ACF 703:
Accounting-Based Equity Valuation
Submitted by:
MANISH KHILNANI
MSc Finance 2006-07
Library Card No: 06055679
Words: 11,000
MSc Finance 2006-07
ABSTRACT
The dissertation focuses on the equity valuation using a small sample and a large
sample analysis. The key conclusions from the small sample study are that the
analysts still prefer DCF over RIVM in most of the cases because of its long
history, familiarity and their client’s comfort of understanding this valuation type.
The analysts if construct a full-blown multi-period forecast than use Multi-Period-
Model as their dominant model. Low growth sector like Beverages depended more
on DCF valuation where the cashflows can be easily forecasted and
Pharmaceuticals & Electronics having relatively High-Intangibles relied on single-
period Multiples-Based-Models, where accounting information is less reliable for
forecasting cashflows.
The key conclusions for the large sample of 3128 observations are that Residual-
Income-Valuation-Model with zero growth in perpetuity performs exactly in the
same way for both High-Intangible and Low-Intangible firm observations and
works the best for High-Intangible firm observations, however Price-to-Sales
multiple was the second best model for High-Intangible firms.
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1. INTRODUCTION ........................................................................................................................ 5
2. LITERATURE REVIEW ............................................................................................................ 6
2.1 OVERVIEW ............................................................................................................................ 6
2.1 EQUITY V/S ENTITY-PERSPECTIVE.................................................................................. 6
2.2 MULTIPLES – BASED VALUATIONS ................................................................................. 7
2.2.1 Methodology..................................................................................................................... 7
2.2.2 Multiples-Based Variants ................................................................................................. 7
2.4.3 Implementation Issue...................................................................................................... 10
2.3 DIVIDEND DISCOUNT MODEL ........................................................................................ 12
2.3.1 Methodology................................................................................................................... 12
2.3.2 Equation ......................................................................................................................... 13
2.3.3 Implementation Issue...................................................................................................... 13
2.4 DISCOUNTED CASHFLOW / FREE CASHFLOW MODEL.............................................. 14
2.4.1 Methodology................................................................................................................... 14
2.4.2 Equation ......................................................................................................................... 14
2.4.3 Implementation Issue...................................................................................................... 15
2.5 RESIDUAL-INCOME-VALUATION-MODEL ................................................................... 15
2.5.1 Methodology................................................................................................................... 15
2.5.2 Derivation ...................................................................................................................... 16
2.5.3 Implementation Issue...................................................................................................... 18
2.6 ABNORMAL-EARNINGS-GROWTH-MODEL ................................................................. 20
2.6.1 Methodology................................................................................................................... 20
2.6.2 Derivation ...................................................................................................................... 21
2.6.3 RIVM v/s AEGM............................................................................................................. 22
2.6.4 Implementation Issue...................................................................................................... 22
3. SMALL SAMPLE ANALYSIS ................................................................................................. 24
3.1 OVERVIEW .......................................................................................................................... 24
3.2 HYPOTHESIS 1..................................................................................................................... 24
3.3 HYPOTHESIS 2..................................................................................................................... 25
3.4 HYPOTHESIS 3..................................................................................................................... 25
3.5 HYPOTHESIS 4..................................................................................................................... 25
3.6 SAMPLE SELECTION.......................................................................................................... 26
3.7 SCORING CONVENTION.................................................................................................... 27
3.8 DESCRIPTIVE ANALYSIS .................................................................................................. 28
3.9 RESULTS .............................................................................................................................. 28
3.9.1 H1................................................................................................................................... 28
3.9.2 H2................................................................................................................................... 29
3.9.3 H3................................................................................................................................... 30
3.9.4 H4................................................................................................................................... 30
3.10 SENSITIVITY TESTS......................................................................................................... 31
3.10.1 Investment-Bank valuation style................................................................................... 31
3.10.1 Type of Recommendation ............................................................................................. 31
4. LARGE SAMPLE ANALYSIS ................................................................................................ 32
4.1 OVERVIEW .......................................................................................................................... 32
4.2 SAMPLE SELECTION.......................................................................................................... 33
4.3 IMPLEMENTATION OF PE & PS MODEL......................................................................... 34
4.4 IMPLEMENTATION OF RIVM MODEL ............................................................................. 36
4.4.1 Methodology................................................................................................................... 36
4.4.2 Dividend Payout Assumption ......................................................................................... 36
4.4.3 Forecast Horizon............................................................................................................ 36
4.4.4 Forecasted Book-Value .................................................................................................. 37
4.4.5 Discount Rate Assumption.............................................................................................. 37
4.4.6 Growth Rate Assumption................................................................................................ 38
4.5 CALCULATION OF VALUATION ERRORS ..................................................................... 38
4.6 TESTING EACH MODEL FOR HIGH AND LOW-INTANGIBLES................................... 38
4.7 TESTING ACROSS MODELS ON THE SAMPLE .............................................................. 39
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4.7.1 Null Hypothesis .............................................................................................................. 39
4.7.2 Year 2001 ....................................................................................................................... 39
4.7.3 Year 2002 ....................................................................................................................... 40
4.7.4 Year 2003 ....................................................................................................................... 41
4.7.5 Year 2004 ....................................................................................................................... 41
4.7.6 Year 2005 ....................................................................................................................... 41
4.7.7 High IN/TA ..................................................................................................................... 41
4.7.8 Low IN/TA ...................................................................................................................... 42
4.7.9 Pooled Sample................................................................................................................ 42
4.7.10 COMBO Regression v/s Univariate Regression........................................................... 42
5. CONCLUSIONS ......................................................................................................................... 44
6. TABLES & FIGURES................................................................................................................ 46
6.1 SMALL SAMPLE TABLES .................................................................................................. 46
6.1.1 Table 1............................................................................................................................ 46
6.1.2 Table 2............................................................................................................................ 47
6.1.3 Table 3............................................................................................................................ 48
6.1.4 Table 4............................................................................................................................ 49
6.1.5 Table5............................................................................................................................. 50
6.1.6 Table6............................................................................................................................. 51
6.1.7 Table7............................................................................................................................. 51
6.1.7 Table7............................................................................................................................. 52
6.1.8 Table8............................................................................................................................. 52
6.1.8 Table8............................................................................................................................. 53
6.2 LARGE SAMPLE TABLES / FIGURES ............................................................................... 54
6.2.1 Selection criteria table ................................................................................................... 54
6.2.2 Selection criteria figure.................................................................................................. 54
6.2.3 Mean & Median equality test 1 ...................................................................................... 55
6.2.4 Mean & Median equality test 2 ...................................................................................... 55
6.2.5 Descriptive Stats for AVE............................................................................................... 56
6.2.6 Regression estimates – Univariate ................................................................................. 57
6.2.7 Regression estimates – COMBO .................................................................................... 58
6.2.8 Terms & Definition......................................................................................................... 59
7. REFERENCES ........................................................................................................................... 60
7.1 REFERENCE BOOK............................................................................................................. 60
7.2 REFERENCE PAPERS.......................................................................................................... 60
7.3 REFERENCE WEBSITE LINKS .......................................................................................... 61
7.4 BROKER REPORTS FOR SMALL SAMPLE ...................................................................... 63
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1. INTRODUCTION
The dissertation basically is divided into four parts, firstly, the Literature review,
where all the equity valuation models are derived and discussed at length along
with the prior research done on these models.
Second part is the small sample study where, a sample of 24 analyst reports of
large international Investment-Banks is considered for 3 sectors, namely,
Beverages, Electronics and Pharmaceuticals to test variations in the analyst
preference of the equity model for each sector, which is an attempt to replicate the
findings of Demirakos et al. (2004).
Third part deals with the Large Sample of 3128 observations of firms, which have
intangibles in their balance-sheet; the sample is divided into two halves i.e. High
and Low-Intangible firm observations and three models are implemented- Price-to-
Earnings, Price-to-Sales and Residual-Income-Valuation-Model to test which
performs the best. The models are tested year-wise from Year-2001 through Year-
2005 as well as for the pooled sample.
Fourth part or the fifth chapter is the conclusion which sums up all the results of
the small and large sample and gives the final comments.
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2. LITERATURE REVIEW
2.1 OVERVIEW
This section focuses on the equity valuation models, which are derived and
discussed at length along with the prior research. Equity-Perspective v/s Entity-
Perspective is explained through out the literature review for each model namely,
Multiples-Based-Model, Dividend Discount Model (DDM), Discounted-Cashflow-
Model (DCF), Residual-Income-Valuation-Model (RIVM) and Abnormal-
Earnings-Growth-Model (AEGM).
2.1 EQUITY V/S ENTITY-PERSPECTIVE
There is a relationship between a firm’s equity, debt and the operating entity.
Operating Entity or Net-Operating-Assets of the firm is equal to the value of equity
plus value of debt. Equity-Perspective distinguishes between capital provided by
shareholders with that by debtholders, whereas in Entity-Perspective the source of
capital is not important.
Balance sheet value under Equity-Perspective = Net-Operating-Assets – Debt (1)
Balance sheet value under Entity-Perspective = Equity value + Debt (2)
The cashflow to owners under Equity-Perspective is dividends paid to shareholders
whereas under Entity-Perspective its free cashflow generated by the business (net
of tax). The value under Equity-Perspective is claimed only by the shareholders
whereas the value of the firm under Entity-Perspective is divided among the
claimants - the debtholders and the shareholders. The accounting profit under
Equity-Perspective is Net-Income after Interest and Tax and under Entity-
Perspective is Operating Income (net of tax) but before Interest. The cost of capital
under Equity-Perspective is cost of equity capital calculated by using beta
technology such as Capital-Asset-Pricing-Model (CAPM), whereas under Entity-
Perspective is Weighted-Average-Cost-Of-Capital (WACC). Firm valuation could
be done from either perspective.
