Appendix 2: DCF Valuation in Emerging Markets, the SPAM
model
The
three-step Stackable Premiums and Adjustments Model (SPAM) is Luis Pereiro
extension model to less efficient arenas than the practices used by US
financial appraisers when valuing privately held companies.
Stage 1 of
the model introduces adjustments that should be made to cash flows of companies
operating in volatile markets.
Stage 2
presents a wide array of conceptual frameworks to compute the cost-of-equity
capital. And because the use of the traditional CAPM is highly controversial in
emerging markets, the included variants are restricted to those that have been
specifically adjusted to deal with such economies. Non-CAPM based models, which
do not use betas as the risk factor, are also included.
Stage 3,
some recommendations are made as to which and how unsystematic risk adjustments
should be made to stock value, as a function of the condition of the
shareholding appraised and the method used for computing the cost of capital in
the first stage of the process.
Typically
in «developed countries» such as the U.S., financial professionals
use DCF following two steps:
1- The
cost of capital is computed via the Capital Asset Pricing Model (CAPM) as if
the target were a public company, the firm value (both equity and debt) is
estimated via a DCF-based fundamental valuation, either by using the weighted
average cost o capital (WACC) as the discount rate, the equity value is finally
computed by subtracting debt from firm total value.
2- In the
second step, the equity value is adjusted for unsystematic risk factors such as
differences in size, control, and illiquidity, usually found between quoting
and non quoting companies.
The second
step is indeed necessary because the first implicitly assumes the appraiser is
valuing a stock minority position in a large, quoting company; this assumption
is based on the fact that the data used for CAPM calculations derive from
comparable large, public companies, which are by definition, trading minority
positions in the capital markets. But when the target under valuation is
instead a small, control shareholding in a non quoting company, unsystematic
risk must be introduced to adjust the value of its stock.
Step
1: Modeling Cash Flows in Emerging Markets
In emerging economies, the DCF presents hard challenges to the
appraisers. Indeed, both cash flows and the discount rate need to be properly
adjusted to take into account the specific features of the «developing
markets».
First,
three types of adjustments should be applied to the cash flows (CF) of
companies operating in these «emerging markets»:
1-
Adjustments for overcompensation (Salaries vs. Dividends)
2-
Adjustments for over expensing (Personal vs. Corporate spending)
3-
Adjustments for currencies (Exchange risk and Inflation)
Step
2: Modeling the Cost of Capital in Emerging Markets
The second
step following the CF adjustments is to determine a «discount rate»
as an «opportunity cost» expressing the minimum cost of capital that
an investor requires from a specific investment project. The investor will
undertake the project when the free cash flows (FCF) generated by the project,
discounted at this rate, create value over and above the initial investment
(also see next part about Capital Budgeting).
Hence, the
cost of capital (CC) is a factor of prime importance when valuing companies
using DCF method. An overestimation of CC may lead to rejecting good investment
opportunities with a probable potential source of «economic
value» for the future, whereas an underestimation of CC may drive the
investor to undertake value-destructive projects. Defining the CC is therefore
a delicate and effortful task.
The most
used technique in DCF is the free cash flows to the firm (FCFF) that requires a
weighted average of both the cost-of-equity and the cost of debt or WACC. Since
the cost of debt is not very difficult to obtain via the market rate of a
marginal debt issuance or loan contract by the operating firm; the challenge
remains how to determine the cost-of-equity that is the minimum rate of return
shareholders require for an investment.
In modern
financial theory, it is assumed that the cost-of-equity (let's call it CE) of a
quoting company reflects the risk that investors perceive in it; if investors
are risk-averse they will require a larger return when the risk perceived is
larger. This behavior is embedded in the CAPM method when computing the CE:
CE
= Rf + Beta * (RM - Rf) + RU
where Rf
is the risk free rate and RM is the market average return, Beta is a measure of
the volatility of the company's shares to the market return, and RU is a
component that accounts for effects no explained by the other terms of the
equation. The term (RM - Rf) is called the «market risk
premium», the product of Beta and the market risk premium is the
«systematic risk premium» of the shares under analysis and
partly explains the returns that move systematically with the market. The RU
factor arises from the so-called «unsystematic risk» and
encompasses the effects of all variables affecting share value and that do not
move in the same direction as the market.
