1. society
2. work
3. unemployment

African Journal of Business Management Vol. 5(11), pp. 4561-4572, 4 June, 2011
Available online at http://www.academicjournals.org/AJBM
DOI: 10.5897/AJBM10.1082
ISSN 1993-8233 ©2011 Academic Journals
Full Length Research Paper
How to diminish the investment systematic risk?
Ming-Yuan Hsieh1, Chung-hsing Huang2, Tzung-Ming Yan3, Wen-Ming Wu4, and
Chih-Sung Lai5
1
Department of International Business, National Taichung University of Education, Taiwan.
2
College of Management, National Taiwan University, Taiwan.
3
Department of Insurance, Chao-Yang University of Technology, Taiwan.
4
Department of Distribution Management, National Chin-Yi University of Technology, Taiwan.
5
Department of International Business, National Taichung University of Education, Taiwan.
Accepted 8 April, 2011
st
Starting in the 21 century and the challenges of the global economy, investors need to take vigorous
tactics to face the competition for globalization. Financial investment environment changes with each
passing day, investors’ satisfaction are more and more discerning, and market demands can fluctuate
unpredictably. While facing the constant changes of the global financial markets, it is important to know
how to break through the current situation, maintain an advantage and continuously make a profit.
Many investors have the pressure of competing to positively adapt, to form a competitive investment
strategy, and to have a great project management strategy. The traditional business investment is not
enough to deal with the issues regarding new and various economic challenges. This study will focus
on answering the topic of this research: Reducing systematic risk through portfolio theory and
macroeconomic model. This dissertation attempts to answer the main question and secondary issues
and stays focus on the comparison of these industrial regions consisting of ten industrial regions
comprised of two developed industrial regions (USA and Japan) and eight high-growth industrial
regions (Four Asia Tigers and BRIC). Significantly, this research deals with quantitative and empirical
analysis of the prominent features and the essential conditions for portfolio theory and macroeconomic
model and to evaluate the relative strengths and weaknesses of twelve stock markets of the ten
industrial regions by examining three hypotheses. The discussion of the invested systematic risk index
among ten industrial regions is presented by measuring competitive comparison of the first hypotheses
under the factor analysis through the use of the principle component method of factor analysis. Further,
in terms of second hypothesis, measurement of the ten industrial regions competitive comparison was
addressed by analyzing the macroeconomic indicators data under the rotated method (Varimax
method). Lastly, in terms of assessing the first and second macroeconomic models (first hypothesis
and second hypothesis), the measurement focused on the scenario analysis and empirical analysis
through the use of the fluctuate percentage of stock price index and stock market capitalization of
twelve stock markets from 2004 to 2008.
Key words: Portfolio theory (PT), systematic risk, macroeconomic model, factor analysis, capital asset pricing
model (CAPM).
INTRODUCTION
Many investors have confronted more challenges due to
the rapid capricious development of the world economic
*Corresponding author. E-mail: [email protected]. Tel:
+886-975-118-922.
st
and financial investment environment. Starting in the 21
century and the challenges of the global economy, investors need to take vigorous tactics to face the competition
for globalization. Financial investment environment
changes with each passing day, investors’ satisfaction are
more and more discerning, and market demands can
fluctuate unpredictably. While facing the constant changes
4562
Afr. J. Bus. Manage.
Invested risks
Unsystematic risk line
Systematic risk line
Invested time
Figure 1. Invested risks relationship.
constant changes of the global financial markets, it is
important to know how to break through the current
situation, maintain an advantage and continuously make
a profit. Many investors have the pressure of competing
to positively adapt, to form a competitive investment
strategy, and to have a great project management strategy. The traditional business investment is not enough to
deal with the issues regarding new and various economic
challenges (Beck et al., 2000). This study will focus on
answering the topic of this research: Reducing systematic
risk through portfolio theory and macroeconomic model.
Further, in order to face the countermeasures regarding
the rapid change in the global and domestic industrial
structures, and the service challenges posed by industrial
regions with rapid economic developing growth, a large
number of global investors have struggled to find out the
resolutions and approaches to subside the invested risks
and to obtain the invested profits from these rapidly
expanding financial markets. In terms of the most efficient
and effective analytical method, the portfolio theory and
the macroeconomic model are the most directly analytical
approach.
In this research, the main research theory is to concentrate on the portfolio theory and the core methodology is
focusing on the macroeconomic model. Further, this
research is going to utilize the factor analysis, scenario
analysis and empirical analysis to select the most impact
of macroeconomic indicators in order to minimize the
invested risks and maximize the invested profits. To take
one step ahead, the invested risks are categorized into
two types. First type is systematic risk (un-diversifiable
risk) which is unavoidable by any invested portfolios and
strategies. The second type is unsystematic risk
(diversifiable risk) which can be avoided more by various
invested portfolios and strategies as expressed in Figure
1.
In addition, the twenty four macroeconomic indicators
from various authorized official government statistic departments and the four academic professional economic
institutes are considered to be the most efficient and
effective macroeconomic model after the consideration of
a large number of related lecture. All research methodology trend to the pared-down measurement procedures
in order to offer the global readers and investors, a
simplified economic and financial paper. Therefore, this
research concentrated on discussing the relationships
discussing the relationships among the expected rate of
return, realized rate of return and systematic risk priority
number. The unsystematic risk is avoided through
diversified invested strategies or activities. Further, this
research created the connection between expected rate
of return and systematic risk in order to achieve the
perfect hedge. In order to define the main concept of this
research, the preliminary and primary research questions
consist of:
(1) how does this research achieve systematic risk
aversion for investors in financial markets and what
evidence indicate that investors are generally systematic
risk averse through macroeconomic model (Arrow, 1951);
(2) what is meant by the covariance between economic
factors in macroeconomic model and how do this
research
measure
covariance
between
each
macroeconomic indicator (Aizenman and Pinto, 2001);
(3) how to minimize the systematic risk in the portfolio
theory through creating the efficient comprehensive
macroeconomic model (Beck and Loayza, 2000).
