The Effects of Economic Uncertainty and Trade Policy Uncertainty on Industry-Specific Stock Markets Equity

Abstract

We use a wavelet based quantile-on-quantiles technique to explore the impact of economic policy uncertainty (EPU) and trade policy uncertainty (TPU) on the Chinese sector's markets. EPU, TPU and Chinese sector stocks monthly data from 2006 to 2022 were obtained from the data stream and separated into short, medium, and long-term datasets. First, the EPU indicates that the equity market for banks is significantly high and more positive, while the equity markets for construction, health care, finance, personal goods, and pharmaceuticals are lower and more consistent. The impact of EPUs on the Chinese sector quantiles is negative and volatile for the stock market in general. Short-term EPUs cause short-term losses in stock market returns in such sectors, but they are useful in redesigning new policies for economic development. In contrast, the long term effect of EPU on Chinese sector quantiles is positive for banks, health care, financial, personal goods, and pharma, but negative and volatile for the construction stock market return. The EPUL has the best long state policies to achieve stable economic development. Second, the TPU’s impact on Chinese sector quantiles is positive for banks but negative for all other sectors’ stock markets. The impact of TPUL on Chinese sector quantiles is negative for banks, construction, and health care but positive for financial, personal goods, and pharma stock markets. In the long run, the state will indeed increase trade, investment, production, and growth. The robustness test also confirms causality at variance and mean values. Finally, the government, stakeholders, and investors can benefit from these insights.

Full text

Vol.:(0123456789)
Computational Economics
https://doi.org/10.1007/s10614-024-10552-1
1 3
The Effects ofEconomic Uncertainty andTrade Policy
Uncertainty onIndustry‑Specific Stock Markets Equity
IjazYounis1· HimaniGupta2· WaheedUllahShah3· ArshianSharif4,5,6,7 ·
XuanTang1
Accepted: 8 January 2024
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature
2024
Abstract
We use a wavelet based quantile-on-quantiles technique to explore the impact of
economic policy uncertainty (EPU) and trade policy uncertainty (TPU) on the Chi-
nese sector’s markets. EPU, TPU and Chinese sector stocks monthly data from 2006
to 2022 were obtained from the data stream and separated into short, medium, and
long-term datasets. First, the EPU indicates that the equity market for banks is sig-
nificantly high and more positive, while the equity markets for construction, health
care, finance, personal goods, and pharmaceuticals are lower and more consistent.
The impact of EPUs on the Chinese sector quantiles is negative and volatile for the
stock market in general. Short-term EPUs cause short-term losses in stock market
returns in such sectors, but they are useful in redesigning new policies for economic
development. In contrast, the long term effect of EPU on Chinese sector quantiles is
positive for banks, health care, financial, personal goods, and pharma, but negative
and volatile for the construction stock market return. The EPUL has the best long
state policies to achieve stable economic development. Second, the TPU’s impact on
Chinese sector quantiles is positive for banks but negative for all other sectors’ stock
markets. The impact of TPUL on Chinese sector quantiles is negative for banks,
construction, and health care but positive for financial, personal goods, and pharma
stock markets. In the long run, the state will indeed increase trade, investment, pro-
duction, and growth. The robustness test also confirms causality at variance and
mean values. Finally, the government, stakeholders, and investors can benefit from
these insights.
Keywords EPU· TPU· Chinese sectoral markets· Wavelet· Quantile on quantile
Extended author information available on the last page of the article

I.Younis et al.
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1 Introduction
Policy uncertainty is an economic and financial risk in which the future direction
of government action is unknown, causing market volatility to rise and businesses
and individuals to delay spending and investing until the uncertainty is addressed.
As a result, global economic activity is contracting. Uncertainty about govern-
ment laws, economic policies such as monetary or fiscal policy, administrative
decisions such as the tax code, or trade policy among domestic or international
sectors can all be referred to as policy uncertainty (Pechman, 2001).
A government-enforced and regulated economic policy is designed to influ-
ence or regulate the conduct of the economy. On the other hand, economic policy
uncertainty (EPU) is described as a danger in which government policies and
regulatory frameworks are unclear for the foreseeable future (Saxegaard et al.,
2022). We might also remark that in the context of economic policy uncertainty,
it is impossible to foresee when and how the government’s existing economic
policy will be revised (Gulen & Ion, 2016). Economic policy uncertainty hasfar-
reaching consequences at the macro and micro levels (Wang & Kong, 2022).
Because of market uncertainty,this phenomenonmay drive enterprises and indi-
viduals to postpone spending and investments Bernanke (1983) also emphasized
the influence of economic policy uncertainty on unemployment and even national
economic development. Bloom (2009) taxonomy has rekindled interest in uncer-
tainty’s macroeconomic repercussions. Prior study indicates that the EPU index
influences spending and investment and has a negative relationship with equi-
ties (Ko & Lee, 2015). Economic policy uncertainty will cause volatility in the
financial market, while fluctuations frequently follow financial sector volatility in
economic activity (Dixit etal., 1994). A practical and stable financial market is
required for industrial expansion and economic prosperity.
Trade agreements are very important. It contributes to the consistency of
domestic as well as foreign relations. High trade policy uncertainty reduces firm
export margins significantly, while the variability effect occursacross businesses
with different levels of viability (Osnago etal., 2015). Recent research has pri-
marily focused on how trade policy uncertainty (TPU) can negatively impact
businesses’ export behaviour. Handley (2014) investigates how trade-related
uncertainty delays a business’s entry into the international market and makes
firms less sensitive to implemented tariff reductions (Al-Thaqeb & Algharabali,
2019).
Autocratic governments also create economic uncertainty. When Russia acquired
Crimea in 2014 and launched military incursions into eastern Ukraine, it triggered
international sanctions. It created an unpredictable situation, reducing overseas
investment in Russia and adding to its poor economic performance (Parliament,
2016). Uncertainty in monetary policy hurt both Russia and Ukraine. Conflict
among them and their unrest has also restricted international investment there in
Ukraine (Morelli, 2017). On the other hand, due to China’s assertive moves, the

