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

financial reports, on par with physical and financial assets. While such attitude concerning 1 See, Baruch lev, Intangibles: Management, Measurement and Reporting, forthcoming from Brookings

Institution Press, June 2001, for elaboration on the unique attributes of intangibles.

2

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

future earnings, or growth potential. A performance measure which is strictly based on past

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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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INTANGIBLE ASSETS

Past Earnings+

Future Earnings

Normalized Earnings

Subtract: Return on Physical Assets

Subtract: Return on Financial Assets

Equal: Intangibles-Driven Earnings

Figure 1

5

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 company-

specific 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 3-

stage 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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The Scope of Intangibles

  Name Industry

Knowledge Capital

8/31/2000Knowledge

Earnings 1999

Change in Knowledge

Earnings '99-'98

Knowledge Capital / Book

ValueMarket Value /

Book Value

Market Value / Comprehensive

ValueMarket Value

8/31/2000Return 8/31/2000 -

2/28/2001

HON HONEYWELL INTL INC COMAerospace & Defense 33,839 2,157 235 3.6 3.3 0.71 30,891 22%

LMT LOCKHEED MARTIN CORP COMAerospace & Defense 27,358 1,417 -333 4.2 1.8 0.34 11,407 32%

BA BOEING CO COMAerospace & Defense 23,447 1,590 614 1.9 3.8 1.30 46,270 17%

NOC NORTHROP GRUMMAN CORP COMAerospace & Defense 15,901 894 65 4.5 1.5 0.28 5,440 21%

RTN.B RAYTHEON CO CL BAerospace & Defense 8,356 800 -595 0.8 0.9 0.50 9,457 21%

DAL DELTA AIR LINES INC DEL COM Airlines 10,792 709 -15 2.1 1.2 0.38 6,071 -15%

AMR AMR CORP COM Airlines 9,230 425 -174 1.4 0.7 0.31 4,920 1%

LUV SOUTHWEST AIRLS CO COM Airlines 6,668 374 68 2.2 3.7 1.17 11,280 23%

U US AIRWAYS GROUP INC COM Airlines 3,420 251 60 NM NM 0.72 2,280 21%

AMGN AMGEN INC COM Biotech 20,876 1,041 136 6.0 22.4 3.20 77,958 -5%

MEDI MEDIMMUNE INC COM Biotech 4,409 124 36 6.1 24.3 3.44 17,651 -48%

BGEN BIOGEN INC COM Biotech 4,377 219 44 4.4 10.2 1.90 10,229 4%

CHIR CHIRON CORP COM Biotech 1,508 80 17 0.8 5.4 2.95 9,863 -13%

DD DU PONT E I DE NEMOURS & CO COM Chemical 49,085 2,543 23 3.7 3.5 0.75 46,779 -1%

DOW DOW CHEM CO COM Chemical 29,091 1,844 748 3.2 2.0 0.47 17,761 28%

PPG PPG INDS INC COM Chemical 9,948 632 63 3.1 2.2 0.53 7,045 28%

APD AIR PRODS & CHEMS INC COM Chemical 6,245 379 42 2.4 2.9 0.87 7,746 13%

ROH ROHM & HAAS CO COM Chemical 4,656 280 -29 1.3 1.8 0.77 6,356 29%

IBM INTERNATIONAL BUSINESS MACHS COM Computer Hardware 128,186 6,597 212 6.7 12.1 1.58 232,413 -24%

DELL DELL COMPUTER CORP COM Computer Hardware 83,519 2,490 547 12.9 17.5 1.26 113,251 -50%

HWP HEWLETT PACKARD CO COM Computer Hardware 49,857 2,598 -340 3.4 8.2 1.85 119,385 -52%

EMC E M C CORP MASS COM Computer Hardware 45,958 1,569 389 6.9 32.2 4.06 213,677 -58%

SUNW SUN MICROSYSTEMS INC COM Computer Hardware 44,560 1,849 470 6.1 27.7 3.91 202,719 -69%

MSFT MICROSOFT CORP COM Computer Software 188,787 8,526 2,406 4.6 8.9 1.60 368,819 -15%

ORCL ORACLE CORP COM Computer Software 54,304 2,314 904 8.4 39.4 4.19 254,509 -58%

CA COMPUTER ASSOC INTL INC COM Computer Software 38,908 1,782 279 5.7 2.7 0.41 18,763 -2%

VRTS VERITAS SOFTWARE CO COM Computer Software 16,988 176 143 5.3 15.1 2.40 48,465 -46%

SEBL SIEBEL SYS INC COM Computer Software 6,180 176 53 6.9 45.6 5.76 40,715 -61%

AES AES CORP COM Electric Utilities 28,486 691 197 7.1 7.3 0.90 29,119 -15%

DUK DUKE ENERGY CORP COM Electric Utilities 15,380 934 211 1.6 2.9 1.10 27,531 10%