The first research question aims at creating the effective
macroeconomic model by the collected macroeconomic
factors though factor analysis in order to achieve risk
averse. The second and third research questions relate to
finding the more outstanding covariance and correlation
between macroeconomic factors through Varimax method
(Varimax rotation) in order to adjust the macroeconomic
model to match the invested market situation. The forth
and final research question is the primary purpose of this
research which is to achieve perfect hedge.
MATERIALS AND METHODOLOGY
In terms of examining the complexity and uncertainty challenges
surrounding portfolio theory and macroeconomic model, Beck et al.,
(2000) argued that five years of data was analyzed along with multimethods and multi-measures field research in order to achieve a
retrospective cross-sectional analysis of industrial regions (Beim
and Charles, 2004). These industrial regions consisted of ten
industrial regions comprised of two developed industrial regions
(USA and Japan) and eight high-growth industrial regions (Four
Asia Tigers and BRIC) (Chang, 2007). This chapter not only characterizes the overall research design, empirical contexts, and research sample and data collection procedures, but is also designed
to compare the ten industrial regions. Additionally, this chapter
Hsieh et al.
Fundamental Investment Concept of John R. Hicks
(1931) to Disperse the Investment Risk
Initial Concept of Macroeconomic Model of
Bautista (1988) and Capros et al. (1990)
Portfolio Theory of
Harry Markowitz
(1959)
Efficient Portfolio
(CML) of Harry
Markowitz (1959)
Macroeconomic
Model (MEM)
Computable General
Equilibrium (CGE)
CAPM of William
Sharpe (1963)
Efficient Portfolio
SIM of William
Sharpe (1963)
MEM of Challen
and Hagger (1983):
Keynes–Klein
(“KK”) model,
Phillips–Bergstrom
(“PB”) model,
Walras–Johansen
(“WJ”) model,
Walras–Leontief
(“WL”) and Muth–
Sargent (“MS”)
model.
Social Accounting
Matrix (“SAM”),
CGE accentuates
three analytical
factors (labor,
manufacture product
and financial
market) in the
macroeconomic
model (Keynes,
1936)
Risks are dispersed
two categories
including systematic
risk and unsystematic
risk.
Arbitrage Pricing Model of Stephen
Ross (1976)
Unsystematic Risk
Different Invested
Objectives and
Portfolios to Decrease
or Eliminate
Unsystematic Risks
4563
Systematic Risk
Analyzing
Macroeconomic
Factors in order to
minimize the
Systematic Risk
Modern Macroeconomic Model
Utilizing Factor Analysis
to Produce the Effective
Macroeconomic Model
for Analytical Industrial
regions
Scenario Analysis & Empirical Analysis
Assessment on the Beta Priority Numbers of the Twelve
Stock Markets of Ten industrial regions
Figure 2. Research design framework.
chapter inductively generates a few economic theories and follows
by qualitative and quantitative data that was collected from many
official government statistic departments and academically
economic institutions for this analysis.
Research design
The fundamental research design in this research is based on
combining the portfolio theory (Hicks, 1931) and macroeconomic
model in order to create the efficient and effective macroeconomic
model (Hicks, 1939) to measure the beta priority number (beta
coefficient) in the capital asset pricing model (CAPM) (Dufey and
Ian, 1984). Further, in order to produce the macroeconomic model,
this research follows earlier procedure of the research theory
development framework as expressed in Figure 2 to build the
research design framework as expressed in Figure 3. This research
design framework not only focuses on the application of the
portfolio theory, the macroeconomic model, and the assessment
method (Sharpe, 1964), but also concentrates on the
macroeconomic environment for industrial regions that is measured
by some major statistic macroeconomic factors (Shaw, 1973) that
included GDP, Economic Growth Rate, Import, Export, Investment
Environment, and Financial Trade which are from official
government statistic departments such as Taiwan’s Bureau of
Foreign Trade (MOEA) and USA’s Federal Reserve Board of
Governors and academic economy institutions such as the IMD,
World Economic Forum (WEF), Business Environment Risk
Intelligence (BERI), and Economist Intelligence Unit (EIU).
Nevertheless, there are two potential macroeconomic factors that
could have an impact on the analysis but are not discussed in this
research design due to these factors being difficult to be quantified
and measured (Shi, 2002). Accordingly, this research also makes
an assessment on the strengths and weaknesses of the ten
industrial regions for the most beneficial invested markets based on
the Invested of Systematic Index (ISRI) and the Rotated Invested of
Systematic Risk Index (RISRI) which will produce in fist macroeconomic model (first hypothesis) and second macroeconomic
4564
Afr. J. Bus. Manage.
Identifying Research Topic
Collecting Related Lecture
1.Outstanding papers and journals regarding research methodology
2.Fundamental concept of portfolio theory
3.Relevant empirical research paper regarding portfolio theory
4.Elemental concept of macroeconomic model
5.Relevant empirical research paper regarding macroeconomic model
6.Review of relevant references
Develop and Apply
Measuring Invested Systematic Risk Indexes
- Utilizing Factor Analysis to Produce the
Effective Macroeconomic Model for Analytical
Industrial regions
Comparison of Invested Systematic Risk
among ten industrial regions (USA,
Japan, Four Asia Tigers and BRIC)
Bring the annual growth rate of
ICI into CAPM model in order
to calculate Beta Priority
Numbers of twelve stock
markets of ten industrial
regions
Factor
Analysis
Macroeconomic
Model
Portfolio Theory
(CAPM)
Scenario Analysis &
Empirical Analysis
Adjustment
Verification of Analysis and Explanation
Conclusion and Recommendations
Figure 3. Research methodological process.
macroeconomic model (second hypothesis) in this research (Duo et
al., 2007).
