Does Trade War Affect Share Market in Long Run-Dissertation Sample

QUESTION

 

Does Trade War affect Share Market in Long Run: A Case Study of Recent Trump- China Trade War

Methodology

One of the promises made by Donald’s during the US presidential election was to limit and rather stop the abuse of trade practices by China towards USA. The assault began in January 2018 [2] when USA imposed 30% tariffs on solar panel imports, a majority of which originated in China. Another set of tariffs came on July 6 when 25% tariffs were imposed on $34 billion of imported Chinese goods, closely followed by an additional addition of $16 billion in mid-August. In august 2017 itself the estimated cost of theft of intellectual property was above $300 billion. In addition to that there is a trade deficit of more than $500 billion a year with China. This has led to a tit for tat response from China as well. The first set came on April 2 when a tariff was imposed on 128 products it imports from America [2].

Though anticipated the imposition of tariffs always comes as a shock to the market. Apart from the actual imposition of tariffs a lot of market shocks could be linked to just the announcement of probable tariff and their news. Thus, the methodology used in the paper will be an amalgamation of both statistical models and an empirical study approach for measuring the impact of new on the stock index values.

Sample Size

When analysing the effect of the trade war it is necessary to have a sample that is completely diversified in both regards:

  1. The nations analysed
  2. The range of data period

The choice of data range has been discussed in detail in the later section, here we discuss the choice of nations. The announcement of any tariffs from either side USA of China) will have both positive and negative impacts of different nations.

To give that context assume USA imposes tariffs on the import of electronic devices from China. Then, it is uncertain if that will cause a good or bad impact on the stock markets of USA. But, the stock prices of organisation exporting electronics devices to China will definitely take a hit and in-turn the whole stock market of China. The stock market of nations which export raw material of electronics like lead will also take a hit. Thus, both China and the lead exporting nation will take a hit and we can call them news losers. At the same time a nation C might see a rise in their stock market which is likely to get more requests for exports of electronics devices. We can call this nation a new winner. Hence the sample needs both news loser and news winners.

While, considering the impact of trade war it is necessary for the research to accommodate for the fact that all the nations might not be impacted by the trade war between the United States of America and China. Thus, the sample should contain neutral nations which remained reluctant to the trade war. Such nations act as a control element for the news losers and news winners. The trade war is not the only factor changing the stock prices of national indices. The control indices will help remove other common shocks apart from the trade war. The list of the 30 nations chosen for the purpose of the research are present in Appendix 1 – List of elected countries and the Stock Indices.

Data Source

For any methodology to work and give reliable results it is necessary to have all the data in a similar format and along the same period. Thus, the websites of various country indexes are not a suitable as there might be data inconsistencies. Some websites use dividend adjusted returns whereas the others prefer raw index prices. The choice of a data source is of utmost importance in the regard that both the statistical and empirical approaches rely on the accuracy of data and how precisely it has been measured. Thus, the choice of source has been a common aggregator site called investing.com [1]. The site also provides the data for almost all of the major indexes of the world and the data is present for the required period as well.

Data Period

The first set of regulations for imposing tariffs on any imports from either side came in the month of January 2018 by USA thus, that can be assumed as the beginning of the trade war. The data has been collected till 19th October 2018. The whole period is termed as the period of trade war as the differences are yet to be settled and regulations and tariffs have been imposed even in the month of September 2018 [2]. A control period is required for the effect of trade war. The time period from January 2016 to December 2017 has been chosen as the control period. Daily index prices have been used to cover up for the lack of data point from the short duration. The duration of the research has been kept low as before 2016 there were other global economic factors which might interfere with the research like the European Sovereign Bond Crisis. Thus, the data period of the complete research is from January 3, 2016 to October 19, 2018 divided into the period of the trade was and the neutral period.

Statistical Techniques

Multiple statistical techniques have been employed to check the impact of the news of tariff imposition on the stock market prices of the equity indexes of various nations. The first of them has been used to analyse the impact of tariffs on the returns of the indices.

