IMPACT OF GOLD PRICES CHANGES ON STOCK MARKET: EVIDENCE FROM MALAYSIA - sersc
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International Journal Of Advanced Science And Technology
Vol. 29, No. 4s, (2020), Pp. 778-790
IMPACT OF GOLD PRICES CHANGES ON STOCK MARKET:
EVIDENCE FROM MALAYSIA
Raed Walid Al-Smadi
Department of Finance and Economics Science, Irbid National University, Irbid, Jordan
Abstract
This research paper focused on assessing the impact of changes in the gold prices on the stock market
taking evidence of Malaysia. The main focus of the research is carried out on the dynamic influences of the
changes in oil prices on the Malaysian stock exchange with the help of econometrics approach. For this
purpose, Data return on gold and capital market have been collected though period Feb 2000 to Dec 2019
in monthly time window. Total number of observations in terms of months are 239 observations and this
makes data equal to 20 years. Meanwhile, diagnosis and preliminary tests have indicated to choose the
VECM model for which JJ cointegration and Granger causality was also prerequisite. The empirical
findings of the study provided consistent findings that return of gold and return on capital market have
relation in long-run but not in short-run. Meanwhile, the VECM findings shows that return on gold has
negative effect on the return on capital market. In addition to the, paper also discusses implications and
provided recommendations for the investors.
Keywords: Gold prices, stock market, VECM, Malaysia
1. Introduction
This research paper has focused on assessing the impact of changes in the gold prices on the stock
market taking evidence of Malaysia. The main focus of the research is carried out on the dynamic influences
of the changes in oil prices on the Malaysian stock exchange with the help of econometrics approach. In
the past few years, there has been an upsurge in the studies pertaining to the gold and oil prices where the
main reason is focused on the increases in the strategic commodities that have irreplaceable roles on the
globalised economy. According to the study of Shiva & Sethi (2015), the gold is considered to be the market
leader of the metal market regardless of the country where it is also treated as a huge investment asset based
on the commodity of the industrial levels. The gold is mainly referred to as the “safe haven” for the avoiding
of the risk in various financial markets (Hussin et al., 2013; Sharma & Mahendru, 2010). In different
countries, gold is used as a management tool of the risk which is focused on the diversification and hedging
of the commodity portfolios which is mainly less susceptible to the fluctuations in the currency rates.
Bearing this in mind, it can be highlighted that gold has that power which can change the purchasing power
of the consumers along with the domestic currency. In addition to the above statement, the research
conducted by Mahmood & Mohd Dinniah (2007) highlighted that gold prices have the adaptability for
adjusting quickly with the inflation rates as it has the ability of value-preserving.
The study conducted by Ghazali, Lean & Bahari (2015) and Baig et al., (2013), identified that the
gold prices were at the peak at the financial crisis in the developed markets for instance US stock markets
and other major European countries. This was also evident in the Malaysian market where there has been
fluctuating. In the year 2001, the country became the 12th country in the world for having the own gold
bullion coins which were launched as Kijang Emas by the Malaysian Royal Mint. Moreover, the gold prices
are traditionally quoted as the USD which is also available in different denominations as per the
commodities that are traded globally (Nordin, Nordin & Ismail, 2014; Mahmood & Mohd Dinniah, 2007;
Zhang & Wei, 2010). Different scholars and economist have focused on evaluating the association between
the gold prices and stock markets where the evidence has also been provided regarding the significance of
the relationship between the variables.
A study conducted by Ibrahim (2012), identified a dynamic relation regarding the variables and
provided an insight about the how policy makers can efficiently manage the gold prices due to the changes
in the currency considering the complexity of the dynamic relations. It is a known fact that Malaysia as an
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emerging market has improved its economy and attracts foreign capital due to capital flows and bilateral
trade between the countries. Furthermore, the prices of gold in Malaysia have increased in the
commencement of the year 2019 because of the fact that the investors have focused on looking into safe
havens from the uncertainty of economics.
At the start of 2020, the trend is still higher from the price brackets. However, considering the
previous years, there has been a decrease in the prices of gold between the year 2013 and the year 2015
which further recovered to touch the new heights. The report also says that it climbed more than 58% for
the past 5 years (Gazi, 2020). The most likely factor which influenced the prices of gold is the exchange
rate as most of the gold is traded in the US dollars and with the increase in the exchange rate; it can result
in the overall increase for the local prices. On the other hand, it has also been highlighted that stronger
ringgit will tend to focus on lower gold prices. Keeping into consideration the exchange rate of the US
dollars, it is significant for understanding the trading of the gold will affect the selling and buying of the
prices. The current gold prices expressed in Malaysian Ringgit are reflected in the table below.