SO SOUTHERN CO COM Electric Utilities 10,351 847 177 1.1 2.1 0.99 19,418 6%

FPL FPL GROUP INC COM Electric Utilities 5,385 391 67 0.9 1.7 0.85 9,488 24%

D DOMINION RES INC VA NEW COM Electric Utilities 3,358 418 77 0.5 1.8 1.22 12,604 26%

EMR EMERSON ELEC CO COM Electrical 24,717 1,426 130 3.9 4.5 0.91 28,273 2%

ROK ROCKWELL INTL CORP NEW COM Electrical 9,431 536 16 3.5 2.8 0.62 7,534 15%

CBE COOPER INDS INC COM Electrical 5,950 363 27 3.3 1.8 0.43 3,292 25%

APCC AMERICAN PWR CONVERSION CORP COM Electrical 4,311 199 32 4.3 4.6 0.87 4,629 -49%

KO COCA COLA CO COM Food/Beverages 67,165 3,484 394 7.3 14.2 1.71 130,326 1%

PEP PEPSICO INC COM Food/Beverages 50,480 2,334 67 7.5 9.1 1.08 61,593 9%

HNZ HEINZ H J CO COM Food/Beverages 18,565 1,064 85 11.4 8.1 0.65 13,223 14%

UN UNILEVER N V N Y SHS NEW Food/Beverages 18,390 1,306 36 3.0 4.4 1.10 27,007 19%

CPB CAMPBELL SOUP CO COM Food/Beverages 13,022 835 47 95.1 81.3 0.85 11,140 20%

KMB KIMBERLY CLARK CORP COM Forest Products 25,308 1,579 201 4.5 5.6 1.02 31,514 23%

IP INTL PAPER CO COM Forest Products 11,369 1,103 841 0.9 1.2 0.63 15,361 20%

GP GEORGIA PAC CORP COM GA PAC GRP Forest Products 8,884 854 369 2.2 1.1 0.35 4,568 13%

WY WEYERHAEUSER CO COM Forest Products 5,762 572 285 0.8 1.5 0.81 10,322 18%

WLL WILLAMETTE INDS INC COM Forest Products 1,044 221 69 0.5 1.5 1.01 3,331 54%

PG PROCTER & GAMBLE CO COM Home Products 63,450 3,882 143 5.2 6.6 1.07 80,719 15%

G GILLETTE CO COM Home Products 26,145 1,343 124 11.0 13.3 1.11 31,590 9%

CL COLGATE PALMOLIVE CO COM Home Products 19,296 1,097 109 11.8 17.8 1.40 29,257 17%

Table 1

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CLX CLOROX CO DEL COM Home Products 8,151 502 96 4.5 4.7 0.86 8,517 0%

AVP AVON PRODS INC COM Home Products 7,675 455 24 NM NM 1.27 9,304 9%

TYC TYCO INTL LTD NEW COM Industrial 56,184 2,970 640 3.7 6.3 1.34 96,177 -4%

UTX UNITED TECHNOLOGIES CORP COM Industrial 25,856 1,564 438 3.4 3.9 0.87 29,231 26%

CAT CATERPILLAR INC DEL COM Industrial 23,132 1,166 54 4.2 2.3 0.44 12,705 15%

ITW ILLINOIS TOOL WKS INC COM Industrial 15,800 957 113 3.1 3.3 0.81 16,922 9%

IR INGERSOLL-RAND CO COM Industrial 14,453 819 77 4.5 2.3 0.42 7,340 -4%

DIS DISNEY WALT CO COM DISNEY Media 53,012 2,126 59 2.2 3.5 1.07 82,396 -20%

VIA.B VIACOM INC CL B Media 16,759 646 188 0.3 2.1 1.55 102,113 -26%

CCU CLEAR CHANNEL COMMUNICATIONS COM Media 9,536 447 119 0.9 2.7 1.40 27,518 -21%

F FORD MTR CO DEL COM PAR $0.01 Motor Vehicles 90,338 6,685 1,680 3.7 2.1 0.44 50,941 18%

GM GENERAL MTRS CORP COM Motor Vehicles 55,026 4,257 282 1.9 1.3 0.46 38,758 -25%

DPH DELPHI AUTOMOTIVE SYS CORP COM Motor Vehicles 13,413 962 97 3.8 2.6 0.54 9,205 -14%

