Proceedings of Annual Spain Business Research Conference
14 - 15 September 2015, Novotel Barcelona City Hotel, Barcelona, Spain
ISBN: 978-1-922069-84-9
Would Investor Sentiment and Macroeconomic Factors Affect
Exchange Market in Asian Country?
Huang Mei-Hung1*, Jeng Yih 2 and Hsu So-De3
Kahneman and Tverskey(1979) established Prospect Theory studying the decisionmaking procedure of the investors from mental part, and the influence of the mental effects
to the financial market. This theory brought in human irrational behavior, a tremendously
new idea, to traditional Finance. Investor sentiment would affect the investor’s trading
behaviors and investment strategy, and further would affect the market returns. To identify
the changes in investor sentiment and macroeconomic factors, and to find out whether the
asymmetric phenomenon exists in the foreign exchange market under the influences of
these factors, this research studies the relations among the investor sentiment,
macroeconomic factors, and foreign exchange market. Using the data from 01/1999 to
03/2011, we processed principal components analysis, general linear model, GARCH, and
quantile regression analysis. The result shows that the investor sentiment factors and the
macroeconomic factors have the different influences to the foreign exchange rates in each
Asian country in different conditions.
JEL Codes: C31, F31 and G02
1. Introduction
With the development of technologies and internet, the international finance markets
and trade liberalization made the investments more globalized. Therefore, to predict
exchange rates more reasonably becomes an important topic. However, in Taiwan, most
studies about investor sentiment are related to the general impacts to stock returns. There
are not many studies about investor sentiment to the foreign exchange markets. From the
documentations, we found that investor sentiment would affect the investment decisions in
different finance markets. However, in foreign exchange market, how investor sentiment
factors could be evaluated? And, what should be the indicators for evaluating the investor
sentiment?
The study object of this research is the foreign exchange markets in Asian countries.
We took the investor sentiment factors studied in Baker and Wurgler(2006) as the
reference. Beside stock turnover rate, index of consumer sentiment, and weather effect
factors selected by Saunders(1993), trade remaining, industrial production index, inflation
rate, money supply, discount rate, and base interest rate are used to study the relation of
investor sentiment factors and exchange market returns. We expect to find out the precise
relations among these factors and the exchange rates. Compared with economic
indicators, investor sentiment factors are easier to interpret and provide more information.
In different exchange market returns, the investor sentiment factors are easy to interpret
as well, and could help to identify the market situations and investment decisions. Through
the related researches from other countries, we found that the investors can be grouped
by different emotion factors. Moreover, these factors could affect the cross-sectional stock
1
Miss. Huang Mei-Hung, Ph.D. Candidate of Financial Management, National Sun Yat-Sen University ,
Kaohsiung, Taiwan ; Lecturer of Dep. Of Business Administration, Overseas Chinese University, Taichung,
Taiwan, Email: tiffanyhuang428@gmail.com
2
Dr. Jeng Yih, Department of Finance management, , National Sun Yat-Sen University, Kaohsiung, Taiwan,
Email: yihjeng2@gmail.com
3
Dr. Shyu So-De, Department of Banking and Finance, Takming University of Science and Technology,
Taipei, Taiwan, Email: dshyu@takming.edu.tw
Proceedings of Annual Spain Business Research Conference
14 - 15 September 2015, Novotel Barcelona City Hotel, Barcelona, Spain
ISBN: 978-1-922069-84-9
market. This research is also expected to find out whether the investor sentiment factors
and macroeconomic factors would impact exchange market. Mainly, we would like to
research further about the influence of the investor sentiment factors to the exchange
market returns in Asian countries. Most the researches are related to the influences of the
investor sentiment to the stock market returns or to the speculative stocks‟ returns. Not
many researches studied about the influence of the investor sentiment to the exchange
market returns. Based on the points in Behavioral Finance, we would like to find out the
correlation of the investor sentiment and macroeconomic factors to the fluctuation of
foreign exchange rates.
The purposes of this study are -1. to study the relations of the exchange market returns among 11 countries in Asia.
2. to study how the exchange market returns in each country are related to investor
sentiment and macroeconomic factors through principal components analysis.
3. to study how the exchange returns in each country are related to investor sentiment
factors and macroeconomic factors through multivariate analysis and quantile regression
analysis.
2. Literature Review
2.1 Exchange Rates and Exchange Market
With the data collected and integrated by International Commercial Bank of China and
Taiwan Stock Exchange from 01/04/2001 to 12/30/2004, Yu(2005) processed unit root
test, Granger causality test, error correction model, and cointegation to process the
research. The result of Yu‟s study shows -- (1) Depending on the countries, the causality
of stock price index and foreign exchange rates has the different lead-lag relationship.
Moreover, different exchange rate objects come up with the different lead-lag
relationships. (2) The stock markets of the countries with the larger capital markets affect
the ones of the countries with the smaller capital markets. And, this causality is not
reversible. Therefore, the U.S. stock price index is followed by the Japan stock price index
and Taiwan stock price index. The stock price index of a small capital market, such as the
one in Taiwan, is impacted by international stock markets. (3) U.S. S&P500 affects TAIEX
for more days than Nikkei 225 index affects TAIEX. This inferred that Japan stock market
affects Taiwan stock market directly and immediately because Japan is the country with
the largest total trade volume for Taiwan. (4) No matter if stock price index or exchange
rates fluctuate ahead, it comes up with the same conclusion that the investors transfer the
capitals to stock market when the stock price rising, which causes currency depreciation
because of the capital loss in exchange market. At this situation, investors would transfer
the capitals to exchange market, which causes the stock price dropped. The currency
value and the stock price of a country are in a negative correlation.
Hsu (2010) studied the correlation between the fluctuations in international stock
market and foreign exchange market. The methods used were ADF and PP unit root test.
The samples were the data from 01/07/2000 to 03/05/2010. Because the stock market off
days are different in each country, this study compared the stock market data from
Thailand, Taiwan, Philippines (with the data for 525 weeks), Japan, Singapore, South
Korea, and Indonesia after U.S. stock market re-opened. Because Indonesia started to
operate the market based on floating rate of exchange at 07/2005, there is only data for
238 weeks for Indonesia. The data was retrieved from Global Financial Database. The
results of the study demonstrated that the correlation coefficient estimated by DCC model
Proceedings of Annual Spain Business Research Conference
14 - 15 September 2015, Novotel Barcelona City Hotel, Barcelona, Spain
ISBN: 978-1-922069-84-9
varies obviously and shifts back and forth with a specific range. If the data is only
estimated by unconditioned fix correlation coefficient model, the fluctuation of correlation
coefficient would be overlooked, which makes the investors cannot identify the correlation
of the factors in the finance markets and get into the higher risk. The samples of the
exchange rates were affected by the fluctuation of the stock market returns in each
country. Except the result of Taiwan, the ones of all other seven countries demonstrated
that the alteration of the exchange rates had the significant influence to the stock market
returns. Furthermore, the more serious the alteration, the wider the fluctuation of the stock
market returns, which makes the investment more risky. Except Taiwan and Singapore,
the mean of θ derived from unconditioned fix correlation coefficient is less than the mean
ofθ derived from dynamic conditional correlation coefficient . This means the influence of
the exchange rate alteration to the stock market return fluctuation would be
underestimated when estimated by unconditioned fix correlation coefficient. Therefore,
when doing the estimate, dynamic conditional correlation coefficient is better than
unconditioned fix correlation coefficient. Exchange rate alteration impacts stock market
return fluctuation positively and obviously. Beside the investment returns and the
investment portfolio, the investors should consider the relations among the finance
markets, the fluctuation in exchange market, and the international finance situations to
make the more reasonable and more correct decisions to get lower risks and higher
benefits.
2.2 Investor Sentiment
DSSW(1990) brought up the points about the influence of the investor sentiments to
the risk-weighted assets (Lo and Lin, 2005). First, when noise traders are more optimistic
at investment, they would carry more risk-weighted assets. Therefore, they carry more risk
premium. However, eventually, the increase of the demand would cause the price of the
risk-weighted assets rising. The investors need to pay the higher price for the riskweighted assets, which makes them get the lower returns in the investment. Second, when
the noise traders have more different attitudes to the future stock market, they would get
more uncertainty at the risk-weighted assets. The uncertainty would trigger the risk
aversion causing that the arbitragers carry less risk-weighted assets and get less returns.
That also means the decrease of arbitragers in the markets would increase the noise
traders bringing up the prices far away from the fundamental values. Because of carrying
the higher risk, the noise traders get higher returns. Third, noise traders would obtain the
risk-weighted assets with the higher price but sell out with the lower prices. Thus, the more
common the situations happen, the lower returns that the noise traders would get. Shleifer
and Vishny(1997) stated that the investor sentiments brought the investment risk which
might be very expensive.