Table 1: Current Price of Gold
Source: Gold Eagle (2020)
The present research fills the gaps pertaining to the literature as the studies are mainly focused on
the physical gold, for instance, the gold coins. In addition, the studies are primarily focused on international
studies where the developed countries are taken into consideration with few factors considering on the
consumer prices, stock prices and oil prices. This study has focused on the contribution of gold prices
considering the nature of both short and long-term relationship between the gold prices and stock market.
Evaluating the short-term influences will assist the investors, portfolio managers and regulators to make
the most formal investment decisions. The main purpose of the study is focused on evaluating the
relationship between gold prices and the stock market in the evidence of Malaysia. For the conclusion, the
study has attempted to provide valuable recommendations to the policymakers and investors regarding the
investment in the gold and whether their decisions are viable in the context of the Malaysian stock market.
The research question of the study is designed as,
Q. How the fluctuations in the gold prices are related to the stock market in the case of
Malaysia?
2. Literature Review
The investments made in gold are considered as the store of value, inflation hedge, a source of
wealth, mean of exchange and a safe asset for the stock market during the troubles in the stock market
(Balcilar et al., 2018; Selmi et al., 2018; Liu et al., 2016). In addition to this, a sense of certainty is provided
by the gold investments to investors during financial downturns and is also considered as the attractive and
alternate investment due to the simplicity of the gold market (Tiwari, Adewuyi & Roubaud, 2019; Raza et
al., 2016). On the other hand, the fluctuation on the prices of gold can affect the stock market in negative
terms. This is due to the reason that pertaining to the fact that gold is considered as an asset for the stock
market and helps in the financial downturns of investors, therefore, the changes in prices of gold creates
uncertainty in the stock market. The study of Beckmann, Berger & Czudaj (2019) suggested that
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fluctuations in the gold prices increases the uncertainty and lack of interest among the investors. In this
manner, most of the investors pull their investments due to the fluctuations and uncertainty in the gold
prices.
Moreover, due to its positive relation with the inflation, the gold is also considered as the good
instrument of inflationary hedge (Ozatac, Kaakeh & Rustamov, 2017; Aye et al., 2017; Maghyereh,
Awartani & Tziogkidis, 2017). In addition to this, during the periods of high inflation, the investment in
gold can retain its purchasing power. This feature of gold make it trustworthy for the investors in terms of
investing. Due to the low correlation with the other assets while lowering the overall risk of portfolio, gold
can also be considered as the portfolio diversifier (Selmi et al., 2018; Klein, Thu & Walther, 2018; Raza et
al., 2016). Particularly, the gold is also retained by the central banks for the purpose of diversification while
safeguarding the economic uncertainties. This shows the significance of gold for the investors and stock
market in terms of generating the profits and gaining the interests. However, the fluctuations in the prices
of gold can create uncertainty in the gold market which has its adverse effects in the stock market. The
study of Nurulhuda, Hasan & Mohd (2018) also supports the argument that fluctuations in the prices of
gold impact the stock market as the investors lack the trust in order to make their investments in gold.
In spite of the role of gold investments in hedging and portfolio diversification, there is a negative
impact of volatility in the prices of gold on stock markets. On the other hand, safe investment conditions
are indicated by the lower volatility in prices of gold (Elie et al., 2019; Rahman & Mustafa, 2018; Raza et
al., 2016). In this manner, it is necessary in order to understand the volatile behaviour of gold market for
the purpose of making hedging decisions. This has also been supported in the study of Junttila, Pesonen &
Raatikainen (2018) that prior to the hedging decisions, it is necessary to capture the volatile behaviour of
gold market. Furthermore, the increased volatility of the gold prices is regarded also as an alert for the
investors while exposing them to increased chances of risks. In addition to this, the increased risk also
enhances the interest of investor in order to understand the reaction of stock market to the volatility of gold
price (Basher, Haug & Sadorsky, 2019; Bampinas, Panagiotidis & Rouska, 2019; Chkili, 2017). In this
manner, the interest of investors regarding the investment in the stock market is influenced due to the
fluctuations in the prices of gold.