1 3
The Effects ofEconomic Uncertainty andTrade Policy Uncertainty…
United States, India, Australia, and other major European nations are concerned
about the South China Sea’s usage as a crucial global commerce gateway.1
Recent instances worldwide demonstrate that governments and politics are sig-
nificant cause of economic instability. Policy uncertainty can also occur due to
severe economic crises and disturbances. For example, the Global Financial Crisis
of 2008–09, the Chinese Crises 2014–15 and COVID-19 presented authorities with
enormous and complicated issues. There was much ambiguity regarding how gov-
ernments should and would react to the problems and the economic implications.
It is critical to have a relevant measure that monitors the uncertainty in the
economy.Because uncertainty may have a negative impact on the whole economy.
Baker etal. (2016) developed a unique EPU index that incorporates and compen-
sates for various factors, including earlier uncertainty measurements. This index
seeks to reflect all causes of uncertainty inside the economy, making it appealing
to and extensively referenced by experts from various professions. The EPU index
has emerged as one of the finest gauges of uncertainty in economics and invest-
ment. Their index has also developed through time, with the creators gradually
adding more nations and sub-indices that account for different types or causes of
uncertainty.
When information or materials flow between two or more industries or sectors,
this is called an industrial link. It demonstrates the interdependence and interaction
of two or more industries (Richardson, 2017). A country must encourage industrial
and technological progress to improve its economic structure. The advancement of
industrial-technological progress is critical for output growth (Zhu & Yu, 2022).
If a country wants its economy to grow and develop, it must connect its various
industries. Previous research, such as (Acemoglu etal., 2007; Bartelme & Gorod-
nichenko, 2015), has also demonstrated this. All industries are interconnected,
beginning with the extraction of raw materials used to manufacture the product and
ending with the goodsor servicesbeingprovided to the end consumer.
As 2019 came to a close, a highly contagious novel coronavirus (hereafter
"COVID-19") spread quickly from its roots in Wuhan, China. With the emergence
of the pandemic, everyone’s life has become more uncertain. Businesses have been
forced to close, and governments have imposed quarantines during the pandemic
resulting in a global "Great Lockdown" affecting every industry. As a result, the
labour market and output of goods and services were drastically reduced (Coibion
et al., 2020). This uncertainty exacerbates a variety of problems and highlights
severe economic consequences. Since the previous decade’s global financial cri-
ses, populist movements have grown globally. Similarly, when the Ukraine conflict
began, financial markets worldwide plummeted, while commodity prices such as
crude oil, natural gas, metals, and food skyrocketed. These crises have also impacted
global market economics at domestic and international levels. Because all sectors
of the economy are interconnected, it impacts everyone’s lives. The global financial
crises, the Chinese crash, the COVID-19 pandemic, and the Ukraine war are just a
few instances of how uncertain economic policies disrupted the economy’s vision,
1 “South China Sea ruling increases uncertainty for shipping, trade,” Wall Street Journal, 14 July 2016.

I.Younis et al.
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affected all counterparties, and demonstrated the interlinkages between different
sectors within the same economy. A change in the interconnectedness of various
sectors should accompany economic policy shifts. Economic policy uncertainty has
grown in amplitude and relevance regarding implications (Baker & Bloom, 2013),
as fast expansion in different industrialeconomies depends on it.
Many researchers, including Ahmad etal. (2018) and Gabauer etal. (2022), have
focused on the interconnectedness of worldwide investments and financial markets.
Various studies on industrial linkage (Chatziantoniou etal., 2022), economic policy
uncertainty (Arouri etal., 2016), unemployment (Caggiano etal., 2017), pandemic,
economic growth, and economic development have been conducted by researchers.
One drawback of published research seems to be that they only look at broad trends
in stock markets, asset classes, economic policy uncertainty, pandemics, and eco-
nomic growth. Though this shows the overall trend of connectedness between the
two nations, it does not show a sense of connection between various industries of the
same economy. It should be noted that each economic sector is inextricably linked
to the others. Inflation, for example, exists in an economy. The government enacts
monetary policy, which influences economic investment. As a result, the investment
will impact goods and services production, employment, and economic growth. As
a result, if a shock is transmitted from one industry to another, it indicates that the
sectors of an economy are interconnected.
Other variables, such as Fiscal Policy Uncertainty (FPU), Monetary Policy
Uncertainty (MPU), and Exchange Rate Uncertainty, are also used to measure eco-
nomic uncertainty (Song et al., 2022). Uncertainty around the future course of a
nation’s fiscal policy is referred to as fiscal policy uncertainty. Uncertainty over the
future course of a country’s monetary policy, such as its interest rate policy, is called
monetary policy uncertainty. Uncertainty over the future value of the Chinese yuan
is referred to as exchange rate uncertainty. Exchange rate uncertainty, FPU, and
MPU may be connected. For instance, if there is ambiguity over China’s fiscal poli-
cy’s future course, there may also be worry regarding the yuan’s future value. This is
due to the fact that China’s fiscal policy frequently affects the value of the yuan. As a
result, the three different types of uncertainty may reinforce one another, making the
overall economic climate more unstable.
Because of this, the Chinese government must take action to make its economic
policies less ambiguous. Song etal. (2022) also discovered that the association is
higher during economic unrest. According to Huang and Luk (2020), there is a con-
nection between China’s exchange rate and monetary policy uncertainty. Addition-
ally, they discovered a more significant association when the Chinese government
is more likely to make market interventions. Leduc and Liu (2016) found that the
association is higher when the Chinese government is more inclined to alter its mon-
etary policies. The Chinese government has a very stable political and economic
climate, in which these issues are not important worries. Therefore, the researcher
chose not to include FPU, MPU, or exchange rate uncertainty in the study. Second,
the researcher is interested in how uncertainty affects various sectors of the Chinese
stock market in the short term (just during crises), while FPU, MPU, and exchange
rate uncertainty may have a longer-lasting effect. Thirdly, according to the author,
FPU, MPU, and exchange rate uncertainty were all related to one another, and