Brown and Cliff(2004) assumed that fundamental investors and speculators exist in
the market in which the fundamental investors have the unbiased expectation and the
speculators have the biased expectation to the asset values. When the speculators think
the asset real value is higher or lower than the current price, they would tend to be
optimistic or pessimistic. In other words, the extent of being optimistic or pessimistic could
be taken as the range of the discount of the real value. The expectation deviation from the
investors is what we take as the investor sentiment. Different scholars have the different
ways to explain and to think about the investor sentiment. Thus, the results of the
researches are different. Baker and Stain(2004) stated that turnover rate is like a liquidity
variable which can be taken as a sentiment variable. Jones(2001) found that the return
rate of future stock will be low when the turnover rate is high. Chen(2001) took odd lot
Proceedings of Annual Spain Business Research Conference
14 - 15 September 2015, Novotel Barcelona City Hotel, Barcelona, Spain
ISBN: 978-1-922069-84-9
trading amount, turnover rate, the ratio of margin purchase and short sale, domestic
institutional investors, foreign investor net buy and net sell, dealer, bond redemption price,
investment trust holding ratio, the amount of initial public offering companies, and fund net
asset value discount rate as the investor sentiment variables. The study found that
investor‟s emotions and stock return are correlative. Brown and Cliff(2004) used principal
components analysis to build up the sentiment index to study the relationship between
sentiment index and short-term stock return. This study revealed that the historical stock
returns could affect the sentiment index. Besides, the stock return and sentiment index are
highly correlated. But, in the study, there was no obvious evidence provided to support that
the sentiment factors could be used to predict stock returns.
2.3 Macroeconomic Factors
Lee(1992) studied the connections of substantial stock returns, industrial production
growth, inflation and real interest rate through Granger causality test and vector
autoregression (VAR) model. In VAR model, substantial stock returns can only interpret a
small part of the variation in inflation rate. Inflation can only interpret a small part of the
variation in industrial production growth and real interest rate. Kwon and Shin (1999)
studied whether Korean economic activities would impact Korea Stock Exchange (KSE)
through vector error correction model, cointegration, and Granger causality test. In the
study of Kwon and Shin (1999), the economic activity factors had exchange rates, trade
remaining, money supply, and industrial production index. The result of this study indicated
that KSE stock price index and economic factors have a stable and lasting relationship.
However, KSE stock price is not a leading indicator. Choudhry(2001) studied the
connections of the stock return rates for Chile, Argentina, Venezuela, and Mexico, the
countries with the high inflation rate. The result of this study indicated that inflation rate
and stock return rate are positively correlated. The historical inflation rate would impact the
current stock return rate. And, some studies indicated that the correlation of substantial
stock returns to inflation rate of the next term is negative. Tsouma (2009) studied the
relationship between stock return and economic activities for developed countries through
Granger causality test and vector autoregression with the index of consumer sentiment
from 01/1991 to 12/2006, nominal rate of stock return, substantial stock return, and
industrial production index. The results of the study indicated that no causal connection
exists between economic activities and stock return. The fluctuation of stock return
prediction and stock returns in emerging markets could interpret the finance development
and risk.
Chen (2001) used multiple regression analysis to analyze the relationship among the
macroeconomic factors, the returns of the bond market, and the returns of the stock
market. The factors were interest rate change, money supply change rate, inflation rate,
and real gross production rate of change. The study shows that the real gross production
rate of change and money supply change rate have the significant and positive correlation
to the stock returns. And, these four factors have the significant negative correlation to the
returns at bond market. Lee et al.(2006) picked industrial production index, 30-day
commercial paper rate, and the difference of the prices as the macroeconomic factors.
And, the investor sentiment factors were replaced by the balance of stock load, total
amount, and stock index returns. Vector autoregression model and forecast error variance
decomposition were used to analyze the relationship among the momentum strategy,
macroeconomic factors and investor sentiment factors. The result of the research shows
up that the investor sentiment and macroeconomic factors is in the causal correlation.
Proceedings of Annual Spain Business Research Conference
14 - 15 September 2015, Novotel Barcelona City Hotel, Barcelona, Spain
ISBN: 978-1-922069-84-9
Besides, the investor sentiment, momentum strategy, and macroeconomic affect one
another.
3. The Methodology and Model
3.1 Data
This research studied the connection between investor sentiment and exchange
market, and the connection of the exchange market and the investor sentiment. The study
object is the exchange markets in Asia. Because of the difficulty of collecting the data in
investor sentiment and macroeconomic factors, Vietnam is not covered in this study. The
exchange market returns of Japan, South Korea, Hong Kong, Singapore, China,
Indonesia, Malaysia, Philippines, Taiwan, and Thailand are the research objects. The
collected data are from 01/1999 to 03/2011 (monthly). The data resources are TEJ, and
Intelligence Winner. Beside turnover rate and index of consumer sentiment, industrial
production index, substantial interest rate, exchange rates, and trade remaining are taken
as the economic factors for studying the connection between investor sentiment and
exchange market return. For the missing data, the previous entry would be used.
3.2 Research Methods
With time series model, we used unit root test, cointegration, vector error correction
model, causal relationship, vector autoregression, impulse response, and variance
decomposition to build up the model of exchange market and stock price index for Asian
countries. We clarify the definitions of the factors involved in this study. We studied the
interaction and the connection of stock price index and exchange rates for Asian countries.
After the analysis, the conclusion and suggestions are provided.
4. Empirical Results
4.1 Data
The factors of exchange rates from 11 countries are selected to process a basic
descriptive statistics (Table 4-1). The exchange return data of China, Hong Kong, India,
Indonesia, Japan, South Korea, Malaysia, Philippines, Singapore, Taiwan, and Thailand
from 01/1999 to 03/2011 were used to process a single differencing. The basic statistics
data is listed in Table 4-1.
4.2 The Result of Unit Root Test
Table 4-2 lists the result of unit root test for the exchange rates. During ADF and PP
unit root test, before autoregression, the unit root test for time series of exchange rates
from Asian countries cannot reject null hypothesis. The result shows the time series has
the unit root phenomenon, which is called nonstationary time series. However, after first
autoregression, the test results of all factors reject the unit root, which means the series is
stable and the features of all the factors could be presented at first level. At KPSS test,
before autoregression, all the factors reject null hypothesis, which means unit root
phenomenon exists. That is nonstationary time series. However, after first autoregression,
the series is stable. Therefore, we can conclude that the results of ADF, PP and KPSS unit
root tests are matched.
Proceedings of Annual Spain Business Research Conference
14 - 15 September 2015, Novotel Barcelona City Hotel, Barcelona, Spain
ISBN: 978-1-922069-84-9
4.3 Principal Components Analysis
For obtaining the most appropriate factors to represent the exchange rates in each
country, we selected the common investor factors and macroeconomic factors.The
principal components analysis of each country is similar. We took the principal
components analysis for Japan as the example. Here is the result. When running
regression analysis, if the factors are highly correlated (please see Table 3), other than the
complexity of the model is increased, the interpretation of the factors would be possibly
dispersed. Therefore, we used principal components analysis to solve collinearity
problems. Generally, when Eigenvalue is less than 1 (Eigenvalues: (Sum = 8, Average =
1)), the interpretation of principal component is not acceptable for not better than the
original variable. Based on the Eigenvalue at Table 1, we figured out that only factor 1,
factor 2, and factor 3 could be selected as the principal components. Furthermore, the
cumulative proportion reaches to 75%, which is with the highly interpretation.
Chart 4-2 shows the interpretation of the eight factors by the principal components.
Therefore, we can conclude that PC1 has the positive correlation with BA, CU, DI, IP, TE,
and TU. PC1 has the negative correlation with CC and TR. PC2 has the positive
correlation with CC, CU, DI, IP, TE, TR, and TU, and negative correlation with BA. These
demonstrate that PC1 can fully interpret most the original factors. PC1 is highly correlated
with BA(BASE_RATE), DI(DISCOUNT_RATE), and TU(TURNOVER_RATE), which
means PC1 has the better interpretation to BA(BASE_RATE), DI(DISCOUNT_RATE), and
TU(TURNOVER_RATE). PC2 has the better interpretation to IP(IPI). And, PC3 has the
better interpretation to TE(TEMPERATURE).
