Modern Economy, 2012, 3, 473-480 Published Online September 2012 (
The Asset Pricing System
Yi-Jang Yu
Department of Economics, Ming Chuan University, Taipei, Chinese Taipei
Received May 5, 2012; revised June 6, 2012; accepted June 15, 2012
Mainstream asset pricing models are all inappropriate when they consistently insist on applying one single model to
deal with a reality filled with different aspects of asset pricing. In addition, those models also treat the right environment
variable too lightly hence can not rightly do the job of asset pricing. In this study, based on the portfolio theory and the
principle of supply and demand, a more reasonable asset pricing system including five different models will be sug-
gested to provide a necessary function of automatic price stabilization and to better serve our financial market.
Keywords: Asset Pricing System; Environment Variable; Portfolio Theory; Automatic Price Stabilization
1. Introduction
Since the mainstream asset pricing models can not restrain
themselves from generating price bubbles in the financial
market (Summers, 1985 [1]; Krugman, 2009 [2]; Colan-
der, 2010 [3]), they certainly deserve our closer attention.
In recent years, final conclusions regarding the major
causes of recent financial crises still focus on market con-
straints such as loose controls over financial derivatives
or credit or monetary policies (Kindleberger, 2005 [4];
Askari et al., 2010 [5]; Bresser-Pereira, 2010 [6]). How-
ever, if those mainstream asset pricing models can not be
tamed by nature, then unleashing their constraints would
only further exaggerate their power of sabotage.
Equally important is that, when asset risk and return
can only be endogenously determined and both are of much
concern to investors, clearly it is impossible to apply just
one single model to price assets, not to mention that there
are still other considerations including the separation be-
tween normative and positive analyses needing to be
taken care of. In reality, we have no choice but to apply a
system instead of one single model to manage the job of
asset pricing.
The main tasks of this study are therefore twofold. The
first is to point out that mainstream asset pricing models
are all inappropriate when they consistently insist on
applying one single model to deal with a reality filled
with different aspects of asset pricing. Accordingly, the
second task of this study is to suggest a necessary asset
pricing system which, by its own properties, can provide
a safer ground to serve our financial market.
1.1. Literature Review
Within the econometric approaches to asset pricing, strictly
using only company variables emerged earlier, and the
MM theorem proposed by Miller and Modigliani in 1961
was deemed to be the pioneering study in this field (Chen
and Zhang, 2007 [7]). By contrast, Fama (1981) [8] started
to lay the econometric foundation between asset price
variation and macroeconomic variables including GDP,
monetary supply and other related variables. Not surpris-
ingly, combining both microeconomic and macroeconomic
variables into one econometric model also attracted some
attention (Lev and Thiagarajan, 1993 [9]; Swanson et al.,
2003 [10]). However, when the set of explanatory variables
with the best explanatory power could not be standard-
ized for different individual assets, requiring more weights
to be put on company variables, or asset portfolios, re-
quiring more weights to be attached to macroeconomic
variables, there was no end to the disputes that arose.
As to those asset pricing models constructed with
theoretical foundations, the first category featured the
return aspect and was described by Graham and Dodd in
1934, later becoming the Dividend Discount Model pro-
posed by Gordon and Shapiro in 1956 (Bettman et al.,
2009 [11]). After that, two more kindred products of the
Earnings Capitalization Model and the Residual Income
Valuation Model had also been suggested (Kothari, 2001
[12]). The problem is, as long as all of them have to ap-
ply time series corporate net incomes to forecast the net
income in the next period, they can easily become econo-
metric models (Collins and Kothari, 1989 [13]; Dechow
et al., 1999 [14]). The second category can be classified
as risk evaluation models, and the CAPM is clearly the
most important representative. The scale of its family is
still growing up to these days. While some consistently
oppose it (Fama and French, 1996 [15]; 2004 [16]), some
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do not (Levy, 2010 [17]). The final category belongs to
quantitative models focusing on the establishment of
certain applicable fundamental indexes (Arnott et al.,
2005 [18]; Mar et al, 2009 [19]). The problem with them
is that they can hardly be applied as standardized tools to
analyze asset risk (Kaplan, 2008 [20]).
In addition, there still have a couple of serious short-
comings that are too lightly concerned by all previously
mentioned attempts at asset pricing. The first is the im-
possibility of applying one single model to handle the
whole job of asset pricing, the second is the negligence
of incorporating a necessary environment variable into
the job of asset pricing in order to faithfully reflect the
reality and automatically restrain the asset price volatility.
1.2. The Essence of Asset Pricing
By stating an asset’s all one-period possible returns as
112 and all corresponding probabilities
of occurrence as
,,,v v
12 n, the expected return
can thus be discounted to obtain a current price
c0 through
1V Ed
where is the uncertain discount rate. With respect
to the uncertainty of 1, usually its corresponding
variance 1
or other related measures will be suggested.
Although Equation (1) simply states what the job of
asset pricing is looked like in general, it actually contains
some important information needing to be further clari-
fied. First of all, since expected return is calculated in a
way similar to that of average value, it can thus possess a
property of lowest bias but with an unknown confidence
level. Second, 1 is undoubtedly a return measure,