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2.2 MULTIPLES – BASED VALUATIONS
2.2.1 Methodology
Valuation through multiples is a very systematic process; it starts by first selecting
the value driver like Earnings, Book-Value, Sales, forecasted Earnings, Earnings-
Before-Interest-Tax-Depreciation-Amortization (EBITDA), cashflow from
operations etc. Secondly, one has to select the set of comparable firms; this could
be done by comparing firms from same sector or industry group, risk, strategy and
Earnings growth etc. Thirdly, calculating the benchmark multiple for the firm to be
valued. Lastly, multiplying the benchmark multiple by the value driver of the firm.
The benchmark multiple is calculated by taking average of the multiples of
comparable firms but by not including the multiple of the firm to be valued, this is
demonstrated further.
2.2.2 Multiples-Based Variants
IV VDi Mean Pj (3)
VD
j
IV stands for Intrinsic-Value of the Equity/Firm if Equity-Perspective/Entity-
Perspective, VD j stands for value driver of the firm to be valued, Pj and
VD j stand for the Market-Value/Price and value driver of the comparable jth firm
respectively. The Mean term becomes the benchmark multiple, which doesn’t takes
into account the multiple of the firm to be valued. For instance, to calculate Price
to Book-Value multiple, under Equity-Perspective, the Pj term will be market
capitalization or Market-Value of equity (share Price) and VDi will be Book-Value
of equity (Book-Value Per share) of the comparable jth firm, whereas under Entity-
Perspective, the Pj term will be market capitalization plus debt and VDi will be
Book-Value of equity plus debt of the comparable jth firm. Penman (2007) in his
text, terms multiples under Equity-Perspective as levered multiples and multiples
under Entity-Perspective as unlevered multiples.
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To calculate Price Earnings multiple (PE), under Equity-Perspective, the Pj term
will be market capitalization or Market-Value of equity (share Price) and VD j will
be Net-Income (Earnings Per share or EPS) of the comparable jth firm, whereas
under Entity-Perspective, the Pj term will be market capitalization plus debt and
VD j will be Net Operating Profit After Tax (NOPAT) of the comparable jth firm,
however, as per Penman (2007) depreciation and amortization methods differ with
firms so to normalize the denominator VD j will in some cases be EBITDA. The
numerator or Pj term under Entity-Perspective is also called as Enterprise value.
There are some variations in the PE shown below under Equity-Perspective as
given in Penman (2007) text:
RollingPE P (4)
4
EPS
i 1
ForwardPE P (5)
EPS1
DivadjPE P DPS (6)
EPS
RollingPE or trailing PE compares the share Price (P) with the most recent four
quarter Earnings. ForwardPE compares P with one-year ahead Earnings forecast
(EPS1); this takes into account the anticipated growth in Earnings in the coming
year. Dividends reduce P as the value is taken out of the firm while EPS is
unaffected. To rectify this error in case of excess dividend payouts, DivadjPE or
Dividend-adjusted PE is used where the numerator is P plus the annual dividend
per share (DPS)
There are some variations in calculating benchmark multiples, which are shown
below:
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1Mean N Pj MSc Finance 2006-07
N j1 VD j (7)
N (8)
Pj
WeightedMean j1
N
VD j
j 1
HarmonicMean 1 (9)
1 N VD j
N j1 Pj
Mean is the most commonly used while calculating benchmark multiples but in
cases where the value driver is a very small, it results in a upward bias in the
estimate, however, this can be solved by trimming such outliers, considering
Median instead of Mean of multiples as used in Alford (1992) or by considering
approaches like calculating theWeightedMean , which divides the sum of P of all
comparable firms in numerator by sum of value driver of all comparable firms in
denominator. HarmonicMean is the reciprocal of the mean of the reciprocals of
the ratio, it is always lower than the simple mean and hence, it helps in rectifying
to some extent the bias of the upward estimate caused due to small denominator.
Valuation could be done by using two or more value drivers. This could be done by
assigning arbitrary weights, which are shown below:
IV W1 VD1i BM1 W2 VD2i BM 2 (10)
W2 and W2 stand for weights one and two say 0.5 each. BM1 and BM 2 stand for
Benchmark multiple one and two calculated in any one of three methods discussed
in equation 7, 8, and 9. Value drivers used could be Earnings and Book-Value for
instance.
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2.4.3 Implementation Issue
Advantages:
Multiples-Based valuation has one special feature that the valuation does not
involve forecasting and therefore it’s very simple to understand. The use of
comparables reduces the chance of mispricing firms relative to others, as it cannot
safeguard against an entire sector being misvalued.
Disadvantages:
Though multiple-based valuation is simple, it’s at a cost of ignoring information.
The Multiples-Based valuation has got some restriction that the value driver has to
be greater than zero; the multiple can’t be computed with negative denominator.
For instance, a firm with negative Earnings can’t be valued with the PE multiple.
Penman (2007) in his text says the method could be dangerous if Price is
considered to calculate value, for instance, firm A is to be valued and the
comparable firms are B, C and D, benchmark multiple is computed using multiples
of firms B, C and D assuming the Prices of the said firms are efficient, likewise if
one was to value firm B, the comparable firms then become A, C and D. The
question is if one uses Price in valuation then why one would do valuation to
challenge the Price. The valuation using one multiple will be different from
valuation using another multiple, for instance, PE multiple and Price to Book
multiple will give out different valuations for the same firm.
Empirical Findings:
Industry-based Multiple
Alford (1992) concludes that PE valuation method is most accurate when
comparable firms are selected on the basis of first three digits of SIC codes i.e.
most closer match in the industry. The accuracy would increase if comparable
firms are selected on the basis of comparable industry and comparable total assets
together. Boatsman and Baskin (1981) compare the valuation accuracy of PE
multiple on two sets of comparable firms from the same industry and finds that
valuation errors are smaller when comparable firms are chosen based on similar
historical Earnings growth, compared to when they are chosen randomly. Liu et al.
(2002) also believe that the common practice of selecting comparable firms from
the same industry improves the performance of the model.
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Large firm v/s medium and small firm’s valuation
Alford (1992) findings suggest that valuation accuracy is greater for large firms in
terms of total assets compared to small firms. Lie and Lie (2002) also finds that
valuations were more precise for large companies than medium and small
companies.
Enterprise multiple or Equity multiple
Alford (1992) finds that using firm or enterprise value is not effective and accuracy
decreases when PE multiples are adjusted for leverage across comparable firms.
Liu et al. (2002) also finds that “using enterprise value rather than equity value for
Sales and EBITDA further reduces performance”. However, Lie and Lie (2002)
finds that asset value multiple or enterprise value multiple generally yields more
precise and less biased estimates of value than do Sales and Earnings multiples,
Kim and Ritter (1998) also find that “using historical accounting information and
controlling for leverage effects, the enterprise value-to-Sales ratio works
reasonably well for both young and old firms”. The selection criteria for Liu et al.
(2002), Kim and Ritter (1998) and Lie and Lie (2002) are quite different where Lie
and Lie (2002) and Kim and Ritter (1998) deal with less than 2000 observations of
non-financial companies on average and 190 IPO observations respectively, Liu et
al. (2002) deals with 19,879 observations, thereby making Liu et al. (2002)
conclusion more reliable.
Use of PE multiple
Liu et al. (2002) finds using Earnings which exclude many one-time or
extraordinary items improves performance of the model. They exclude firm-years
with negative values for any value driver. They find that forward Earnings perform
the best and the performance improves if the forecast horizon lengthens from one-
year to two-year to three-year ahead EPS forecast, paper also included that among
value drivers derived from historical data, Sales performs the worst and Earnings
performed better than Book-Value. Lie and Lie (2002) also finds that “using
forecasted Earnings rather than trailing Earnings improves the estimates of the PE
multiple” and for all the company sizes, the asset multiple performed the best and
the Sales multiple performed the worst. Consistent with the said findings, Kim and
Ritter (1999) while investigating how initial public offering Prices are set also
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finds that forward PE multiple dominates all other multiples in terms of accuracy
and the EPS forecast for next year dominates the current year EPS forecast.
Harmonic Mean
Liu et al. (2002) finds performance of the model improves when multiples are
computed using harmonic mean relative to mean or median ratio, while calculating
benchmark multiple. Beatty Riffle and Thompson (1999) derived and documented
the benefits of using harmonic mean, they found that the best performance is
achieved by using weights derived from harmonic mean book and Earnings
multiples. Baker and Ruback (1999) showed in their paper that the industry
multiples estimated using harmonic mean approach were close to minimum-
variance estimates based on Monte Carlo simulations. However, Alford (1992) and
Lie and Lie (2002) use median ratio to calculate benchmark multiples which again
can’t be relied as the observations tested are relatively very small.
Industry preferred multiples or not
Working paper Tasker (1998) finds that “the acquisitions in the banking industry
are valued on book and Net-Income multiples, those in the hotel, oil and real estate
industries are valued on operating cashflow multiples, and software targets are
valued on revenue”, the paper argues that the choice of preferred multiple is driven
by the reliability and relevance of the accounting numbers. However, Liu et al.
(2002) findings discussed above are consistent with almost all the industries and
contradict the popular belief of “different industries have different “best”
multiples”.
2.3 DIVIDEND DISCOUNT MODEL
2.3.1 Methodology
The Dividend Discount Model (DDM) is an Equity-Perspective valuation and is
one of the oldest methods used today, which involves forecasting cashflow to
shareholders – Dividends. The dividends are forecasted and discounted at r (cost of
equity) to fetch the present value and this present value of all the expected
dividends is called the Intrinsic-Value. The discount rate r is calculated by using
the beta technology such as Capital-Asset-Pricing-Model (CAPM).
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2.3.2 Equation
There are two variants, analyst can forecast dividends for initial years and then
assume it as constant dividends or dividends growing at a constant growth rate (g)
until perpetuity.
IVE Div1 Div2 Div3 .... DivT DivT 1 /1 rT (11)
r
1 r 1 r2 1 r3 1 rT
IVE Div1 Div2 Div3 .... DivT DivT 1 /1 r T (12)
r g
1 r 1 r2 1 r3 1 rT
2.3.3 Implementation Issue
Advantages:
According to Penman (2007) text, Dividend Discount model is an easy concept as
the dividends are what shareholders get and so forecasting dividends makes sense.