Various economic factors those are interrelated and have a complex connection among them
impacts the stock market. In contrast to this, the macroeconomic variables such as the prices of gold, prices
of crude oil along with the volatilities have more profound impact on the prices of stock (Najafabadi,
Qazvini, & Ofoghi, 2020;Hashim et al., 2017; Jain, & Biswal, 2016). In this manner, the oil, gold and stock
linkages have been analysed in a linear settings (Akbar, Iqbal & Noor, 2019; Jain & Biswal, 2016; Raza et
al., 2016). It has also been argued in the study of Ma et al. (2019) that one of the most basic issue with the
linear modelling is that there are linear time series while in real times they are not linear. On the other hand,
there have been little attention towards the nonlinearities between the stock, gold and oil nexuses. This
shows that there is a certain impact of the fluctuations in the gold prices on the stock market. Moreover, on
the basis of the above discussion, the following hypothesis has been developed and is being tested
throughout the study:
Ho: There is no impact of fluctuations in gold prices on the stock returns.
2.1 Conceptual Framework
Based on the above discussion and the hypothesis on relationship of both the variables, the
conceptual framework of this study is provided below:
2.2 Theoretical Framework
There are two important reasons in the analysis of relationship among the variables that have
nonlinear setting. Firstly, if the negative and positive components of a series are cointegrated, there can be
a hidden cointegration in the time series (Irandoust, 2018; Alexakis, Pappas & Tsikouras, 2017; Bekhet and
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Al-Smadi, 2015). On the other hand, structural and asymmetry breaks are the kind of nonlinearities which
can affect the dynamic of market certainly when the sample period is market with the crisis related to
finance (Awartani et al., 2019; Ngene, Benefield & Lynch, 2018). In this manner, for the purpose of
achieving the aim, nonlinear ARDL (NARDL) approach has been employed which allow in order to test
the short run and long run asymmetries Bekhet and Al-Smadi, 2016. In this manner, the respective responses
are quantified by the dynamic multipliers in the presence of asymmetries in response to the negative and
positive changes in the stock market in each of the explanatory variables. This is done by considering the
negative and positive partial sum of decomposition of these variables. In this manner, the volatile behaviour
of the gold prices has been tested in respect to its impact in the stock market. The study of Jain & Biswal
(2016) suggests that prior making the investment in the gold, it is necessary for the investor to understand
the economy and fluctuation in the prices of gold for that particular country. In this manner, the clear
insights will be gained about the behaviour of gold market and fluctuations due to several macroeconomic
factors.
1. Method
3.1 Data
Data is defined as set of quantitative observations based on the empirical analysis are being
conducted through suitable statistical techniques. This paper has also been dealing with the quantitative
data in which two variables are selected; return of capital market and return on gold. Data of these two
variables have been collected though period Feb 2000 to Dec 2019 in monthly time window. Total number
of observations in terms of months are 239 observations and this makes data equal to 20 years. Meanwhile,
the data was collected from the authentic sources that are considered as free public sources; where data of
capital market was collected from the Yahoo Finance which provides data into different time window of
daily, weekly, monthly, and annually as well; in contrast the data of gold prices were extracted from the
IndexMundi which is also another trusted authentic data source.
In addition to the collection of data was ensured through the publicly available sources since other
sources had required subscription to get data. Hence, publicly available data sources were used that have
also been used by scholars into their researches. Furthermore, attributes of the two variables were time
series; each variable had closing prices of respective month; and to capture true effect return in terms of
percentage change was computed for each variable. Therefore, analysis were conducted on the percentage
change in following paper in which the aim was to investigate either return of capital market is being
affected by the variation into the gold prices specifically in case of Malaysia. On the other hand, a study
conducted by Robiyanto et al., (2017) has also used the percentage change formula to investigate effect of
gross domestic product (GDP) on the capital markets. Hence, utilization of the percentage change formula
is also supported by scholarly work. Thus, followed the empirical literature, it is plausible to form the
relationship between return of capital market (RCM) and return on gold (ROG) as in Equation (1):
RCMt= μ+ α1ROGt +εt (1)
where μ is the intercept, t is the time period, while ε stands for a residual or error term that is assumed to
be normally distributed and α1 is the coefficients of the variable.