relates to the measure of risk and the reason follows. To
take one single asset for instance, after setting an identi-
cal lower boundary 1and a fixed but sufficiently large
enough return range all the times, it is clear that varia-
tions of the asset’s investment risk can be presented as
changes in cumulative probabilities upon those returns
below . By contrast, this measurement of investment
risk is superior to the traditional one of standard devia-
tion. This is because, taking the way to calculate the cu-
mulative probabilities in the case of log-normality as the
example, if the lower boundary 1 is greater than the
expected value , then, other things being equal,
increase in standard deviation would mean a decrease in-
stead of an increase in investment risk. However, as long
as both 1 and 1
V can remain constant, the only
variable capable of affecting the cumulative probability is
clearly the standard deviation. Therefore, choosing standard
deviation to define the asset’s risk really can have certain
convenience, only needs to be done with caution.
Second, any decision to directly take the uncertain
discount rate as the sole solution to the job of asset pric-
ing is obviously inappropriate. When information of cur-
rent price c0 is always conveniently available, it seems
that the asset’s future price could be directly inferred
after working out the uncertain discount rate. Obviously,
this is a saying of mistaking cause for effect. Without
having any information about the expected value, no un-
certain discount rate could be brought into the uncertain
discount process. Furthermore, with respect to Equation
(1), the following inequality must hold after introducing
a constant impact K on the expected value as
Ed Ed
However, since
can not reflect any information of
this kind, the outcome of 0 will remain un-
changed accordingly. In other words, although
can not be excluded from the uncertain discount process,
however, it can only do the job as its name stands.
Finally, directly links to environment condition
which, by definition, is not under control of any asset
investor; on the contrary, 1 relates to individual factor
and, in general, can be improved by self-effort; as for
, acting as the denominator in Equation (1), it is
required to incorporate especially the uncertainty contents
within both and 1 plus additionally a unique time
factor. Obviously, so long as there are three independent
variables in effect, there is no way to apply just one sin-
gle model and still can well consider every aspect in the
job of asset pricing.
1.3. Normative and Positive Analyses
Statistically speaking, the expected value
1 is a
first-moment measure, its uncertainty or risk is a sec-
ond-moment measure and can not be directly informed
1 itself. Therefore, in the sense of positive analy-
sis, both the process of uncertain discount and the uncer-
tain discount rate can not legitimately exist. In other words,
Equation (1) can only be a definition in the sense of
normative analysis.
Since individual risk is unobservable, therefore, the
portfolio theory and its concept of risk diversification can
only be applicable in the world of normative analyses
(Fabozzi et al., 2002 [21]). In addition, by definition, the
systematic risk is non-diversifiable, hence can represent a
unique environment constraint generally applicable to every
asset in the market. After referring to the statement about
the probability variable provided in Section 2.1, it can
now be easily inferred that directly relates to the sys-
tematic instead of individual risk component of an asset.
In turn, this fact can provide a convenient path to present
the right environment variable needed in the job of asset
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Conclusively, for those three independent variables
, and functioning in Equation (1), when two
out of three are applicable for normative analyses, it must
be that the whole job of asset pricing is a synthesis of
normative and positive analyses. Furthermore, the way to
construct the asset pricing system must be started form
the asset’s risk but not return identity. This is because, by
definition, one asset’s uncertainty or risk is measured
based on its return information, never the opposite.
112 2
1.4. The Environment Variable
Other than self-effort, there still has luck or environment
factor that we rely. This is a common sense and its mean-
ing is straightforward but substantial. First, while self-effort
is what can be within our grasp, luck or environment usu-
ally can not. Second, a certain environment variable can
not be excluded from the job of asset pricing, otherwise
all factors affecting asset prices would be deemed as if
they could be totally under our control. For example, any
decrease in international oil price can immediately raise
the profits of oil companies, other thing being equal, and
certainly this is not an outcome contributed by self effort
from those companies. Another fact is that there is al-
ways a systematic risk generated by the global economy
that can not be kept away even for a well-diversified
multinational corporation. Finally, since it is an issue of
either-or choice, therefore, the weights accompanying
both self-effort and environment factors must be summed
to equal one. This seems trivial but can be extremely im-
portant to jobs of asset pricing and market management.
Traditionally, by applying only return information in
the asset market, the principle of supply and demand
provides no clue to present the right environment vari-
able needed for the job of asset pricing. That is, an envi-
ronment definition that can be generally applicable to all
members within the same environment. In addition, al-
though the probability variable in Section 1.2 can be
numerically provided, however, there is still no way to
directly infer or derive the systematic risk variable solely
based on the this information. Therefore, the way to
present the right environment variable for an asset really
has to rely on the portfolio theory and its concept of risk
Assume a two-factor asset pricing model can be ex-
pressed as
 