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The Effects ofEconomic Uncertainty andTrade Policy Uncertainty…
accounting for all three elements would have made it difficult to determine the rela-
tive importance of each one (Huang & Luk, 2020; Leduc & Liu, 2016; Song etal.,
2022).
The preceding raises two important questions. What is the nature of intercon-
nectedness across sectors, and how has it changed over time because of uncertain
economic policy? Responding to this question will provide insights into the time-
varying links that have already formed among all sectors of the economy due to
economic policy uncertainty. Second, how has inter-sectoral connectivity evolved,
particularly during financial crises, the Chinese crises, the COVID-19 pandemic,
and the Ukraine war? Exploring this question will assist policymakers and financial
investors in identifying the economy’s leading and lagging sectors during difficult
times of crisis. However, to the researcher’s knowledge, no study has been con-
ducted on the relationship between economic policy uncertainty and industrial link-
age in China during financial crises, Chinese crises, COVID-19, and Ukraine war.
According to our research, construction, health care, finance, personal goods,
and pharma financial sectors have low but stable EPU values, whereas banks have
higher but positive EPU values. EPUs have a negative impact on quantiles across
all Chinese industries. The EPUL’s influence on Chinese sector quantiles benefits
banks, healthcare, financial, personal goods, and pharma. At the same time, the con-
struction stock market is negativebut unstable. The EPUL long-term state policies
have more effectively achieved long-term stable economic growth. Additionally, the
quantiles of Chinese sectors are positively impacted by TPU for the banking indus-
try and negatively for all other sectors’ stock markets. The TPUL’s influence on
Chinese industries is mixed, with positive quantiles for financial, personal products,
and pharmaceuticals and negative quantiles for banks, construction, and healthcare
stocks.
Exploring these issues is especially important for the Chinese economic system,
especially in light of recent events. The global financial crisis, first and foremost,
had a significant negative impact on the Chinese economy, affecting exports, foreign
exchange reserves, and structural changes. Second, China’s economic growth in the
aftermath of COVID-19 and, more recently, the Ukrainian war has prompted Chi-
nese banks to take drastic measures to increase loan loss reserves, tapping China’s
bond markets for 30% more funds than they did last year.
In this paper, we estimate the connectedness and impact of economic and trade
policy uncertainty across various sectors of the Chinese economy. The monthly
returns of the Baidu EPU index values are used to measure economic policy uncer-
tainty, and different sectors such as financial and banking, energy, pharmaceutical,
personal goods and materials, and construction will be considered. We use Wave-
let Quantile on the quantile approach. According to Khalfaoui etal. (2020), wave-
let analysis is a popular method for analysing data in the temporal and frequency
domains to study economics and finance. This benefit allows wavelet transform to
accurately break time series into frequency components (Kassouri et al., 2022).
Additionally, it can track every piece of data in the time series and link it to specific
periods and horizons (Younis etal., 2023). Further, with this approach, the station-
ary assumption for variables is relaxed, potential asymmetric responses are distin-
guished, the temporal lag effect is taken into account, and the endogeneity issue is

I.Younis et al.
1 3
alleviated (Wang etal., 2021). The link between TPU and EPU and industry-specific
stock market equity may change at various time scales, which provides the theoreti-
cal foundation for considering the quantile-on-quantile model at various time scales.
The link, for instance, can be stronger on a daily time scale than on a monthly time
period. This is due to the possibility that short-term effects of TPU and EPU on
stock prices may be more noticeable than long-term effects. The quantile-on-quan-
tile approach will also identify the time scale at which the association between TPU
and EPU and industry-specific stock market equity is strongest. Investors and deci-
sion-makers attempting to comprehend how these factors affect stock prices over
time may find this helpful. The QQ regression approach has been widely applied in
the financial market (Hashmi etal., 2021), energy market (Wang etal., 2022; Yang
etal., 2021), industrial economy (Li & Yuan, 2021), and has been expanded to the
ecological and environmental protection area based on these benefits (Xu et al.,
2021).
Investors can use the results of this study to help them decide whether to buy and
sell stockswhile considering the economic ramifications. If the analysis shows that
the relationship between TPU and EPU and stock prices is strongest in the short
term or during crises, investors may focus on short-term trading strategies. Policy-
makers can use the findings of this investigation to create measures that lessen eco-
nomic uncertainty. For instance, if the research reveals that TPU and EPU have a
detrimental effect on stock prices, regulators may wish to concentrate on lowering
these variables. Furthermore, researchers might use the findings of this investigation
to generate fresh hypotheses regarding the connection between TPU and EPU and
stock prices. This can result in fresh perceptions of how these elements impact the
economy.
This study, however, cannot be repeated inmultiple time frames. This implies
that changing one variable does not necessarily cause the other to change, and vice
versa. The reason could be that numerous factors affect stock prices, and on some
occasions, these factors may be more important than TPU and EPU. In the long run,
for instance, the economy’s overall health might be more important than TPU and
EPU.
The following is how this paper is organized. Section1 introduces the topic, Eco-
nomic and trade policy uncertainty, as well as the purpose of the paper. Section2
discusses previous research on Trade Policy and EPU, industrial linkage, and crises.
Section3 describes the study’s methodology. Section4 discusses the empirical find-
ings, and Sect.5 concludes the paper.
2 Literature Review
The literature on economic and trade policy uncertainty in Chinese sectors dur-
ing various crises is noticeably lacking. The topic at hand has received extensive
research from both researchers and policymakers. As an outcome, the article con-
tains very negligible information about the current topic on academic aspects,
making the conceptual contribution of the study significant and worth studying.
Unpredicted activities, like the rising youth unemployment, economic disparity,

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The Effects ofEconomic Uncertainty andTrade Policy Uncertainty…
large-scale movement of people, and fluctuating fuel prices, had already confounded
economies’ growth paths even further.
Policy uncertainties have always played a significant role in determining eco-
nomic outcomes, as nations currently experiencing policy uncertainties demonstrate
sluggish economic growth. For example, China has been going through a massive
transformation since 2014, prioritizing environmental goals. The interconnected-
ness of different sectorsis reflected in their linkage. Because all sectors are inter-
connected, we can say that goods, services, and information flowacross industries.
This flow determines how much one industry’s development contributes to the
expansion of another (Miller & Blair, 2009; Richardson, 2017). Demand for down-
stream industries’ output (i.e., industries close to customers) increases investment
and capacity utilization or technological capabilities between many companies in
upstream industries (Hauknes & Knell, 2009).
Our review of the research is divided into four sections. In the first section, we
reviewed the previous study conducted in the context of economic policy uncer-
tainty. The second section will cover a systematic review of trade policy uncertainty,
followed by uncertainty in various sectors of the economy and, finally, studies of
various crises.
According to Christou etal. (2017), whether economic policy uncertainty affects
any country depends on the strength of its economy and the volume of its stock
market. However, according to (Boutchkova etal., 2012; Yu etal., 2017), the effect
of EPU seems to depend on the kind of sector the economic system has. China is
a significant player in the global energy market. Wang and Kong (2022) attempted
to investigate the impact of uncertain economic policy on the Chinese energy stock
market. They used Baker etal. (2016) index. They employed the Structural Vec-
tor Autoregressive (SVAR) model as their methodology. They discovered an inverse
relationship between EPU and the price of energy stocks. The price of energy stocks
falls as the EPU rises. The current state of the stock market benefits the energy sec-
tor. Financial risk management is difficult in any economy but becomes even more
difficult when policies are uncertain. Many researchers, including Alexopoulos and
Cohen (2015), concentrated on the relationship between uncertainty shocks, mar-
kets, and the economy. Researchers such as (Ajmi etal., 2015; Almeida & Divino,
2015; Caporin & Velo, 2015), on the other hand, focused on the relationship
between EPU and the contribution of macroeconomic and microeconomic factors,
predicting investment potential, which uses block structure multivariable conditional
variance models, the time-varying causality among futures and spotprices,crude oil
prices and a government evaluation of the information transfer.
The previous work on trade policy uncertainty has primarily focused on its meas-
urement and the impact of micro and macroeconomic factors on trade policy uncer-
tainty. Baker et al. (2016) created an index of economic policy uncertainty based
on how frequently news stories appeared in the most widely circulated newspapers.
He discovered that the index was largely consistent with macroeconomic variables,
which helped to explain the significance of the policy uncertainty index. Handley
and Limão (2017) created the trade policy uncertainty index using media data and
information. They unearthed that the index peaked following Trump’s nomina-
tion and election as president. Transactions between firms constitute a trade. Thus,