Research methodology
In the research framework, the observations and investigations of
this research are according to the established insights from the
literatures on the portfolio theory and macroeconomic model and
the research methodologies to assess high potential of the ten
industrial regions of becoming the most profitable invested market.
In terms of first step of the statistic measurement, the “Principle
Component Method” of “Factor Analysis” was utilized to measure
and test the first hypothesis (Macroeconomic Model MeasurementCovariance) (Tian and wan, 2004) in order to uncover the solution
to the first research question. This step is to take advantage of 5
years of collected data from the ten industrial regions to build
competition of marketed market indexes (CMI) based on twenty-five
macroeconomic factors. Then, in terms of the second step of
statistic measurement, the “Principle Component Method - Varimax
Method” of “Factor Analysis” was exploited to measure the second
hypothesis (Macroeconomic Model Rotation Measurement Correlation) in order to find out the solution to the second and third
research questions (Tong, 2003). The measure step is to adjust the
ISRI through the rotation method of the collected data. Ultimately, in
terms of the third statistic measurement, the third hypothesis
(Maximize Return Rate – Scenario analysis and empirical analysis)
is measured by the scenario analysis and empirical analysis to
resolve the final two research questions (Yang et al., 2005). The
statistic measurement in this step is to utilize the ISRI annual
growth rate to be the market portfolio return rate (Rm), the fluctuated
rate of stock index to be the expected return rate of C invested
Hsieh et al.
portfolio ( E ( Rc ) ) and government bonds rate to be risk-free
returned rate of invested objective (capital asset) to put into the
CAMP in order to calculate the Beta Priority Numbers (Beta
Coefficient) (Fama, 1968) for the analytical twelve stock markets
(USA New York, USA NASDAQ, Japan Tokyo, Taiwan, Singapore,
Korea, Hong Kong, Brazil, Russia, India, China Shanghai and
China Shenzhen) (Graciela et al. 2002) of ten industrial regions including USA, Japan, Taiwan, Singapore, Korea, Hong Kong, Brazil,
India, Russia and China. Correspondingly, quantitative statistical
methods provided the required evidence for the results in this study
(Jao, 2002). The consequence is a grounded, more accomplished
and indicative theory of portfolio theory in Figure 3 (Hsieh, 2009).
Research specification of research sample and data collection
4565
different theories. As noted in the lecture review of the elemental
concept of the portfolio theory, the invested risks are always the
key-point to impact the invested portfolio researches of Hicks
(1931) and Markowitz (1959). Since Sharpe (1964) created the
CAPM model, the invested risks began to be quantified as the
named (beta coefficient) and associated with the expected return
rate of the C invested portfolio ( E ( Rc ) ), market portfolio return
rate of C invested objective ( E ( Rm ) ), and risk-free returned rate
of invested objective (capital asset) ( R f ). Further, the invested
risks are classified as systematic risk and unsystematic risk. To take
the next step, in terms of systematic risk, Stephen (1976) created
the APM model that explicitly expressed the systematic risk result
from multiple economic factors. In order to decrease or eliminate
the systematic risk, it is important to create the most directly
efficient mathematical equation to measure the systematic risk for
financial and economic researches with a long time horizon. Hence,
macroeconomic model is the most compatible for measuring
systematic risk due to the analytical economic factors that are considered in the macroeconomic model. Further, through a series of
statistic calculation and analysis, the correlation, standard deviation
and covariance among measured factors are able to be directly
expressed. This is groundbreaking measurement for economic
scholars and financial researchers. Therefore, in this research,
factor analysis is used as a statistic measurement to create the
most effective macroeconomic model. Ultimately, this study utilized
scenario analysis and empirical analysis to verify the model. The
research theory development framework is expressed in Figure 4.
In Figure 4, in terms of research theory, the relationship of the
three theories (portfolio theory, macroeconomic model (MEM) and
assessment method) in this research are briefly presented through
the related lecture review study in detail. The first research theory
application of this research is to maximize expected return rates
and to minimize systematic risk in the invested objectives and
portfolios through utilizing and combining the portfolio theory and
macroeconomic model. The second research theory exercise of this
research is to verify the macroeconomic model correlation among
analytical macroeconomic factors through utilizing and combining
the portfolio theory and macroeconomic model. The ultimate
research theory practice of this research is to examine the stock
market of ten industrial regions in order to verify the macroeconomic model through portfolio theory, through utilizing and combining
the macroeconomic model and the assessment method (scenario
analysis and empirical analysis).
In terms of the representativeness and correction of the efficient
macroeconomic model though factor analysis, the research sample
must collectively and statistically constrain all impacted macroeconomic factors as far as possible. Further, the sample in this
research contains large and complicated macroeconomic factors
that are collected from two authoritative and professional channels
(Greenwood and Jovanovic, 1995). One is the official government
statistic departments and the other is from the four economic
statistics for macroeconomic factors data, including IMD World
Competitiveness Yearbook (WCY)-National Competitive Index
(NCI), World Economic Forum (WEF)-Global Competitiveness
Index (GCI), Business Environment Risk Intelligence (BERI)Business Environment, and Economist Intelligence Unit (EIU)
Business Environment. The content of research sample consists of
the vertical range and horizontal scope. Specifically, in terms of the
validity and reliability of collected data, this study focused on the
three important measuring aspects (Hsu and Tang, 2002): (1)
Content validity, which was judged subjectively; (2) construct
validity, which was examined by factor analysis and (3) reliability,
which concluded that the seven measures of quality management
have a high degree of criterion-related validity when taken together.
Given the sensitive nature of research data, time was devoted to
cite the impacted macroeconomic factors of academic institutions
(James, 2002). A database of all macroeconomic factors was
created using public and primary economic reports including press
releases, newspapers, articles, journal articles and analyzing
reports. These sources provided a macroeconomic-level
understanding of the motivations and objectives, basic challenges,
target characteristics, financial market contexts and general sense
regarding portfolio theory and macroeconomic model. Otherwise,
the economic indicators data includes the annual economic
indicators data (leading and lagging indicators) (Joe et al., 2002).