in which all elements affecting asset returns Y are inte-
grated into X1 as a systematic risk or an environment
variable, and X2 as a self-identity variable. Estimators of
both coefficients can be listed separately as
21 112
12 12
yx xyxx
xx xx
 
According to the portfolio theory, since the covariance
between x1 and x2 or between x1 and y is exactly the
variance of x1, therefore, 2
and 1
are equivalent
and equal to 1
, and
must have a sum
of one.
In contrast to a model having just one self-identity
variable, since it has no systematic risk variable that can
especially act like an automatic price stabilizer, there is
no way to directly restrain its variable’s coefficient from
generating price bubbles. If the strength of this coeffi-
cient can be further exaggerated by its host’s volatility,
then the whole model will become explosive by nature.
Take the CAPM as an example, it can be reduced into
 
im i
if mf
Er rErr
 
 (5)
with all terms to be defined in their usual ways for asset
#i. It is clear that
i directly links to σi especially
when having a positive ρim. Therefore, if the CAPM would
serve as the only asset pricing tool in the market, then
most likely self-destruction could be destined by encour-
aging higher and higher σi.
2. The Asset Pricing System
On one hand, market information contains supply infor-
mation, the opposite is never possible; on the other hand,
uncertainty or risk has to be calculated based on the re-
turn information of an asset, not vice versa. Therefore,
the way to construct the asset pricing system has to be
initiated from the market information of an asset’s risk
2.1. The Risk Pricing Model
For a company i running strictly within a well-diversified
industry T, if its asset’s systematic risk can be expressed
in the form of variance as T
, then its individual risk as
. According to the portfolio theory and its principle
of risk diversification, for variables T and id
r gener-
ating T
 
and id
, respectively, since both of them are
perfectly uncorrelated, there must have
Clearly, this is just an expression presenting the simple
fact that the uncertain rate of return for asset i is the sum
of its systematic and individual risk components. Obvi-
ously, the tradeoff relationship between risk and (ex-
pected) rate of return is identical for T but usually is
not for among different assets. Therefore, the resulted
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tradeoff relationship between risk and (expected) rate of
return per unit of total variance risk must be heterogene-
ous among different assets. As a consequence, Sharpe
ratios obtained from different assets are not comparable
by nature. This is not only a concern between ordinal and
cardinal numbers but also is a question of having hetero-
geneous Sharpe ratios.
Without any doubt, in Equation (6), the behavioral
pattern for variable T or id
r must be quite different. By
definition, the former usually can not but the latter can be
improved by self effort of individual participants in the
market. Furthermore, since id still can be indirectly ob-
served through a difference variable iT
, both behav-
ioral equations for and can thus be presented as
 
i Tid
2id i
where T or iT
is the real systematic or indi-
vidual risk variable for T or id, respectively; eT and
eid are the corresponding demand components and will be
treated as residuals as usual.
After substituting both Equations (8) and (9) into
Equation (6), the outcome is
rR i
in which iTid
. Similar to Equations (3), (4a) and
(4b), having the systematic risk variable T can make the
coefficient 1
here to equal 1, and Equation (10) can
become a risk pricing model as
iT i
 