T-test

The hypothesis being tested via the t-test here is that there is no significant impact of the news of tariffs on the returns of the indices. The t-test is a statistical tool which can be used to examine the means of different populations. A simple two sample t-test can be used to compare the mean of two different populations. The test can be used when we do not know the variances of both the populations and even in case of a smaller sample (sizes less than 30 even)

F-test

The hypothesis being tested via the use of an F-test is that there is no significant difference between the volatility of returns before and after the imposition of tariffs. It can happen that the return of the indices remains the same thought the risk in the market changes and an F-test will help us identify that. It is used to test the F distribution for the null hypothesis. F-test is best appropriate when the data is fitted to the population using least squares and that is similar to what is done in calculation the variances of the population.

Correlation

The hypothesis being tested here is that whether trade war has led to an increased spill over effect or contagion in the global stock markets. Correlation is a statistical technique use to see the movement of one variable with respect to the other. We will use Pearson’s Correlation to calculate the correlation coefficient that ranges from -1 to +1. -1 represents a complete negative correlation which means that a change of 1% in the first variable means a change of -1% in the second variable. A correlation of +1 means a complete positive correlation which that a change of 1% in the first variable means a change of 1% in the second variable. The formula for calculating the Pearson’s Correlation is as follows:

Correlation = Cov1,2/(StdDev1*StdDev2)

Where Cov1,2 is the covariance between variable 1 and 2

StdDevi is the standard deviation of the ith variable

Method Used

Initial Impact Assessment (January 2018)

The daily closing price data for all the 30 indices from January 2016 to October 2018 has been collected from investing.com. The time period of the study ha been classified into two parts: the pre-war period that ends at December 2017 and the trade war period.

The analysis used the following two approaches:

  1. The logarithmic returns of the indices have been calculated using the closing price of the indices the previous day using the data of the entire period under study.

  2. Then the effect is estimated from the first news of imposition of tariffs that was on 22 January 2018 by taking the average return of short, medium and long term.

Our classification of short, medium and long term is as follows: the duration short term is 10 days before and after the imposition of tariffs, similarly medium term stands at 20 days and the long term stands at 30 days. The method for calculation of returns is as follows:

R­I = ln (P­i/Pi-1)

Where,

ln is the natural log of a number.

Ri is the return of a value (index) for the period/date i

Pi is the closing price of the index on period/date i

Pi-1 is the closing price of the index on period/date i-1

  1. Similar to the average returns before and after the initial news of January 22, 2018 we have calculated the variance of the index returns in the short, medium and long term before and after the first news of imposition of tariffs.

  2. To compare the average returns we have applied a paired t-test

  3. To compare the variances an F-test has been used.

The F statistics is the ratio of the variances of the two populations under study.

Contagion Analysis

Further we wish to analyse if there is an increase in the correlation between the world markets after the beginning of the trade war. For the same purpose we divide our data into two periods. The first period is the period leading up to the trade war. In all of our data the period leading till the trade war will be the data till December 2017 as the uncertainty of tariff became high in January 2018. The time period from January 2018 to October 2018 will be used as the period of trade war.

Correlation among all the selected countries will be calculated for both the periods separately and then compared. The purpose of the process here is to make an empirical analysis of the correlations between the stock market index returns if it has changed during the trade war.

Empirical Analysis

The individual news events and their probable impact on any stock market has been analysed. The process used is simple. The returns of the markets around all the tariff events were plotted and any significant change in the stock markets during such time was highlighted. For the same all the news events have been analysed for any outlier in data and if any return can be spotted which is out of the ordinary. A simple scatter plot is used for the same purpose which can quickly highlight any outlier during such an event.

Data Analysis

News Events and Empirical Analysis

The following is the timeline of the news events from USA and the reaction of Chana for them. The news events can be linked to the correlations discussed in the later sections and how the adverse effect of the news events can lead to a contagion on the other countries of the world. All the dates are for 2018