The vertical range stretch over five years from 2004 to 2008 and
the horizontal scope consists of twelve stock markets (USA New
York, USA NASDAQ, Japan Tokyo, Taiwan, Singapore, Korea,
Hong Kong, Brazil, Russia, India, China Shanghai and China
Shenzhen) of ten industrial regions including USA, Japan, Four
Asia Tigers and BRIC. With regard to the analysis method,
whichever gains a higher score will be given full marks and other
methods are in accordance with relative value to decide who wins
the score (James and Kam, 2007). Table 1 utilized these macroeconomic factors as the measure items of analysis (James, 2007).
In conclusion, this chapter summarizes the findings and
consequences of this thesis research and discusses the
study’s limitations. The contributions of this research
utilize appropriate theories, methods and practices and
follow the future direction in the development of
researching systematic risk through the portfolio theory
and macroeconomic model.
Research theory
Conclusion
The fundamental research design in this research is based on
combining the portfolio theory and macroeconomic model in order
to create the efficient and effective macroeconomic model to
measure the beta priority number (beta coefficient) in CAPM. The
anticipatory problem is how to combine these two completely
After the measurement of this research, the five research
questions are resolved in detail, which are outstanding
findings for global investors who desired to invest in
these twelve stock markets from ten industrial regions.
RESULTS
4566
Afr. J. Bus. Manage.
Table 1. Research sample contents of published data.
Measure Institution
Official Government Statistic
Department
Measure Items
The Economic Growth Rate (%) (EG) - A positive change in the level of production of goods
and services by a country over a certain period of time. Nominal growth is defined as
economic growth including inflation, while real growth is nominal growth minus inflation.
The Gross National Product (GDP) per capital (USD) (GDPPC) - GNP is divided by total
national population.
The Inflation Rate (consumer prices) (IRCP) –This entry furnishes the annual percent
change in consumer prices compared with the previous year's consumer prices.
The National Imports (Billions, USD) (IP) – This entry provides the total US dollar amount of
merchandise imports on a c.i.f. (cost, insurance, and freight) or f.o.b. (free on board) basis.
These figures are calculated on an exchange rate basis, i.e., not in purchasing power parity
(PPP) terms.
The National Exports (Billions, USD) (EP) –This entry provides the total US dollar amount of
merchandise exports on an f.o.b. (free on board) basis. These figures are calculated on an
exchange rate basis, that is, not in purchasing power parity (PPP) terms.
The GDP (purchasing-power parity) (Billions, USD) (GDPPP) – This entry gives the gross
domestic product (GDP) or value of all final goods and services produced within a nation in a
given year. A nation’s GDP at purchasing power parity (PPP) exchange rates is the sum
value of all goods and services produced in the country valued at prices prevailing in the
country. This is the measure most economists prefer when looking at per-capita welfare and
when comparing living conditions or use of resources across countries.
The National Current Account Balance (Billions, USD) (NCAB) – This entry records a
country’s net trade in goods and services, plus net earnings from rents, interest, profits, and
dividends, and net transfer payments (such as pension funds and worker remittances) to and
from the rest of the world during the period specified. These figures are calculated on an
exchange rate basis, that is, not in purchasing power parity (PPP) terms.
The National Reserves of foreign exchange and gold (Billions, USD) (NRFEG) –This entry
gives the dollar value for the stock of all financial assets that are available to the central
monetary authority for use in meeting a country’s balance of payments needs as of the enddate of the period specified. This category includes not only foreign currency and gold, but
also a country’s holdings of Special Drawing Rights in the International Monetary Fund, and
its reserve position in the Fund.
The Investment (gross fixed) of GDP (%) (IPY) - This entry records total business spending
on fixed assets, such as factories, machinery, equipment, dwellings, and inventories of raw
materials, which provide the basis for future production. It is measured gross of the
depreciation of the assets, that is, it includes investments that merely replaces worn-out or
scrapped capital.
The Industrial production growth rate (%) (IPG) –This entry gives the annual percentage
increase in industrial production (includes manufacturing, mining, and construction).
The Unemployment Rating (%) (UE) - An economic condition marked by the fact that
individuals actively seeking jobs remain not hired. Unemployment is expressed as a
percentage of the total available work force.
The Consumer Price Index (CPI) - An inflationary indicator that measures the change in the
cost of a fixed basket of products and services, including housing, electricity, food, and
transportation.
The Interest Rate (%) (IR) - A rate which is charged or paid for the use of money. An interest
rate is often expressed as an annual percentage of the principal.
The Exchange Rate (ER) –The price of one currency expressed in terms of another
currency.
IMD World Competitiveness
Yearbook (WCY)
The Comprehensive Index of Economic Performance from World Competitiveness Yearbook
by the International institute for Management Development (IMDCEP)
World Economic Forum (
WEF )
The Comprehensive Index of Business Competitiveness form World Economic Forum
(WEFCBC)
The Basic Requirements of Global Competitiveness form World Economic Forum (WEFGR)
The Efficiency Enhancers of Global Competitiveness form World Economic Forum (WEFGE)
The Innovation Factors of Global Competitiveness form World Economic Forum (WEFIF)
Hsieh et al.
4567
Table 1. Contd.
Business Environment Risk
Intelligence ( BERI )
The Comprehensive Index of Profit Opportunity Recommendation of Global Business
Environment Index (BERIPOR)
The Operation Risk of Global Business Environment Index (BERIOR)
The Policy Risk of Global Business Environment Index (BERIPR)
The Exchange Risk of Global Business Environment Index (BERIER)
Economist Intelligence Unit ( The Comprehensive Index of Business e-readiness Environment from Economist
Intelligence Unit (EIUER)
EIU )
'
%
!
$! " % #
(! ) *
'
)
# + , -
" #
*
'
1
.'
/ 0
2&
0!
.3
0
!
'
"
42
&
0
.
)
*
.