I.Younis et al.
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Caldara etal. (2020) designed and built a firm-level trade policy uncertainty index
using transcript text analytics. Transcripts include quarterly earnings reports from
publicly traded companies. Handley and Limão (2017) developed another uncer-
tainty index based on the gravity model. Pierce and Schott (2016) created another
index based on the tariff difference between non-permanent and permanent normal
trade ties. Handley (2014) investigated the impact of trade policy uncertainty on
Australian export enterprises. The export rate slowed by 7% in 2001 compared to
1993. If all tariffs and restrictions are removed, it is expected that more than half
of the predicted new product growth will be accounted. Chen (2018) looked into
the effect of trade policy uncertainty on employment in China and discovered an
inverse relationship. As trade policy uncertainty decreases, employment in Chinese
enterprises rises. As a result, the range of trade products has grown. As demand for
goods rises, so does the demand for labour. This, in turn, boosts work opportunities
in Chinese businesses. Shepotylo and Stuckatz (2017) studied the effects of trade-
related uncertainty on foreign investment for almost a decade. They discovered a
link between trade policy uncertainty and foreign investment. Investment in Ukraine
is increasing as trade policy uncertainty. Schott et al. (2017) assessed the effects
of trade policy uncertainty on corporation acquisition trends and found that when
trade policy uncertainty is high, companies will opt for American-style acquisition
to avoid a trade war. When there is lessuncertainty about trade policy, the possibil-
ity of a trade conflict is low. Companies will prefer Japanese-style acquisition as it’s
less costly and therefore, can increase social welfare.
A growing body of literature shows that the global financial crises, Chinese cri-
ses, COVID-19 pandemic, and Ukraine war all impacted various aspects of the
economy. This literature can be extracted across many segments, namely, those
demonstrating that the crises affected: company effects, such as cash reserves and
holdings of cash(Shen etal., 2020), share prices (Haroon & Rizvi, 2020), energy
markets (Devpura & Narayan, 2020; Prabheesh etal., 2020), Foreign investment and
financial services markets (Vidya & Prabheesh, 2020), and global politics (Apergis
& Apergis, 2020) to name a few. Altig etal. (2020) discovered a significant increase
in economic uncertainty indicators in the United Kingdom and the United States
throughout the disease outbreak. A long-term inverse relationship between US EPU
and renewable energy use is demonstrated by (Shafiullah etal., 2021). According to
Qin etal. (2020), the oil market and US economic policy uncertainties are closely
associated. Conversely, according to Appiah-Otoo (2021), the impact of EPU
onrenewable energy is insignificant.
Debata and Mahakud (2018) discover that economic policy uncertainty reason-
ably affects share returns throughout usual circumstances. Regrettably, during the
globalfinancial crisis, economic policy uncertainty plays a vital role in evaluating
liquidity position. The researchers furthermore unearthed that investors considered
a significant fraction of the changes in financial liquidity in the market throughout
the financial collapse. During the global financial crisis, Ozili (2021) discovered
that EPU predictors are extremely important for Europe, non-Europeancountries,
and theUnited States, suggesting that financial meltdowns are a contributing factor
driving the association of economic policy uncertainty in some of these provinces.
According to Belcaid and El Ghini (2019), before the 2008 recession, the connection

1 3
The Effects ofEconomic Uncertainty andTrade Policy Uncertainty…
between the Moroccan stock market and the EPU was normally negligible for the
long-run market returns component, specifically for France and Spain. However,
this effect was relatively significant due to uncertainty around economic policies in
the United States and Germany. This implies that the short-run volatility component
generated by daily stock market returns attempted to explain a greater portion of the
amount of variance.
3 Data andMethods
3.1 Data andFundamental Analysis
This study made use of monthly data from 2006:M01 to 2022:M08. Datastream
offers information on Chinese sectoral markets. In addition, the external economic
environment in China in 2013 was complex and severe; for example, the European
debt crisis, volatility of oil prices, and the flow of international capital to emerg-
ing markets brought uncertainty to the world economy. The other variables are Chi-
na’s EPU and TPU; we obtain the data from a website.2 The EPU is a news-based
indicator provided by Baker etal. (2016) that measures monetary, fiscal, and other
economic policy uncertainties. The index has attracted increasing attention and is
widely used in finance and economic studies. Further, EPU and TPU dataset has
been divided into the short, medium and long term.
Table 1 provides descriptive statistics, and the lowest mean represented EPUM
and EPUs (0.547 & 0.583), although the highest mean is shown to banks and finan-
cial sectors (3.45 & 3.39), respectively. The lowest mean indicates minimum return,
while the highest is maximum return in study sample sectors. Similarly, variance
shows risks; here, the highest variance to TPUL is 0.919 and the lowest to banks is
0.014.
Moreover, the banks and financial sectors are maximum mean (3.45 & 3.39) and
minimum variance (0.014 & 0.021), respectively. Similarly the construction-mate-
rial and health care sector mean are (2.732 & 2.675) while its variance (0.024 &
0.431) respectively. Finally, the personal goods and pharm-biotech sectors are min-
imum mean (2.448 & 2.27) while its variance (0.032 & 0.058) respectively. The
above outcomes indicate that banks and financial sectors are the best options for
investors as the lowest variance improve maximum returns overall. However, some-
times due to uncertainty, higher variance (higher risks) provides higher returns and
vice versa. Further uncertainty, risks, stock market and investments portfolio and its
relevant theoretical justification provide deep analysis for investors and speculators
to adjust their maximum return in the market through diversifications, hedging and
innovative approaches.
The skewness and kurtosis values are used to find the data’s normal distribution
curve and shape. According to George and Mallery (2010), values for asymmetry
and kurtosis between − 2 and + 2 are deemed acceptable to demonstrate a normal
2 http:// www. polic yunce rtain ty. com/

