbers in the tables are risk-adjusted monthly return. It is clear that for both C/M and AC/M, the portfolio returns are sharply increasing from low C/M (AC/M) to high C/M (AC/M) portfolios. The abnormal returns are economically very meaningful. For example, the monthly return for portfolio Q4, 0.236 (Table 11), translate to an annual return of over 3.0 percent above risk benchmark. Summarizing, the extensive, large sample empirical tests reported in this section indicate that the market-to-comprehensive value metric, based either on analysts’ forecasts or on a sales growth model, exhibit a consistent ability to generate subsequent abnormal stock returns, whether evaluated against a market-to-book strategy, or a combination of risk factors.
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Table 6
Market Value / Comprehensive Value (8/31/2000) and Subsequent Stock Performance (8/31/2000-12/31/2000)
Master Table Weighted Subsequent Market Value / Return Comprehensive Averages for Value Group low 7.0% high -15.5%
Count in Sample 53 52
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Table 7
27
Figure 4
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Table 8
29
Table 9
30
Table 10
31
Table 11
32
Table 12
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6.
Takeaway Points
The Intangibles Scoreboard adds an essential, and hitherto missing, valuation tool for managers and investors concerned with intangible (intellectual) assets, and with the optimal resource allocation of intangible and physical assets. R&D, advertising, information technology and various human resource practices were empirically identified as drivers of intangible capital, and in turn corporate value. Intangibles measures provide more relevant information than conventional performance measures, as indicated by the strength of correlations with stock returns. Intangibles measures successfully distinguish between over-and under-valued stocks, as indicated by the research presented above. Lastly, the data and findings reported above are based on publicly available information, and uniform return and discount rates. It can be expected that substantially improved valuations will be obtained by tailoring the intangibles measures to the specific circumstances of companies, subsidiaries, or stocks.
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