)
Figure 4. Research theory development framework.
Further, global investors are able to forecast the variation
of fluctuating systematic risk by measuring the ISRI and
RISRI through the combined utilization of CAPM
( E ( RStock ) = R f + β Stock × E ( RMin ) − R f ), macroeconomic
model and rotated macroeconomic model. In this
research, the invested systematic risk index (ISRI, first
hypothesis) and the rotated invested systematic risk
index (RISRI, second hypothesis) of the ten industrial
regions are obviously measured which created the
related macroeconomic model (1) and rotated
macroeconomic model (2) as expressed:
Assumption: All collected data are correct and the formula
inaccuracy is given and constant.
Invested Systematic Risk Index (Competition of invested
financial markets) (df)
= Academic Economic Institute Score Factor + Economic
Production Factor + Economic Trade Factor + Economic
Exchange Rate Factor + Economic Interest Rate Factor +
Economic Consumer Price +e (formula inaccuracy)
= (WEFCBC, WEFGE, BERIFOR, BERIOR, GDPPC,
EIUER, WEFGR, BERIPR, WEFIF, IMDCEP and
BERIER) + (IPY, NRFEG and IPG) + (GDPPP, IP and
EP) + (ER) + (IR) + (CPI) +e (formula inaccuracy)
4568
Afr. J. Bus. Manage.
=0.957*WEFCBC+0.951*WEFGE+0.913*BERIFOR+0.89
8*BERIOR+0.891*GDPPC+0.882*EIUER+0.881*WEFGR
+0.881*BERIPR+0.868*WEFIF+0.833*IMDCEP+0.778*
WEFGR, GDPPC, EIUER, IMDCEP, BERIER and
WEFIF) + (GDPPP, IP and EP) + (IPY, IPG and EG) +
(NRFEG) + (CPI) + (ER) +e (formula inaccuracy)
BERIER+0.857*IPY+0.792*NRFEG+0.729*IPG
+0.954*GDPPP+0.852*IP+0.677*EP+0.807*ER+0.456*IR
+0.527 *CPI+ e (formula inaccuracy) (1)
=0.954*BERIFOR+0.931*BERIPR+0.908*BERIOR+0.893
*WEFCBC+0.886*WEFGE+0.861*WEFGR+0.843*
i. The Gross National Product (GDP) per capital (USD)
(GDPPC).
ii. The National Imports (Billions, USD) (IP).
iii. The National Exports (Billions, USD) (EP).
iv. The GDP (purchasing-power parity) (Billions, USD)
(GDPPP).
v. The National Reserves of foreign exchange and gold
(Billions, USD) (NRFEG).
vi. The Investment (gross fixed) of GDP (%) (IPY).
vii. The Industrial production growth rate (%) (IPG).
viii. The Consumer Price Index (CPI).
ix. The Interest Rate (%) (IR).
x. The Exchange Rate (ER).
xi. The Comprehensive Index of Economic Performance
from World Competitiveness Yearbook by the
International Institute for Management Development
(IMDCEP).
xii. The Comprehensive Index of Business Competitiveness form World Economic Forum (WEFCBC).
xiii. The Basic Requirements of Global Competitiveness
form World Economic Forum (WEFGR).
xiv. The Efficiency Enhancers of Global Competitiveness
form World Economic Forum (WEFGE).
xv. The Innovation Factors of Global Competitiveness
form World Economic Forum (WEFIF).
xvi. The Comprehensive Index of Profit Opportunity
Recommendation of Global Business Environment Index
(BERIPOR).
xvii. The Operation Risk of Global Business Environment
Index (BERIOR).
xviii. The Policy Risk of Global Business Environment
Index (BERIPR).
xix. The Exchange Risk of Global Business Environment
Index (BERIER).
xx. The Comprehensive Index of Business e-readiness
Environment from Economist Intelligence Unit (EIUER).
Assumption: All collected data are correct and the formula
inaccuracy is given and constant.
Rotated Invested Systematic Risk Index (Competition of
invested financial markets) (df)
= Academic Economic Institute Score Factor + E
Economic Trade Factor + Economic Profit Factor +
Economic Reserves Factor + Economic Consumer Price
Index Factor + Economic Exchange Rate Factor
= (BERIFOR, BERIPR, BERIOR, WEFCBC, WEFGE,
GDPPC+0.835*EIUER+0.799*IMDCEP+0.796*WEFIF+0.
778*BERIER+0.959*GDPPP+0.984*IP+0.688*EP+0.889*
IPG+0.948*IP+0.751*EG+0.91*NRFEG+0.884*CPI+
0.972*ER +e(formula inaccuracy) (2)
i. The Economic Growth Rate (%) (EG).
ii. The Gross National Product (GDP) per capital (USD)
(GDPPC).
iii. The National Imports (Billions, USD) (IP).
iv. The National Exports (Billions, USD) (EP).
v. The GDP (purchasing-power parity) (Billions, USD)
(GDPPP).
vi. The National Reserves of foreign exchange and gold
(Billions, USD) (NRFEG).
vii. The Investment (gross fixed) of GDP (%) (IPY).
viii. The Industrial production growth rate (%) (IPG).
ix. The Consumer Price Index (CPI).
x. The Exchange Rate (ER).
xi. The Comprehensive Index of Economic Performance
from World Competitiveness Yearbook by the
International institute for Management Development
(IMDCEP).
xii. The Comprehensive Index of Business Competitiveness form World Economic Forum (WEFCBC).
xiii. The Basic Requirements of Global Competitiveness
form World Economic Forum (WEFGR).
xix. The Efficiency Enhancers of Global Competitiveness
form World Economic Forum (WEFGE).
xx. The Innovation Factors of Global Competitiveness
form World Economic Forum (WEFIF).
xxi. The Comprehensive Index of Profit Opportunity
Recommendation of Global Business Environment Index
(BERIPOR).
xxii. The Operation Risk of Global Business Environment
Index (BERIOR).
xxiii. The Policy Risk of Global Business Environment
Index (BERIPR).
xxiv. The Exchange Risk of Global Business Environment
Index (BERIER).
xxv. The Comprehensive Index of Business e-readiness
Environment from Economist Intelligence Unit (EIUER).