Moreover, the predictability is also high as dividends are usually fairly stable in the
short run so forecasting becomes easy for the short run atleast. The model would
work best in case of firms who have constant dividend payout like a fixed dividend
payout ratio (dividend/Earnings) such as in case of utilities companies.
Disadvantages:
According to Investopedia, there are some issues like growth rates in dividend
exceeding the discount rate, which in this case will turn the continuing value term
as negative and therefore the one can’t get IV. The dividend payout ratio is very
difficult to predict as there is no fixed pattern followed by firms and there are
exceptions such as Microsoft, which did not pay dividends for decades and thus it
becomes difficult to forecast the dividends even for short term. These issues could
be further explored from the empirical findings in the papers explained in the next
section.
Empirical studies:
Barker (1999) in his field work of semi-structured interviews with 40 finance
directors, 32 analysts and 39 fund managers and questionnaires from 42 analysts
on evaluating the role of dividends in valuation finds that due to the nature of the
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dividends, the forecast horizon becomes typically two-year where information is
based on past and current trend in dividends and beyond which, there is a high
dependence on terminal value where information is based on subjective judgment
rather than on quantitative analysis. He finds Dividend yield valuation is used in
preference to the DDM and it’s not inconsistent with economic rationality as “in
practice, it is not possible to quantify expectations of state-contingent future
cashflows and to discount these at the risk-adjusted cost of capital”. The paper also
states that dividend yields are directly comparable across companies and can be
used as a basis for understanding relative pricing differences.
2.4 DISCOUNTED CASHFLOW / FREE CASHFLOW MODEL
2.4.1 Methodology
Discounted-Cashflow-Model (DCF) is the Entity-Perspective valuation. This
involves forecasting of the Cashflow from operations and Investments to arrive at
Free-Cashflow (FCF), which is then discounted at the Weighted-Average-Cost-Of-
Capital (WACC) of operations to fetch the present value of all FCF. This present
value of all the expected FCF will be the IV.
2.4.2 Equation
There are two variants, analyst can forecast FCF for initial years and then assume
it as constant FCF or FCF growing at a constant growth rate (g) until perpetuity.
FCF C I (13)
IVF FCF1 FCF2 FCF3 .... FCFT FCFT 1 /1 W T (14)
W
1W 1 W 2 1 W 3 1 W T
IVF FCF1 FCF2 FCF3 .... FCFT FCFT 1 /1 W T (15)
W g
1W 1 W 2 1 W 3 1 W T
IVE IVF VD (16)
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Where C is cashflow from operations (inflows) and I is cash investment (outflows)
made in operations. W stands for the WACC for operations, T stands for the time
horizon and VD stands for Market-Value of debt.
2.4.3 Implementation Issue
Advantages:
According to Penman (2007) text, DCF is an easy concept as the cashflows are not
affected by the accounting rules, they are easy to forecast and the cashflow
valuation is straight-forward application of the present value concept. DCF works
best when the firm produces positive constant FCF or FCF growing at a constant
rate such as in a “Cashcow” business.
Disadvantages:
According to Penman (2007), FCF does not measures value in the short run as the
investment is treated as a loss of value, for instance if the investments in assets for
operations exceeds the cashflow generated from operations in that year the FCF
turns negative showing a loss of value in that year, whereas the assets bought in
that year could be generating future income, likewise, if the firms which is a
continuous growth firm and makes these investments every year for some years
and if DCF is used the present value of all the FCF including the Continuing value
term will be negative and thus IV cant be calculated as it cant be negative. If one
has to get rid of this problem to tap the future income flows, one will have to
predict extreme long term to tap the value, which again would involve typically
long forecast horizons leading to inefficiency in predicting FCF. DCF doesn’t
follow the concept of accruals where the income is matched with the expense for
an any given year. Analysts generally forecast Earnings rather than cashflow as the
stock market appears to value firms on the basis of expected Earnings.
2.5 RESIDUAL-INCOME-VALUATION-MODEL
2.5.1 Methodology
Residual-Income-Valuation-Model (RIVM) or Edwards-Bell-Ohlson valuation
technique is a forecast-based model where one forecasts Residual-Income (RI). RI
is also commonly known as Abnormal-Earnings. Residual-Income is defined as:
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RI E NIT r BVT 1 (Equity-Perspective) (17)
T
RI F NOPATT W NOAT 1 (Entity-Perspective) (18)
T
NIT stands for Net-Income/profit and the term r BVT1 is the capital charge which
is calculated by multiplying r by the opening Book-Value of equity BVT1 . The
term NOPAT is net operating profit after tax. The term W NOAT1 is the capital
charge under Entity-Perspective, which is calculated by multiplying W or WACC
by the opening Net-Operating-Assets NOAT1 .
The valuation typically involves an anchor which is Book-Value of equity (BV) in
case of Equity-Perspective or Net-Operating-Assets (NOA) in case of Entity-
Perspective. The anchor is added to the present valued of all the RI including the
continuing value term to fetch the IV. The discount rate in case of Entity-
Perspective is r and Entity-Perspective is W. The RIVM could be derived from both
DDM (Equity-Perspective) and DCF (Entity-Perspective).
The Clean-Surplus-Relationship (CSR) is assumed which means change in the
balance sheet value of the business is equal to profit for the period less cash
payments to owners. The equation is shown below:
BVT BVT 1 NIT DivT (Equity-Perspective) (19)
NOAT NOAT1 NOPATT FCFT (Entity-Perspective) (20)
2.5.2 Derivation
All the terms with sub-script 0 (zero) are actual values and rest all are
estimated/expected values. The term expected is not shown in the formulae below
to reduce the clutter in the formulae.
Provided that, YT 0 as T
(1 r)T
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Then, 0 Y0 Y1 1 rY0 Y2 1 rY1 Y3 1 rY2 .........
1 r 1 r2 1 r3
As, 0 Y0 Y1 1 rY0 Y2 1 rY1 Y3 1 rY2 .........
1 r 1 r2 1 r3
Where Y could be anything,
Replacing Div in the DDM, IVE Div1 Div2 Div3 ....
1 r 1 r2 1 r3
IVE Y0 Y1 Div1 1 rY0 Y2 Div2 1 r Y1 Y3 Div3 1 rY2 .........
1 r 1 r2 1 r3
IVE YT DivT 1 rYT 1
Y0 1 rT (21)
T 1
If Y is Book-Value then, BVT DivT 1 rBVT 1
IVE BV0 1 rT
T 1
BVT DivT BVT 1 r BVT 1
1 r T
T 1
IVE
BV0
If Clean-Surplus-Relationship holds, then NIT BVT DIVT BVT1 (from eq.
19)
NIT r BVT 1
1 r T
T 1
IVE
BV0
RI E
IVE T
BV0 (Numerator from eq. 17) (22)
1 r T
T 1
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Or, IVE BV0 RI E RI E .... RI E RI E 1 /1 r T (23)
1 2 T T
1
1 r 1 r2 r T r
In case of constant growth in RI,
IVE BV0 RI E RI E .... RI E RI E 1 / 1 r T (24)
1 2 T r T g
1
1 r 1 r2 r T
Likewise from Entity-Perspective it will be,
RI F
IVF T
NOA0 (25)
1W T
T 1
Or, IVF NOA0 RI F RI F .... RI F RI F 1 /1 W T (26)
1 2 T T
1 W 1 W 2 1 W T W
For constant growth in RI,
IVF NOA0 RI F RI F .... RI F RI F 1 /1 W T (27)
1 2 T T
1 W 1 W 2 1 W T W g
2.5.3 Implementation Issue
Advantages:
According to Penman (2007) text, RIVM focuses on profitability and growth of
investment, which drive value as the earning over and above the expected rate of
return in recognized. RIVM incorporates fundamentals such as BV and NOA in
Equity-Perspective and Entity-Perspective respectively as anchors of which the
value is already recognized in the balance sheet. RIVM uses properties of accrual
accounting that recognize value added much ahead of cashflows, matches value
added to value given up, and treats investment as an asset rather than a loss of
value unlike DCF. RIVM manages to have forecast horizon shorter than those
required in DCF. RIVM is also aligned with what people generally forecast i.e.
people fore cast income statement and balance sheet rather than cashflows.
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Disadvantages:
According to Penman (2007) The concept is a bit complex as it requires an
understanding of how accrual accounting works. The model relies on the
accounting numbers which can be suspected, for instance if the anchor is not
rightly valued on balance sheet.
Empirical findings on DDM v/s DCF v/s RIVM:
Francis et al (2000) RI value estimates are more accurate and explain more of the
variation in security Prices than do FCF and Div value estimates. However,
Lundholm & O’Keefe (2001a CAR) find that getting same value estimate out of
RIVM and DCF is only a matter of care, they point out three things as a reason for
getting different value estimates for both models. Firstly, inconsistent forecasts
errors caused by starting the perpetuity of valuation off with wrong amounts;
Secondly, while calculating the value estimates for DCF, firm value is calculated
first using WACC and then value of debt is deducted to arrive at equity value
estimate to compare it with the RIVM equity value estimates calculated using r,
labeled as incorrect discount rate error, Finally, where financial statement forecasts
for RIVM don’t satisfy CSR, labeled as missing cashflow error. Lundholm &
O’Keefe (2001b CAR) while answering to Penman (Summer 2001 CAR)
counterpoints, stick to their findings in their original paper and comment that
calling RIVM value estimates better than DCF value estimates would be like
claiming “the Celsius thermometer is more accurate than the Fahrenheit
thermometer”.
Empirical studies on RIVM:
Francis et al (2000) finds that greater reliability of RIVM is likely driven by the
sufficiency of Book-Value of equity as a measure of IV coupled with the greater
precision and predictability of RI. Moreover, articulation of CSR ensures that IV is
unaffected by conservatism or accrual practices.
