The effect of asymmetric information on the cost of capital.
Bellalah, Makram ; Belhaj, Riadh
ABSTRACT
In this paper we propose a new method to compute the cost of
capital in domestic and in international settings. Our formulas show the
effect of information costs on the cost of capital and give the
conditions under which the domestic and the international approach yield
the same results in the presence of this imperfection. Information costs
are defined within the context of Merton's (1987) model of capital
market equilibrium with incomplete information, CAPMI. We argue that the
cost of capital in small countries should be estimated using a global
CAPMI rather than a local CAPMI. Our simulation results show that the
error on the cost of capital for small firms is greater than the large
one.
JEL: G3, G15, G31, G32
Keywords: Cost of capital; Information costs; Local CAPM; Global
CAPM
I. INTRODUCTION
Despite the considerable controversies surrounding the capital
asset pricing model of Sharpe (1964), most valuation approaches in the
USA take the CAPM as given and compute the discount rate for equity cash
flows. In small countries, academics mimic US practices and use the CAPM
with a broad based local index as a proxy for the home country market
portfolio. This is referred to as the local CAPM. In Stulz (1995a), the
global CAPM refers to the implementation of the standard CAPM with a
broad based global index proxying for the collective wealth of countries
with easily accessible capital markets for investors who live in any of
these countries.
As it appears in the keynote speech of Stulz (1995b), most
computations of the cost of capital use the CAPM applied to individual
countries as if capital markets were not integrated. In that speech, the
main question is to know whether the cost of capital differ for firms
located in different countries. The cost of capital refers to a hurdle
rate that a project must earn for owners of a firm not to suffer a
wealth loss if the project is taken. The definition of this hurdle rate
in the neoclassical sense ignores the presence of agency costs. In this
spirit, Stulz (1995b) argues that projects that satisfy the neoclassical
hurdle rate can destroy shareholder wealth in firms with high agency
costs.
As it is well known, the CAPM assumes costless information. Or, as
it appears in Merton's (1987) model of capital market equilibrium
with incomplete information, CAPMI, this model can better explain the
expected returns and some anomalies in financial markets. The type of
incomplete information in Merton's (1987) model has something to do
with agency costs. Therefore, using the CAPMI can lead to a better
estimation of the cost of capital than the standard CAPM since the CAPMI
accounts for shadow costs of incomplete information.
Merton (1987) adopts most of the assumptions of the original CAPM
and relaxes the assumption of equal information across investors.
Besides, he assumes that investors hold only securities of which they
are aware. This assumption is motivated by the observation that
portfolios held by actual investors include only a small fraction of all
available traded securities. The main distinction between Merton's
(1987) model and the standard CAPM is that investors invest only in the
securities about which they are "aware". This assumption is
referred to as incomplete information. However, the more general
implication is that securities markets are segmented. The main intuition behind this result is that the absence of a firm-specific risk component
in the CAPM comes about because such risk can be eliminated (through
diversification) and is not priced. It appears from Merton's model
that the effect of incomplete information on expected returns is greater
the higher the firm's specific risk and the higher the weight of
the asset in the investor's portfolio. The effect of Merton's
non market risk factors on expected returns depend on whether the asset
is widely held or not.
The CAPMI can be used in the reexamination of corporate risks under
incomplete information and in particular in the computation of the cost
of capital as in Bellalah (2001). Following Stulz (1995a), we can define
in the same spirit a local CAPMI and a global CAPMI. We study the use of
the CAPMI for small countries. Since several markets are today
accessible, the cost of capital in small countries is not determined
locally but globally. We present a formula to show the magnitude of the
mistake made if the local CAPMI is used instead of a global CAPMI.
The paper is organized as follows: Section II studies the effect of
incomplete information in domestic and global markets on the cost of
capital. It is assumed that information costs are similar for domestic
and foreign investors. Section III investigates the impact of
differential shadow costs of incomplete information on the cost of
capital. It is assumed that information costs are different for domestic
and foreign investors.
II. THE EFFECT OF INCOMPLETE INFORMATION IN DOMESTIC AND GLOBAL
MARKETS ON THE COST OF CAPITAL
This section provides expressions for the cost of capital when it
is determined locally and when it is determined globally in the presence
of shadow costs of incomplete information. These shadow costs reflect in
some sense the asymmetric information problems and agency costs as in
Stulz (1995b). These costs are documented in several studies including
Kang and Stulz (1997), Brennan and Cao (1997), Coval and
Moskowitz(1999), Bellalah (2001), etc. The use of Merton's (1987)
model in the computation of the cost of capital offers an appropriate
method to account for these shadow costs in an international setting.