3.2 Data Analysis Method
It is discussed by Gunst (2018) that appropriateness and unbiased results depend on type of data
analysis methods being used in the empirical analysis. Similarly, Darlington and Hayes (2016) argues that
in time series data, it is critical that data has to meet with the certain assumptions to apply certain tests. It
has also been discussed by DeFusco et al., (2015) that ordinary least square (OLS) is most commonly used
technique to investigate how a variable has been affected by another variable in given model. Therefore, in
order to proceed with the regression, it is prerequisite that time series data has to meet with the certain
assumptions including normality, multicollinearity, stationarity and autocorrelation (Fox, 2019; Al-Smadi,
& Omoush, 2019). Meanwhile, results of these tests showed that data is not normal, which means the
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residuals or error terms are not approximately normally distributed. In this regard, DeFusco et al., (2015)
has suggested that use of regression for the forecasting is not appropriate based on the fact that it could not
correct the certain errors associated with the data, and secondly regression is also restricted to be
implemented on the data that is normal otherwise the expected outcome would be biased. Furthermore,
when regression empirical model could not be used for the analysis due to violation of the assumptions,
then vector autoregressive (VAR) model is being used for the forecasting (Suharsono, Aziza & Pramesti,
2017). However, due to associated with problem with the data and that VAR model is also limited to
estimate at some extent but vector error correction model (VECM) has been used as replacement of VAR
since it holds an advantage in terms of correcting the errors and is more powerful in coefficient estimation
(Bekhet, Yasmin, & Al-Smadi, 2017; Kuo, 2016). Hence, as compare to the linear regression and vector
autoregressive (VAR) model, VECM model is more appropriate as per the type of data in following paper.
Therefore, in following paper VECM was used to investigate either variations into gold prices have
influence over the return on capital market return. Thus, the general form of the VECM model could be
formulated as in equation (2).
k k
ΔRCMt = μ1 +
j 1
β11ΔRCMt-j +
j 0
β12ΔROGt-j +ΠiECMt-1 +εt (2)
Where, μ denote intercept; β11, β12 represent the short-run coefficients of the variables; and Πi represents the
coefficients of the error correction terms (ECMt-1) that are used to explore the long-run relationship and εt
represent the error term.
2. Findings and Analysis
4.1 Descriptive Summary
Capital market refers to the stock exchange market trading of the shares undertakes between the
investors and brokers. Table 1 elucidates the summary of statistics for two variables of the paper. The return
of capital market in Malaysia on average has been 0.003 that could also be stated 0.3% in given month and
it also has standard deviation 0.0396 or can be stated as 3.96%. It can be interpreted as that an investor
could have a return of 0.3% on average from the Malaysian capital market; but this return could also move
away in either direction by value of standard deviation. In contrast, during period of sample size 2000 to
2019; minimum and maximum profit was reported as -15.222% and 13.545%; hence the return of
Malaysian capital market could also fall under the given range.
Table 2 Statistics Summary
Referring to the gold return, on average an investor can achieve a return of 0.007 or can be stated
as 0.7% and it also has standard deviation 0.0356 that means the average return on the gold could also move
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away in either direction. Meanwhile, it can be determined that on average an investor could earn a return
0.792% on the purchase of gold, and this return could also go as high as 12.048% and go as low as 10.526%
in any given time. In addition to this, normality of the data was also checked through the Jarque-Bera;
where the value of Jarque-bera is 29 [p=0.000] and 3.45 [p=0.1777]; this implies data of capital market
return is not approximately normally distributed but the data of return of gold is approximately normally
(Park, 2015). In contrast, the normality was also checked through regression on the residuals and following
results was extracted as elucidated into the figure 1.
Figure 1 Histogram and Jarque-Bera
The figure 1 explains that value of Jarque-bera is 28.131 [p=0.0000] this implies that data as whole
is not approximately normally distributed. It is because the null hypothesis of the normal distribution is
rejected that data is approximately normally distributed.
4.2 Diagnostic Analysis
Diagnostic analysis were conducted with an intention investigate extent to which attributes of the
data meet with empirical methods being used in the following study. In this regard, Fox` (2019) has
suggested various method to diagnose the time series data; hence the issue of unit root and autocorrelation
were investigated. However, the test of multicollinearity was dropped as suggested by Kalnins (2018) that
multicollinearity investigates that whether two independent variable are strongly related or not. Since, in
following paper there were only two variables; one independent variable and second dependent variable,
hence the issue of multicollinearity was not possible. Meanwhile, other diagnosis tests are presented and
analyuzed as follows:
4.2.1 ADF Results
Augmented Dickey-Fuller (ADF) is being used to investigate stationarity of the data; and the null
hypothesis of the unit root states that time series contains unit root. Meanwhile, results of ADF test
presented into the appendix shows that t-statistics of capital market return and gold return are -13.766
[p=0.000] and -14.544 [p=0.000] respectively (Paparoditis and Politis, 2018). Therefore, it can be stated
that both time series variables do not have unit root problem and are stationary.