Hence, the Invested Systematic Risk Index (ISRI), the
Rotated Invested Systematic Risk Index RISRI, the
Growth Rating of the Invested Systematic Risk Index,
and the Growth Rating of the Rotated Invested
Systematic Risk Index of each of the ten industrial
regions are presented in Table 2. To take the further step,
the five-year beta priority numbers of the twelve stock
markets (USA New York, USA NASDAQ, Japan, Taiwan,
Singapore, Korea, Hong Kong, Brazil, Russia, India,
Hsieh et al.
4569
Table 2. The Invested systematic risk index of the related macroeconomic model (1) and rotated macroeconomic model (2) of factor analysis from 2004 to 2008.
USA 2004
USA 2005
USA 2006
USA 2007
USA 2008
JAPAN 2004
JAPAN 2005
JAPAN 2006
JAPAN 2007
JAPAN 2008
TAIWAN 2004
TAIWAN 2005
TAIWAN 2006
TAIWAN 2007
TAIWAN 2008
SINGAPORE 2004
SINGAPORE 2005
SINGAPORE 2006
SINGAPORE 2007
SINGAPORE 2008
KOREA 2004
KOREA 2005
KOREA 2006
KOREA 2007
KOREA 2008
HONG KONG 2004
HONG KONG 2005
HONG KONG 2006
HONG KONG 2007
HONG KONG 2008
BRAZIL 2004
BRAZIL 2005
BRAZIL 2006
BRAZIL 2007
Invested systematic risk
index of the related
macroeconomic model (1)
47939.05
50829.85
53582.36
55452.83
57553.24
30578.81
32269.18
34259
35928.98
37330.76
22895.45
24564.83
26429.42
28424.24
29692.29
36420.57
39479.08
42671.31
45346.12
47350.66
20210.04
21547.26
23266.19
24958.46
26395.33
29752.28
32750.99
35714.21
38808.21
40751.45
8456.545
9044.654
9296.606
9921.351
Rotated invested systematic
risk index of the related
macroeconomic model (1)
47452.32
50476.59
53399.25
55332.69
57523.32
29678.99
31352.22
33336.81
35016.01
36426.45
21885.17
23524.94
25309.2
27234.41
28442.72
34631.95
37566.93
40631.67
43236.08
45142.68
19636.25
20934.21
22606.01
24273.97
25685.36
28453.7
31345.82
34170.09
37158.84
39027.63
8129.662
8707.226
8964.129
9582.526
Growth rating of invested
Growth rating of rotated invested
systematic risk index of the related systematic risk index of the related
macroeconomic model (%) (1)
macroeconomic model (%) (1)
5.69
5.99
5.69
5.99
5.14
5.47
3.37
3.49
3.65
3.81
5.24
5.34
5.24
5.34
5.81
5.95
4.65
4.80
3.76
3.87
6.80
6.97
6.80
6.97
7.05
7.05
7.02
7.07
4.27
4.25
7.75
7.81
7.75
7.81
7.48
7.54
5.90
6.02
4.23
4.22
6.21
6.20
6.21
6.20
7.39
7.40
6.78
6.87
5.44
5.49
9.16
9.23
9.16
9.23
8.30
8.27
7.97
8.04
4.77
4.79
6.50
6.63
6.50
6.63
2.71
2.87
6.30
6.45
4570
Afr. J. Bus. Manage.
Table 2. Contd.
BRAZIL 2008
RUSSIA 2004
RUSSIA 2005
RUSSIA 2006
RUSSIA 2007
RUSSIA 2008
INDIA 2004
INDIA 2005
INDIA 2006
INDIA 2007
INDIA 2008
CHINA 2004
CHINA 2005
CHINA 2006
CHINA 2007
CHINA 2008
11010.61
11265.09
12433.47
13918.89
15635.57
17398.09
5955.555
6448.421
7086.197
8015.897
6055.779
10686.48
12260.32
14676.83
16902.61
15107.45
10675.02
10840.45
11980.63
13445.71
15157.75
16928.12
5917.939
6418.842
7069.151
8065.254
6207.333
11005.24
12748
15261.61
17641.32
16007.7
9.89
9.40
9.40
10.67
10.98
10.13
7.64
7.64
9.00
11.60
-32.37
12.84
12.84
16.46
13.17
-11.88
10.23
9.52
9.52
10.90
11.29
10.46
7.80
7.80
9.20
12.35
-29.93
13.67
13.67
16.47
13.49
-10.21
Source: SPSS Data Analysis.
China Shanghai and China Shenzhen) were taken
from the ten industrial regions (USA, Japan,
Taiwan, Singapore, Korea, Hong Kong, Brazil,
Russia, India and China) through CAPM
( E ( RStock )
= R f + β Stock × E ( RMin ) − R f
)
(Kindleberger, 1972) of portfolio theory and
macroeconomic
model
(1)
and
rotated
macroeconomic model (2) as detailed in earlier.
RESEARCH LIMITATIONS
Despite the measuring significance of all the
consequences, this study comes with some
research limitations as expected (Chuang and
Hsu, 2003). The most apparent of these
limitations is the generalization of the findings.
The sample consisted of 5 years of economic
indicators data for the ten industrial regions from
2004 to 2008 with related macroeconomic factors.
Further, these macroeconomic factors were
collected from the official government statistic
departments and four main academic economic
institutions: IMD World Competitiveness Indexes
(WCY); World Economic Forum Indexes (WEF);
Business Environment Risk Intelligence (BERI);
and Business Information Unit (BIU). For all that,
the conclusions of this research are not able to
take into entire consideration other macroeconomic sectors (for example, political, legal, technology) (Chien, 2002). This will require additional
data collection, greater discussion and further
investigation. The related limitations are listed:
1. Initially, this research focuses on the portfolio
theory so that the fundamental assumption and
designed methodology of this research is developed
using the basic research content of portfolio
theory (King and Ross, 1993).