However, Sougiannis & Yaekura (2001) find that consensus analysts’ Earnings
forecast in Multi-period accounting-based models could not be used to set equity
Prices for the following reasons: First, the models employed could be theoretically
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correct but they are not the model that the market uses. Second, the presence of
measurement error in the calculations introduced by the errors in discount rates and
extrapolation of two-year to four-year ahead forecasts. Fourth, the quality of
forecasts could be questioned as the poor performance results due to missing
information, this could also be because four-year analysts forecast horizon is not
long enough. Fifth, the quality of Generally Accepted Accounting Principles
(GAAP) Earnings could be questioned as the Earnings of young firms with losses
are not very informative even if they are predicted with accuracy, use of
conservative accounting such as expensing R&D expenses also reduces the
information in BV. Finally, it could be the case of accurate model Prices but
inaccurate or inefficient market Prices. I believe if the mean forecasted Earnings
reported by IBES were used rather than median forecasted Earnings then the
results could be different.
2.6 ABNORMAL-EARNINGS-GROWTH-MODEL
2.6.1 Methodology
According to Penman (2007) Abnormal-Earnings-Growth-Model (AEGM), which
is also called as Ohlson/Juettner-Nauroth Model expresses the Intrinsic-Value of
equity as the capitalized next-period expected Earnings i.e. the estimate of normal
forward PE ratio capitalized at the rate of r for perpetuity plus the present value of
the capitalized forecast Abnormal-Earnings growth (Z) of subsequent periods,
where Z is the excess of period Earnings change over a normal return on previous-
period retained Earnings. Z can be expressed in the following way:
1 ZT
r NI T r DivT 1 r NIT 1 (28)
Where, the second term on the right-hand side can be said as normal Earnings i.e.
one plus normal return times Net-Income at T-1. This would be Net-Income of the
firm at T if it maintained its time T-1 Earnings and if it retained all of those
Earnings and earned a normal return on those retained Earnings. First term on the
left-hand side is cum dividends, Net-Income at T plus additional Earnings that
would have been earned if the T-1 dividend had been retained and invested to earn
a normal return.
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ZT1
Z can also be expressed as: r NI T NIT 1 r NIT 1 DivT 1 (29)
This shows the extra Earnings that the firm would generate at T if it were to earn a
normal return on the T-1 reinvested Earnings.
2.6.2 Derivation
From equation 21,
If Y is expected next-period Earnings capitalized as perpetuity then,
IVE NI1 NIT 1 / r DivT 1 r NI T / r
r r T
T 1 1
IVE NI1 1 NIT 1 r DivT 1 r NI T
r T 1 r 1 rT
IVE NI1 1 NIT 1 rNIT DivT
r T 1 r 1 rT
The Term in square brackets in the summation is Earnings growth in excess of a
normal return on prior-period retained Earnings. The model is therefore called as
Abnormal-Earnings-Growth-Model.
ZE 1NIT 1 rNIT DivT (From equation 28 and 29) (30)
r
IVENI1 ZT T (31)
r 1 r
T 1
Or in case of constant growth in Abnormal-Earnings growth,
IVE NI1 T Z2 Z3 ........ 1 ZT 1 g (32)
r T 1
1 r 1 r2 rT 1 r
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2.6.3 RIVM v/s AEGM
RI E IVENI1 ZT
IVE T r 1 r
BV0
1 r T T
T 1 T 1
In RIVM anchor is Book-Value and in AEGM its capitalized next-period Earnings.
In RIVM excess flows is residual earning and in AEGM excess flows is capitalized
Abnormal-Earnings growth. Capitalized next-period Earnings can be written as
current Book-Value plus capitalized next-period Residual-Income. Thus, more is
captured by AEGM anchor and there is less dependence on the flows and therefore
the continuing value term would be less important in AEGM compared to RIVM.
The equation can be expressed as follows:
NI1 r BV0 RI E BV0 RI E (33)
r 1 1
r r
The flows to be capitalized in RIVM are RI based on CSR, While the flows to be
capitalized in AEGM have no requirement that the Earnings number should be
computed on a CSR basis. The CSR in RI can also be expressed as follows:
RI E BVT DivT 1 rBVT1 (From eq. 17 and 19) (34)
T
2.6.4 Implementation Issue
Advantages:
The advantages are very similar to that of RIVM. Penman (2007) texts lists down
that the concept it is easy to understand as investor think in terms of future
Earnings, and the model focuses directly on the most common multiple used such
as PE multiple. Model uses accrual accounting and is aligned with what people
forecast – Earnings and Earnings growth.
Disadvantages:
The concept involves accounting complexity in terms of requiring an
understanding of accrual accounting. The concept is complex as it involves cum-
dividend Earnings i.e. the income for reinvestment of dividends is taken into
account. Model is sensitive to the required return estimate as the value derives
completely from the forecast that are capitalized at the required return, whereas
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RIVM valuations derive partly from the anchor that does not involve a required
return.
Empirical studies on RIVM v/s AEGM:
Ohlson (2005) compares RIVM and AEGM and finds AEGM is relatively superior
model. He finds some disadvantages associated with RIVM which AEGM
overcomes. He finds that while considering RIVM, BV comes into focus which is
directly related to firm’s expected profitability and that a firm’s profitability in turn
relates to conservative accounting. He illustrates that the contrived nature of
relying on CSR to introduce Earnings affects expected BV, adjusted for dividends.
He also relabeled RIVM as “ABG”, which stands for Abnormal-Book-Value-
Growth. For AEGM, he firstly finds that the model demands no BV construct, nor
does the model rely on CSR, EPS is as easy to work with as total dollar Earnings
and the possibility of changes in shares outstanding has no adverse implications.
Secondly, he finds that a focus on Earnings can never be worse than a focus on BV,
but the reverse would be false. Finally, he concludes that investment practice
revolves around Earnings and their subsequent growth and not BV and their
subsequent growth therefore, AEGM and not RIVM should build the central
organizing principle of equity valuation.
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3. SMALL SAMPLE ANALYSIS
3.1 OVERVIEW
Demirakos et al (2004) (henceforth, paper) adopts a very structured approach to
explaining the valuation practices of financial analysts by studying the valuation
techniques contained in 104 analyst reports from International Investment-Banks.
The paper focuses on three industries: Beverages, Electronics and Pharmaceuticals,
which are chosen intentionally to give potential variation in analysts’ valuation
practices. The paper finds that Pharmaceuticals have the highest annualized Sales
growth, volatility of Earnings, R&D-to-Sales and market-to-book-value relative to
Electronics and Beverages, where Beverages is the lowest in these measures and
Electronics falls between these two sectors.
I have tried to test most of the hypothesis and the sensitivity tests given in the
paper. In order to contradict or accept the findings of the paper with the findings in
my sample, I have kept my sample selection methodology consistent with paper. I
have tried to replicate most of the things given in the said paper with an aim to
achieve the same results in framework given to me for the dissertation.
3.2 HYPOTHESIS 1
The prior research given in the paper and discussed above in the second chapter
finds that valuation using multiples is the most pervasive form of valuation. The
research suggests that due to the complexity of the forecast based models,
Multiples-Based approach is attractive to the analysts. According to the paper’s
findings Beverages is a sector which shows fairly uniform and stable growth
compared to Electronics and Pharmaceuticals. Thus, valuation by Single-Period-
Comparatives in case of Beverages might yield a reasonable first approximation,
while Electronics and Pharmaceuticals are sectors for which the ideal conditions
for valuation by comparatives are less likely to hold. Therefore consistent with the
paper, my hypothesis is:
H1: Use of valuation by Single-Period-Comparatives is higher in the Beverages
than in Electronics or Pharmaceuticals.
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3.3 HYPOTHESIS 2
The paper says DCF and RIVM both techniques are theoretically equivalent, but
while making the choice, analyst will consider the technique, which they and their
clients are most familiar and comfortable with. DCF has had a long history of use
relative to RIVM, thus DCF is in essence a default. For analyst to pick RIVM over
DCF, they have to be confident that the published accounting information captures
the essence of business and the paper tries to test whether the confidence of the
analysts on the accounting information, which is used for the value generation
process, may vary across the sectors. The paper points out that the accounting is
relatively strong in valuing tangible assets and is weak in valuing intangible assets.
Therefore, the choice between RIVM and DCF will reflect the nature of the firm’s
assets. In particular, the paper says accounting measures of performance to be less
relevant for high-intangibles firms or the firms with high growth opportunities.
Pharmaceuticals is characterized in this category. Therefore, the hypothesis is:
H2: Use of multi-period DCF relative to multi-period RIVM is higher in the
Pharmaceuticals sector than in the Beverages sector, which Electronics sector
falling between the two extremes.
3.4 HYPOTHESIS 3
Thirdly, Penman (2007) says “Free-Cashflow does not measure value added in the
short run; value gained is not matched with value given up”. According to the
literature in the chapter 2 and the paper’s findings, it’s suggested that firm
valuation based on a multiple of a single year’s free cashflow is not sensible. Thus
the hypothesis is as follows:
H3: Given the limitations of single-period cashflow as a measure of value
generation, no analyst will use it as their dominant model.
3.5 HYPOTHESIS 4
Finally, the paper believes that the analyst view valuation by
multiples/comparatives as a simplified form of valuation. “If they incur the cost to
produce a full-blown Multi-Period-Model, they present it as their dominant
model”. Thus the hypothesis is:
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H4: Analysts who construct a multi-period valuation analysis of either type do not
adopt Multiples-Based valuation as their dominant model.
3.6 SAMPLE SELECTION
Sample firms are selected from a dataset which contains largest 200 non-financial
firms listed on the London Stock Exchange as of December 2004. Sample Broker
Reports are downloaded from Investext Plus. The selection criteria is by and large
consistent with the paper and is as follows:
Three sectors – Beverages (FTAG4–3570, which is the industry
classification code), Pharmaceuticals (FTAG5–4577) and Electronics and
Electrical equipment (FTAG4–2730)
Top three firms in each sector in terms of market-cap on Balance sheet date
MY(FYE).
Report greater than or equal to seven pages.
Reports selected are from the period 3-months before and after Balance
sheet date (6-months)
Where an analyst publishes more than one report for a particular company
in the given period, I select largest report and if pages are same for both the
larger reports than I select the latest one.