A. Market Segmentation and Incomplete Information
Consider a firm i in a small country (the home country) referred to
as country H. When investors in the home country cannot invest abroad
and foreign investors cannot invest in the home country, this situation
refers to a segmented market case. In the absence of taxes, transaction
costs and other markets imperfections within countries, the CAPMI can be
used. We consider that capital markets are segmented and that the
economy is characterized by incomplete information as in Merton (1987).
In this context, the CAPMI of Merton (1987) applies within the home
country and the required expected return on shares of firm i is given
by:
E([R.sub.iH]) = r + [lambda].sub.i]
+[[beta].sub.jH](E([R.sub.H])-r-[[lambda].sub.H]) (1)
where E([R.sub.iH]) : the expected rate of return of asset i when
the domestic market portfolio is used; E([R.sub.H]) : the expected rate
of return of the domestic market portfolio; [[beta].sub.iH] =
cov([R.sub.i], [R.sub.H])/var([R.sub.H] : the beta of asset i when the
domestic market portfolio is used; [[lambda].sub.i] the shadow cost of
incomplete information for asset i; [[lambda].sub.H] : the shadow cost
of incomplete information for the domestic market; r : the domestic risk
free rate of interest.
If Equation (1) is used in the computation of the cost of capital,
this is equivalent to mimicking the US approach. This means that the
home country is assumed to be isolated from the rest of the world. In
the opposite, if the market portfolio comprises all markets that are
freely accessible for investors of the home country, the market
portfolio refers to a global market portfolio. Equation (1) can be used
for example to value a company in France using as the market portfolio a
proxy like the CAC 40 or the Morgan Stanley Capital International (MSCI)
index for France. Equation (1) is not very appropriate for any capital
market that is not isolated from the other markets.
B. Market Integration and Incomplete Information
When home country investors can access foreign capital markets and
investors in these countries can access the market of the home country,
all these markets represent one capital market or a global capital
market. For example, the French market is not segmented from the world
and it seems more appropriate to calculate the cost of capital of the
French companies by a global CAPMI.
Under the same assumptions used in Stulz (1995a), it is possible to
write Merton's (1987) model for the computation of the cost of
capital
E([R.sub.iG]) = r + [[lambda].sub.i] + [[beta].sub.iG]
(E([R.sub.G])- r - [[lambda].sub.G]) (2)
where: E([R.sub.iG]) : the expected rate of return of asset i when
the global market portfolio is used; E([R.sub.G]) : the expected rate of
return of the domestic market portfolio; [[beta].sub.iG] =
cov([R.sub.i], [R.sub.G])/var ([R.sub.G]): the beta of asset i when the
global market portfolio is used; [[lambda].sub.i]: the shadow cost of
incomplete information for asset i; [[lambda].sub.G] : the shadow cost
of incomplete information for the global market; r : the domestic risk
free rate of interest
Equation (2) can be used for example to value a company in France
using as the market portfolio a proxy like the MSCI World index. In
relations (1) and (2), it is assumed that the shadow cost of incomplete
information about the firm i is the same for the domestic and the
international investor. In the next section, we will relax this
assumption and explain how it affects the results. This is because
information costs can be higher or at least different for foreign
investors.
When the home country is integrated in world capital markets, the
expected return on the market portfolio of the home country is computed
using equation (2):
E([R.sub.H]) = r + [[lambda].sub.H] + [[beta].sub.HG]
(E([R.sub.G])-r-[[lambda].sub.G]) (3)
where [[beta].sub.HG] = cov([R.sub.H], [R.sub.G])/var([R.sub.G])
Equation (3) gives the risk premium on the home country market
portfolio when the country is integrated in global markets. Inserting
relation (3) into (1) gives:
E([R.sub.iHG]) = r + [[lambda].sub.i] +
[[beta].sub.iH][[beta].sub.HG](E([R.sub.G])- r -[[lambda].sub.G]) (4)
where the subscript iHG refers to the required return obtained for
security i when markets are global and the local CAPMI is used.