  1. On January 22 USA imposed tariffs on Chinese solar panels and cells and a 20-50% tariff on the parts of South Korean washing Machine. In a response to the same the Commerce Ministry of China initiated an anti-dumping and anti-subsidy investigation into the US sorghum on February 4
  2. On March 2, a 25% tariff on steel import and 10% tariff on aluminium imports was applied to all the nations from which even allies were not exempted. This was the first instance of a direct impact of the trade war on other nation. In response to that EU imposed a $3.5 billion package penalty to US exports including motorcycles like Harley-Davison and garments like denim
  3. US keeps Canada and Mexico exempt from the NAFTA deals and allows other nation to negotiate tariff reductions on March 8. A quick reply from EU and China came on March 9 with China urging US to reduce tariffs and EU threatening another $3.5 billion worth of tariffs.
  4. On 3rd and 5th April US threatens to impose tariffs on a total of $150 billion worth of imports. As a response China threatens to impose the same number of tariffs on April 3. It mandated Shorghum importers to pay 178.6% of the net value of US imports they make.
  5. On 29 May US again takes an offensive position against China. On May 31 US imposed steel an aluminium tariff on EU, Mexico and Canada. In response, on June 5, China offered a purchase of $70 billion worth of US products if US stopped tariff threat.
  6. From June 14 to June 19 another 25% tariff imposed on $50 billion worth of Chinese imports and a total pf $200 billion of Chinese goods put under scrutiny. On June 15 China says it will impose 25% tariff on $50 billion of US goods.

From the above data we can see that it is not just China which has been affected by the trade-war, but the effect has also spilled over to other nations, specially Canada, Mexico and EU. Thus, an escalation in trade war might possibly impact all the stated nations.

Initial Impact Assessment (January 2018)

Here we first test the hypothesis that trade was does not have any impact on stock market returns. The hypothesis can be stated as follows:

Ho: The mean return of the stock market is same before and after the beginning of trade war

HA: The mean return of the stock market is same before and after the beginning of trade war

The hypothesis is tested for all the three-time ranges, i.e. the short term, medium term and the long term. We as the researcher want to be 95% confident about our analysis and thus we choose and alpha of 5%. The test chosen is a t-test because the sample size is less than 30 for all the three decided periods.

Appendix 2, – Short, Medium- and Long-Term returns before and after January 22, shows the average return for the short medium and long term before and after the first news of January 22. A general overview of the returns makes it apparent that foremost of the indices the returns after the news announcement are lower than the returns before the news announcement. Thus, our initial analysis says that there is a difference in the stock market returns. But the confirmation of the same is only possible via the use of a paired t-test. The critical value for 10-day return is 2.365, for 20 days return it is 2.131 and for 30 days it is 2.101. Table 1 shows the results of the t-test conducted for each country.

Table – 1: The calculated t-scores for short, medium- and long term returns of the country indices

Last 10 days return & Next 10 days return Last 20 days return & Next 20 days return Last 30 days return & Next 30 days return
Australia

5.08619718

4.347612

4.874822
Belgium

2.84517923

6.177053

5.347584
Brazil

2.54554443

5.788935

5.445049
Canada

3.93792947

3.982923

5.495489
China

7.38518168

3.715571

5.08297
Egypt

3.57472134

6.142359

6.160804
France

6.77938608

6.823034

7.029084
Germany

5.74665706

7.422415

7.115684
Hong Kong

3.4537522

3.754696

4.33161
India

6.42024754

4.764006

5.049432
Indonesia

2.9904982

3.714677

5.615209
Italy

3.32106302

6.355041

6.672803
Japan

5.18660977

4.958671

5.181839
United Kingdom

2.41054828

4.774059

4.226657
Malaysia

2.81028358

4.067588

5.303147
Mexico

4.31333112

4.78928

5.638873
Netherland

3.83704154

5.742018

5.857869
Oman

2.62208501

4.531038

5.069407
Pakistan

10.0940844

5.961303

7.808547
Poland

2.51869341

3.616732

7.078546
Russia

3.70044222

6.403473

7.044627
Singapore

2.72590731

4.492043

5.42321
Saudi Arabia

2.72590731

4.492043

5.42321
South Africa

2.34896273

4.120129

4.586655
South Korea

3.46531463

6.264735

5.854835
Spain

2.36281718

6.48857

7.291208
Switzerland

2.01303506

5.484796

5.202552
Taiwan

2.74973522

3.540532

3.831016
Thailand

2.41054828

4.774059

4.226657
USA

4.19308376

3.502524

4.217555

From Table – 1: The calculated t-scores for short, medium- and long term returns of the country indices it is very evident that except for three countries all other countries see an impact of the trade war in their stock market returns. The three countries, namely South Africa, Spain and Switzerland, were not impacted by the trade war in the short run. The causes for the same can be multiple ranging from the investors in these countries from being indifferent to the trade war to a domestic shock neutralising the impact of the trade war. These three countries also have been impacted by the trade war in the long run as we can see that there is a significant difference in the mean for medium- and long-term impact.