I.Younis et al.
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Table 1 Descriptive statistics of EPU, TPU and Chinese sectoral stock markets
a, b and c represented the significant at 0.05%, 0.5% and 1%
Indica-
tors
TPU TPUSTPUMTPULEPU EPUSEPUMEPULBanks Const&Mats Health.
care
Financials Personal.
goods
Pharm&biotech
Mean 1.945 0.664 0.656 0.944 2.146 0.583 0.547 0.874 3.452 2.732 2.675 3.392 2.448 2.27
Var i -
ance
0.411 0.755 0.624 0.919 0.088 0.548 0.564 0.664 0.014 0.024 0.431 0.021 0.032 0.058
Skew-
ness
− 0.952a0.770a0.712a0.141 0.484a0.849a0.903a− 0.082 − 1.614a0.452a0.115 − 3.840a0.072 − 0.601a
Ex.Kur-
tosis
1.424a− 0.987a− 0.981a− 1.795a1.032b− 0.322 − 0.046 − 1.736a6.375a3.968a− 0.971a31.863a− 0.164 0.265
JB 47.113a27.903a24.943a27.513a16.699a24.878a27.197a25.342a425.501a138.038a8.301b8952.041a0.395 12.627a
ERS − 2.088b− 2.821a− 5.050a− 1.606 − 0.122 − 3.812a− 1.793c− 1.479 − 1.357 0.140 0.391 − 1.197 − 1.610 0.195
Q(10) 483.928a19.915a256.329a896.060a616.681a10.943b183.250a852.773a372.433a447.973a976.717a193.364a714.415a852.054a
Q2(10) 573.152a19.136a252.744a927.951a562.843a6.474 175.265a871.932a401.398a399.607a996.022a262.666a708.490a878.911a

1 3
The Effects ofEconomic Uncertainty andTrade Policy Uncertainty…
univariate distribution. According to Hair etal. (2011), data is normal if skewness is
between 2 and + 2 and kurtosis is between 7 and + 7. So we could say that the spread
and height of your normal distribution are described by skewness and kurtosis.
A higher positive skewness was recorded for EPUs, which is 0.903, and posi-
tive skew while negative for the financial sector − 3.840, indicating the distribution
curve’s tail at the left side. The kurtosis values of banks, financial and construc-
tion-material are larger than 3, showing that their tails are fatter and leptokurtic.
Conversely, the kurtosis values of TPUS&L and EPUS&L, healthcare, and personal
goods are negative and demonstrate platykurtic distribution. Also, the JB test has
indicated the normal range values and significance at 0.05%. Similarly, ERS and
quantile values are also significant in this study.
Figure 1 indicates that all sector’s stock indices, TPU and EPU showed an
increasing trend excluding the financial sector in the graph but a sharp decline in
response to the global financial crisis subsample, oil shocks, the Chinese stock mar-
ket crash, and Covid-19. However, EPU, banks and healthcare indices in China have
similar patterns, while the construction-material, pharma and personal goods sector
have similar resemblances in the graph. The TPU index shows increasing trends but
with small fluctuations, while financial sectors have opposite decline trends in graph
value. As demonstrated by Feng etal. (2017), a reduction in trade policy uncertainty
lowers company expectations regarding the amount of tariff payments and promotes
export activity because of the expectation of higher export profits. However, the
lower trade policy uncertainty may cause consumers to choose imported items over
those made domestically, which would diminish current domestic consumption and
investment; claim (Caldara etal., 2020).As a result, the domestic industrial linkage
would eventually decline.
1.2
1.6
2.0
2.4
2.8
3.2
06 08 10 12 14 16 18 20 22
TPU
3.1
3.2
3.3
3.4
3.5
3.6
3.7
06 08 10 12 14 16 18 20 22
EPU
2.2
2.4
2.6
2.8
3.0
3.2
06 08 10 12 14 16 18 20 22
Banks
1.0
1.5
2.0
2.5
3.0
3.5
4.0
06 08 10 12 14 16 18 20 22
Const. & Mats
2.0
2.2
2.4
2.6
2.8
3.0
06 08 10 12 14 16 18 20 22
Financials
3.0
3.2
3.4
3.6
3.8
06 08 10 12 14 16 18 20 22
Health Care
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
06 08 10 12 14 16 18 20 22
Personal Goods
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
06 08 10 12 14 16 18 20 22
Pharm. & Biotech
Fig. 1 Dynamic Indices of TUP, EPU and Chinese sector markets

I.Younis et al.
1 3
From this analysis’s perspective, Fig.2 evaluates the risk-return from the TPU
and EPU to the Chinese Sector Stock Market. Almost all Chinese sectors’ stock
market returns fell below zero during several crises. Chinese stock returns across all
industries are very similar, particularly during the financial crisis when the Shang-
hai stock exchange fell. Likewise, the healthcare and pharmaceutical sectors had a
higher variation weight throughout the sample period. The risk-volatility in the Chi-
nese healthcare sector is in a negative pattern. Similarly, there is a continuous vari-
ation in crises in the banking and financial sectors. The Chinese stock markets are
found to be riskier, providing investors with an average return during normal times
but suffering losses during the global financial crisis, the debt crisis, the crash of
the Chinese stock market, the oil shocks, and Covid-19. Losses and variations in the
pharmaceutical and healthcare sectors are always rising, and they are notably bigger
during times of crisis. All the sectors under consideration, except the healthcare sec-
tor, show bidirectional spillovers in both the upside and negative risk-return.
3.2 Wavelet Multi‑Scale Decomposition (WMD) Method
Wavelet analysis is a widely used technique for investigating economics and finance
in both the time and frequency domains (Khalfaoui et al., 2021). Based on this
advantage, wavelet transform can decompose time series into frequency components
without misrepresentation.
(Kassouri etal., 2022). Moreover, it can trace all information in the time series
and connect it with particular time locations and horizons (Mo et al., 2019). The
basic wavelet generally contains two types, namely, the father wavelet
𝜑
and mother
wavelet
𝜓,
which are shown as follows:
-300
-200
-100
0
100
200
300
06 08 10 12 14 16 18 20 22
TPU
-100
-50
0
50
100
150
06 08 10 12 14 16 18 20 22
EPU
-30
-20
-10
0
10
20
30
06 08 10 12 14 16 18 20 22
Banks
-60
-40
-20
0
20
40
60
06 08 10 12 14 16 18 20 22
Const. & Mats
-40
-20
0
20
40
06 08 10 12 14 16 18 20 22
Financials
-40
-20
0
20
40
60
06 08 10 12 14 16 18 20 22
Health Care
-40
-20
0
20
40
60
06 08 10 12 14 16 18 20 22
Personal Goods
-30
-20
-10
0
10
20
30
40
06 08 10 12 14 16 18 20 22
Pharm. & Biotech
Fig. 2 Dynamic returns of TUP, EPU and Chinese sector markets