Multiple Investment-Banks are chosen so that a particular bank does not
dominate the results. The resulting Investment-Banks are Citibank (Citi),
ABN Amro (ABN), Deutsche Bank (DB), Independent International
Investment Research (IIIR), Societe Generale (SG) and Credit Suisse First
Boston (CSFB). Each investment house is included in all three sectors with
an exception of IIIR, which is included in 2 sectors. The rationale is that
differences in valuation methodologies across sectors should reflect
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genuine differences between the sectors and not the differences in which
Investment-Bank covering each sector.
All reports should be workable i.e. stating valuation models.
Each company should have atleast one report to include the company in the
sample
Total number of reports should be equal in all sectors (Eight reports per
sector)
If 6-month period is less for the said criteria than look for reports in the
period of 2 years from Jan-04 to Dec-05.
3.7 SCORING CONVENTION
I make a distinction among valuation models and classify them as single-period
models, Multi-Period-Model, hybrid models and other model(s) – consistent with
the paper, the definition of the models is given in the point 6.1.3-Table3.
If the model is shown in the valuation or is mentioned on the first page of
the report, the model is counted in point 6.1.4-Table4.
For classifying a model as dominant, following criteria is used:
If model, is closely associated with analyst own stock Price
recommendation.
If only one model than its dominant
If more than one, then analyst preference is checked in the valuation
section.
Checking First page or Investment thesis/Executive summary to see which
model is highlighted
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If none of above then calculate the difference between analyst alternative
value estimates and analysts final target Price, selecting the one closest to
the target Price.
If no target Price, no specific value estimate then, highest amount of space
given by the analyst for a model is considered
Score of 1 to sole dominant model
Score of 0.5 to model if jointly used with another model
Score of 0.33 to a model if jointly used by 2 other models
3.8 DESCRIPTIVE ANALYSIS
All the sectors in all the reports use some kind of Earnings multiple shown in
6.1.4-table4, this fact is contrary to the findings of the paper, where Electronics and
Pharmaceuticals show less frequency of use of Earnings multiple compared to
Beverages. For Beverages, there was a highest use of DCF to the extent of 5
reports out of 8, while in Electronics the usage of DCF was just in 3 reports out of
8. There has been no instance of the use of SOP in the Pharmaceuticals and
maximum use of SOP in the Electronics sector, suggesting that the model works
very effectively for the said sector.
3.9 RESULTS
3.9.1 H1
H1: Use of valuation by Single-Period-Comparatives is higher in the Beverages
than in Electronics or Pharmaceuticals.
6.1.5-Table5 presents the test of this hypothesis. Panel A being consistent with the
paper shows the heavy use of Single-Period-Comparatives in all sectors and the use
of Single-Period-Comparatives as a starting point even if its not a preferred
valuation model. A very interesting result could be seen in the Panel B, which
shows the choice of dominant valuation models between Single-Period-
Comparatives and multi-period valuation model. It’s observed that the use of
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Single-Period-Comparatives is the least in Beverages compared to that of
Pharmaceuticals and Electronics – this result is not consistent is rather opposite to
the findings in the paper. Moreover, the use of multi-period DCF is the highest in
Beverages compared to that of Pharmaceuticals and Electronics.
Interestingly, The use of Single-Period-Comparatives is the highest and the use of
multi-period DCF is the lowest in case of Electronics - this is again not consistent
and rather opposite to the findings in the paper.
An alternative approach as used in the paper to test this hypothesis is to see how
many of the reports that use a multi-period valuation models choose this as the
dominant model. Panel C shows that in Beverages 5 reports use multi-period
valuation and the use is as high as 4.5 (90%) as per the scoring convention.
Corresponding figures for Electronics are 1.33 times out of 3 (44.3%) and for
Pharmaceuticals are 2.5 times out of 4 (62.5%). The figures are highly not
consistent with H1 and the findings in the paper. However, a formal chi-square test
could not reject the null hypothesis, so the results can’t be full warranted.
3.9.2 H2
H2: Use of multi-period DCF relative to multi-period RIVM is higher in the
Pharmaceuticals sector than in the Beverages sector, which Electronics sector
falling between the two extremes.
6.1.4-Table4 shows that no reports in all three sectors has used RIVM either as
dominant model or jointly with some other model. Although RIVM could have
worked better for Beverages (high tangible assets and consistent growth) and
Electronics (fairly tangible assets and fairly consistent growth), RIVM was not
implemented. This re-affirms the belief that despite a lot of advantages, analysts
still prefer DCF to RIVM because of its long history, familiarity and their client’s
comfort of understanding this valuation type. The result is directionally consistent
with H2 and the paper, however chi-square test could not reject the null hypothesis.
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3.9.3 H3
H3: Given the limitations of single-period cashflow as a measure of value
generation, no analyst will use it as their dominant model.
6.1.6-Table6 lists the types of models that I classify as being dominant in each
report based on the scoring convention consistent with paper. In total E (Earnings
multiples) is used in 38% of the cases, DCF in 34% and SOP in 18% of the cases
as dominant model. There is clearly no one model dominant in all three sectors. It
shows in the Beverages sector, DCF is the dominant approach, used in 54.1% of
reports, in the Electronics sector, SOP (sum of parts valuation, explained in 6.1.3-
table3) is the dominant approach used in 41.6% of reports, in the Pharmaceuticals
sector E is the dominant approach used in 68.8% of the reports. This consistent
with the H3 and the paper, I find no report that uses a single-period cashflow
multiple as its dominant valuation methodology. As voiced out in the paper “ Cash
is king”, I consistent with the paper find no evidence to support this view in my
data.
The evidence reported in 6.1.4-table4 shows that a cashflow multiple is used 7 out
of 24 reports (29.2%), this is used by the analyst to do a sensitivity test for the
companies. Thus I accept the hypothesis which is consistent with paper.
3.9.4 H4
H4: Analysts who construct a multi-period valuation analysis of either type do not
adopt Single-Period-Comparatives as their dominant model.
A total of 12 reports used multi-period valuation, all using DCF (6.14-table4). Of
this 12 reports that implement a multi-period valuation model, 6.1.7-table6 shows
that 6 reports identify Multi-Period-Model as their sole dominant model, while 4
other reports used DCF along with Single-Period-Comparatives model and 1 report
used DCF along with Single-Period-Comparatives and SOP model. There was only
one instance in the Electronics sector where DCF was employed but was not used,
where the focus seemed to be on the Earnings multiples (not consistent with the
hypothesis). Barring this exception, the result is consistent with H4.
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3.10 SENSITIVITY TESTS
3.10.1 Investment-Bank valuation style
The paper suggest that almost all the equity research reports include some Single-
Period-Comparatives valuation, however Investment-Bank might differ in their
preference for other models. Panel A in 6.1.7-table7 shows that DCF is the most
preferred model for CSFB(75%), Citi(60%) & ABN(60%), DB(50%), SG(33.3%)
in the order of ranking from highest to lowest. Panel B shows that SOP is the most
preferred model for SG(66.7%), CSFB(25%) & DB(25%), Citi(20%) &
ABN(20%) in the order of ranking from highest to lowest. Interestingly, whenever
analyst uses SOP, they used it either as a sole dominant or along with other models
jointly. IIIR never preferred either DCF or SOP, it always used Earnings multiple.
3.10.1 Type of Recommendation
While carrying out the analysis, I recorded the recommendation (Buy, Market
Outperform, and Add) as positive. There were 10 reports out of 24 which showed
this recommendation. The categories Market Perform, Neutral and Hold are neutral
recommendations, again 10 out of 24 showed this and only the remaining 4 reports
showed either Market Underperform or Sell. Counting neutral as weak negatives,
in total there were 10 positives and 14 negatives – not consistent with the findings
of the paper, analysts recommendations are biased towards sell. 6.1.8-table8 shows
that the proportion of the positive recommendations is 37.5% in beverages, 12.5%
in electronics and 75% in pharmaceuticals. To test if these differences could drive
the choice of valuation model, I therefore (being consistent with the paper)
examine whether the choice of DCF as valuation model varies significantly across
types of recommendations. Of the 10 (14) reports that have positive
(negative/neutral) recommendation, 6 (6) use DCF i.e., 60% (42.9%) of the reports.
A chi-square test reveals that this difference is not significant [chi-square score =
0.225 < 3.84 (critical value, Significance level = 5%)] – consistent with the paper.
For reports containing DCF valuation, DCF is dominant in 4.5 out of 6 (75%) in
positive recommendation cases and 3.83 out of 6 (63.83%) in negative
recommendation cases as per the scoring convention followed. This difference is
also not significant (chi-square = 0.032 < 3.84). Thus, results suggest that the type
of the recommendation does not drive the choice of the valuation model - results
consistent with paper.
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4. LARGE SAMPLE ANALYSIS
4.1 OVERVIEW
Large Sample analysis tests general universe of firms with intangibles in the
balance sheet as well as the subset of the pooled sample. Four models are
implemented on a set of 3128 observations, namely, Price-to-Earnings-Model,
Price-to-Sales-Model, Residual-Income-Valuation-Model assuming perpetual
growth at 4% and zero growth.
When the firm is born, its balance sheet represents total assets and liabilities plus
some increases in the tangible assets like plant, machinery, equipments, receivables
and debt, but when the firm grows over time it acquires intangible assets like
royalty, patents, copyrights, brand, and goodwill etc. The valuation technique
would essentially differ across type of asset in the balance sheet, as intangible
assets would promise growth in future. Thus, with this curiosity, I made a
distinction between high and Low-Intangibles firms to find if there is any valuation
technique which works in the same way for both kinds of firms irrespectively and
or is there any valuation methodology which works best for each type.
Furthermore, I also test if the data is segregated year-wise, are there any year
specific models, in other words, I try to test, is their any model which works best
for all years or does each year has a particular model performing the best in terms
of accuracy and explainability in the variation in the stock Price. The comparison
of the pattern followed is also done with the pooled sample.
Finally, I try to test, if value estimates from all models combined together explain
the variation in the stock Price better than the value estimate from any single
model.
