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
1
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.
2
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
3
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.
4
Figure 1
INTANGIBLE ASSETS
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
Past Earnings
+
Future Earnings
Normalized Earnings =
(3 to5 Yr.) Core Earnings =
Earnings AT-(Extraordinary Items, etc.)
Normalized Earnings %
Subtract:
Return on Physical Assets
-7% Estimate
Subtract:
Return on
Financial Assets
Economy-Wide Average
-4.5% Estimate
Economy-Wide Average
5
Equal:
Capitalize:
Intangibles-Driven Earnings Knowledge Earnings %
Intangible Assets
Estimated 1
Footnote 1
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 longterm 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.”
6
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).
7
 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.”
8
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.
9
Table 1
The Scope of Intangibles
Name
Industry
Knowledge
Capital
8/31/2000
Change in
Knowledge
Market Value /
Knowledge
Knowledge
Capital / Book Market Value / Comprehensive
Earnings 1999 Earnings '99-'98
Value
Book Value
Value
Market Value
8/31/2000
Return 8/31/2000 2/28/2001
HON
HONEYWELL INTL INC COM
Aerospace & Defense
33,839
2,157
235
3.6
3.3
0.71
30,891
22%
LMT
LOCKHEED MARTIN CORP COM
Aerospace & Defense
27,358
1,417
-333
4.2
1.8
0.34
11,407
32%
BA
BOEING CO COM
Aerospace & Defense
23,447
1,590
614
1.9
3.8
1.30
46,270
17%
NOC
NORTHROP GRUMMAN CORP COM
Aerospace & Defense
15,901
894
65
4.5
1.5
0.28
5,440
21%
RTN.B
RAYTHEON CO CL B
Aerospace & 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
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%
NM
10
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%
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
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%
NM
11
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%
NM – Not Meaningful
12
Table 2
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
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 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
13
14
Figure 2
15
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.
16
Table 3
17
Table 4
18
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.”
19
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.
20
Figure 3
Correlation Between Stock Returns
and Annual Changes in Three
Performance Measures
0.53
0.6
Operating Cash-Flow Growth
Earnings Growth
0.5
Knowledge Earnings Growth
0.4
0.29
0.3
0.2
0.11
0.1
0
21
22
Table 5
23
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.
24
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.
25
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.
26
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
27
Table 7
29
Figure 4
30
Table 8
31
Table 9
32
Table 10
33
Table 11
34
Table 12
35
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.
36