4.4 Ordinary least squares Regression
From the exchange market data at Table 4-5, we found that both basic interest rate
and index of consumer sentiment have the negative correlation with exchange market
return in Japan. But, both do not have the significant negative correlation with exchange
market return in South Korea. For Hong Kong, the temperature to exchange market return
has a significant negative correlation. The Constant is significant and keeps the positive
correlation with the temperature. This means, other than basic interest rate, inflation rate,
discount rate, industrial growth index, temperature, and turnover rate, there is some other
factors affecting Hong Kong exchange market return.
From Table 4-6, for Singapore, we found the turnover rate to exchange market return
has the significant and negative correlation. For China, both index of consumer sentiment
and industrial production index have the significant and negative correlation to exchange
market return. For Indonesia, trade remaining to exchange market return is in a significant
and negative correlation. From Table 4-7, for Malaysia, the inflation rate to exchange
market return is in a significant and positive correlation. For Philippines, the discount rate
to exchange market return is in a significant and positive correlation. For India, all are not
significant. From Table 4-8, for Taiwan, the index of consumer sentiment to exchange
market return is in significant and negative correlation. The temperature to exchange
market return is in significant but positive correlation. Constant to exchange market return
is in a significant and positive correlation. Therefore, other than basic interest rate, index of
consumer sentiment, inflation rate, temperature, and turnover rate, there are other factors
affecting Taiwan exchange market return.
Proceedings of Annual Spain Business Research Conference
14 - 15 September 2015, Novotel Barcelona City Hotel, Barcelona, Spain
ISBN: 978-1-922069-84-9
4.5 Quantile Regression
The data at Table 4-9 shows that the basic interest rate, and the index of consumer
sentiment both have significant and negative correlation with exchange market return. For
getting the area of the points of significance, we processed Quantile Regression Analysis.
For Japan, the result of the analysis shows that basic interest rate has the significant and
negative correlation in the interval of 0.2, the index of consumer sentiment has the
significant and negative correlation in the interval of 0.7. The results of both general
regression and quantile regression of discount rate are not significant. The result of
industrial production index has the significant and positive correlation in the interval of 0.7.
And, the result of quantile regressions of the temperature, trade remaining, turnover rate
are not significant.
The data, for South Korea, at Table 4-10 shows that the results of quantile regression
for basic interest rate, index of consumer sentiment, discount rate, industrial production
index, trade remaining, and turnover rate are insignificant. The result of the whole inflation
rate is insignificant. However, with quantile regression, the inflation rate is in significant
and positive correlation in the interval of o.9.The data, for Hong Kong, at Table 4-11 shows
that the exchange rate return to the whole basic interest rate, inflation rate, discount rate,
industrial production index, and turnover rate are insignificant. But, through quantile
regression, exchange market return to basic interest rate is in significant and negative
correlation in the interval of 0.6, 0.7, 0.8, and 0.9, to inflation rate is in significant and
positive correlation in the interval of 0.8, and 0.9, to discount rate is in significant and
positive correlation in the interval of 0.6, 0.7, 0.8, and 0.9, and to industrial production
index is in significant and negative correlation in the interval of 0.1, and 0.2, The exchange
market returns to turnover rate is in significant and negative correlation in the interval of
0.1, which means insignificant to the whole system but significant to the interval. The
exchange market return to temperature is in significant and negative correlation in the
interval of 0.1.
The data, for Singapore, at Table 4-12, shows that the exchange market return to the
whole basic interest rate and temperate are both in insignificant correlation. Hence, the
quantile regression result is insignificant. The exchange market return to inflation rate is
insignificant, but is in significant and negative correlation in the interval of 0.1 and 0.2. The
exchange market return to trade remaining is significant, and the quantile regression result
is in significant and negative correlation in the interval of 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9.
The data, for China, at Table 4-13, shows that the general regression of index of consumer
sentiment and temperature are insignificant. Thus, the result of quantile regression is
insignificant. The general regression results for inflation rate, discount rate, and trade
remaining are insignificant. The result after quantile regression shows that inflation rate is
in significant and negative correlation when in the interval of 0.1, 0.2, and 0.3. The quantile
regression for discount rate is in significant and negative correlation while in the interval of
0.2, 0.3, and 0.4. The quantile regression for trade remaining is in significant and negative
correlation while in the interval of 0.1, and 0.2. The quantile regression for industrial
production index is in significant and negative correlation while in the interval of 0.1, 0.3,
0.6, 0.7, and 0.8.
The data, for Indonesia, at Table 4-14, shows that the results of general regression for
inflation rate, and discount rate are insignificant. Thus, the result of quantile regression for
discount rate is insignificant.The data, for Malaysia, at Table 4-15, shows that the general
regression for basic interest rate, industrial production index, and trade remaining are
Proceedings of Annual Spain Business Research Conference
14 - 15 September 2015, Novotel Barcelona City Hotel, Barcelona, Spain
ISBN: 978-1-922069-84-9
insignificant. But, the result of quantile regression for basic interest rate is in significant and
negative correlation while in the interval of 0.2. The result of quantile regression for
industrial production index is in significant and negative correlation while in the interval of
0.2 and 0.3. The result of quantile regression for trade remaining is in significant and
negative correlation while in the interval of 0.1 and 0.9. The result of quantile regression
for inflation rate is in significant and positive correlation while in the interval of 0.9. The
result of general regression for temperature is insignificant, so the result of quantile
regression for temperature is insignificant as well.
The data, for Philippines, at Table 4-16, shows that the results of general regression
for the whole basic interest rate, inflation rate, and trade remaining are insignificant. But,
the result of quantile regression for basic interest rate is in significant and negative
correlation while in the interval of 0.2, 0.4, 0.5, and 0.8. The result of quantile regression
for inflation rate is in significant and negative correlation while in the interval of 0.1, 0.2,
and 0.3. The result of quantile regression for trade remaining is in significant and negative
correlation while in the interval of 0.1, and 0.9. The result of general regression for
discount rate is in significant. The result of quantile regression for discount rate is in
significant and positive correlation while in the interval of 0.2, 0.3, 0.4, 0.5, 0.7 and 0.8.
The result of general regression for temperature is insignificant. Hence, the result of
quantile regression for temperature is also insignificant.
At Table 4-17, the data, for India, shows that the results of general regression for
basic interest rate, inflation rate, discount rate, and industrial production index are
insignificant. Therefore, the quantile regression results are insignificant. The result of
general regression for temperature is insignificant, but the result of quantile regression
result is in significant and positive correlation while in the interval of 0.8 and 0.9. At Table
4-18, the data, for Taiwan, shows that the general regression results for basic interest rate,
money supply, and turnover rate are insignificant. However, the result of quantile
regression for basic interest rate is in significant and negative correlation while in the
interval of 0.1, 0.4, and 0.5. The result of quantile regression for money supply is in
significant and negative correlation while in the interval of 0.4, 0.5, and 0.6. The result of
quantile regression for turnover rate is in significant and negative correlation while in the
interval of 0.9. The result of general regression for the index of consumer sentiment is in
significant and negative correlation. The result quantile regression for the index of
consumer sentiment is in significant and negative correlation while in the interval of 0.1,
0.2, 0.3, 0.4, 0.6, and 0.7. The result of general regression for the temperature is in
significant and positive correlation. The result of quantile regression for the index of
consumer sentiment is in significant and positive correlation while in the interval of 0.1, 0.2,
0.3, 0.4, 0.5, 0.6, and 0.9. The result of general regression for inflation rate is insignificant.
The result of quantile regression for inflation rate is also insignificant.At Table 4-19, the
data, for Thailand, shows that general regression results for basic interest rate, inflation
rate, discount rate, and temperature are all insignificant. The result of quantile regression
for basic interest rate is in significant and negative correlation while in the interval of 0.1.
The result of quantile regression for inflation rate is in significant and positive correlation
while in the interval of 0.7. The result of quantile regression for discount rate is in
significant and negative correlation while in the interval of 0.2. The result of quantile
regression for temperature is in significant and positive correlation while in the interval of
0.8.