Yet, two
major conditions that exist when trading «financial assets»
in the developed and efficient markets are not normally present when trading
«real or closely held assets» in the emerging
markets: diversification and efficiency.
First,
diversification is imperfect when a single or only a handful of acquisitions is
made in a market where only few interested buyers ad sellers are operating;
this is the case in majority of the deals in emerging markets. Imperfect
diversification, in turn, generates unsystematic risk, and the
traditional CAPM, as explained earlier, has not been structured to deal with
this condition. Unsystematic risk is particularly important in
emerging markets, where the dominant transaction is the «small and non
quoting company». Most transactions in practice correspond to private
assets where private risk plays an important role in defining the firm
value.
Second,
the final price of a transaction is not a transparent reference determined by
financial analysts, it is rather a blend of different viewpoints from a small
group of entrepreneurs, strategic investor or Venture Capital and Private
Equity firms' negotiating the deal.
Finally,
practice shows that even among financial professionals, the existence of
efficiency is truly questionable in emerging markets for many reasons, among
them (Pereiro Luis E., 2002):
- Emerging
stock markets tend to be relatively small,
- The
importance of stock markets in emerging markets is also small,
- Emerging
stock markets are highly concentrated,
- Market
and cost of capital information is scarce, unreliable and volatile,
- Data
series are extremely short,
- Very few
comparable companies are available.
Hence, to
mitigate the drawbacks of the use of CAPM for the determination of the CE in
emerging markets, specific adjustments should be applied. Luis E. Pereiro
suggested five CAPM-based and two non-CAPM based models to cope with the
difficulties and constraints faced in the emerging economies. The non-CAPM
based models are basically models designed to take out Beta coefficient from
the CAPM in order to avoid the use of beta approach in emerging markets that
are highly volatile, with betas not correlated with returns when computed
against the world market.
The CAPM
based models are:
-
Global CAPM Variant: uses global market parameters into the CAPM
equation,
- Local
CAPM Variant: uses local market parameters and country risk premium into the
CAPM equation,
-
Adjusted Local CAPM Variant: adjusting the previous model by
avoiding the double-counting of the «macroeconomic country
risk» by correcting the «systematic market risk
premium».
-
Adjusted Hybrid CAPM Variant: this model calibrates the
«global market premium» to the «domestic
market» through the use of a «country beta».
-
Godfrey-Espinosa Model: it is and ad hoc «beta
model» that adjusts Beta to deal with CAPM in emerging markets.
The
Non-CAPM based models are:
-
Estrada Model: this model better reflects the partial integration
under which many emerging markets operate by using a «downside
risk» as the risk measure as opposed to «total
risk».
-
Erb-Harbey-Viskanta (EHV) Model: this model is designed for
economies without a stock market by using the credit-risk rating-based
model.
Now that
these models are presented and partially explained, the challenge remains what
model to choose since each variant will naturally lead to a different value for
the cost of equity. What can be verified is that the CE depends strongly upon
the volatility generated by both the «country risk premium»
and the «market risk premium». Non-CAPM based models on the
other hand give higher values the CAPM-based models; this may be due to the
fact that they are capturing a portion of «unsystematic
risk».
In fact,
there is no right model to choose or to recommend, the appraiser is solely
responsible for which model fits his preferences. If a CAPM based model is
used, the selection of the specific variant should undergo two
decisions:
1-
Deciding the true degree of integration between the local financial market and
the global market.
2-
Deciding on the reliability and usefulness of data available for the target
country.