2. Based on the portfolio theory and related
financial investment researches, the risks are
categorized by two kinds of risks. One is
systematic risk and the other is unsystematic risk.
However, there are still some potential risks that
cannot be measured but in this research, these
risks are not addressed (Desheng, 2007).
3. In terms of unsystematic risk priority, the
unsystematic risk is able to be diversifiable
through comprehensive effective invested portfolio
strategies. However, this research did not discuss
and verify what is the best effective invested
portfolio strategies, due to the invested objectives
which are different characteristics, including cash,
checking, securities, fund, options, future market
and others invested objectives. Therefore, in order
not to be deviation of this research, the unsystematic
Hsieh et al.
priority number is given through scenario analysis and
empirical analysis (De Sheng, 2007).
4. Further, there is a little impact of unavoidable potential
risk be included in the unsystematic market that is not
discussed in this research. For example, the investors’
preference is the one critical impact of unsystematic risk
priority number due to the assumption of portfolio theory
is based on that all investors pursue the maximum return
rate (Kuznets, 1995). Therefore, in order not to be
deviation of this research, the unsystematic priority
number is given through scenario analysis and empirical
analysis.
5. Moreover, this research centralizes on macroeconomic
environment and market. The impacts of accounting and
financial factors of invested objectives are not covered in
this research (Liu, 2001). For example, accounting return
on assets (“ROA”), accounting return on equity (“ROE”),
cash-flow return rate, internal rate of return (“IRR”), etc.
6. This research does not do any investigation and study
regarding the impact of policy, even though there is close
and related dependency between economy and policy
through the a few factors in the macroeconomic model of
this research (Lin, 2001), there is no directly or strongly
positive indicators to represent the characteristics of a
policy to be a variance or indicator.
7. This research focuses on the ten selected industrial
regions including two global developed economic entities
(United of States and Japan), one global developing economic entity – four Asia tigers (Hong Kong, South Korea,
Singapore, and Taiwan) and one highly industrialized and
emerging economic entities (Brazil, Russia, India and
China). Hence, this does not discuss worldwide industrial
regions. Also, due to the shortcomings of previous literature pertinent to create effective macroeconomic model
into portfolio theory introducing a complete indicator of
estimation, this research has not concluded the entire
worldwide economy macroeconomic model (Martin,
2001).
8. The collected data is from 2004 to 2008 which only
cover the first financial crisis year in 2008 (Maxwell,
1995). The objective macroeconomic factors are supposed to be affected by financial crisis (McKinnon, 1973).
The macroeconomic factors in this research may be not
able to deeply and widely discuss the impact of implied
volatility of financial crisis (McKinnon, 2001). The resolution of barrier on this research limitation depends on the
further larger research data in the future.
An eventual caveat is that the consequences are
depended on data collected retrospectively (Rao et al.,
2009). The utilization of sources mitigates this problem to
a larger extent, but inaccuracies are not to be rule out
completely and totally.
RESEARCH CONTRIBUTION
Notwithstanding its limitations, this thesis is on reducing
4571
the invested systematic risk in twelve stock markets from
ten industrial regions through portfolio theory and
macroeconomic model makes some contributions to the
literature and future direction. Further, looking back, this
research used an integration of three kinds of research
fields,
“Macroeconomic
Model
Measurement
–
Covariance”,
“Macroeconomic
Model
Rotation
Measurement – Correlation” and “Maximize Return Rate
– Scenario Analysis and Empirical Analysis”, across ten
industrial regions (USA, Japan, Taiwan, Singapore,
Korea, Hong Kong, Brazil, Russia, India and China) by
analyzing five-year beta priority numbers (beta
coefficients) of twelve stock markets (USA New York,
USA NASQAQ, Japan Tokyo, Taiwan, Singapore, Korea,
Hong Kong, Brazil, Russia, India, China Shanghai and
China Shenzhen) from 2004 to 2008. Besides the
theoretical contributions, this study also offered
methodological insights and visions. Fundamentally, the
work provides an example of how such research could
combine the depth of development conduct of portfolio
theory and macroeconomic with the quantitative
analytical power of sufficiently large sample size of
macroeconomic indicators data. Most of the past work on
systematic risk has pursued qualitative discussion,
explanation and investigation at macroeconomic and
microeconomic environment level. Further, this thesis is
one of the few to offer operational measures of performance to the portfolio theory and macroeconomic model
literature among ten industrial regions, documenting
actual value creation, compared to the majority of work
out there. Finally, this thesis introduces a way to
empirically apply the study of dependent research.
REFERENCES
Aizenman J, Pinto B (2001). Finance and Volatility – Managing
Economic Volatility and Crises: A Practitioner’s Guide. Boston:
Cambridge University Press.
Arrow KJ (1951). Alternative Approaches to the Theory of Choice in
Risk-Taking Situations. Econometrica. 19: 404 - 437.
Beck L, Loayza N (2000). Financial Intermediation and Growth:
Causality and Causes and Finance and the Sources of Growth. Econ.
Growth Res., 3: 3-5.
Beck T, Ross L, Norman L (2000). Finance and the Sources of Growth.
J. Financ. Econ., 58 (1): 261-300.
Beim D, Charles WC (2004). Statement of Charles W. Calomiris Before
a joint meeting of the Subcommittee on Housing and Community
Opportunity and the Subcommittee on Financial Institutions and
Consumer Credit of the Committee on Financial Services. U.S.
House Representatives, 30: 1-34.
Chang C (2007). A Study of the relationships of United States, East
Asian Tigers and BRICs Stock Indices. Master Dissertation, Shih Hsin
University. Taiwan.