Proceedings of Annual Spain Business Research Conference
14 - 15 September 2015, Novotel Barcelona City Hotel, Barcelona, Spain
ISBN: 978-1-922069-84-9
5. Conclusion and Suggestions
Integrated with the ideas in Finance and Psychology, this research studied the factors
affecting exchange markets in Asian countries. We used unit root test ADF and PP to
check if exchange rates of the 11 Asian countries are in stationary series, and used KPSS
for the confirmation. The result demonstrates that the level 1 difference would make the
data a stationary series. Moreover, in Japan, exchange market return to basic interest rate
is in significant and negative correlation, to index of consumer sentiment is in significant
and negative correlation, and to industrial production index is significant and positive
correlation. For South Korea, the exchange market return to inflation rate is in significant
and positive correlation. For Hong Kong, the exchange market return to basic interest rate
is in significant and negative correlation, to inflation rate is in significant and positive
correlation, to discount rate is in significant and positive correlation, to industrial production
index is in significant and positive correlation, to temperature is in significant and negative
correlation, and to turnover rate is in significant and negative correlation. Other than these
factors, there might be some other factors affecting Hong Kong exchange market. For
Singapore, exchange return to inflation rate is in significant and negative correlation, and
trade remaining is in significant and negative correlation. For China, inflation rate, discount
rate, industrial production index and trade remaining are the factors affecting the exchange
market. These factors are all in significant and negative correlation with the exchange
market. There are other factors impacting the exchange market in China. For Indonesia,
trade remaining to exchange market is in significant and positive correlation. For Malaysia,
to exchange market, basic interest rate is in significant and positive correlation, inflation
rate is in significant and positive correlation, industrial production index is in significant and
negative correlation, and trade remaining is in significant and positive correlation. For
Philippines, to exchange market, basic interest rate is in significant and negative
correlation, inflation rate is in significant and negative correlation, discount rate is in
significant and positive correlation, and trade remaining is in significant and negative
correlation. For India, to exchange market, temperature is in significant and positive
correlation. For Taiwan, basic interest rate is in significant and negative correlation, index
of consumer sentiment is in significant and negative correlation, money supply is in
significant and negative correlation, and temperature is in significant and positive
correlation. For Thailand, basic interest rate is in significant and negative correlation,
inflation rate is in significant and positive correlation, discount rate is in significant and
negative correlation, and temperature is in significant and positive correlation,
From this study, the controllable factors were found for the government of each
country to control the exchange rates. Japanese government can drop the basic interest
rate to make exchange rates risen and Japanese Yen appreciated. South Korean
government can increase the money supply to make exchange rates going up. Hong Kong
government can decrease basic interest rate and increase money supply to make
exchange rates going up, and make HKD appreciated. Singapore government can
decrease money supply to lower inflation rate (increase exportation and decrease import)
to rise the exchange rates to make Singapore Dollar appreciated. Chinese government
can decrease money supply to lower inflation rate, or lower trade remaining (increase
exportation and decrease import) to make exchange rates going up (CNY appreciated).
The government of Indonesia can rise trade remaining (increase exportation) to rise
exchange rates (IDR appreciated). The government of Malaysia can rise basic interest
rate, rise money supply or rise trade remaining (increase import and decrease exportation)
to rise exchange rates (MYR appreciated). The government of Philippines can lower basic
interest rate, lower money supply, or lower trade remaining (increase exportation and
Proceedings of Annual Spain Business Research Conference
14 - 15 September 2015, Novotel Barcelona City Hotel, Barcelona, Spain
ISBN: 978-1-922069-84-9
decrease import) to rise exchange rates (PHP appreciated). The government of Taiwan
can lower basic interest rate to rise exchange rates (TWD appreciated). The government
of Thailand can lower basic interest rate, and rise money supply to rise exchange rates
(THB appreciate). The index of consumer sentiment and temperature would affect investor
sentiment, than to fluctuate exchange market in Japan, Hong Kong, India, Taiwan and
Thailand. All the factors selected in this study would affect the exchange market in one or
more countries, depending on the countries. However, there is no factor can affect all the
countries at the same time.
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Brown, Gregory W. and Michael T. Cliff, 2004, „Investor Sentiment and the Near-Term
Stock Market, Journal of EmPSrical Finance, Vol 11, pp 1-27.
Kwon, Chung S. and Tai S. Shin, 1999, “Cointegration and Causality between
Macroeconomic Variables and Stock Market Returns,” Global Finance Journal Vol
10, pp 71-81.
Lee, Bong-Soo 1992, “Causal Relations among Stock Returns, Interest Rates, Real
Activity, and Inflation,” Journal of Finance Vol 47, pp 1591-1603.
Lyons, R. K. 1995. “Tests of microstructure hypotheses in the foreign exchange market,”
Journal of Financial Economics, Vol 39: pp 321-351.
Lyons, R. K. 1997. “A simultaneous trade model of the foreign exchange hot potato,”
Journal of International Economics, Vol 42: pp 275-298.
Omitted some of the papers for space constraints
Proceedings of Annual Spain Business Research Conference
14 - 15 September 2015, Novotel Barcelona City Hotel, Barcelona, Spain
ISBN: 978-1-922069-84-9
Appendix
CH
HK
ID
IN
JA
KO
MA
PH
SI
TA
TH
Mean
Median
7.341491
7.775543
44.68482
9521.453
103.7105
1075.393
3.470996
48.07893
1.486149
32.16679
35.116
7.1133
7.7719
44.64
9230
105.42
1030.1
3.47
47.52
1.4819
32.278
34.32
Table 4-1 Basic Statistics
Max
Min
Standard
Deviation
8.2767
6.561
0.585996
7.8246
7.7501
0.021719
51.065
39.27
2.842656
12350
8715
749.2965
123.52
80.76
12.71448
1534
900.7
142.6095
3.8
3.0225
0.227237
56.3
40.46
4.135247
1.6916
1.2609
0.117489
34.95
29.3
1.061078
41.7
29.98
3.179134
Skewness
Kurtosis
0.283941
0.466864
-0.11002
2.059393
-0.28779
0.975468
-0.20756
0.356124
0.086186
-0.55054
0.477492
1.434403
1.964429
2.670889
7.041425
1.738657
3.537506
1.960307
2.299458
2.048059
3.402719
2.320816
Table 4-2 The ADF, PP, KPSS Unit Root Test Result of Exchange Rates for Asian Countries
Method
ADF
PP
KPSS
Country
Level 0
Level 1
Level 0
Level 1
Level 0
Level 1
-1.68907
-16.2325 ***
-1.25408
NA
0.33108***
0.00000322
CH
( 0.7513)
(0)
( 0.8948)
(0.5)
(0)
[0.052978]
-3.5612**
-11.8265***
0.109558***
0.00000099
-3.507**
-64.7633***
HK
(0.0368)
(0)
(0.0423)
(0.0001)
(0.007571)
[0.033873]
-2.42297
-12.8637***
-2.2443
-46.3198***
0.114252
-0.00022
ID
(0.3662)
(0)
(0.4613)
(0.0001)
(0.647871)
[-0.11004]
-3.27803
-11.5279***
-3.26917
-70.9886***
0.069448*
0.309844
IN
( 0.074)
(0)
(0.0755)
(0.0001)
(0.000001)
[0.262645]
-1.69097
-15.1813***
-1.71997
-51.0627***
0.23521***
0.000933
JA
(0.7506)
(0)
(0.7376)
(0.0001)
(0)
[0.11088]
-1.88194
-11.458***
-1.93674
NA
0.172314**
-0.01271
KO
(0.6589)
(0)
(0.6302)
(0.5)
(0.000607)
[-0.10168]
-2.7484
-7.12857***
-1.37817
NA
0.230681***
0.00000508
MA
(0.2192)
(0)
(0.8635)
(0.5)
(0)
[-0.04909]
-1.62945
-9.46257 ***
-1.62945
NA
0.294717***
-0.00023
PH
(0.7767)
(0)
(0.7767)
(0.5)
(0.650433)
[-0.09279]
-2.26915
-10.3596***
-2.28441
-113.126***
0.298729
0.00000913
SI
(0.4477)
(0)
(0.4394)
(0.0001)
(0)
[0.143168]
-2.39981
-13.9961***
-2.1438
-46.2358***
0.1668**
0.0000355
TA
(0.3781)
(0)
(0.5168)
(0.0001)
(0.000542)
[-0.03353]
-2.50992
-14.7176***
-2.52441
-82.9833***
0.259469 ***
0.0000467
TH
(0.323)
(0)
(0.3161)
(0.0001)
(0)
[0.022284]
Note: 1.CH is China. HK is Hong Kong. ID is India. IN is Indonesia. JA is Japan. KO is South Korea. MA is
Malaysia. PH is Philippines. SI is Singapore. TA is Taiwan. TH is Thailand.
2.( ) is p value. [ ] is t value
3.For 10%, 5%, and 1% of ADF unit root test statistics, the significant critical values for level 0 are -4.02, 3.44, and -3.1. The significant critical values for level 1 are -4.02, -3.44, and -3.15.
For 10%, 5%, and 1% of PP unit root test statistics, the significant critical values for level 0 are -4.02, -3.44,
and -3.14. The significant critical values for level 1 are -4.02, -3.44, and -3.15.