For the
first decision, the literature recommendation is to use a «Global
CAPM» when strong financial integration is perceived, and to use a
«local CAPM» when the domestic market is partially or
nonintegrated with the world market. For the latter case, the «local
adjusted» version is preferred over the single «local
CAPM» because of the double-counting of «country
risk».
For the
second decision, the appraiser should gauge the usefulness and availability of
the historical domestic market data series to be used as a reference in the
forecasting process. When series are considered to be short, or incomplete, or
when the market is expected to be very volatile in the future, the appraiser
may opt to use data from the global market and adjust for the country risk as
the 2 last models suggest («adjusted hybrid CAPM» and
«Godfrey-Espinosa» models).
Otherwise,
if the appraiser doesn't trust the use of Beta as a risk measure, he or she may
use the Estrada or EHV models. The available suggestion here is to apply the
Estrada model to markets where a local sock exchange exists and the EHV model
where it doesn't.
Finally,
the analysts' team may consider that the different models provide a
«range» of values for the CE; hence the team can compute a
«synthetic value» as a combination of the different models
instead of choosing a «single value» derived from a single
model.
Now, once
the CE is determined, the WACC can be computed using these three parameters:
Proportion of Debt to Equity (D/E), the Cost of Debt and the Corporate rate Tax
(usually both Cost of debt and tax are computed using the
«marginal» values).
Step
3: Modeling the Unsystematic Risk
To be able
to model the unsystematic risk (UR), the following questions should be
answered:
- How
important is UR in company valuation and why isn't it popular among academics
and practioners alike?
- What are
the specific drivers of unsystematic company risk?
- How is
UR computed by U.S. practioners?
- What is
the size of the private company risk adjustment for non US markets, and how can
this adjustment be computed in an emerging market?
- How are
UR adjustments transformed into risk premiums to be introduced directly into
the discount rate?
In fact,
diversification is usually imperfect in the world of real assets. For many
Corporate Finance deals such as M&A's or Private Equity transactions
involving closely held companies, money is allocated to a single or just a few
investment projects; this creates a component of «unsystematic» or
«idiosyncratic» or «private» risk which affects positively
or negatively the company value. Such risk can be introduced as a premium or
discount into the discount rate, or simply as a straight adjustment (decrease
or increase) to the final stock value computed via the DCF analysis.
Computing
the UR is an indeed an intricate task for the appraiser. Academics have not yet
developed a full set of models to tackle the issue, simply because the CAPM
mind-set ignores it by design. As a result, most practioners resist dealing
with UR, yet this matter cannot be pushed aside because there is empirical
evidence that shows UR may greatly affect company value. In fact, it can
diminish by about half the company value of US firms, and more than that in
emerging markets! Said differently, «unsystematic risk» may have a
larger impact on firm value than «systematic risk».
In the US,
UR is composed of three different value-affecting drivers:
- Company
size
-
Shareholding size
-
Shareholding liquidity
The
Size Effect:
Practioners
do recognize the influence of size on value and adjust it accordingly.
Alternatively, the influence of size on value can also be estimated as the
spread between the bank rates at which smaller and larger firms may take a
loan.
Control
Premiums:
A majority
shareholding is less risky than a minority one, since the former carries
several control and restructuring privileges than the latter does not. As a
result, a minority interest is worth less than a control interest. In other
words, the former trades at a «minority discount» or, alternatively,
the control interest carries a «control premium» over the minority
interest.
Illiquidity
discounts:
The shares
of a quoting company are more liquid than those of a non quoting firm, as they
can be rapidly and easily traded in the stock market, with considerable
certainty on the realization value and with minimum transaction costs.
Illiquidity risk translates into a discount on the price at which shares of a
private company are sold compared to the selling price of shares belonging to a
public and similar company.
In the non
US and Emerging markets, if we assume that CAPM based methods, by definition,
capture systematic or undiversifiable risk only, the analyst choosing one of
the variants in this subset of models must apply any adjustments for size,
control, and/or illiquidity, depending on the condition of the stock under
appraisal.