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4.2.2 Autocorrelation Results
Autocorrelation refers to the presence of pattern in the data where the lagged value of timer series
can be used to estimate its own future values based on the historical pattern present. In time series
forecasting, series must be free from the issue of autocorrelation since it also enables error terms to have
pattern and this makes forecasting biased and method worthless (King 2018). It was checked through
Breusch-Godfrey LM test and results of the autocorrelation test are presented in appendix. The null
hypothesis of the test is that no autocorrelation exists up to 2 lags, since Prob. F value of the test is 0.127
that is greater than alpha 0.05; hence it is sufficient to claim that autocorrelation does not exists in the data
up to 2 lags (Abdulhafedh, 2017).
4.3 Johansen Cointegration results
Johansen Cointegration that has also been known as JJ cointegration is widely used for the purpose
of identifying the cointegrating relationship in which cointegrating vectors are identified based on the
forecasting could be conducted. Similarly, Dwyer (2015) argues that Johansen cointegration can only be
used on the condition that there must be linear combinations in the time series that could be used to estimate
on variable from another. Therefore, cointegration test could not be ignored since it could lead to spurious
regression in which r-squared could approach the sky but would be still useless (Ahmed et al., 2017). Hence,
in following paper, this technique is use to investigate the presence of cointegrating relationships or vectors
based on which association of variable could be estimated as a fact of long-run interconnection with time
series. Meanwhile, the results of the tests are presented as follows
Table 3 Cointegration
The Johansen cointegration outcomes elucidates that within the and between the two time series
there are at most two cointegrating vectors; hence these vectors could be used estimate return of stock
market through return on gold. However, presence of cointegrating vectors is an evidence that there is long-
run association of return on gold and return on the capital markets (Ahmed et al., 2017). Therefore, it is to
be interpreted as that if the prices of gold in Malaysia has been rising then this could have a positive or
negative effect on the return of the capital market.
4.4 Granger Causality Results
Granger causality is being used for the forecasting purposes to investigate if the certain time series
could be used to forecast. It is more like the cause-and-effect but is quite different in concept; where if a
time series is granger caused by another time series then in this condition it is said that lagged values of one
time series can estimate another time series (Ahmed et al., 2017). In addition, it is mainly used for the
purpose of exploring short-run association within the variables, and outcome of the test as follows
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Table 4 Granger Causality
The first hypothesis of the test is that return of capital market is not being granger caused by the return on
gold; since the F-statistic 1.521 [p=0.220] this suggest to accept the hypothesis and state that return on the
capital market is not being granger caused by the return on gold. Hence, it infers that return on capital
markets does not has short-run association. Similarly, last hypothesis states that gold return is not be granger
caused by return on capital market and it has F-statistic 1.183 [p=0.308] and this also suggests to accept the
null hypothesis that gold return is not be granger caused by return on capital market (Puente-Ajovin &
Sanso-Navarro, 2015) . It can also be interpreted as that both time series variables have neither bi-directional
nor unidirectional relation implying that both variables cannot be used for forecasting purposes since none
of them have short-run association.
4.5 VECM Model Results
VECM model has been used by various scholars with purpose to control the errors that could not
be controlled by other conventional statistical techniques such as regression and VAR. Meanwhile, VECM
model was conducted and results of the model as follows
Table 5 Summary of Model
Coefficient of determination of the model is 0.351 which means 35.17% of regressand could be
estimated by the regressors of the model. Meanwhile, this can also be interpreted as that 35.17% variance
of the return on capital market could be explained by the return on the gold; this means that certain variables
that have not been included into the model are reasons of low explanatory power that has led to residual of
the model (Zhang, 2017). Meanwhile, the explanatory power of the model in terms of coefficient estimation
is elucidated in table below
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Table 6 Coefficients Table
The table explains that if the return on the gold changes by 1 unit then it is expected that a change
of -2.87 would incur in return of capital market of Malaysia. This implies that return of gold has negative
influence over the return on capital markets; which means that if the return on the gold increases by 1%
then this could decline the return of capital markets (Hussain & Saaed, 2015).