Hence, we have enough evidence to reject the null hypothesis that the trade was does not impact the stock market returns at least in the medium and long term. We accept the alternate hypothesis that there is an impact of the trade war on the stock market returns. For short term we reject the null hypothesis for 27 countries and do not reject the null hypothesis for 3 countries. We now proceed to analyse if the impact is positive or negative for the countries.

Table 2: The direction of impact of the trade war

Country Short Term Medium Term Long Term
Australia Positive Negative Positive
Belgium Negative Negative Negative
Brazil Negative Negative Negative
Canada Negative Negative Negative
China Negative Negative Negative
Egypt Positive Negative Negative
France Negative Negative Negative
Germany Negative Negative Negative
Hong Kong Negative Negative Negative
India Negative Negative Negative
Indonesia Negative Negative Negative
Italy Negative Negative Negative
Japan Negative Negative Negative
United Kingdom Negative Negative Negative
Malaysia Positive Negative Negative
Mexico Negative Negative Negative
Netherland Negative Negative Negative
Oman Positive Positive Positive
Pakistan Negative Negative Negative
Poland Negative Negative Negative
Russia Negative Negative Negative
Singapore Negative Negative Negative
Saudi Arabia Negative Negative Negative
South Africa Negative Negative Negative
South Korea Negative Negative Negative
Spain Negative Negative Negative
Switzerland Negative Negative Negative
Taiwan Negative Negative Negative
Thailand Negative Negative Negative
USA Negative Negative Negative

Table 2, The direction of impact of the trade war, shows whether the trade war has a positive effect on the stock market returns or a negative impact. Except for Oman no other country show a complete positive effect of the trade war and thus it is the only news winner in our selected sample of countries. All other countries are news-losers as they see a decrease in their stock market returns.

Malaysia, Egypt and Australia can be termed a news-winners in the short run as they see a rise over their past 10-day returns. Australia is also a news winner in the long run as well. South Africa, Spain and Switzerland cannot be termed as news winner as we failed to reject the null hypothesis for them and thus they will be referred to as control countries in the short-term returns.

From the analysis it is very evident that the trade war in general has a negative impact on the stock market returns of the nations including that of the one imposing the tariffs, in our case United States of America. We can see that the stock market returns for USA also decreased after the initial news of the imposition of tariffs which shows a negative impact of the US-China trade war on the economy of the entire world, or at least most of the countries in the selected sample of 30 countries.

Further we move towards the analysis of variance to determine if the trade war has also impacted the variance of return before and after the initial imposition of tariffs. Here we first test the hypothesis that trade was does not have any impact on stock market variances. The hypothesis can be stated as follows:

Ho: The variance of the stock market is same before and after the beginning of trade war

HA: The variance of the stock market is same before and after the beginning of trade war

The hypothesis is tested for all the three-time ranges, i.e. the short term, medium term and the long term. We as the researcher want to be 95% confident about our analysis and thus we choose and alpha of 5%. The test chosen is a t-test because the sample size is less than 30 for all the three decided periods.

Appendix 3, – Short, Medium- and Long-Term variances before and after January 22, shows the variance for the short medium and long term before and after the first news of January 22. A glance over the appendix suggests that there is a general increase in variances after the imposition of tariffs on January 22. Thus, out initial analysis predicts that the volatility in the market has increased after US imposed tariffs on import from China. This can me attributed to the uncertainty that remains in the market over the final outcome of the trade war and the speculations over the upcoming steps that either side will take to counter the tariffs. But the confirmation of the same is only possible via the use of a paired F-test. The critical value for 10-day return is 4.28, for 20 days return it is 2.04 and for 30 days it is 1.86. Table 3, calculated F-scores for short, medium- and long term returns of the country indices, shows the results of the F-test conducted for each country.