For 10%, 5%, and 1% of KPSS unit root test statistics, the significant critical values for level 0 are 0.22, 0.15,
and 0.12. The significant critical values for level 1 are 0.22, 0.15, and 0.12.
* means null hypothesis is rejected at 10% significance level. ** means null hypothesis is rejected at 5%
significance level. *** means null hypothesis is rejected at 1% significance level.
Proceedings of Annual Spain Business Research Conference
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ISBN: 978-1-922069-84-9
Table 4-3 The Codes of Sentiment Factors and Macroeconomic Factors
country
variable
Japan
Korea
Hong
Kong
Singapore
★
★
BA
BASE RATE
★
★
CC
★
★
★
★
★
DI
CCI
CURRENCY
INFLATION
DISCOUNT RATE
★
★
★
IP
IPI
★
★
★
TE
TEMPERATURE
TRADE
REMAINING
★
★
★
★
★
TU
TURNOVER RATE
★
★
MO
MONEY SUPPLY
CU
TR
China
Indonesia
Malaysia
Philippines
India
Taiwan
Thailand
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
★
Table 4-4 The Result of Principal Components Analysis for Japan
《Table 1》Principal Components Analysis- Japan
Date: 11/09/11 Time: 12:32
Sample: 2005M12 2011M03
Included observations: 64
Computed using: Ordinary correlations
Extracting 8 of 8 possible components
Eigenvalues: (Sum = 8, Average = 1)
Number
Value
Difference
Proportion
1
2.984
1.008341
2
1.975659 0.907201
3
1.068458 0.180454
4
0.888004 0.404203
5
0.483801 0.196092
6
0.287709 0.068608
7
0.219101 0.125833
8
0.093268 --《Table 2》Eigenvectors (loadings):
Variable
PC 1
PC 2
BA
0.497681
-0.09731
CC
-0.393806
0.405659
CU
0.42673
0.18951
DI
0.421299
0.296976
IP
0.133469
0.608737
TE
0.03329
0.137495
TR
-0.189727
0.556557
TU
0.427458
0.051382
《Table 3》Ordinary correlations:
BA
CC
BA
1
CC
-0.719068
1
CU
0.375619
-0.26228
DI
0.688133
-0.233668
IP
0.045822
0.270443
TE
0.04659
0.139141
TR
-0.246306
0.503414
TU
0.467019
-0.370315
0.373
0.247
0.1336
0.111
0.0605
0.036
0.0274
0.0117
Cumulative Value
CumulativeProportion
2.984
4.95966
6.028118
6.916122
7.399923
7.687632
7.906732
8
0.373
0.62
0.7535
0.8645
0.925
0.961
0.9883
1
PC 3
0.11546
0.059879
-0.217789
0.143439
-0.284869
0.910524
0.057091
0.040161
PC 4
-0.434007
0.250796
0.433004
-0.378498
0.062751
0.223739
-0.371921
0.475041
PC 5
0.077531
-0.260894
-0.442428
-0.381217
0.100877
-0.06036
0.403361
0.639011
PC 6
0.04129
0.580611
-0.273608
0.432142
-0.354522
-0.297806
-0.129941
0.410267
PC 7
-0.2447
-0.155675
0.458618
0.071622
-0.611259
-0.08941
0.561416
0.060059
PC 8
0.688155
0.429493
0.257154
-0.482984
-0.130525
-0.023418
0.133756
-0.084269
CU
DI
IP
TE
TR
TU
1
0.511936
0.430051
-0.005162
-0.205992
0.571799
1
0.393807
0.160625
0.13402
0.351912
1
-0.046362
0.511806
0.228609
1
0.102051
0.135067
1
-0.224247
1
Proceedings of Annual Spain Business Research Conference
14 - 15 September 2015, Novotel Barcelona City Hotel, Barcelona, Spain
ISBN: 978-1-922069-84-9
Chart 4-1 (Scree Plot(Ordered Eigenvalue)
Scree Plot (Ordered Eigenvalues)
3.0
2.5
2.0
1.5
1.0
0.5
0.0
1
2
3
4
5
6
7
8
From Table 2, principal components are shown as PC1＝ 0.497681-0.393806+0.42673+0.421299+0.133469+0.03329-0.189727+0.427458
PC2＝-0.09731+0.405659+0.18951+0.296976+0.608737+0.137495+0.556557+0.051382
PC3＝0.11546+0.059879-0.217789+0.143439-0.284869+0.910524+0.057091+0.040161
Chart 4-2 Orthonormal Loadings Biplot
Orthonormal Loadings Biplot
8
IPI
T RADE_REM AINING_SUM
Component 2 (24.7%)
6
CCI
4
07M 03
DISCOUNT _RAT E
08M 03 CURRENCY_INFLAT ION
T EM PERAT URE
2
T URNOVER_RAT
E
08M
09
0
08M 10
BASE_RAT E
-2
-4
09M 01
-6
-8
-8
-4
0
4
8
Com ponent 1 (37.3% )
Table 4-5 Regression Analysis for Exchange Markets in Japan, South Korea, and Hong Kong
Proceedings of Annual Spain Business Research Conference
14 - 15 September 2015, Novotel Barcelona City Hotel, Barcelona, Spain
ISBN: 978-1-922069-84-9
Table 4-6 Regression Analysis for Exchange Markets in Singapore, China, and Indonesia
Table 4-7 Regression Analysis for Exchange Markets in Malaysia, Philippines, and India
Proceedings of Annual Spain Business Research Conference
14 - 15 September 2015, Novotel Barcelona City Hotel, Barcelona, Spain
ISBN: 978-1-922069-84-9
Table 4-8 Regression Analysis for Exchange Markets in Taiwan and Thailand
Table(4-9) The Quantile Regression Analysis Result of Exchange Return for Japan
Japan
OLS
Intercept (c)
0.262092
(0.5157)
Slope
Basic
Interest
Rate
Index
of
Consumer
Sentiment
Discount
Rate
Industrial
Production
Index
Temperature
-0.169902
(0.0449)**
Trade
Remaining
0.000962
(0.8663)
-0.133649
(0.0317)**
0.007611
(0.4356)
0.060424
(0.5264)
-6.77E-05
(0.9899)
QT
0.1
0.000862
(0.9987)
-0.15772
(0.1622)
0.2
0.718813
(0.3115)
-0.201776
(0.0697)***
0.021434
(0.8309)
0.006696
(0.5623)
0.014093
(0.9145)
-0.04415
(0.6683)
0.002864
(0.5904)
0.000874
(0.9022)
0.031455
(0.4686)
-0.000826
(0.8784)
0.015326
(0.2875)
-0.14311
(0.4194)
0.006889
(0.3342)
0.3
0.365584
(0.5072)
0.128427
(0.2607)
0.067188
(0.567)
0.007483
(0.5305)
0.129618
(0.4003)
0.4
0.111969
(0.852)
0.6
-0.07661
(0.8875)
0.7
0.152735
(0.8117)
0.8
0.319675
(0.7068)
0.145908
(0.2269)
0.102219
(0.4102)
0.008839
(0.5094)
0.041855
(0.7984)
0.5
0.169907
(0.7567)
0.106191
(0.3967)
0.105101
(0.4078)
-0.00108
(0.9277)
0.110306
(0.4317)
0.117632
(0.3313)
-0.128
(0.2824)
-0.173263
(0.1415)
-0.00018
(0.9877)
0.120038
(0.338)
0.004132
(0.7329)
0.183254
(0.0992)***
0.104101
(0.405)
0.104986
(0.3364)
0.009837
(0.4405)
0.080847
(0.5695)
0.000885
(0.8725)
0.001005
(0.8906)
0.000357
(0.9507)
0.003122
(0.6646)
0.000871
(0.8805)
0.003567
(0.6325)
0.000808
(0.8883)
-0.002205
(0.8052)
0.002648
(0.7955)
0.00431
(0.558)
0.005134
(0.6152)
-0.229785
(0.0317)**
0.001513
(0.8881)
Turnover
0.014268
0.054558
0.027651 0.04936
0.029527 0.006798 -0.034124
Rate
(0.7025)
(0.2031)
(0.5123)
(0.2726)
(0.5443)
(0.8889)
(0.6044)
0.092375
(0.2192)
Remark : For all P values in the quote, *** means P value is significant at 1% level, ** means P value is significant at 5% level, * means
significant at 10% level.