3. Discussion of Findings
The findings of the present study suggests that return of gold has negative influence over the return
of capital market of Malaysia. Thus, if the investors invest into the capital markets and they could bear a
loss if the return on gold increases. In contrast, a rise into the capital market could also lead to a slump into
the return on the gold in case of Malaysia. There is also a theoretical explanation behind this empirical
finding as discussed and argued by various scholars such as Balcilar et al., (2018); Selmi et al., (2018); Liu
et al., (2016) these scholars have argued that gold has been used as one most valuable investment
opportunity as hedging strategy against the inflation and is also used when other sources of investment are
not attractive into the markets. Similarly, Tiwari, Adewuyi & Roubaud, (2019); Raza et al., (2016) have
also discussed that when stock market has not been performing as per the expectations of the investors, then
investors turn to other attractive investment opportunities that could entirely protect them from the inflation.
In this condition, investors find government securities and investment into the gold more attractive
opportunity.
However, choice of the investors in financial down turn has also been gold since various previous
studies have explained that association between the gold and stock market has been negative since investors
are aware that return from the government securities tend to less than their expectations and that it could
also not compensate against the inflation. Therefore, as per the rule of economics the shift into the aggregate
demand assuming the supply constant, the price would surely skyrocket (Klein, Thu & Walther, 2018; Raza
et al., 2016). Hence, investor’s frequent investment into the gold increases prices of gold which further
attracts more investors into the market, and the instant withdrawal from the stock market becomes choice
of investors. Consequently, on one hand the capital market has already been under the slump but after
investors focus on the gold further lead to worst down fall into the capital market (Selmi et al., 2018).
Similarly, based on this explanation and discussion, it can also be evidently stated that return on the gold
also has negative association with the capital market return in case of Malaysia and this is also empirically
and theoretically possible.
4. Recommendations
Based on the findings of the study, the following recommendations are provided,
1. There should be an appropriate analysis on the future gold demand as it has been estimated that the global
demand of the gold will likely to continue with the supply. However, with limited mining capacity coming
through, the gold should be recycled so that the fluctuations in the prices can be controlled. Also, the
inflationary pressures should also be taken into consideration as the positive drivers for the gold prices.
2. The consumption demand for the gold will likely to be increased in the Malaysian culture due to the
increasing desire for financial protection. Therefore, gold will be considered as the safety of investment and
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the main rationale behind purchases. For this purpose, the hedging should be carried out while identifying
the downside risk associated with the gold prices fluctuations.
5. Limitations
This study has focused on the identification of impact of fluctuations in gold prices and the stock
market which is the study based on the Malaysian stock market. However, there are certain limitations of
the study. Firstly, the study has focused on the stock market of Malaysia which restricts the scope of this
study to one country. Therefore, the scope of the study can be broaden with the help of including the stock
markets of other Asian countries. On the other hand, the study has focused on the impact in stock market
because of the fluctuations in the prices of gold. In addition to this, there may be other factors which may
influence the stock market of the country. Including rage of factors may increases the scope of the study
while providing broader picture of the stock market of that particular country. These were the certain
limitations of the study which must be considered by the researchers conducting the study in same field in
the near future.
6. Conclusion
This study has examined the impact of fluctuation in gold prices on the stock market in the context
of Malaysia. In this manner, the study has involved the descriptive findings for the purpose of relating the
trends in the gold industry with respect to the fluctuations in the prices of gold. On the basis of discussion
and analysis, it has been identified that the gold is deliberated to be the market leader of the metal market
irrespective of the country where it is also treated as a heavy investment asset centred on the commodity of
the industrial levels. In this regard, the gold is mainly denoted to as the “safe haven” for the avoiding of the
risk in various financial markets. On the other hand, the fluctuations in the gold prices upsurges the
uncertainty and lack of interest among the investors. In this method, most of the investors pull their
investments due to the variations and uncertainty in the gold prices. Moreover, the analysis identified that
there is an impact of fluctuations in gold prices on the Malaysian stock market. In this manner, it is
recommended to have an appropriate analysis on the future gold demand as it has been projected that the
global demand of the gold will likely to continue with the supply. In addition to this, hedging should be
carried out while identifying the downside risk associated with the gold prices fluctuations.
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