High-Intangibles firm observations belong to industries such as Radio and TV
broadcast stations, Newspaper Publication, Pre-packed Software, Pharmaceutical
Preparations, Motor/Aircraft Accessories, Bottled Can and Soft-drink Water,
Chemicals, Fabricated Metal Products, Lab Analytical Instruments, Ortho &
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Dental Equipments and Credit Reporting services etc., while Low-Intangibles firm
observations belong to diverse industries.
Consistent with Alford (1992) and Liu et al. (2002) findings, all models were
considered from Equity-Perspective. The rationale behind selecting Price-to-
Earnings-Model (PEM) is that, PE multiple is the most widely used and accepted
valuation model. Moreover, Earnings as a value driver is also used to value
intangibles – consistent with Rics consulting valuation report on valuation of
intangible assets and World Intellectual property Organization document on the
value of intellectual property, intangible assets and goodwill.
The rationale behind selecting Price-to-Sales-Model (PSM) is to test if I get
different results compared to Liu et al. (2002) and Lie and Lie (2002) findings.
Moreover, brand valuation is done based on PSM - consistent with Al Ehrbar and
Mich Bergesen (Winter 2001).
The reason why I used Residual-Income-Valuation-Model (RIVM) is because of its
advantages and advantages over DCF explained in the chapter 2 of this dissertation
and the use of Economic Value added model in the paper on valuation of brands by
Al Ehrbar and Mich Bergesen (Winter 2001), which works the same way as RIVM.
4.2 SAMPLE SELECTION
The data is taken from COMPUSTAT and consists of 8850 observations from year
2000-2005. The analysts’ mean Earnings forecast is taken from IBES. The data
used from IBES is multiplied by the IBES adjustment factor to make it comparable
to COMPUSTAT. Dividend payout ratio was calculated first by taking the last
two-years’ mean of the actual dividend payout for each observation - consistent
with Sougiannis & Yaekura (2001). Year-2001 observations were needed only for
calculating the mean dividend payout ratio. Year-2001 observations were then
deleted leaving behind 7375 observations. Negative Earnings were deleted for the
purpose of calculating PE leaving behind 5060 observations. There were only two
observations, which had zero Sales, which were deleted, as one of the value drivers
tested is Price-to-Sales. Mean one-year and two-year ahead EPS forecast, which
were negative, zero and missing were deleted, as there could be potential problems
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in arriving at Intrinsic-Value for RIVM leaving behind 4118 observations, where
EPS stands for Earnings per share and RIVM stands for Residual-Income-
Valuation-Model. Moreover, observations with negative Book-Value (BV) were
also deleted as BV acts as an anchor in RIVM and could also cause problems while
arriving at the value estimates, leaving behind 4070 observations. All models were
then implemented at this stage.
While implementing the PE & PS model all such observations were deleted where
for each firm observation, comparable firm observations were less than five -
similar to the methodology adopted by Alford (1992) and Liu et al. (2002). After
implementing RIVM, there were some observations where the value estimates were
negative explained further, all such observations were deleted. The said two cases
make a total of 170 observations that were deleted, leaving behind 3900
observations. All 3900 observations were sorted in the descending order as per
Intangibles-to-Total-Assets Ratio (IN/TA). Observations with zero intangibles or
zero Intangibles-to-Total-Assets ratio were deleted leaving behind 3292
observations. The data was then divided into two half’s the upper half is being
High-Intangible rich observations and lower half is being Low-Intangible
observations as shown in the tables 6.2.1 and 6.2.2.
To further deal with the problem of outliers there was trimming done of
approximately 5% on the sample of 3292 observations. The trimming was done so
that approximately 1.25% of 3292 observations or 41 observations were deleted
each from the upper and lower fence of the upper half (High-Intangibles with 1646
observations) and similarly 41 observations each from the upper and lower fence of
the lower half (Low-Intangibles with 1646 observations) leaving behind 3128
observations and effectively 1564 observations each for High-Intangibles and
Low-Intangibles. The IN/TA in the High-Intangible group range from 16.75% to
72.38% and in the Low-Intangible group from 0.01% to 15.62%.
4.3 IMPLEMENTATION OF PE & PS MODEL
PEIV N 1 EPSi (35)
34
EPj EPi
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(36)
SPj SPi
Harmonic Mean approach was used for calculating Intrinsic-Value as per PE and
PS model, where PE stands for Price-to-Earnings and PS stands for Price-to-Sales
– consistent with Liu et al. (2002), Riffle and Thompson (1999) and Baker and
Ruback (1999). Industry classification used was as per COMPUSTAT code,
DNUM3 – consistent with Alford (1992) findings of defining industries by the first
three SIC digits is reasonable. Industry benchmark multiples were calculated for
each observation from the dataset of 4070 observations. This included maximum
possible number of industry observations where multiples could be computed.
PEIV stands for Price-to-Earnings Intrinsic-Value, N stands for a total number of
observation for a particular industry sorted as per DNUM3 code, EPj stands for
summation of all the Earnings-to-Price multiples in a given industry, EPi stands
for Earnings-to-Price multiple of the given observation in a particular industry and
EPSi is actual Earnings per share for a given observation in a particular industry
which excludes extraordinary items. While calculating EPi and EPj, the numerator
excludes extraordinary items as used in Alford (1992). For the calculation
of PEIV , benchmark multiple for each observation is calculated by taking the
harmonic mean of the summation of Earnings-to-Price ratio for the industry and
then deducting EPi and by doing this we obtain benchmark multiple for every
single observation which then is multiplied by the actual Earnings per share.
For the calculation of the PEM, Historical Earnings were used to be able to
compare it with PSM as while comparing both Multiples-Based-Models, forward
Earnings v/s Historical Sales won’t make sense to conclude if one is better than
other, so to be fair in comparison historical figures of Earnings (reported Earnings)
are used.
PSIV stands for Price-to-Sales Intrinsic-Value, SPj stands for summation of all
the Sales-to-Price multiples in a given industry, SPi stands for Earnings-to-Price
multiple of the given observation in a particular industry and SPSi is Sales per
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share for a given observation in a particular industry. For the calculation of PSIV ,
benchmark multiple for each observation is calculated by taking the harmonic
mean of the summation of Sales-to-Price ratio for the industry and then deducting
SPi and by doing this we obtain benchmark multiple for every single observation
which then is multiplied by the Sales per share.
4.4 IMPLEMENTATION OF RIVM MODEL
4.4.1 Methodology
RIVMIV BPS EPS1 r BPS EPS 2 r BPS1 (37)
r 1 rr g
1
RIVMIV stands for Residual-Income-Valuation-Model Intrinsic-Value, BPS stands
for the Book-Value per share, which also acts as an anchor; EPS1 and EPS2 are the
one-year and two-year ahead forecasts, BPS1 is the one-year ahead Book-Value
forecast, r stands for the discount rate and g stands for the growth rate. Firstly, the
Residual-Income is calculated by deducting the capital charge from the EPS1 and
EPS2 and then discounting with the appropriate discount rate. Secondly, growth
rate is incorporated in the formula assuming the EPS2 grows until perpetuity at a
certain growth rate. Finally, the discounted Residual-Income and the continuing
value term is added to the anchor or BPS to obtain the Intrinsic-Value.
4.4.2 Dividend Payout Assumption
Firm specific dividend-payout ratio is assumed. Dividend payout ratio was
calculated by taking the last two-years’ mean of the actual dividend payout for
each firm observation – consistent with Sougiannis & Yaekura (2001). If the mean
dividend-payout ratio worked out to be zero, then zero dividends were assumed for
the following years while forecasting Residual-Income.
4.4.3 Forecast Horizon
Forecast Horizon is considered for two-years as one-year and two-year ahead EPS
forecast were given for maximum number of observations. The idea was to keep
the sample size as large as possible. Mean instead of median one-year and two-year
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ahead forecasts were used to find if doing this contradicts Sougiannis & Yaekura
(2001) findings.
4.4.4 Forecasted Book-Value
To implement RIVM, Clean-Surplus-Relationship (CSR) is assumed to get the
future Book-Value – consistent with Francis et al (2000). The equation is as
follows:
BPS1 BPS EPS1 Div (38)
Where, BPS1 stands for forecasted one-year ahead Book-Value and Div stands for
dividend calculated as per the mean of last two-year dividend payout ratio for each
firm observation – consistent with Sougiannis & Yaekura (2001).
4.4.5 Discount Rate Assumption
Year specific discount rate/cost of equity is calculated using the CAPM equation.
The equation is as follows:
r ERF MRP (39)
where r stands for the cost of equity or the discount rate, ERF stands for yearly
effective Risk-Free-Rate, stands for the beta of the observation and MRP stands
for the Market-Risk-Premium. ERF is different for each year. The mean of
monthly three-month Risk-Free-Rate is computed for each year, its then converted
to a 3-month yield and subsequently into effective annual risk-free-rate and the
effective annual risk-free-rate for the year 2001, 2002, 2003, 2004 and 2005
worked out to be 3.6023%, 1.6208%, 0.9970%, 1.3606% and 3.1844%
respectively, methodology–consistent with Lee, Myers and Swaminathan (1999).
The was assumed to be one for all the observations as the firm specific effects
in aggregate are considered to get nullified. MRP for all the observations in the
sample was assumed to be at 6% - consistent with Sougiannis & Yaekura (2001)
and Francis et al (2000)
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4.4.6 Growth Rate Assumption
While assuming the g , various option were considered like assuming firm specific
or the industry specific g , assuming US GDP growth rate as suggested in (Penman
2001), but the literature (Sougiannis & Yaekura 2001) looked convincing and
therefore g is considered at 4% constant for all observations irrespective of the
years. However, sensitivity analysis is done by comparing Intrinsic-Values with
zero g and g with 4% - consistent with Francis et al. (2000).
After implementing the model with g equal to 4%, the results showed some
observations where the Intrinsic-Values worked out to be negative and this was due
to the capital charge which exceeded the forecasted Earnings turning Residual-
Income negative and subsequently turning the continuing value term negative.