0.9
0.184172
(0.9646)
-0.09992
(0.7955)
0.122082
(0.7634)
0.002765
(0.9498)
0.157568
(0.7908)
-0.01232
(0.9069)
0.006714
(0.7985)
0.037528
(0.9133)
P value is
Proceedings of Annual Spain Business Research Conference
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ISBN: 978-1-922069-84-9
Table(4-10) The Quantile Regression Analysis Result of Exchange Return for South Korea
South Korea
OLS
Intercept (c)
0.220826
(0.8923)
0.002987
(0.9749)
0.072742
(0.7935)
0.018583
(0.4795)
0.015552
(0.6446)
0.027275
(0.8247)
Slope
Basic Interest
Rate
Index
of
Consumer
Sentiment
Inflation Rate
Discount Rate
Industrial
Production
Index
Temperature
0.000224
(0.9821)
Trade
-0.00598
Remaining
(0.5382)
Turnover Rate
0.004519
(0.9204)
Remark : For all P values in the quote,
significant at 10% level.
QT
0.1
-0.98129
(0.6795)
-0.00592
(0.9745)
0.144757
(0.7888)
0.2
0.602735
(0.7933)
-0.0663
(0.6489)
-0.09249
(0.8277)
0.3
-0.13445
(0.9404)
-0.02501
(0.8066)
0.126574
(0.6799)
0.4
0.09167
(0.9728)
-0.03261
(0.7718)
0.089128
(0.8409)
0.5
-0.51799
(0.8552)
0.038416
(0.6989)
0.155876
(0.7383)
0.6
-0.28434
(0.9104)
0.044478
(0.6313)
0.155876
(0.7383)
0.7
0.384955
(0.8769)
-0.02046
(0.8381)
-0.20985
(0.4419)
0.8
0.824216
(0.7204)
-0.0143
(0.8797)
-0.19075
(0.4131)
0.9
1.693522
(0.2669)
-0.01533
(0.8742)
-0.21852
(0.3899)
-0.01807
(0.6445)
0.017975
(0.7879)
0.090303
(0.4966)
-0.00963
(0.8472)
0.030652
(0.5671)
0.000836
(0.9964)
0.010062
(0.8094)
-0.01658
(0.6928)
-0.02774
(0.8789)
0.01324
(0.8383)
-0.00257
(0.9592)
-0.07603
(0.7696)
0.014889
(0.8286)
-0.0018
(0.971)
-0.03581
(0.894)
0.013225
(0.8437)
0.009184
(0.8401)
0.057865
(0.8445)
0.020503
(0.7648)
0.02895
(0.5795)
0.084845
(0.8046)
0.045286
(0.5348)
0.00173
(0.9734)
-0.01543
(0.9635)
0.084752
(0.019)**
-0.03301
(0.3147)
-0.15701
(0.2615)
0.021397
(0.3134)
0.001062
(0.9477)
0.003049
(0.7439)
-0.00296
(0.7659)
-0.00404
(0.6712)
0.000386
(0.964)
0.003858
(0.6732)
0.000848
(0.9155)
-0.00261
(0.7687)
-0.01315
(0.1663)
-0.03315
(0.7651)
0.004246
(0.7657)
-0.04973
(0.4086)
-0.00799
(0.4284)
-0.08264
(0.1535)
-0.00072
(0.9541)
-0.0301
(0.6962)
-0.00233
(0.8462)
-0.02956
(0.7191)
-0.00633
(0.5558)
0.02532
(0.7075)
-0.00499
(0.687)
0.072587
(0.2447)
-0.00144
(0.9091)
0.044538
(0.4507)
0.001656
(0.8683)
0.012965
(0.8242)
*** means P value is significant at 1% level, ** means P value is significant at 5% level, * means P value is
Table (4-11) The Quantile Regression Analysis Result of Exchange Return for Hong Kong
Hong Kong
OLS
QT
Intercept (c)
0.029278
(0.0618)***
0.1
0.070782
(0.0011)*
0.2
0.041632
(0.0546)***
Slope
-0.004917
(0.4012)
-0.005173
(0.8985)
-0.022684
(0.5203)
Basic
Interest
Rate
Inflation
Rate
Discount
Rate
Industrial
Production
Index
Temperature
0.3
0.020482
(0.2466)
0.4
0.005778
(0.7363)
0.5
0.003887
(0.8309)
0.6
-0.004372
(0.7936)
0.7
0.012497
(0.4487)
0.012372
(0.0038)*
0.000287
(0.151)
0.012288
(0.0036)*
0.002351
(0.4495)
0.8
-0.02064
(0.2598)
-0.00771
-0.010629
0.002655 0.006855 (0.1219)
(0.0178)**
0.013562
(0.6044)
(0.1262)
(0.0021)*
0.000404
0.000938
0.000711
0.000144 0.000177 0.000312 0.000255
0.000346
(0.1704)
(0.2469)
(0.3606)
(0.6307)
(0.4035)
(0.1821)
(0.1865)
(0.087)***
0.005093
0.005763
0.023071
0.002788 0.00697
0.007806 0.010742
0.013243
(0.3826)
(0.8874)
(0.515)
(0.5834)
(0.1187)
(0.1133)
(0.0153)**
(0.002)*
-0.004861
-0.012048
-0.006949
0.000746
0.004345
(0.1163)
(0.004)*
(0.0489)**
0.003297 0.001332 0.000732 (0.8082)
(0.2205)
(0.2958)
(0.6798)
(0.8248)
-0.001839
-0.003279
-0.002857
-0.000404 -3.75E-0.00018
(0.0216)**
(0.0705)*** (0.167)
0.001961 0.000291 0.000591 (0.7215)
05
(0.8642)
(0.1738)
(0.7436)
(0.6446)
(0.9741)
Turnover
-0.000827
-0.004576
-0.001406
9.21E-05 0.000592 0.000705 0.001391
0.001384 0.001268
Rate
(0.3624)
(0.012)**
(0.474)
(0.9375)
(0.5828)
(0.5193)
(0.1522)
(0.2209)
(0.2326)
Remark : For all P values in the quote, *** means P value is significant at 1% level, ** means P value is significant at 5% level, * means
significant at 10% level.
0.9
-0.016175
(0.4687)
-0.013823
(0.0025)*
0.000488
(0.0316)**
0.01345
(0.0024)*
0.003761
(0.396)
-0.00018
(0.8642)
0.000529
(0.6478)
P value is
Proceedings of Annual Spain Business Research Conference
14 - 15 September 2015, Novotel Barcelona City Hotel, Barcelona, Spain
ISBN: 978-1-922069-84-9
Table (4-12) The Quantile Regression Analysis Result of Exchange Return for Singapore
Singapore
OLS
QT
0.1
0.239539
(0.762)
0.4
0.5
0.6
0.7
0.8
0.9
0.24407 0.62033
0.6793
0.5495
0.26206 6
0.45130 7
7
21
8
(0.7707) 4
(0.5871)
(0.3705 (0.468) (0.7395)
(0.5887)
)
Slop Basic
0.01932 0.119298 0.007282 0.5395
0.5385
0.39396 0.08204 0.34692 0.06758
e
Interest
3
0.075641 (0.7933)
(0.988)
94
39
4
7
8
1
Rate
(0.959)
(0.876)
(0.2471 (0.2359 (0.3808) (0.8574) (0.4726) (0.8931)
)
)
Inflation
-0.00389
-5.37E0.00417
Rate
0.00107 0.006496 (0.0588)*
0.003633 0.0021
0.0015
0.00083 0.00083 05
5
8
(0.0802)*
**
(0.0958)*
92
75
6
7
(0.9824) (0.1775)
(0.6067) **
**
(0.2998 (0.451) (0.7097) (0.7069)
)
Temperat ure
0.03864 0.036565 0.055685 0.037268 0.0365
0.0715
0.07287 0.06956 0.00323 0.17268
1
(0.5181)
(0.3117)
(0.548)
67
54
9
7
9
2
(0.4489)
(0.4816 (0.162) (0.2027) (0.3149) (0.9456) (0.4311)
)
Trade
Remainin 0.01193 0.001429 0.014731 0.012146 0.0139
0.0151
0.01996 0.01899 0.01456 0.01859
g
9
(0.9306)
(0.132)
(0.2416)
3
66
6
8
(0.0048) 2
(0.0054)
(0.0989 (0.0541 (0.0002) (0.0005) *
(0.0025)
*
)***
)***
*
*
*
Remark : For all P values in the quote, *** means P value is significant at 1% level, ** means P value is significant at 5% level, *
means P value is significant at 10% level.