Table – 3: The calculated F-scores for short, medium- and long term returns of the country indices

Last 10 days volatility & Next 10 days volatility Last 20 days volatility & Next 20 days volatility Last 30 days volatility & Next 30 days volatility
Australia

5.27774291

9.7835

9.448305
Belgium

1.49438838

8.557352

8.143488
Brazil

6.24610071

7.671965

5.166222
Canada

1.09129029

5.745191

7.003837
China

3.10605015

14.80096

11.95982
Egypt

1.0471749

1.875775

1.507775
France

2.60505777

4.147092

4.2592
Germany

1.91097458

3.239341

3.259555
Hong Kong

2.82833632

8.596484

10.1006
India

1.39634788

3.575397

3.539385
Indonesia

14.0583188

3.0323

2.83473
Italy

5.81596081

3.864156

3.188774
Japan

6.14008119

3.306311

4.164918
United Kingdom

3.24361042

2.082133

1.751922
Malaysia

3.75841412

3.687851

3.017291
Mexico

1.76978291

1.487308

1.570154
Netherland

3.60284528

9.719403

8.108111
Oman

2.03999754

3.219182

1.396643
Pakistan

6.31349941

4.734681

2.66327
Poland

1.44996313

2.338884

2.075135
Russia

3.71400223

3.165573

3.435289
Singapore

5.70297025

1.031023

1.064197
Saudi Arabia

5.70297025

1.031023

1.064197
South Africa

9.86849106

9.534002

13.53324
South Korea

3.02752297

4.201838

3.427817
Spain

1.15369305

4.218186

2.942604
Switzerland

1.21486582

5.105021

6.033244
Taiwan

9.86975099

16.33302

11.85552
Thailand

3.24361042

2.082133

1.751922
USA

1.03176923

18.14996

14.23394

F-test shows that the most significant impact on the volatility is during the medium term. This must be the time of maximum speculations as the invertors speculate further policy changes. Countries like Egypt and Mexico do not have much impact on their volatility. The most interesting result here is that of both USA and China as none of the two countries actually show any difference on their volatility in the short term whereas there is a decrease in their stock market returns. This can be explained from an increased uncertainty in both the countries before the imposition of tariffs. Thus, near the date of tariff imposition both the markets were already uncertain, and the volatility did not change much. The difference can however be seen in the medium term and long-term volatility of both the indices. Singapore and Saudi Arabia only see a change in the short term and not the long term which shows that the uncertainty only remained right after the news of tariffs.

Hence, we have enough evidence to reject the null hypothesis that the trade was does not impact the stock market variances at least in the medium and long term. We accept the alternate hypothesis that there is an impact of the trade war on the stock market variances. For short term we reject the null hypothesis for 10 countries and do not reject the null hypothesis for 20 countries. The number changes to 26 and 4 in case of medium term and 23 and 7 in case of the long term. Thus, we can also say that the effect of trade war on the uncertainty decreases in the long term and it starts to revert back to normal at least till more market shocks appear. We now proceed to analyse if the impact is positive or negative for the countries.

Table 4: The direction of impact of the trade war

Country Short Term Medium Term Long Term
Australia Positive Positive Positive
Belgium Positive Positive Positive
Brazil Positive Positive Positive
Canada Positive Positive Positive
China Negative Positive Positive
Egypt Positive Positive Positive
France Positive Positive Positive
Germany Positive Positive Positive
Hong Kong Positive Positive Positive
India Positive Positive Positive
Indonesia Positive Positive Positive
Italy Positive Positive Positive
Japan Negative Positive Positive
United Kingdom Positive Positive Positive
Malaysia Positive Positive Positive
Mexico Positive Positive Positive
Netherland Positive Positive Positive
Oman Negative Negative Negative
Pakistan Positive Positive Positive
Poland Positive Positive Positive
Russia Positive Positive Positive
Singapore Negative Positive Negative
Saudi Arabia Positive Positive Positive
South Africa Positive Positive Positive
South Korea Positive Positive Positive
Spain Negative Positive Positive
Switzerland Positive Positive Positive
Taiwan Positive Positive Positive
Thailand Positive Positive Positive
USA Negative Negative Negative

Table 4, the direction of impact of the trade war, shows if the volatility has increased or decreased after January 22. Positive represents an increase in the volatility and negative represents a decrease. Upon looking at the change in volatility of USA and China the results of the F-test makes more sense. The uncertainty in both the markets actually decreased in the short run. Leading up to the news there is more uncertainty of the number of tariffs and the sectors hit. After the news the uncertainty decreases as the results become clear. In case of USA the trend continues, and variance falls further. In case of China it increases in the medium and long run in anticipation of the reaction from the nation which eventually came in April.