JCI JOHNSON CTLS INC COM Motor Vehicles 8,573 480 74 3.5 1.9 0.42 4,589 26%

PCAR PACCAR INC COM Motor Vehicles 4,159 306 -4 1.9 1.5 0.51 3,246 13%

GCI GANNETT INC COM Newspapers 17,733 1,087 137 3.8 3.2 0.67 14,928 18%

TRB TRIBUNE CO NEW COM Newspapers 10,388 502 140 1.7 1.7 0.66 10,999 14%

NYT NEW YORK TIMES CO CL A Newspapers 5,619 336 44 4.2 4.9 0.95 6,594 13%

KRI KNIGHT RIDDER INC COM Newspapers 4,921 329 12 3.0 2.5 0.63 4,127 10%

DJ DOW JONES & CO INC COM Newspapers 3,562 210 10 6.6 10.1 1.33 5,467 -1%

XOM EXXON MOBIL CORP COM Oil 114,347 8,544 878 1.7 4.2 1.57 284,382 0%

RD ROYAL DUTCH PETE CO NY REG GLD1.25 Oil 27,258 3,818 585 0.8 3.7 2.10 131,204 -5%

CHV CHEVRON CORPORATION COM Oil 24,559 2,210 1,026 1.3 2.9 1.27 55,150 3%

P PHILLIPS PETE CO COM Oil 8,697 877 198 1.7 3.1 1.14 15,756 -13%

UCL UNOCAL CORP COM Oil 8,453 376 42 3.4 3.3 0.74 8,106 7%

PFE PFIZER INC COM Pharaceuticals 128,610 5,796 3,017 8.6 18.2 1.90 273,069 5%

MRK MERCK & CO INC COM Pharaceuticals 109,217 6,583 902 8.6 12.6 1.32 160,694 15%

JNJ JOHNSON & JOHNSON COM Pharaceuticals 76,446 4,336 699 4.3 7.1 1.35 127,891 7%

BMY BRISTOL MYERS SQUIBB CO COM Pharaceuticals 74,002 4,254 424 8.3 11.7 1.26 104,255 21%

PHA PHARMACIA CORP COM Pharaceuticals 55,373 2,193 543 4.7 6.5 1.13 75,998 -11%

LLY LILLY ELI & CO COM Pharaceuticals 48,163 2,641 328 8.7 15.0 1.54 82,453 10%

WMT WAL MART STORES INC COM Retail 81,239 4,867 1,167 2.9 7.5 1.94 211,872 6%

S SEARS ROEBUCK & CO COM Retail 23,457 1,421 115 3.6 1.7 0.36 10,697 33%

TGT TARGET CORP COM Retail 15,406 885 128 2.6 3.5 0.98 20,999 68%

COST COSTCO WHSL CORP NEW COM Retail 6,006 349 40 1.5 3.8 1.52 15,404 21%

KSS KOHLS CORP COM Retail 5,504 250 50 2.9 9.8 2.50 18,486 18%

INTC INTEL CORP COM Semiconductors 208,641 9,502 2,749 5.7 13.7 2.05 502,711 -62%

AMAT APPLIED MATLS INC COM Semiconductors 44,667 1,858 1,090 7.3 11.4 1.38 70,011 -51%

TXN TEXAS INSTRS INC COM Semiconductors 39,390 1,860 1,012 3.1 8.7 2.11 109,810 -56%

BRCM BROADCOM CORP CL A Semiconductors 5,704 137 38 6.8 65.8 8.48 55,509 -80%

HD HOME DEPOT INC COM Specialty Retail 48,849 2,230 621 3.5 8.0 1.77 111,287 -11%

LOW LOWES COS INC COM Specialty Retail 10,962 567 171 2.1 3.3 1.06 17,154 25%

CVS CVS CORP COM Specialty Retail 10,320 512 84 2.6 3.7 1.02 14,504 65%

WAG WALGREEN CO COM Specialty Retail 9,243 510 73 2.3 8.2 2.50 33,231 35%

RSH RADIOSHACK CORP COM Specialty Retail 4,552 271 60 6.3 15.2 2.08 10,962 -27%

VZ VERIZON COMMUNICATIONS COM Telecom 114,464 6,462 1,277 3.3 3.5 0.80 118,573 15%

SBC SBC COMMUNICATIONS INC COM Telecom 113,618 6,903 2,730 4.0 5.0 1.00 141,514 15%

T AT&T CORP COM Telecom 81,221 4,851 -222 0.7 1.1 0.62 118,288 -26%

BLS BELLSOUTH CORP COM Telecom 53,812 3,568 660 3.3 4.3 1.00 70,185 13%

WCOM WORLDCOM INC GA NEW COM Telecom 23,277 1,772 30 0.4 1.9 1.35 104,734 -54%

CSCO CISCO SYS INC COM Telecom Equipment 162,218 4,910 2,434 6.1 18.5 2.60 489,845 -65%

LU LUCENT TECHNOLOGIES INC COM Telecom Equipment 62,824 3,220 315 2.4 5.3 1.57 139,633 -70%

MOT MOTOROLA INC COM Telecom Equipment 26,947 1,684 1,016 1.3 3.7 1.62 78,639 -58%

GLW CORNING INC COM Telecom Equipment 24,786 867 210 3.3 12.6 2.97 96,184 -75%

QCOM QUALCOMM INC COM Telecom Equipment 19,317 672 192 3.3 7.7 1.78 44,610 -8%

11

NM – Not Meaningful

12

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 drivers-

their 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.

23

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

26

Table 7

27

Figure 4

28

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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