Intercept (c)
0.18583
3
(0.7692)
0.2
0.087969
(0.8978)
0.3
0.196163
(0.7889)
Table (4-13) The Quantile Regression Analysis Result of Exchange Return for China
China
OLS
Intercept(C)
0.225097
(0.0217)*
*
Slope
Index
of
Consumer
Sentiment
Inflation Rate
-0.00459
(0.6603)
Discount Rate
QT
0.1
0.390893
(0.0073)*
0.2
0.182571
(0.3158)
0.017872
(0.2917)
0.001995
(0.0469)*
*
0.003115
(0.7347)
-0.00171
(0.0534)*
**
0.006303
(0.1487)
0.008217
(0.222)
0.013599
(0.0976)*
**
Industrial
Production
Rate
-0.03932
(0.0518)*
**
-0.06012
(0.0296)*
*
0.030283
(0.4194)
Temperature
-4.85E05
(0.9383)
0.001168
(0.2332)
0.000361
(0.69)
0.000616
(0.5784)
-0.00195
(0.0358)*
*
-0.00135
(0.0592)*
**
Trade
Remaining
0.001345
(0.1789)
0.3
0.18011
6
(0.0267)
**
-0.00259
(0.7456)
0.00196
1
(0.0194)
**
0.01557
9
(0.0093)
*
0.03077
3
(0.0793)
***
0.00068
3
(0.4931)
0.00073
9
(0.2083)
0.4
0.153323
(0.0258)*
*
0.5
0.143494
(0.0352)*
*
0.6
0.218258
(0.0155)*
*
0.7
0.203405
(0.015)**
0.8
0.194939
(0.0076)*
0.9
0.126701
(0.1824)
0.008591
(0.1819)
0.001093
(0.1444)
0.006337
(0.2825)
0.001114
(0.1253)
0.006337
(0.2825)
0.000777
(0.2631)
0.004543
(0.4641)
0.000841
(0.1827)
-0.004728
(0.3693)
-0.00101
(0.8771)
-0.00077
(0.1595)
-0.000125
(0.8601)
0.009196
(0.0982)*
**
0.007723
(0.1509)
-0.00362
(0.3765)
0.003287
(0.3581)
-0.003548
(0.2335)
-0.002294
(0.4641)
0.020765
(0.1419)
0.020653
(0.1462)
0.038769
(0.0408)*
*
0.036054
(0.04)**
-0.034227
(0.0246)**
-0.025059
(0.1504)
0.000298
(0.6044)
-0.0005
(0.4261)
0.000316
(0.5652)
0.000762
(0.2047)
0.000596
(0.2141)
-0.00074
(0.295)
0.000498
(0.2448)
0.000639
(0.3104)
-0.00045
(0.2169)
-0.000221
(0.5358)
-0.000546
(0.3109)
7.85E-05
(0.8937)
Note : For all P values in the quote, *** means P value is significant at 1% level, ** means P value is significant at 5% level, * means P value is significant
at 10% level.
Proceedings of Annual Spain Business Research Conference
14 - 15 September 2015, Novotel Barcelona City Hotel, Barcelona, Spain
ISBN: 978-1-922069-84-9
Table (4-14) The Quantile Regression Analysis Result of Exchange Return for Indonesia
Indonesia
OLS
Intercept(C)
0.972
824
(0.409
3)
S
lo
p
e
0.001
168
(0.233
2)
0.077
83
(0.451
4)
Inflation Rate
Discount Rate
QT
0.1
0.16056
(0.1804)
0.2
-0.16056
(0.1804)
-0.004193
(0.7173)
0.006022
(0.5015)
-0.032794
(0.1611)
0.026143
(0.1814)
0.3
0.0027
92
(0.962
5)
2.02E05
(0.998
3)
0.0179
47
(0.371
8)
0.0036
66
(0.631
7)
0.4
0.00806
4
(0.9016)
0.5
0.0132
85
(0.8597
)
0.6
0.073
524
(0.363
6)
0.7
0.071642
(0.3731)
0.8
0.074256
(0.4403)
0.9
0.112004
(0.2792)
0.00025
9
(0.9774)
0.0064
36
(0.5246
)
0.009
176
(0.394
3)
0.010801
(0.3267)
0.015964
(0.1968)
0.016986
(0.2101)
0.01006
4
(0.5733)
0.004277 0.020931 0.04794
0.0083
0.010
(0.8352)
(0.414)
(0.0184)
27
427
(0.6426 (0.565
)
3)
Trade
0.013
0.026743
0.010001
0.00268 Remaining
285
(0.0919)*** (0.3282)
3
0.0011
0.008
0.011657 0.016585 0.027723
(0.053
(0.7503) 21
306
(0.2398)
(0.1566)
(0.0175)
9)***
(0.9078 (0.408
)
8)
Remark : For all P values in the quote, *** means P value is significant at 1% level, ** means P value is significant at 5% level, * means
P value is significant at 10% level.
Table(4-15) The Quantile Regression Analysis Result of Exchange Return for Malaysia
Malaysia
OLS
0.063726
(0.6354)
QT
0.1
0.250959
(0.1481)
Intercept (C)
0.2
0.200348
(0.1978)
0.3
0.24939
8
(0.0342)
**
0.4
0.14742
9
(0.1655)
0.5
9.99E-16
(1)
S Basic
Interest
l Rate
o
p
e
Inflation Rate
0.007267
(0.7513)
-0.008294
(0.7705)
0.011309
(0.6926)
0.03264
1
(0.1424)
-2.91E-16
(1)
0.005349
(0.0501)*
**
0.001509
(0.8469)
-0.000284
(0.955)
0.04089
8
(0.0497)
**
0.00166
4
(0.5602)
0.00138
9
(0.3554)
-1.08E-18
(1)
Industrial
Production
Index
0.022044
(0.1578)
-0.022044
(0.1578)
-0.034352
(0.0225)**
-0.02182
(0.0282)
**
Temperature
0.003167
(0.914)
-0.00092
(0.9842)
-0.015138
(0.6995)
0.01695
7
(0.4268)
0.01433
7
(0.1259)
0.00444
(0.7499)
Trade
Remaining
0.000959
(0.8536)
-0.007623
(0.0925)***
-0.003229
(0.4993)
0.00323
6
(0.2454)
Remark : For all P values in the quote, *** means P value is significant at
significant at 10% level.
0.00141
5
(0.5505)
1% level,
0.6
0.0033
69
(0.9782
)
0.0011
97
(0.9625
)
0.7
0.061885
(0.6638)
0.8
-0.205604
(0.1252)
0.9
-0.307331
(0.1067)
0.016245
(0.5478)
0.0162
45
(0.5478)
0.042352
(0.2406)
0.000767
(0.721)
0.006767
(0.0493)**
0.008332
(0.5268)
0.014617
(0.3632)
0.019993
(0.3254)
0.033915
0.4444)
0.003972
(0.2833)
0.007395
(0.0658)***
5.37E0.000194
05
(0.9237)
(0.9765
)
-2.10E-16 0.0004
0.00274
(1)
74
(0.8293)
(0.9676
)
1.34E-16
0.002322
(1)
0.0003
(0.9044)
82
(0.9817
)
9.54E-18
4.80E4.80E-05
(1)
05
(0.9869)
(0.9869
)
** means P value is significant at 5%
level, * means P value is
Proceedings of Annual Spain Business Research Conference
14 - 15 September 2015, Novotel Barcelona City Hotel, Barcelona, Spain
ISBN: 978-1-922069-84-9
Table(4-16) The Quantile Regression Analysis Result of Exchange Return for Philippines
Philippines
OLS
QT
0.1
0.2
0.3
Intercept
0.0693
0.068202 0.009857
(C)
0.021148 86
(0.6115)
(0.9477)
(0.9175)
(0.7164
)
S Basic
-0.05482
l Interest 0.060026 0.0243
0.054557 (0.1322)
o Rate
(0.1142)
7
(0.0733)*
p
(0.4849 **
e
)
Inflation -0.01121
Rate
0.013409 0.0168
(0.0523)* 0.011689
(0.1868)
78
**
(0.0868)*
(0.0874
**
)***
Discou
0.04274
0.0311
0.033265 0.033265
nt Rate (0.0355)* 34
(0.0251)* (0.0251)*
*
(0.1013 *
*
)
Temper 0.032432 0.005938 0.022834
ature
(0.5787)
0.0104
(0.8705)
(0.5753)
53
(0.8383
)
Trade
Remain 0.000475 0.0047
0.002318 0.001898
ing
(0.8491)
59
(0.1515)
(0.2994)
(0.007)
*
Remark : For all P values in the quote, *** means P value
means P value is significant at 10% level.