Oman is another interesting result here as for medium term it has a significant impact on its uncertainty and more importantly it was a news winner in case of return analysis. We also see here that the volatility has decreased for Oman. Thus, Oman is in general gaining from the trade war with decreased risk in its market.

Apart from USA and Oman all other nations see an increase in volatility, at least in magnitude and a definite increase where we are able to reject the null hypothesis. Thus, we can say that irrespective of the returns the general risk in the stock markets of the nations saw a rise after the beginning of the trade war.

Contagion

The effect of contagion has been calculated using correlation among the stock market prices f different countries. The correlations have been calculated for all the nations however the most important ones are the ones with USA and China.

Table 5: Correlation of US stock market with other countries

Pearson Correlation Sig. (2-tailed) Pre-Trade War During Trade War

Australia

0.146769858

8.57E-05

0.166127

0.118936

Belgium

0.486102319

1.93E-43

0.547404

0.400965

Brazil

0.10723544

0.004202

0.13378

0.392495

Canada

0.690830261

5.9E-102

0.694577

0.733228

China

0.138779245

0.000206

0.137711

0.139989

Egypt

0.088217153

0.018637

0.15434

-0.05293

France

0.556160274

5.69E-59

0.637797

0.439805

Germany

0.545437739

2.37E-56

0.622149

0.4298

Hong_Kong

0.203711046

4.26E-08

0.237984

0.153434

India

0.273742336

1.1E-13

0.328493

0.185553

Indonesia

0.07027594

0.061083

0.061017

0.078152

Italy

0.477889707

7.58E-42

0.587936

0.284827

Japan

0.226268137

1.05E-09

0.2493

0.19771

UK

0.450134794

9.14E-37

0.515327

0.358595

Malaysia

0.149370851

6.38E-05

0.137989

0.161767

Mexico

0.458124601

3.51E-38

0.53084

0.345965

Netherland

0.567717817

6.65E-62

0.66

0.423435

Oman

0.067637599

0.071479

0.147284

-0.11207

Pakistan

0.031178279

0.406485

0.058734

-0.01704

Poland

0.333107333

6.94E-20

0.377607

0.265371

Russia

0.373125556

6.6E-25

0.437076

0.273664

Singapore

0.198401184

9.6E-08

0.235447

0.142193

Saudi_Arabia

0.168061874

6.62E-06

0.172777

0.180839

South_Africa

0.377909256

1.48E-25

0.37592

0.383753

South_Korea

0.249613204

1.47E-11

0.230143

0.275631

Spain

0.501776677

1.31E-46

0.579688

0.401881

Switzerland

0.5101397

2.29E-48

0.58455

0.396731

Taiwan

0.181710161

1.08E-06

0.116018

0.262157

Thailand

0.171076845

4.49E-06

0.17785

0.161272

Table 5, correlation of US stock market with other countries, shows the correlation of the stock market index of USA with the indices of other countries. Here the significance level for the entire data has been calculated using SPSS tool. Here we can see that for the entire period the correlation of Oman and Pakistan has been poor with that of S&P 500 index and thus, the results of these two countries cannot be used as for empirical analysis of contagion. The correlation of 9 other countries has increased during the time of the trade war when compared to the correlation of the pre-trade war period. Of these China is the obvious nation which was bound to have a higher correlation during this period. This suggests that during trade wars both the nations are more susceptible to shocks in each other’s economy. Apart from China, Brazil and Taiwan have seen a significant rise in the correlation of their stock market indices with that of USA. Taiwan has a dependency on China and Brazil on USA which has led to this increase in correlation. Oman on the other hand has seen a fall in the correlation with USA which accounts for the fact that is has been a news winner and rather the solo news winner among all the remaining 29 nations.

Countries like Netherlands, Mexico, UK and Italy have seen a fall in correlation from the previous values suggesting a gradual delinking of their stock markets from that of USA as the trade war has progressed. It is also interesting to note that there is very less correlation between the stock market returns of USA and China to begin with and the change is also less than the change for many other countries. This suggests that the trade war might have a worse effect on other nations of the world when compared to its effect on the economy and stock market of China and USA.