1 3
The Effects ofEconomic Uncertainty andTrade Policy Uncertainty…
where
j=1,
⋯
,J
indicates the scale and
k=1,
⋯
,2j
indicates the translation.
The smooth and low frequency parts of the series are captured by
𝜑j,k,
while the
detailed and high frequency parts are recognized by
𝜓j,k
. Therefore, a time series
f(t)
can be decomposed as Eq.(3) with wavelet transform.
The coefficient of
dJ,k
indicates an increasingly finer-scale deviation from the
smooth trend.
sJ,k
is the smooth coefficient that captures the trend. The coefficients
can be simplified as
Sj(t)=∑ksJ,k
𝜑
J,k(t)
and
Dj(t)=∑kdJ,k
𝜓
J,k(t)
, respectively.
Therefore,
f(t)
is written as
where
Sj(t)
is the approximation and captures low-frequency dynamics.
Dj(t)
indi-
cates wavelet details and recognizes higher-frequency features. The maximal overlap
discrete wavelet transform (MODWT) is employed to estimate the scale and wave-
let coefficients. The series are decomposed through a Daubechies least asymmetric
filter with a length of eight (called LA8).
Dj
presents the decomposed series at a
time scale from
2j
to
2j+1
. For this paper, D1 = Short term, D2 = Medium term and
D3 = Long term represent decomposed Baidu Index series, which have a time hori-
zon of 2–4 months (j = 1), 4–8 months (j = 2), and 8–16 (j = 3) months, respectively.
3.3 Quantile‑on‑Quantile (QoQ) Method
The quantile of quantile approach determines two aspects between the variables. For
instance, QoQ approach measures the performance of the stocks under study dur-
ing the crisis period and it points out the size and sign of the shocks on the sector
returns. In the current study, the QoQ approach is applied to highlight how Chinese
sectoral markets performed given a large time frame from 2006:M01 to 2022:M08
and focusing particularly on the linkage between EPU and TPU at short, medium,
long and crisis (Global recession, Chinese market crash, Covid-19 and Russian-
Ukraine war) with Chinese sectoral markets. For this purpose, the quantile of the
Chinese sectors is taken as a dependent variable as it informs how well or poorly the
Chinese sectors are performing. Secondly, we model the quantile of EPU and TPU
as explanatory variables as this quantile provides information on the sign and size of
the shock on Chinese sectoral stock markets. This paper employs the QQ method to
(1)
𝜑
j,k=2−j∕2𝜑
(
t−2jk
2j
)
with ∫𝜑(t)dt =
1
(2)
𝜓
j,k=2−j∕2𝜓
(
t−2
j
k
2j
)
with ∫𝜓(t)dt =
1
(3)
f(t)=
∑
k
sJ,k𝜑J,k(t)+
∑
k
dJ,k𝜓J,k(t)+⋯+
∑
k
dJ,k𝜓J,k(t)+
∑
k
dJ,k𝜓J,k(t
)
(4)
f(t)=Sj(t)+
J
∑
j=
1
Dj(t
)

I.Younis et al.
1 3
verify the hypothesis and a full introduction to QoQ may be found in (Sim & Zhou,
2015), which we recommend to interested readers.
4 Results andDiscussion
4.1 Time‑Varying Quantile onQuantile Estimations
Using a QQ regression technique, Fig.3 depicts the impact of EPU on the Chi-
nese stock markets. The colour bar shows the correlations between the EPU and
the Chinese stock markets. The highest and lowest values are shown, respectively,
by the dark red and dark blue bars. The coefficients between EPU and the Banks
are range from -0.6 to 0.6, as illustrated in Fig.3. This study discovers that the
relationship is favourable when EPU falls within the quantile of (0, 0.3), indi-
cating that a stable monetary policy will boost banks’ returns while lowering
market volatility and risk factors. Comparable to the construction sector stock
market, the impact of EPU on other sector stocks quantiles is low (− 0.5, 1) for
construction (− 0.5, 0) for health care, (− 0.2, 0) for finance, (− 0.1, 0)for per-
sonal goods, and (− 0.1, 0.1)for pharma sector stock markets but constant. All
the sectors except construction can enhance their business activities to higher
earning’s and boost economic growth. Steady and prosperous economic develop-
ment conditions always accompany periods of low EPU, which are advantageous
for trade, investment, and business interest (Yu etal., 2021). Similarly, in Fig.4,
the EPUs (short-term) impact on Chinese sectors quantiles is negative for Banks
(− 0.1, 0.1), Construction (− 0.5, 0.5), Health care (− 0.6, 0.2), Financial (− 0.3,
0), Personal goods (− 2, 0) and Pharma (− 1, 1). Short-term EPU adverse effects
on these sectors can reduce one sector’s production activities while enhancing
the other sector’s business activities towards potential growth. These are findings
consistent and similar to the outcomes of Wang and Kong (2022) for the Chinese
energy stock market and inconsistent with (Appiah-Otoo, 2021). The EPUs short
term has created short-term losses in these sectors but is helpful to redesigned
new policies for economic development. Similar findings were observed for
Fig. 3 Quantile on Quantile impact of EPU on Chinese sectors