On the
contrary, the Estrada and EHV models certainly capture some portion of
unsystematic risk.
However,
in both models, data on returns come from the stock market where, by
definition, only minority shareholdings of quoting companies are traded. It is
reasonable to assume that the models are already capturing the size effect
(plus any other unsystematic factor), with the exclusion of control and
illiquidity effects. Hence, only control and/or illiquidity adjustments must be
applied when using these models.
Once the
analyst has selected which unsystematic risk effects apply, he or she must
decide on the method to combine them. Directly adding discounts may lead to
overestimating risk, since effects may be correlated with each other; ad a
straight addition may double-count risk. So what is the solution? In fact, the
double-counting of UR effects may in practice be at least partially countered
by multiplying (instead of adding) them. The reason is that a multiplicative
combination gives a lower value than the straight addition sum would
give.
Now, all
these adjustments must be transformed into Risk Premiums. Hence, instead of
applying size, control, and illiquidity adjustments to the stock value, the
analyst may prefer to introduce unsystematic risk straight into the DCF
discount rate. The implied risk premium corresponding to a specific
unsystematic risk adjustment can be computed as follows:
1- Obtain
the present value of the company via a DCF analysis.
2-
Subtract debt to obtain stock value.
3- Apply
the unsystematic risk adjustments to stock value.
4- By
trail and error, find out which risk premium (discount), added to the discount
rate, and produces the stick value found in the previous step.
An
iterative method such as this is necessary because the premium (discount) in
the rate implied by a specific final decrease (increase) in stock value is a
function of the cash flow structure.
Alternatively,
Arzac (.....) has suggested a formula for determining the implied illiquidity
risk premium, which has been expanded to cover all three components of UR
(size, control, and illiquidity):
UR
rate premium = d * (k - g) / (1 - d)
Where
d is the discount on the stock value,
k is the DCF discount rate, and
g is the cash flow growth rate.
At this
point, the relevant conclusion is that whatever the computational method used,
the implied risk premium for a closely held company may be substantial. Indeed,
it is UR that may explain large cost of capital values (from 30% to up) in
private companies. As an illustration of that, the average Venture Capital fund
may diversify away only part of unsystematic risk (maybe size and/or control)
by making a portfolio of carefully chosen acquisitions, but it cannot avoid the
marketed discounts imposed by the private and non quoting nature of companies
the fund is entering to as a shareholder.
Computing a Synthetic Company Value:
Using
different variants for computing the cost of capital will naturally lead to
different discount rates, and these in turn will generate a set of alternative
values for the same company. The analyst may hence opt for estimating a
singular, or «synthetic» company value from that set.
Also,
using multiple value scenarios is an effective way to visualize the
«downside» risk involved in any project. Downside risk is the
maximum monetary loss expected in an investment situation and its probability
of occurrence, or simply, the inability to achieve a monetary goal above zero.
In that case, the investor should consider three different scenarios:
optimistic, expected, and pessimistic. Each scenario is then modeled after a
carefully selected set of assumptions on the operation of the business, and
each set defines a specific cash flow. In fact, a substantial empirical
evidence suggests that investors and managers should carefully consider the
«downside risk» when making investment decisions.
Finally,
the analyst may opt for one of the following variants:
- No
Synthesis: the analyst reports the value of each scenario but does not attempt
to combine them.
- Assume
Centrality: the analyst assumes the «most» likely scenario is the one
that counts, and used the value corresponding it as the synthetic value.
- Compute
the Average: the analyst computes the simple average f values for the three
scenarios.
- Compute
the Median: The analyst computes the median value of values.
-
Probability-weighted scenarios: The analyst estimates subjective probabilities
of occurrence for each scenario, and used them as weights to compute a
synthetic value.
See
exhibits here after annexed for the illustration of the steps for a Buy/Sell
deal using a DCF Valuation involving in an Emerging Markets.
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