Table 6: Correlation of China stock market with other countries

Pearson Correlation Sig. (2-tailed)

Pre -Trade War

 During Trade War

Australia

0.242552866

5.59E-11

0.204786

0.353332

Belgium

0.120864068

0.001242

0.083297

0.219367

Brazil

-0.115425076

0.002052

-0.14598

0.132903

Canada

0.13864215

0.000209

0.098957

0.231441

China

1

1

1

Egypt

0.068262136

0.068896

0.06083

0.071446

France

0.142096153

0.000144

0.074929

0.33856

Germany

0.171330165

4.34E-06

0.106263

0.323745

Hong_Kong

0.535318054

5.76E-54

0.443623

0.691786

India

0.267001922

4.53E-13

0.260011

0.275385

Indonesia

0.154593324

3.48E-05

0.101265

0.228439

Italy

0.082560976

0.027712

0.048365

0.182765

Japan

0.257910167

2.88E-12

0.177482

0.478898

UK

0.112061318

0.002769

0.056355

0.273378

Malaysia

0.249742014

1.43E-11

0.23086

0.280142

Mexico

0.166140875

8.46E-06

0.098647

0.303337

Netherland

0.142972294

0.000131

0.06723

0.339511

Oman

0.103733383

0.00563

0.123017

0.046781

Pakistan

0.071344036

0.057245

0.052313

0.105871

Poland

0.195240708

1.54E-07

0.106977

0.369434

Russia

0.18834949

4.22E-07

0.14475

0.274823

Singapore

0.362766413

1.55E-23

0.295692

0.48122

Saudi_Arabia

0.155782532

3.02E-05

0.131567

0.237041

South_Africa

0.230418905

5.07E-10

0.133186

0.40441

South_Korea

0.325532062

5.16E-19

0.192576

0.547437

Spain

0.103935466

0.005537

0.056028

0.274853

Switzerland

0.181100645

1.17E-06

0.106815

0.341212

Taiwan

0.305020844

8.95E-17

0.261686

0.370367

Thailand

0.255363547

4.78E-12

0.204167

0.353295

USA

0.138779245

0.000206

0.137711

0.139989

Table 6, correlation of China stock market with other countries, shows the correlation of the stock market index of China with the indices of other countries. Here the significance level for the entire data has been calculated using SPSS tool. Except Pakistan and Egypt all other countries have a significant correlation with China when the entire period is considered. The correlation of China has increased with all other nations except for Oman. We can say that is the trade war causes the stock market prices of China crash due to the trade war then it will have a negative impact on the stock markets of the entire world. This was not the case when USA was considered and thus, the effect of contagion from China to the world is much higher that the contagion from USA to the world.

Empirical Analysis of Japanese Nikkie

The Graph below shows the Japanese Nikkie and how it has moved so far in 2018. The first three troughs are from the beginning of February to the end of March showing the impact of the imposition of tariffs on the Japanese stock market. The Japanese Nikkie remained low for two continuous weeks due to the imposition of tariffs.

Graph 1: Japanese Nikkie

 

 

References

  1. Anon., 2018. Investing.com. [Online]
    Available at: http://www.investing.com
    [Accessed 19 October 2018].
  2. Staff, M. F., 2018. The US-China Trade War, Explained. [Online]
    Available at: https://www.fool.com/investing/2018/09/30/the-us-china-trade-war-explained.aspx
    [Accessed 30 September 2018].

 

 

Appendix 1 – List of elected countries and the Stock Indices

 

Sr. No. Country Index

1

 Australia S&P/ASX 200

2

 Belgium Dow Jones Belgium

3

 Brazil Brazil All Shares

4

 Canada S&P/TSX Composite

5

 China Shanghai Composite

6

 Egypt EGX 100

7

 France CAC 40

8

 Germany DAX

9

 Hong Kong Hang Seng

10

 India Nifty 500

11

 Indonesia Jakarta SE

12

 Italy FTSE Italy

13

 Japan Nikkie 225

14

 United Kingdom FT Ordinary Share

15

 Malaysia FTSE KLCI

16

 Mexico Dow Jones Mexico

17

 Netherland AEX

18

 Oman MSM 30

19

 Pakistan Karachi 100

20

 Poland WIG

21

 Russia RTSI

22

 Singapore FTSE Singapore

23

 Saudi Arabia Tadawul All Share

24

 South Africa FTSE/JSE

25

 South Korea KOSPI 200

26

 Spain IBEX 35

27

 Switzerland SMI

28

 Taiwan Taiwan Weighted

29

 Thailand SET

30

 USA S&P 500

 

 