Finally, the summation of negative Residual-Income and the negative continuing
value when added to BPS eroded the value to an extent that IV turned negative.
Intrinsic-Value can’t be negative while the stock Price in the market is positive and
therefore such observations were not included in the final selection of sample.
4.5 CALCULATION OF VALUATION ERRORS (40)
VE IV P
P
VE stands for Valuation Error, IV stands for Intrinsic-Value and P stands for the
observed stock-market Price. Valuation Error is calculated in two forms: the signed
difference between value estimate and stock Price and the absolute difference
between value estimate and stock Price, which is termed AVE henceforth.
However, for further analysis only AVE are taken into observation
4.6 TESTING EACH MODEL FOR HIGH AND LOW-INTANGIBLES
The research question addressed here is: Are mean & median AVE from each
model different between High-Intangible and Low-Intangible groups? Two sample
t-test for comparing two mean and non-parametric test such as Wilcoxon rank sum
test for comparing two medians are used to see if one or more models is consistent
with both industry groups.
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The said tests are testing equality of means & medians in AVE of High IN/TA and
Low IN/TA groups for each model. The null hypothesis is that the means/medians
of AVE from two independent samples namely, High IN/TA and Low IN/TA are
equal to zero at 95% confidence level.
The findings are: RIVM0 performs exactly the same way for both Firm types as
both mean and median AVE of High IN/TA are equal to the mean and median AVE
of Low IN/TA respectively. All other models show different mean or median AVE
for each group. The null hypothesis is rejected either for mean or for median AVE.
4.7 TESTING ACROSS MODELS ON THE SAMPLE
The research question addressed here is: Which model performs best in terms of
accuracy and explainability for each year-wise sample i.e. year-2001 through year-
2005 sample, each industry group i.e. High-intangible firms and Low-Intangible
firms and the pooled sample? Paired sample t-test for comparing two means,
Wilcoxon signed rank test for comparing two medians and descriptive stats of the
absolute valuation errors (AVE) from all models (shown in table 6.2.5) are used to
find the accuracy of the model and Univariate regression estimates and Adjusted
R2s are observed to answer explainability.
4.7.1 Null Hypothesis
The null hypothesis is that the mean/median difference between the AVE from two
models for a paired sample is equal to zero at 95% confidence level. It means that
the mean/median difference between the AVE is said to be zero (significantly not
different from zero) or we fail to reject the null hypothesis if the probability value
(P-value) > 0.05 (5%) shown in table 6.2.4. Similarly, table 6.2.6 shows regression
estimates and the P-values, all testing the null hypothesis that the estimates are
equal to zero. If the P-values < 0.05 we reject the null hypothesis which means the
estimates are significantly different from zero.
4.7.2 Year 2001
In this year, the mean and median difference between the AVE of PEM and RIVM4
is zero being case one. The mean difference between the AVE of RIVM0 and PEM
is zero being case two. The median difference between the AVE of RIVM0 and
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PSM is zero being case three. The mean difference between the AVE of RIVM0 and
RIVM4 is zero being case four. In cases two, three and four we have either mean or
median but not both rejecting the null hypothesis, which means if mean equality
test rejects the null hypothesis then median equality test fails to reject the null
hypothesis.
Ideally, mean difference and median difference between the AVE of two models
should both either reject or fail to reject the null hypothesis but its not the case here
because the no. of observations in the year 2001 is very small and due to which the
sample becomes skewed and increases the difference between the mean and
median thus, fetching two different results from mean difference and median
difference in the AVE. For further analysis, the null hypothesis is considered to be
as rejected even if one between mean or median difference rejects the null
hypothesis.
In other words, PEM, RIVM0 and RIVM4 all three performed in the same way for
all year 2001 observations as one can say the mean differences in the AVE were
equal to zero with 95% confidence. All three said models had very close mean
AVE which can be seen the tables above. Although the median difference in the
AVE between RIVM0 and PSM is equal to zero with 95% confidence, PEM works
out to be the best model in this year on all counts as the mean and median AVE is
lower than PSM and at 95% confidence level is equal to RIVM0, and RIVM4.
Moreover, explainability of PEM depicted by Adj R2 while running the regression
of IV over P is the highest at 73.82%, which means PEM value estimates or IV
explain variation in observed Price better than variation by any other model tested
here. PEM IV closely follows the P for year 2001 observations.
4.7.3 Year 2002
In Year 2002 observations, no model showed the same mean or median AVE, all
models worked differently. However, RIVM0 could by far be called the best model
in this year as mean and median and the S/D, which shows the volatility in AVE is
the lowest all models implemented. Moreover, Adj. R2, which is highest is at
76.59%. Interestingly, the constant or the intercept in the regression of IV of PSM
over P is insignificant, which means the slope captures all variation of IV over P
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leaving constant equal to zero depicting efficiency in the equation and making it
second best model with the Adj. R2 of 73.31%
4.7.4 Year 2003
For Year 2003 observations, the mean difference between the AVE of PEM and
PSM is equal to zero and the mean difference between the AVE of RIVM0 and
PEM is equal to zero at 95% confidence level but the mean difference between the
AVE of RIVM0 and PSM is not equal to zero. PSM is the best model for year 2003
observations as the S/D of AVE is lowest of all and the Adj. R2 is highest, which is
at 67.89% though the mean and median AVE are not the lowest.
4.7.5 Year 2004
In Year 2004 observations, again no model showed the same mean or median AVE,
all models worked differently. However, RIVM0 and PEM worked almost similar
overall although the mean and median difference in AVE is significantly different
from zero at 95% confidence level. RIVM0 explains the variation in P better only
marginally as Adj. R2 of RIVM0 is 79.61% and 78.71%. Furthermore,
interestingly, the constant or the intercept in the regression of IV of PEM over P is
insignificant, which means the slope captures all variation of IV over P leaving
constant equal to zero depicting efficiency in the equation.
4.7.6 Year 2005
In Year 2005 observations, the median difference between the AVE of PEM and
RIVM4 is equal to zero and the median difference between the AVE of RIVM0 and
PEM is equal to zero at 95% confidence level but the median difference between
the AVE of RIVM0 and RIVM4 is not equal to zero, which means PEM and RIVM4
performed in the same way, RIVM0 and PEM performed in the same way but
RIVM0 and RIVM4 performed fairly different. However, PEM here works out to be
better in terms of explainability of Adj. R2, which is at 75.44% compared to
second and third best, RIVM0 and PSM at 70.79% and 69.00% respectively.
4.7.7 High IN/TA
In High IN/TA group, no model showed the same mean or median AVE, all models
worked differently. However, RIVM0 was the best model on all counts as the
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mean, median, IQ range and S/D of AVE were the lowest and the Adj. R2 was the
highest at 84.09% of all models implemented. Surprisingly, PSM was the second
best model where the variation in IV over variation in P explained was 79.06%.
PSM had less S/D in AVE than S/D in AVE of PEM.
4.7.8 Low IN/TA
In Low IN/TA group, it again followed a similar pattern; no model showed the
same mean or median AVE, all models worked differently. Although PEM
performed better comparatively in terms of explainability of IV over P but Adj. R2
of 56.60% is not justified for calling it best and therefore no conclusion can be
given for Low IN/TA group of observations.
4.7.9 Pooled Sample
Finally, while testing all 3128 observations again no model showed the same mean
or median AVE, all models worked differently. However, multiple-based models
i.e. PEM and PSM performed better than the forecast-based models RIVM0 and
RIVM4. Interestingly, PSM was marginally better than PEM in terms of
explainability measure Adj. R2, i.e. 67.19% compared to 65.38% in case of PEM,
however PSM was less accurate compared to PEM as the mean and median AVE
of PSM were significantly greater than that of PEM. Adj. R2s for RIVM0 and
RIVM4 were at 58.23% and 43.34% respectively. Although RIVM0 had lower
mean and median AVE compared to those of PEM, PEM clearly explained more
variation in P than RIVM0 did, thus, PEM was the best.
4.7.10 COMBO Regression v/s Univariate Regression
The research question addressed here is: Does a combination of value estimates
from all models explain the variation in observed Price better than value estimates
from any single model? Coefficients and Adjusted R2s from Multivariate and
Univariate regressions are observed to answer this.
The answer is yes, for all years, High IN/TA group, Low IN/TA group and for all
the observations, COMBO regression explained the variation in P better than any
one single model. Adj. R2 for High IN/TA group was as high as 90.22% in case of
COMBO regression, whereas RIVM0 explained only 84.09%. Adj. R2 for Low
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IN/TA was 62.99% with COMBO regression whereas PEM showed only 56.60%
explainability. Likewise, for COMBO showed 77.61% explainability for Year-
2003 observations whereas PSM explained 67.89% variation. For all other
samples, COMBO regression explained more than 80% variations in P.
Interestingly, all the slope estimates for RIVM4 in the COMBO regression are
negative.
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5. CONCLUSIONS
In equity valuation, each approach has its own advantages and
disadvantages. There are a lot of insights for both small and large sample, for
Small Sample study, the conclusions are as follows: First, the use of Single-Period-
Comparatives is high in Beverages irrespectively but a Multi-Period-Model is used
as the dominant model in most of the cases – contrary to the findings of Demirakos
et al. (2004). Second, analysts still prefer DCF over RIVM in most of the cases
because of its long history, familiarity and their client’s comfort of understanding
this valuation type – consistent with Demirakos et al. (2004). Third, no single
model is dominant for all sectors but analysts support the valuation with sensitivity
check based on cashflow multiple in 29.2% of the cases – second half but not first
half is consistent with Demirakos et al. (2004). Fourth, analysts who construct a
multi-period valuation analysis of either type use Multi-Period-Model as a sole
dominant model or along with other models jointly. These reports do not adopt
Single-Period-Comparatives as their dominant model in almost all cases. Fifth –
dominant model analysis - interestingly, for Pharmaceuticals being high-intangible
and high growth sector, there was a high use of Earnings multiple (68.8% of
reports) compared to 10.4% in case of Beverages, which is a low-intangible and
low growth sector; for Electronics sector, there was a high dependence on Single-
Period-Comparatives (75% of reports), for Beverages, there was a high dependence
on DCF (54.1% of reports), there was no use of hybrid model as a dominant model
in any given sectors.