Relation (4) gives the cost of capital when the markets are global
but the domestic market index is used. This relationship is different
from that in Stulz (1995a) due to the effect of incomplete information
regarding the firm and the global market. The domestic CAPMI and the
global CAPMI give the same results only if
E([R.sub.iG]) - E([R.sub.iHG]) = 0
Using equations (2) and (4), we obtain:
E([R.sub.iG])-E([R.sub.iHG) = ([[beta].sub.iG] -
[[beta].sub.iH][[beta].sub.HG]) (E([R.sub.G])-r-[[lambda].sub.G]) (5)
Relation (5) shows that the information cost on the global market
decreases the risk premium. Since this premium on the global market
portfolio is positive, the global CAPMI and the local CAPMI give the
same cost of capital of firm i when [[beta].sub.iG] = [[beta].sub.iH]
[[beta].sub.HG]. When the return on the home country portfolio is always
equal to the return of the global portfolio, the local CAPMI and the
global CAPMI approaches give the same result when [[beta].sub.iG] -
[[beta].sub.iH] [[beta].sub.HG].
In relation (5), we can get a risk premium in the global market
equal to zero if the information cost in global market is equal to the
excess return on the global market; that is when E([R.sub.G])-r =
[[lambda].sub.G].
The case where the global information cost exceeds the risk premium
in global market remains possible, due to the effect of incomplete
information in an international setting.
Simulation results for error on cost of capital are reported in
table 1. The parameters value used are r = 3%, [[beta].sub.iH] = 0,75,
PHG = 0,5 and E([R.sub.G]) = 4,5%. Table 1 shows the effects of the
shadow cost of incomplete information of the global market
([[lambda].sub.G]) and the beta of assets when the global market
portfolio is used ([[beta].sub.iG]) on error on cost of capital. When
[[lambda].sub.G] increases, the error on cost of capital increases. For
instance, when [[beta].sub.iG] = 0,5, as [[lambda].sub.G] increases from
3% to 7%, the error on the cost of capital increases of about 0,5%. It
is also apparent from Table I that the error increases with
[[beta].sub.iG]
III. THE DIFFERENCE IN INFORMATION COSTS BETWEEN DOMESTIC AND
GLOBAL MARKETS AND THEIR EFFECTS ON THE COST OF CAPITAL
In the previous section, we assume that information costs supported
by home investors are of the same magnitude as the costs supported in
global markets. Now, we consider the case where the information costs
supported by the domestic investors are different from those paid by the
foreign investors. This may be a reasonable assumption because there are
in general some additional costs to access to information about foreign
markets. In the case of market segmentation, the cost of capital can be
computed using the following relationship:
E([R.sub.iH]) = r + [[lambda].sub.iH] + [[beta].sub.jH]
(E([R.sub.H])-r-[[lambda].sub.H]) (6)
where [[lambda].sub.iH] is the information cost of asset i in the
domestic market.
If we consider the case where the markets are integrated, the
information cost paid by the international investor will differ from the
one paid by the domestic one. This suggestion is consistent with the
empirical evidence since domestic investors are in general better
informed about their markets than foreign investors.
In the case of market integration, the cost of capital can be
calculated under incomplete information using:
E([R.sub.iG]) = r + [[lambda].sub.iG] + [[beta].sub.iG]
(E([R.sub.G])- r-[[lambda].sub.G]) (7)
where [[lambda].sub.iG] indicates the information cost of asset i
in the global market. Since the international markets are integrated,
relation (3) holds. Inserting relation (3) into (6) gives:
E([R.sub.iHG]) = r + [[lambda].sub.iH] +
[[beta].sub.iH][[beta].sub.HG](E([R.sub.G])-r-[[lambda].sub.G]) (8)
In a domestic and an international setting, the global and domestic
markets yield the same result under information uncertainty when
E([R.sub.iG]) = E([R.sub.iHG]).
From Equations (7) and (8), we obtain:
E([R.sub.iG]) - E([R.sub.iHG]) = ([[lambda].sub.iG] -
[[lambda].sub.iH]([[beta].sub.iG] - [[beta].sub.iH][[beta].sub.HG])
(E([R.sub.G])-r-[[lambda].sub.G]) (9)
Relation (9) reflects the important effect of incomplete
information on the cost of capital. Expression (9) shows that the cost
of capital depends not only on the domestic index and the global index
but also on the information cost in the domestic and the global market.