or an intermediate model linking to the world of funda-
mental analyses as
rR i
 
First of all, the fact that both coefficients in this Equation
(12) have a sum of one can perfectly meet our common
sense when making an either-or choice. Next, since the
coefficient 2i
is usually a positive number smaller
than one, therefore, the sensitivity of a company’s effort
i upon its asset’s expected rate of return
can only be evaluated with conservative manner. In other
words, Equation (12) can itself provide a unique function
of automatic price stabilization and can forcefully make
price movements in the stock market no more too exciting.
Beside Equation (12), a more detailed expression to
additionally deal with the national environment variable
can also be considered and expressed as
rR i
 
It seems that more varieties of risk diversification effects
could be coped with under such an arrangement. However,
since either
or 2
can itself represent a unique
meaning of systematic risk, it must be the case that, eco-
nometrically, 3
cov ,var
ii Ri R
rR R
equals one minus 3
, and 1
equals one minus
the sum of 2i
plus 3i
hence is zero and functionless.
This means that there is no need to apply more environ-
ment variables when the right one has already been used.
However, if the real industrial variable T is not well
diversified, then Equation (13) is still needed in order to
obtain the true
2.2. The Return Pricing Model
Whenever fundamental analysis is concerned, the main
focus is to investigate what and how much a real economic
factor or more factors can affect stock price. Clearly, this
is exactly the function that can be served by Equation
(12). For convenience the real industry variable T will
not be discussed here, only the real company varia
will be explained to show how to design the asset’s
return pricing model.
Basically, for a number of n common stocks issued,
the present value per share should be exactly equal to the
company’s total equity per share
qQn, represent-
ing the part of immediate liquidity value, plus the long-
term profitability per share
, representing the
part of already discounted future value. Accordingly,
in Equation (12) can be transformed into
The problem is, owing to the nonexistence of reliable
long-term company information, the long-term profitabil-
ity per share is still hardly measurable. Since only shorter
than annual forecast reports can be provided by a corpo-
ration, therefore, it is quite incredible for anyone to per-
form any longer term company evaluation without using
reliable information. Under the consideration of applying
only acquirable information, it is thus necessary to trans-
form Equation (14) into a myopic one as
If fixed shares are issued, this equation can also be pre-
sented as
In general, variations in equities mainly come from the
company’s realized profits or losses, plus several finan-
cial activities including new issued shares, purchases of
treasury stocks, distributed dividends and so on. After ex-
cluding those directly involving the company’s financing
activities, Equation (16) can be transformed into (Mand-
leker and Rhee, 1984 [22]):
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 
in which 1i is the uncertain net profits after taxes in
the next period; i is the corresponding current equity;
1i, 1i, 1i
and 1i represent in order the uncertain
total revenues, uncertain total variable costs, fixed costs
and fixed total interest costs; and τ is the corporate tax
rate. Whenever necessary, more details can always be
displayed by following the accounting code.
2.3. The Two-Dimensional Evaluation Model
In a risk-return world, each individual asset is defined as
a set of (σ, E) measures. Without any doubt the most dif-
ficult situation confronting the job of performance evalua-
tion is when both measures increase or decrease simulta-
neously. Clearly, the most convenient way to manage the
job is to merge both definition measures into one number.
Since either measure has its own unique attributes, the
most reliable way to mix them has to be division but not
addition or subtraction, neither multiplication. In terms of
economic meaning, the outcome E
can be interpreted
directly as “the expected rate of return that can be acquired
on average by assuming one unit of the asset’s total risk”.
Furthermore, since it is only rational to bear risk when a
rate of return higher than the opportunity cost can be
expected, E
is thus had to be modified to become
which, in turn, coincides with the Sharpe ratio.
As been explained in Section 2.1, per unit total risks
for different assets are heterogeneous by nature. They
have to be further reshaped in order to be comparable.
Under the hypothesis of lognormal distribution (Jarrow
and Turnbull, 1996 [23]; Limpert, et al., 2001 [24]), after
transforming all rates of return into logarithmic values
and calculating E, σ and rf accordingly, the correspond-
ing cumulative probability measured with respect to rf
can be listed as
Nr E
, representing the asset’s
investment risk, or as
, representing the
possibility of obtaining better outcomes over the oppor-
tunity cost and can be named as the “WINDEX”.
Once the Sharpe ratio can be transformed into the
WINDEX based on the hypothesis of lognormal distribu-
tion, even different assets positioning on the same line
with an identical and positive Sharpe ratio or slope can
be further evaluated according to the stochastic rules of
dominance (SRD). By combining both Sharpe ratio and
the SRD, the outcome is a more realistic approach of
asset evaluation and is called “the two-dimensional rules
of dominance” (Yu, 2007 [25]). As to the outcome of
evaluation, it can be found that assets locating on the
farther upper right part of the line are relatively superior.
A point directly supports the benefit of financing asset
investment especially when having a bull market.
2.4. The Asset Evaluation Model
Based on the way to construct the Sharpe ratio, Modigliani
and Modigliani (1997) [26] suggested that, by maintain-
ing the Sharpe ratio unchanged, that is, by equalizing
Er EEr
 and
 , the linear
trade-off relationship between Δσ and ΔE can be directly
inferred accordingly. However, as been already well ex-
plained with respect to Equations (6) and (7), this kind of
trade-off relationship is non-linear in essence.
As an example, assuming there are two different stocks
having the same Sharpe ratio but different definitions of
. When increment of total variance comes identi-
cally from the individual but not the systematic risk
component, clearly, the resulted trade-off between Δσ
and ΔE for either stock can be different. Not only the
trade-off ratio for individual risk can vary, but also the
ratio between systematic and individual risk components
per unit total variance risk can rarely be the same among
different stocks. Furthermore, even if increment of total
variance can come identically from the systematic risk
component, the trade-off ratio between Δσ and ΔE per
unit of total variance risk will still vary if initial total
variance risks are different.
Accordingly, the way to examine the relationships be-
tween the Sharpe ratio
01 234
Sr r
r and its explicit as well as
implicit components for an asset can only be performed
in an econometric way as
 