1 3
The Effects ofEconomic Uncertainty andTrade Policy Uncertainty…
EPUM in the medium term in connection with all the Chinese sectors in Fig.5.
Conversely, in Fig. 6, the EPUL (long-term) impact on Chinese sectors quan-
tiles is positive for Banks (0, 0.03), Health care (0, 0.4), Financial (0.01, 0.02),
Personal goods (− 0.01, 0) and Pharma (0, 0.4) respectively while negative for
construction (− 0.5, 0.15) but unstable. The EPUL long-term state policies have
better to obtain the long run stable economic development. As a result, micro-
economic entities would eventually increase production and growth. Additionally,
increasing EPU would change and lessen the focus on environmental governance,
undermining the impact of adopting ecological protection, according to Jiang
etal. (2019). Higher EPU degrade and deteriorate the micro–macro condition of
the state and reduce uncertainty in all the industrial and financial sectors. In com-
parison, lower EPU positively affects all sectors of production, economic growth,
and economic development.
Fig. 4 Short-term Quantile on Quantile impact of EPU on Chinese sectors
Fig. 5 Medium-term Quantile on Quantile impact of EPU on Chinese sectors

I.Younis et al.
1 3
Additionally, Fig.7 illustrates the impact of TPU on the Chinese stock markets
using the QQ regression method. Yet again, the colour bar shows the correlation
between the TPU and the, and the Chinese stock markets’ dark red and dark blue
values, respectively, show the highest and lowest values. The coefficients between
TPU and the Banks range from 1.5 to 2, as illustrated in Fig.7. This study discov-
ers that the relationship is positive when TPU is within the quantile of (0, 1), indi-
cating that stable economic policy would boost banks and financial sectorsreturns
while lowering risk factors and market volatility. TPU impact on sectors quan-
tiles is also stronger for construction (0, 2), Health Care (− 0.5, 0.5), Financial (0,
0.5), Personal Goods (− 2, 1), and Pharma (0, 2), respectively. Economic expan-
sion, growth, investment, trade, and corporate activity are always more prevalent
during times of higher TPU. Moreover, in Fig.8, the TPUs (short-term) impact
on Chinese sectors quantiles is positive for Banks (0, 0.6) and negative for con-
struction (− 1, 2), Health care (− 0.4, 0), Financial (− 0.5, 0.1), Personal goods
Fig. 6 Long-term Quantile on Quantile impact of EPU on Chinese sectors
Fig. 7 Quantile on Quantile impact of TUP on Chinese sectors

1 3
The Effects ofEconomic Uncertainty andTrade Policy Uncertainty…
(− 1, 0) and Pharma (− 3, 3) respectively. Consistent findings were observed for
TPUM at the medium term in connection with all the Chinese sectors in Fig. 9.
Consistently in Fig.10, the TPUL (long term) impact on Chinese sectors quan-
tiles is negative as for Banks (− 0.2, 0.2), Construction (− 0.1, 0.1) and Health
care (− 0.05, 0.4) while positive as for Financial (0, 0.2), Personal goods (0, 0.1)
and Pharma (0, 0.1). The TPUL long-term state policies have diverse effects on
different sectors in the long run due to economic development policy. The low
TPUL further creates business expansion, higher exports, and industrial growth
opportunities in the country and is linked with higher potential economic devel-
opments, as similar demonstrated by (Feng etal., 2017). Still, it also has adverse
effects explained by (Caldara etal., 2020). As a result, the domestic industrial
Fig. 8 Short-term Quantile on Quantile impact of TUP on Chinese sectors
Fig. 9 Medium-term Quantile on Quantile impact of TUP on Chinese sectors

I.Younis et al.
1 3
linkage would eventually decline. Hence, the state would increase trade, invest-
ment, production, and growth in the long run.
In conclusion, the relationship between EPU, TPU, and the sectors exhibits
unique characteristics at various EPU and TPU time frames. Low EPU and TPU
have a short-term favourable or negative effect on the sector’s return, but the rela-
tionship shifts when EPU and TPU’s effects increase or decrease over time. High
EPU is thought to have this effect by degrading the macroeconomic environment,
which causes businesses to change their operations, affecting production and trade.
In contrast to EPUs, EPUL has a beneficial impact on the sectors. This illustrates
how increased EPU raises industry risk and funding restrictions. Additionally, it
should be highlighted that although the correlations between TPU and sectors are
always negative or positive, the values decrease as TPU increases, consistent with
the government’s sustainable initiatives. Macroeconomic conditions are changed by
a greater EPU, which can result in reduced economic growth and decreased energy
consumption (Su etal., 2022; Yin et al., 2019). For instance, at the end of 2019,
COVID-19 broke out and rapidly spread worldwide. As a result, China had to make
several policy adjustments, leading to EPU reaching a respectably high level. The
GDP growth rate in the first quarter of 2020, which was impacted by the virus, was
6.8%; this was the first quarter of negative growth since 1978 (Andreoni, 2022).
The empirical findings also show that enterprises are likely to utilise traditional
and investment strategies as TPU increases, increasing demand and sector trades. In
light of this, policymakers should adopt a harder posture to decrease the swings of
the sectors to stabilise the market and public expectations. Stock markets and market
participants should use policy tools, such as stable monetary and fiscal policies and
investment, to affect EPU and TPU, given the complicated linkages between various
frequencies of EPU, TPU, and sectors.
4.2 Robustness Test: Causality atVariance andMean
We employ robustness to support our conclusions by looking for causality in the
variance and mean. Figure11 depicts our use of the causality variance and mean of
Fig. 10 Long-term Quantile on Quantile impact of TUP on Chinese sectors

1 3
The Effects ofEconomic Uncertainty andTrade Policy Uncertainty…
EPU and TPU with Chinese sector stock markets. The forecasts are from the non-
parametric causality tests plotted in the figure for the different quantiles. Quantiles
are displayed on the X-axis, whereas statistics are displayed on the Y-axis. In Fig.11,
the causality in variances and mean between TPU and banks graph shows small vari-
ation at the upper and lower quantile. While causality in variance and mean between
EPU and banks show a higher variation at extreme upper and lower quantiles in the
graph. The Chinese banking sector’s Risks and return level are affected mainly by
EPU because the TPU are linked with EPU. Similarly, the causality in variances and
mean between TPU, EPU and the financial sector graph show small variation nor-
mally but higher at the upper and lower quantile. These outcomes justified that dur-
ing economic and trade uncertainty financial sector was directly effecting while the
other sectors of the economy some later after the financial sector. Economic stable
and thriving conditions are always present during times of low EPU, which is advan-
tageous for trade, commerce, and investment activities (Yu etal., 2021). As a result,
microeconomic entities would boost energy use and grow output, which results in
significant carbon emissions.
Conversely, the causality in variance between EPU and personal goods and
causality in a mean between TPU and personal goods sector stock markets graph
Fig. 11 Quantile causality-in-Mean and and in-variance for TUP and EUP on Chinese sectors market
Note: that the graph depicts the estimates from the various quantiles’ non-parametric causality tests. Sta-
tistics are shown on the Y-axis, while quantiles are shown on the X-axis