The cost of capital is the same in the two contexts if:
([[lambda].sub.iH] - [[lambda].sub.iG]) = ([[beta].sub.iG] -
[[beta].sub.iH] [[beta].sub.HG]) (E([R.sub.G])-r-[[lambda].sub.G]) (10)
If we consider a positive risk premium on the global market in the
case of complete information, we get the same result as in Stulz (1995):
[[beta].sub.iG] = [[beta].sub.iH][[beta].sub.HG]. If the information
cost in the domestic market is the same as the information cost in
global market [[lambda].sub.iG] = [[lambda].sub.iH] = [[lambda].sub.i],
we obtain the same condition as the one in the previous section of this
paper.
Table 2 shows the effects of [[lambda].sub.iG] and [[beta].sub.iG]
on error on cost of capital. When [[lambda].sub.iG] increases, the error
on cost of capital increases. For example, when [[beta].sub.iG] = 0,5,
as [[lambda].sub.iG] increases from 1% to 3%, the error on the cost of
capital increases of about 2%. It is also apparent from table I that the
error increases with [[beta].sub.iG].
In international setting the information costs are more important
than in domestic case. Tables I and 2 show that the information costs
are more important for an international investor than the domestic one
([[lambda].sub.iG] > [[lambda].sub.iH]). Kang and Stulz (1997) have
shown that the information costs are higher for small firms than the
large one. From our simulations we expect that the error on the cost of
capital for small firms are greater than the large one. This conclusion
justifies the fact that we have a difficult to get information about
small firms.
We conclude that the appropriate way to compute the cost of capital
in international and domestic setting in the case of incomplete
information is to determine the optimal conditions on the beta and the
information costs for domestic and international investors.
IV. CONCLUSION
Following the analysis in Stulz (1995a), this paper shows that the
cost of capital in small countries is determined globally and not
locally in the presence of incomplete information. Information
uncertainty is defined with respect to Merton's (1987) simple model
of capital market equilibrium with incomplete information, CAPMI. The
analysis implies that the valuation approaches in small countries should
be based on a global capital asset pricing model as the CAPMI rather
than a local CAPMI. In both, cases, the cost of capital must account for
the effects of shadow costs of incomplete information. Further research
can be done to quantify the magnitude of the mistake made using a local
CAPMI rather than a global CAPMI.
The neo-classical cost of capital and the agency-adjusted cost of
capital in the presence of incomplete information can provide different
answers as to whether the cost of capital differ between countries. Our
analysis extends the standard analysis in Stulz (1995a, 1995b) to
account for the effects of incomplete information in the computation of
the cost of capital.
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Makram Bellalah (a) and Riadh Belhaj (b)
(a) Conservatoire National des Arts et Metiers, Finance Chair, 2
rue conte 75003 Paris, France, Bellalah@cnam.fr
(b) Conservatoire National des Arts et Metiers, Finance Chair, 2
rue conte 75003 Paris, France, Belhaj@cnam.fr
Table 1
Effects of [[lambda].sub.G] and [[beta].sub.iG] on error on the cost
of capital. r = 3%, [[beta].sub.iH] = 0,75, [[beta].sub.HG] = 0,5 and
E([R.sub.G]) = 4,5%.
[[lambda] [[beta] Error on the
.sub.G] .sub.iG] cost of capital (%)
0.3 0.11
3% 0.5 -0.19
0.7 -0.49
0.3 0.26
5% 0.5 -0.44
0.7 -1.14
0.3 0.41
7% 0.5 -0.69
0.7 -1.79
0.3 0.56
9% 0.5 -0.94
0.7 -2.44
Table 2
Effects of [[lambda].sub.iG] and [[beta].sub.iG] on error on the cost
of capital in case of different information cost. r = 3%, [[lambda]
.sub.iH] = 0,5%, [[beta].sub.iH] = 0,75, [[beta].sub.HG] = 0,5 and
E([R.sub.G] = 4,5%.
[[lambda] [[beta]. Error on the
.sub.iG] sub.iG] cost of capital (%)
0.3 -0.04
0,5% 0.5 0.06
0.7 0.16
0.3 0.46
1% 0.5 0.56
0.7 0.66
0.3 1.46
2% 0.5 1.56
0.7 1.66
0.3 2.46
3% 0.5 2.56
0.7 2.66