0.4
0.076163
(0.6103)
0.5
0.017934
(0.9044)
0.6
0.066875
(0.6618)
0.7
0.148806
(0.3269)
0.8
0.041724
(0.8701)
0.9
0.07175
2
(0.7231)
0.07426
7
(0.1177)
0.065428
(0.0897)*
**
0.069049
(0.0861)*
**
0.046639
(0.3001)
0.075347
(0.1476)
0.092921
(0.0844)*
**
0.005339
(0.4378)
0.005695
(0.4064)
0.005057
(0.4964)
-0.00632
(0.4378)
0.016426
(0.2389)
0.01902
4
(0.1348)
0.030882
(0.0684)*
**
0.037848
(0.0301)*
*
0.023712
(0.183)
0.035371
(0.0514)*
**
0.048536
(0.0568)*
**
0.03962
(0.1103)
0.009394
(0.8145)
0.026209
(0.5137)
.003382
(0.9328)
0.007784
(0.836)
0.033074
(0.6532)
0.06005
1
(0.3537)
0.000928
(0.624)
0.000957
(0.6)
0.000443
(0.8179)
0.000544
(0.7741)
0.002772
(0.2355)
0.00443
2
(0.0842)*
**
is significant at 1% level, ** means P value is significant at 5% level, *
Table(4-17) The Quantile Regression Analysis Result of Exchange Return for India
India
OLS
Intercept (C)
0.18450
6
(0.8625)
S Basic Interest
l Rate
o
p
e Inflation Rate
0.01875
5
(0.3699)
0.00267
6
(0.8136)
0.00388
(0.8533)
-0.009641
(0.5405)
Discount Rate
0.02838
7
(0.6706)
0.17487
5
(0.5334)
0.222644
(0.3915)
Industrial
Production
Index
0.00741
3
(0.7816)
0.01862
(0.5204)
0.03531
(0.4163)
-0.003121
(0.92)
0.01829
1
(0.6446)
-0.018218
(0.5779)
Temperature
QT
0.1
0.27024
6
(0.6787)
0.02483
2
(0.6813)
0.2
-0.397468
(0.4798)
0.032441
(0.5032)
0.3
0.29806
6
(0.6505)
0.01180
1
(0.7621)
0.4
0.21058
1
(0.7655)
0.00503
3
(0.9129)
0.00841
8
(0.4879)
0.10116
8
(0.7836)
0.5
0.38290
9
(0.592)
0.00552
7
(0.8979)
0.01356
2
(0.2355)
0.10795
6
(0.7727)
0.6
0.34076
3
(0.6285)
0.00172
4
(0.9613)
0.01150
3
(0.3099)
0.06828
1
(0.8503)
0.00426
7
(0.8729)
0.00923
(0.7581)
0.02688
4
(0.3576)
0.01821
8
(0.5779)
5.78E05
(0.9985)
0.01532
2
(0.631)
0.01177
4
(0.3648)
0.13760
3
(0.6783)
0.8
0.217836
(0.7867)
0.9
0.51514
5
(0.6877)
-0.026839
(0.2843)
0.02348
8
(0.5689)
-0.003887
(0.7998)
0.00199
9
(0.9354)
-0.312513
(0.4345)
0.03047
4
(0.2786)
0.7
0.03692
5
(0.9583)
0.01136
6
(0.6672)
0.00880
8
(0.475)
0.14300
6
(0.6896)
0.03066
1
(0.2708)
0.61337
9
(0.2547
0.02838
7
(0.6706)
0.02316
1
(0.4916)
0.05332
1
(0.1717)
0.083025
(0.0523)**
*
0.027874
(0.4205)
0.11833
6
(0.001)*
Remark : For all P values in the quote, *** means P value is significant at 1% level, ** means P value is significant at 5% level, * means
P value is significant at 10% level.
Proceedings of Annual Spain Business Research Conference
14 - 15 September 2015, Novotel Barcelona City Hotel, Barcelona, Spain
ISBN: 978-1-922069-84-9
Table (4-18) the Quantile Regression Analysis Result of Exchange Return for Taiwan
Taiwan
OLS
Intercept (C)
0.668518
(0.0878)**
*
-0.027801
(0.1422)
S
l
o
p
e
Basic
Interest
Rate
Index
of
Consumer
Sentiment
Inflation
Rate
Money
Supply
QT
0.1
1.169362
(0.1076)
0.2
0.467291
(0.4053)
0.3
0.60018
(0.3177)
0.4
1.09089
(0.0377)**
0.5
1.271351
(0.0101)**
0.6
0.985412
(0.0362)**
0.7
0.652877
(0.1571)
0.8
0.670867
(0.1933)
0.9
0.954214
(0.1848)
-0.073131
(0.0342)**
-0.033813
(0.2122)
-0.019993
(0.3976)
-0.043136
(0.043)*
-0.026591
(0.1935)
-0.017529
(0.3935)
-0.016664
(0.5016)
-0.027256
(0.4667)
-0.036692
(0.0195)**
-0.07037
(0.0046)*
-0.052343
(0.0051)*
-0.050811
(0.0192)**
-0.03881
(0.0781)**
*
-0.041528
(0.0374)**
-0.032018
(0.106)
-0.044736
(0.0241)**
-0.040398
(0.0364)**
-0.033815
(0.1106)
-0.015187
(0.6079)
0.000668
(0.7584)
-0.031733
(0.1672)
0.003298
(0.3512)
-0.056799
(0.2017)
0.003116
(0.4738)
-0.014444
(0.6842)
0.001427
(0.6092)
-0.024344
(0.5204)
-7.95E-05
(0.9819)
-0.049591
(0.0838)**
*
0.016781
(0.0267)**
0.00299
(0.3247)
-0.029237
(0.2958)
-0.002142
(0.5953)
-0.030501
(0.3183)
-0.00579
(0.1808)
-0.050473
(0.2158)
0.011166
(0.1639)
0.014153
(0.1043)
0.019282
(0.0417)**
0.001102
0.001111
(0.6993)
(0.6982)
-0.055776
-0.069089
(0.0865)**
(0.0227)**
*
Temperatu 0.018542
0.030326
0.017331
0.017692
0.017285
0.015003
re
(0.002)*
(0.0314)**
(0.0698)**
(0.0702)**
(0.0527)**
(0.0588)**
*
*
*
*
Turnover
-0.011766
0.007898
-0.015662
-0.013036
-0.012833
-0.009937
Rate
(0.1204)
(0.6703)
(0.1434)
(0.1478)
(0.1516)
(0.2491)
Remark: For all P values in the quote, *** means P value is significant at 1% level, ** means P
significant at 10% level.
-0.007685
-0.009216
-0.021567
-0.037133
(0.3532)
(0.3054)
(0.1208)
(0.0223)**
value is significant at 5% level, * means P value is
Table(4-19) The Quantile Regression Analysis Result of Exchange Return for Thailand
Thailand
Intercept (C)
OLS
0.04761
5
(0.648)
s Basic Interest l Rate
0.00796
o
1
p
(0.6632)
e Inflation Rate
0.00418
3
(0.2839)
Discount Rate
0.00328
9
(0.5818)
Temperature
0.01740
9
(0.5346)
Remark : For all P values in the
significant at 10% level.
QT
0.1
-0.336167
(0.5052)
0.2
-0.025911
(0.8016)
0.3
0.4
0.5
0.6
0.7
0.8
-0.03676
-0.076119
-0.19122
0.01806
(0.6466)
0.10009
0.03893
(0.4868)
(0.1599)
1
7
3
(0.8283)
(0.2779)
(0.7101)
-0.057899
-0.015788
0.00038
-0.018077
(0.0557)**
(0.3653)
0.01228
0.00134
2
0.01768
(0.3485)
0.006854
*
5
8
(0.9835)
5
(0.7575)
(0.4799)
(0.9393)
(0.3672)
-0.008666
0.0031
0.00514
0.00274
0.00122
0.00461
0.007258
0.00249
(0.1784)
(0.4493)
9
5
2
5
(0.0718)*** (0.5905)
(0.1957)
(0.4618)
(0.7875)
(0.2803)
-0.005
-0.009441
0.00169
0.000285
0.005578
(0.7213)
(0.0829)**
0.00770
0.00478
0.00098
9
(0.959)
(0.4042)
*
6
1
5
(0.7528)
(0.1177)
(0.3325)
(0.8556)
0.12846
0.015124
0.01063
0.01058
0.02868
0.01976
0.031303
0.060054
(0.4167)
(0.6402)
5
8
8
7
(0.2205)
(0.0478)*
(0.6628)
(0.6411)
(0.2329)
(0.4366)
*
quote, *** means P value is significant at 1% level, ** means P value is significant at 5% level, * means
0.9
0.23367
3
(0.2243)
0.02358
2
(0.3413)
0.00492
4
(0.3201)
0.00922
2
(0.5118)
0.05605
9
(0.2389)
P value is