Chien Y (2002). Cong Dalu Rushi Waizi Yinhang Zai Dalu zhi Fazhan,
From Mainland China to the World: the development of foreign
invested banks in Mainland China. Taiwan Bus. Teachers’ Monthly,
49: 235-255.
Chuang C, Hsu H (2003). Taiwan Gushi Xiangguan Zhengce dui Gushi
zhi Yingxiang, Influence of Taiwan’s Stock Market Policy on the Stock
Market. Securities Exchange Transact. Inform., 489: 21-33.
De Sheng H (2007). 2000-2007 Taiwan Macroeconomic Forecast
Report Taiwan: Taiwan Institute of Economy Research.
4572
Afr. J. Bus. Manage.
Dufey G, Ian HG (1984). Eurocurrency Deposit Risk. J. Bank. Finance,
8: 567-89.
Duo Q, Marie AC, Geoffrey D, Xinhua H, Rui L, Shiguo L, Nedelyn M,
Pilipinas Q (2007). A macroeconometric model of the Chinese
economy. Asian Development Bank (ADB). Philippines, Institute of
World Economics and Politics (IWEP), Chinese Academy of Social
Sciences (CASS), China, Queen Mary, University of London, United
Kingdom and University of the Philippines, Philippines
Fama EF (1968). Risk, Return and Equilibrium: Some Clarifying
Comments. Journal of Finance, 23 (1): 29-40.
Graciela K, Lyons RK, Schmukler SLR (2002). Managers, investors,
and crises: mutual fund strategies in emerging markets. J. Int. Econ.,
98 (1): 113-134.
Greenwood J, Jovanovic B (1995). Financial Development, Growth and
the Distribution of Income. J. Polit. Econ., 98(5): 1076-1107.
Hsu C, Tang C (2002). Gongying Yinhang Minyinghua Wenti, Problem
of Public Bank Privatization. National Instit. Econ. Rev., 91(66): 1-29.
Hicks JR (1931). The Theory of Uncertainty and Profit. Econ., 32: 170 189.
Hicks JR (1939). Value and Capital, Econ. J., 72: 87-102.
James L (2007). Financial sector regulation, bank franchise values and
savings mobilization. Appl Econ., Taylor Francis J., 39(4): 519-525.
James L (2002). External Financial Liberalization and Foreign Debt in
China. Discussion Papers Series 304. Australia: School of
Economics, University of Queensland.
James L, Kam K (2007). Opening China’s Capital Account: Modeling the
Capital Flow Response. J. Chinese Econ. Bus. Stud., Taylor Francis
J., 5(1): 1-18.
Joe C, Davis, Jorge G, Gonzalez (2002). Scholarly Journal Articles
about the Asian Tiger Economies: Authors, Journals, and Research
Fields, 1986-2001. Trinity University.
Kindleberger ?? (1972). The Benefits of International Money, Journal of
International Econ. 2: 425-442.
King RG, Ross L (1993). Finance and Growth: Schumpeter Might Be
Right. Q. J. Econ., 108: 717-737.
Kuznets S (1995). Economic Growth and Income Inequality. Am. Econ.
Rev., 45 (1): 1-28.
Liu C (2001). Shanghai Weilai Fazhan Wei Guoji Jinrong Zhongxin zhi
Taolun, Discussion on Future of Shanghai as Internation Finance
Center. Unpublished Master’s thesis, National Tung-Hwa University
Mainland China Research Institute.
Lin T (2001). Chen Chien-liang Dalu Ge Shengshi Zongti Jingzhengli
Fenxi, Analysis of Competitivety of Cities and Provinces within
Mainland China. Taiwan Financ. Q., 2(3): 1-15.
Martin P (2001). The Economic Freedom of Asian Tigers – An Essay on
Controversy. Department of Economics in University of Aarhus.
Maxwell JF (1995). Money, Interest and Banking in Economic
Development. Johns Hopkins University Press.
McKinnon RI (1973). Money and Capital in Economic Development.
Washington, DC: Brookings Institution, 23: 134-243.
Rao B, Tamazian A, Singh R (2009). What is the Long Run Growth
Rate of the East Asian Tigers? University of Western Sydney.
Australia.
Ronald I, McKinnon (2001). After the Crisis, The East Asian Dollar
Resurrected: An Interpretation of High Frequency Exchange Rate
pegging. Journal of J. Stiglitz and S. Yusuf (eds). Rethinking the East
Asian Miracle, 2001. World Bank and Oxford University Press.
Sharpe WF (1964). Capital asset prices: A theory of market equilibrium
under conditions of risk. J. Finance, 19 (3): 425-442
Shaw ES (1973). Financial Deepening in Economic Development. New
York: Oxford University Press.
Shi M (2002). Chiu Shi-min Zhongguo Jinrong Shichang zhi Yanjin yu
Zhanwang, Development and Overview of Mainland China’s
Financial Markets. Taiwan Econ. Financ. Monthly, 38(4): 11-22.
Tian GG, Wan GH (2004). Interaction among China-related stocks:
evidence from a causality test with a new procedure. Appl. Financ.
Econ., 14: 67-72.
Tong Ch (2003). Zhonggong Shiliu Da Qianhou Jingji Jushi Pinggu,
Evaluation of 16 Main Economic Bureau’s in the People’s Republic,
Overview of Cross-strait Economic Development Sympoisum for
2003, organized by Executive Yuan Research Association, Taiwan
Thinktank and Sun Yat-sen University Social Sciences Institute.
Yang Y, Hung W, Sung J, Tsai M (2005). The Impact of Oil Prices
Change on Asian Stock Market- Evidence From Hong Kong,
Singapore, South Korea and Taiwan: An AR(1)-GARCH(1,1) Model.
Department of Finance in Ling Tung University. Taiwan.
Jao YC (2002). Shanghai and Hong Kong as International Financial
Centers: Historical Perspective and Contemporary Analysis.
Published Thesis, University of Hong Kong. Hong Kong, 54.