 
Based on the hypothesis of lognormal distribution and
calculating all related variables accordingly, it is clear
that, after 1
can be obtained from this Equation (18),
how much changes in Sharpe ratios i
r should affect
changes in expected prices can now be analyzed
 
10 1
The reasonable expected price change can therefore be
measured as
Certainly, if the environment variable T falls short of
the required standard, a more detailed econometric model
that can additionally include national environment vari-
ables G and G
is needed to obtain the true 1
Furthermore, if the company’s equity is the focus, then,
the asset evaluation model in Equation (18) has to be
replaced by an equity evaluation model. In the meantime,
all variables have to be replaced by their corresponding
real representatives in order to link the company’s per-
formance to its equity value.
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2.5. The Two-Factor Discount Model
In essence, the way to apply a standard discount factor
has to be selective. For example, since the risk-free rate
is usually taken as the discount factor in the one-dimen-
sional return world, it must be a two-factor discount model
in a two-dimensional risk-return environment. As been
explained in Section 1.3, with no uncertain discount rate
available in the sense of positive analysis, a normative
one has to be created. The rationale is that if the task of
discounting would be managed without any standard, then
the whole financial market could be filled with maneu-
vered discount factors. As to the way of designing the
needed two-factor discount model, a geometric concept
can be borrowed. That is, to select two different bench-
marks first and then to define accordingly the relative loca-
tions of all other members within the same environment.
In a two-dimensional risk-return environment repre-
senting a certain well diversified asset market, on the
efficient frontier of this market there are not too many
candidates that can be chosen as the two benchmarks
applicable in the two-factor discount model. Other than
the minimum-risk portfolio (u), representing the system-
atic risk common to all individual assets in the market,
only the so-called optimal portfolio (m) can have another
unique feature of having the largest Sharpe ratio hence
the highest possibility to make more than the opportunity
cost. In statistics, different expected rates of return are
not directly comparable, especially when their reliabil-
ities have yet been identified. In order to be applicable in
any evaluation job, they have to be standardized by nor-
malizing their uncertainties. That is, as been explained in
Section 2.3, they have to be transformed into first the
Sharpe ratios and next the WINDEXes.
As a result, for asset i, the expected uncertain discount
rate generally applicable in the market can be
listed as
 
Er Er
th ˆ
Again, with the inclusion of a systematic risk variable u
e outcome of 12
can be guaranteed when
both estimators are calculated as
ˆiu mim
um im
 