For Large sample study, the conclusions for the best model are given on the
basis of the accuracy and explainability of the model put together. Following are
the conclusions: First, RIVM0 works exactly in the same way for both High and
Low IN/TA firm types, however it’s not the best model for both groups. Second,
there is no one particular model of the models implemented as the best model for
all the year-wise samples, every year has its own best model for instance, PEM
performed better for year-2001 and year-2005, RIVM0 performed better for year-
2002 and year-2004 and PSM was the best model for Year-2003. Third, between
the industry groups, RIVM0 performed the best for High IN/TA group and no
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conclusion could be given for Low IN/TA as the explainability of the models was
less then 60%. However, for High IN/TA group PSM was the second best model by
just 5% (84% - 79%) less explainability in terms of Adj. R2 vis-à-vis RIVM0,
which contradicts the findings of Liu et al. (2002) and Lie and Lie (2002) that say
Price-to-Sales performs worst. The finding also proves that Price-to-Sales is a
better multiple than Price-to-Earnings for valuing Intangible-rich firms as used in
Al Ehrbar and Mich Bergesen (Winter 2001). Fourth, The reasons for PEM instead
of RIVM0 is the best model for the pooled sample and RIVM4 being the worst
model for all samples could be attributed to the reasons given in the paper
Sougiannis & Yaekura (JAAF 2001), which are also explained on Page19 of this
dissertation. Interestingly, PSM had slightly more explainability compare to PEM
but was less accurate for the pooled sample. Fifth, The variation in the P by
COMBO regression was explained by far more than the Univariate regression. On
average, more than 80% of the variation in P was explained by COMBO
regression. Interestingly, RIVM4 showed the negative slope for all samples in
COMBO regression.
It can be logically inferred that in the small sample, Beverages uses DCF as
it is a sector which has high tangible assets and stable growth for which cashflows
can be forecasted effectively; Pharmaceuticals and Electronics have a relatively
high growth and High-Intangible assets, for which, the accounting information is
not reliable to forecast the cashflow for a Multi-Period-Model and therefore there
is a high dependence on Single-Period-Comparatives. These findings are consistent
with the literature review and could also be called consistent with the large sample
results, where for high-intangible firms, Price-to-Sales or a Single-Period-
Comparative worked really well. Further research on the analyst preference of
RIVM in case of intangibles could shed more light on the results as RIVM worked
marginally better than Price-to-Sales in case of high-intangibles but is still not a
preferred model.
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6. TABLES & FIGURES
6.1 SMALL SAMPLE TABLES
6.1.1 Table 1 MV(FYE) B/S date Pgs I-bank Pgs I-bank Pgs I-bank
Panel A: 10049190 Jan-05 48 SG 11 ABN 12 IIIR
BEVERAGES - (FTAG4 - 3570)
Cadbury Schweppes Plc 5205253 Sep-04 24 ABN 9 Citi
Associated British Foods Plc
Tate & Lyle Plc 1435317 Mar-04 24 CSFB 7 DB 7 Citi
Panel B MV(FYE) B/S date Pgs I-bank Pgs I-bank Pgs I-bank Pgs I-bank
ELECTRONICS - (FTAG4 - 2730)
Invensys Plc 1109035 Mar-04 44 CSFB 12 SG 9 DB
The Laird Group Plc
The Morgan Crucible Company Plc 516422 Dec-04 7 ABN
Panel C: 502046 Jan-04 36 CSFB 25 ABN 10 DB 9 Citi
PHARMACEUTICALS - (FTAG5 - 4577)
Glaxosmithkline Plc MV(FYE) B/S date Pgs I-bank Pgs I-bank Pgs I-bank Pgs I-bank Pgs I-bank
Astrazeneca Plc
Shire Plc 71703680 Dec-04 28 Citi 18 IIIR 13 ABN 12 SG 7 CSFB
31075010 Dec-04 19 DB
2652489 Dec-04 14 IIIR 11 Citi
SG refers to Societe Generale, ABN refers to ABN Amro, Citi to Citibank, IIIR to Independent International Investment Research, DB to Deutsche Bank
CSFB to Credit Suisse First Boston
MY(FYE) is the market capitalization on Balance Sheet close date, B/S date is Balance Sheet close date
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6.1.2 Table 2
Summary Statistics for the Sampled Companies and Reports
Sector No. of Firms No. of Reports TNPg MVPg MdVPg Rp/IB RP/F
Bvr 3 8 142 17.75 11.5 1.33 2.67
(7 - 48) (1-2) (2-3)
Elec 3 8 152 11 1.60 2.67
Phar 3 8 122 19 13.5 (1-2) (1-4)
(7 - 36) 1.33 2.67
Totals 9 24 416 15.25 11.5 (1-2) (1-5)
(11 - 28) 4.00 2.67
17.3333 (3-5) (1-5)
Sectors examined are beverages (Bvr), Electronics and Electrical Equipment (Elec), and Pharmaceuticals (Phar)
TNPg total number of pages of the reports
MVPg mean value of pages per report (range of pages per report in parentheses)
MdVPg median value of pages per report
Rp/IB number of reports per sell-side Investment bank (range of reports per sell side I-bank in paranthesis)
RP/F number of reports per firm (range of reports per firm in paranthesis)
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6.1.3 Table 3
Major Earnings Definitions for the Valuation Scoring Convention
Valuation Multiples (E)
Models Definition
Single - Period price to earnings (PE) - earnings include historical as well as forward earnings, Enterprise
Comparative value to earnings before interest taxes, depreciation and amortization (EV/EBITDA),
enterprise value to earnings before interest and taxes (EV/EBIT), PEG ratio (PE multiple
scaled by earnings' growth rate)
Sales price to sales (P/S) and enterprise value to sales (EV/S) multiples.
Multiples (S)
Price-to-book stock price to book value per share
(BV)
Price to cash price to cash flow multiple, cash flow including free cash flow and cash flow yield
flow (CF)
Dividened the dividend yield method
Yield (DY)
Ratio to ratio of the market - to - book value of the enterprise to the return on invested capital scaled
economic by the weighted average cost of capital (WACC). REP includes all forms of analyis like
profit (REP) EV/IC and ROIC/WACC multiples, where ROIC stands for return on investment capital
Sum of Parts it’s a valuation used by analyst where the value of a company is dermining by what its
(SOP) division would be worth if it was to be broken up and spun off or acquired by another
company, the valuation of parts/divisions is done using single period multiples , the
valuation is done with a view that value of parts put together is often worth more than the
Hybrid Accounting the return of equity (ROE) and return on investment capital (ROIC) ratios when analyst
rates of return use these as valuatin models and not simply as indicators of economic profitability or if
(ARR) displayed on the first page of the analyst report
Economic the return spread times the book value of the firm's assets
Value added
(EVA)
Multiperiod DCF Discounted Cash Flow
Other RIVM Residual Income Valuation Model
CSFB HOLT This is Credit Suisse First Boston HOLT valuation framework, where the warranted share
price is calculated by assuming the present value of the the assets and deducting for it the
NPV of the future investments, adding market value of investments and finally deducting all
the debt and equivalantes including minority interest
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6.1.4 Table 4
Valuation Models Employed in Analysts' Reports
No. of Single-Period Comparative Valuation Techniques Hybrid Multiperiod
Reports Valuation Valuation
Sector E S BV CF DY REP SOP Other
Bvr 8 8 2 Models Models
100.0% 322 75 ARR EVA DCF RIVM
25.0%
37.5% 25.0% 25.0% 87.5% 62.5% 4 5
50.0% 62.5%
Elec 8 8 4 4 3 5 5421 3 1
100.0% 50.0% 50.0% 37.5% 62.5% 62.5% 50.0% 25.0% 12.5% 37.5% 12.5%
Phar 8 8 222 62 4
50%
100% 25% 25% 25% 75% 25%
Totals 24 24 9 8 7 18 12 6 6 1 12 0 1
100.0% 37.5% 33.3% 29.2% 75.0% 50.0% 25.0% 25.0% 4.2% 50.0% 0.0% 4.2%
Sectors examined are beverags (Bvr), electronics and electrical equipment (Elec) and pharmaceuticals (Phar)
E refers to earnings multiples, S to Sales multiples, BV to price to book, CF to price to cashflow, DY to divident Yield, REP to rating to economic
profit, SOP to Sum of parts, ARR to accounting rate of return, EVA to economic value added, DCF to discounted cash floe and RIVM to residual
income valuation model. " Other" refers to CSFB's HOLT model.
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6.1.5 Table5
Panel A: Use of Alternative Valuation Models
Single-Period Comparative Multiperiod Valuation
Industry Valuation Techniques Models
Beverages 29 5
Electronics 33 3
Pharmaceuticals 22 4
Chi-Square Test of beverages versus pharmaceuticals = 0.005 < 3.84 (Critical value, alpha=5%)
Chi-Square Test of beverages versus electronics + pharmaceuticals = 0.234 < 3.84
Panel B: Choice of Dominant Valuation Models
Single-Period Comparative Multiperiod Valuation
Industry Valuation Techniques Models
Beverages 3.50 4.50
Electronics 6.33 1.33
Pharmaceuticals 5.50 2.50
Chi-Square Test of beverages versus pharmaceuticals = 1.02 < 3.84
Chi-Square Test of beverages versus electronics + pharmaceuticals = 2.35 < 3.84
Panel C: Frequency of Use of Multiperiod Valuation Models as Dominant Model
Use of Multiperiod
Dominance of Multiperiod Valuation Model as a
Industry Valuation Model Nondominant Model
Beverages 4.50 0.5
Electronics 1.33 1.67
Pharmaceuticals 2.50 1.5
Chi-Square Test of beverages versus pharmaceuticals = 0.97 < 3.84
Chi-Square Test of beverages versus electronics + pharmaceuticals = 1.71 < 3.84
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