I.Younis et al.
1 3
shows higher at upper and lower quantiles. Moreover, the causality in a variance
of EPU and the mean of TPU with pharma sector stocks graph shows higher at
upper and lower quantiles. Higher causality shows strong links among these stock
markets with EPU and TPU. Furthermore, the causality in variance between EPU
and healthcare and causality in the mean between EPU and healthcare sector
graph shows higher at upper and lower quantiles. In contrast, construction sector
stocks are small and stable variations at extreme quantiles. Hence these verified
the above quantile results of EPU and TPU with the Chinese stock markets.
5 Conclusion andImplications
We investigated the effects of EPU and TPU on the Chinese stock markets using a
wavelet and QoQ regression technique. EPU and TPU with Chinese sectors monthly
data are obtained from data stream MSCI and CSMAR from 2006 to 2022. Fur-
ther, the EPU TPU dataset has been divided into short, medium and long-term, and
crises. The EPU shows that banks’ sector stock market is better and more positive,
while construction, healthcare, finance, personal goods, and pharma stocks are low
while stable. EPUs negatively and volatilely impact the stock market’s quantiles in
all Chinese sectors. The EPUs have caused these industries to experience short-term
losses, yet they are useful in redesigning new strategies for economic development.
In contrast, the impact of the EPUL on Chinese sector quantiles is beneficial for
banks, healthcare, financial, personal goods, and pharmaceutical stocks. At the same
time, it is negative but unstable for the construction stock market. The long-term
state policies of EPUL have been more successful in achieving long-term stable eco-
nomic growth. Additionally, TPU positively impacts Chinese sector quantiles for
sectors’ stock market of banks and negatively impacts all other sectors. TPUL has a
mixed influence on Chinese industries, with negative quantiles for banks, construc-
tion, and health care and positive quantiles for finance, consumer goods, and phar-
maceuticals stock markets. The state would consequently boost trade, investment,
production, and growth over the long run. The robustness test also verifies the cau-
sality at variance and means values.
Our review adds to the growing research literature on factors affecting stock
volatility and predictability from the perspective of macroeconomic policies (EPU
and TPU). It guides decision-makers as they develop appropriate responses to these
issues in the sectors for higher yields. Our findings imply that policymakers should
closely examine ways to reduce uncertainty because it is a major cause of changes in
economic cycles and financial markets. The Chinese economy is resilient to shocks
of uncertainty, but governments must also closely monitor uncertainty that spills
over from its economic fundamentals and trades to other countries.
Quantile regression is used to identify heterogeneous influences in various sec-
tors stock market during different conditions. All sectors in the upstream, mid-
stream and downstream provide variability of the stock market return. EPU and
TPU have a policy-specific effect on each sector stock market during different
time zones. Therefore, government policymakers and investors should focus on

1 3
The Effects ofEconomic Uncertainty andTrade Policy Uncertainty…
the specific effects of these uncertainty policies on different sectors stock market
to reduce losses and enhance their return in the stock markets.
Sector stock markets react differently to EPU and TPU. We examine the causal
relationships between stock costs and uncertainty in this situation. The review
thus makes it possible to objectively investigate the effects of both positive and
negative stock market sectors from EPU and TPU. The findings demonstrate that
the stock market index returns correlate with fundamental macroeconomic con-
cepts; some stock sectors are shock-sensitive. This discovery might help inves-
tors start their processes from these modifications. Government and their policies
should be as necessary to concentrate on the development of the stock market.
EPU can impact stock prices by changing the consumption and investment
patterns of micro-subjects through anticipated effects. EPU specifically affects
investors’ and corporations’ expectations for the future, which impacts the
stock market. With policy uncertainty, investors’ expectations for the future will
alter. Investors will demand higher risk premiums as uncertainty rises, impact-
ing stock market values (Wang & Kong, 2022). Linking macroeconomic factors
and identified macroeconomic uncertainty to stock market volatility may suggest
a risk-set-up justification for the profits concerning these portfolios. Due to the
increasing EPU and TPU, the un-predictableness that permeates the area has fre-
quently grown more grounded, rendering the individual market more susceptible
to shock. Regarding the policy implications, this evidence supports the argument
for more policy coordination to handle a potential drop in risk. Additionally, it
implies that institutional investors and mutual funds have fewer opportunities for
portfolio expansion in these sectors’ stock markets.
Numerous topics are left unexplored in this analysis for future study. A wave-
let-based quantile-on-quantile regression approach primarily focused on EPU
and TPU to explain the changes in the sector’s stocks. A similar study can be
conducted between EPU, TPU and the Chinese stock market regarding dynam-
ics times and frequency outcomes. Further, future research may focus on politi-
cal and global risk uncertainty and micro-firm-level risks uncertaintyto investi-
gate the micro-mechanism of EPU and TPU on the Sectors from the companies’
perspective.
Acknowledgements Thankful to anonymous reviewers and the National Nature Science Foundation of
China (52270180) for providing funds to undertake this study.
Data Availability The datasets used and analyzed during the current study are available from the corre-
sponding author on reasonable request.
Declarations
Conflict of interest The authors declare that they have no competing interests.
Ethical Approval and Consent to Participate Not Applicable.
Consent for Publication Not Applicable.

I.Younis et al.
1 3
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The Effects ofEconomic Uncertainty andTrade Policy Uncertainty…
Authors and Aliations
IjazYounis1· HimaniGupta2· WaheedUllahShah3· ArshianSharif4,5,6,7 ·
XuanTang1
* Arshian Sharif
arshian.aslam@gmail.com
* Xuan Tang
txtangxuan@gzhu.edu.cn
Ijaz Younis
ranaijaz232@yahoo.com
Himani Gupta
himanigupta8476@gmail.com
Waheed Ullah Shah
shahfin01@gmail.com
1 School ofManagement, Guangzhou University, Guangzhou, People’sRepublicofChina
2 Jagannath International Management School, VasantKunj, India
3 Business School, Shandong Normal University, Jinan, China
4 Department ofEconomics & Finance, Sunway Business School, Sunway University,
SubangJaya, Malaysia
5 Adnan Kassar School ofBusiness, Lebanese American University, Beirut, Lebanon
6 University ofEconomics andHuman Sciences inWarsaw, Warsaw, Poland
7 College ofInternational Studies, Korea University, Seoul, SouthKorea