222 2
ˆim uiu um
um um1
im um
 
 
 
where βim or βum are the betas in the CAPM meaning.
After redefining β2 to be βi, Equation (21) can be rewrit-
ten as
 
Ed ErEr
 (23)
This equation states that the required expected rate of
return for asset i equals to the sum of a common reward
u for assuming the systematic risk in the market
and a specific reward
Er Er
for assuming
the asset’s individual risk. Contrasting to a similar ex-
pression in the shape of the CAPM, the only difference is
clearly between the systematic risk variable
the opportunity cost rf.
First of all, the inclusion of an opportunity cost will
certainly transform the initial definitive environment into
an investment world. Because, for companies, financing
and cash management are selective but not compulsory
decisions. Therefore, by introducing the opportunity cost
into the model, the CAPM should be more appropriate to
deal with the job of asset investment instead of asset pric-
ing. Second, according to the CAPM, it is always possi-
ble that an asset’s total variance risk can be smaller than
the non-diversifiable systematic risk in the market. Ob-
viously, this can never be true in the world of definition.
Finally, although there are mixed empirical findings
regarding the CAPM (Blume and Friend, 1973 [27]; Fama
and French, 2004 [28]), general conclusions can still be
summarized in the followings. When the model’s beta is
approximately equal to one, its pricing outcome can be
unbiased. However, when the beta is far greater or far
smaller than one, its pricing result will be significantly
under- or over-estimated, respectively. In contrast, as
long as
u, also capable of representing the average
performance of the market, can outperform the opportu-
nity cost and when βum can only be positive and smaller
than one, it is easy to see that either problem of under- or
over-estimation with the CAPM can be automatically
corrected in Equation (23).
3. Conclusions
Within the two-dimensional risk-return world, each indi-
vidual asset can be defined as a set of measures.
Basically, understanding how each measure (or both) can
be affected by either risk or return pricing factors (or
both) will only accomplish the task partially. Since how
much can this effect exactly change the asset price is the
final concern, therefore, three additional jobs must be
done accordingly. The first relates to the way of identi-
fying the direction of price change caused by this effect;
the second, the way to exactly quantify the expected
price change; and the third, the way to calculate the pre-
sent value of expected price change. All together, it is
clear that only an asset pricing system, including proba-
bly at least five different models, can reasonably handle
the job of asset pricing.
In our common sense, we usually separate the part of
our life that can be under our control from the other part
that can not. In addition, we also try very hard to break
loose current environment constraints by moving outward
Copyright © 2012 SciRes. ME
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to a larger or even a new environment. Undoubtedly, so
far, no one can leave the earth. Therefore, discarding the
necessary environment variable is equivalent to disregard-
ing the constraint and opportunity conditions confronting
our life. As a consequence, this negligence can most likely
overestimate any contribution from self effort.
Traditionally, the right environment variable needed in
the job of asset pricing can not be precisely presented
based on the principle of supply and demand. This is
because the taxonomy of micro versus macroeconomics
can only define the environment in a very rigid manner.
Another fact is that the necessary environment variable
has to be presented case by case. Fortunately, the portfo-
lio theory can have the needed flexibility and framework
to work out the right environment variable for the job of
asset pricing. In general, within the same environment,
systematic risk is the part of constraints that is not under
any investor’s control, hence can represent the common
environment condition necessary for the job of asset pric-
ing. Accordingly, this concept plus another one that an
asset’s uncertainty has to be measured based on its return
information can therefore provide two very important clues
to construct the asset pricing system. Furthermore, since
fundamental analysis is especially concerned in the job
of asset pricing, therefore, the principle of supply of de-
mand also can not be absent.
After bringing the needed environment variable into
the job of asset pricing, the pricing mechanism can thus
be shown to be very classical. That is, the market, to be
represented as the systematic risk or the environment
variable, can automatically restrain the asset price. Ac-
cordingly, all asset markets will probably be no more
attractive as they are now.
Historically, the financial market was established to
associate our real economy, it should not become an am-
bitious place to stimulate our greedy. Basically, the asset
market can itself do the right job of asset pricing if its
way of doing things is correct. Without any doubt, our
respectful earlier workers have already contributed their
very best effort in economics. Therefore, as followers, it
is our responsibility to provide necessary improvements
in order to make a better future world.
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