Appendix 2 – Short, Medium- and Long-Term returns before and after January 22

 

Country Last 10 days return Last 20 days return Last 30 days return Next 10 days return Next 20 days return Next 30 days return
Australia

-0.18%

-0.08%

-0.06%

0.21%

-0.21%

-0.04%
Belgium

0.02%

0.23%

0.16%

-0.32%

-0.45%

-0.22%
Brazil

0.41%

0.30%

0.43%

0.38%

-0.03%

0.27%
Canada

0.05%

0.06%

0.05%

-0.52%

-0.47%

-0.23%
China

0.31%

0.38%

0.31%

-0.08%

-0.67%

-0.28%
Egypt

-0.02%

0.10%

0.18%

0.21%

0.05%

0.07%
France

0.14%

0.28%

0.15%

-0.33%

-0.48%

-0.17%
Germany

0.28%

0.28%

0.14%

-0.55%

-0.60%

-0.33%
Hong Kong

0.57%

0.53%

0.52%

0.12%

-0.60%

-0.18%
India

0.20%

0.21%

0.22%

-0.36%

-0.26%

-0.27%
Indonesia

0.25%

0.15%

0.26%

0.23%

0.03%

0.07%
Italy

0.33%

0.58%

0.36%

-0.24%

-0.39%

-0.23%
Japan

0.06%

0.30%

0.21%

-0.25%

-0.76%

-0.40%
United Kingdom

0.01%

0.03%

0.08%

-0.38%

-0.51%

-0.26%
Malaysia

0.13%

0.13%

0.24%

0.25%

0.00%

0.06%
Mexico

0.32%

0.09%

0.16%

0.15%

-0.24%

-0.06%
Netherland

0.30%

0.31%

0.20%

-0.38%

-0.55%

-0.29%
Oman

-0.28%

-0.14%

-0.08%

0.14%

0.02%

-0.01%
Pakistan

0.49%

0.69%

0.78%

0.03%

-0.10%

-0.06%
Poland

0.42%

0.36%

0.31%

-0.32%

-0.44%

-0.28%
Russia

0.41%

0.72%

0.69%

0.02%

-0.35%

0.10%
Singapore

0.22%

0.35%

0.32%

-0.09%

-0.34%

-0.07%
Saudi Arabia

0.28%

0.28%

0.19%

0.16%

-0.05%

0.03%
South Africa

0.34%

0.17%

0.20%

-0.42%

-0.54%

-0.20%
South Korea

0.03%

0.01%

0.11%

-0.08%

-0.45%

-0.24%
Spain

0.20%

0.35%

0.14%

-0.29%

-0.47%

-0.26%
Switzerland

0.04%

0.10%

0.06%

-0.34%

-0.50%

-0.26%
Taiwan

0.55%

0.36%

0.36%

-0.02%

-0.45%

-0.19%
Thailand

0.17%

0.26%

0.26%

0.04%

-0.08%

-0.08%
USA

0.33%

0.39%

0.28%

-0.19%

-0.38%

-0.17%

 

 

Appendix 3 – Short, Medium- and Long-Term variances before and after January 22

 

Country Last 10 days variance Last 20 days variance Last 30 days variance Next 10 days variance Next 20 days variance Next 30 days variance
Australia

0.0006%

0.0013%

0.0011%

0.0029%

0.0119%

0.0088%
Belgium

0.0009%

0.0019%

0.0018%

0.0024%

0.0165%

0.0129%
Brazil

0.0035%

0.0042%

0.0049%

0.0226%

0.0256%

0.0213%
Canada

0.0010%

0.0012%

0.0009%

0.0027%

0.0067%

0.0058%
China

0.0022%

0.0017%

0.0020%

0.0058%

0.0238%

0.0200%
Egypt

0.0069%

0.0043%

0.0043%

0.0059%

0.0081%

0.0054%
France

0.0012%

0.0033%

0.0034%

0.0050%

0.0143%

0.0125%
Germany

0.0032%

0.0048%

0.0046%

0.0070%

0.0159%

0.0131%
Hong Kong

0.0041%

0.0036%

0.0031%

0.0088%

0.0296%

0.0285%
India

0.0042%

0.0031%

0.0025%

0.0120%

0.0116%

0.0079%
Indonesia

0.0009%

0.0031%

0.0030%

0.0097%

0.0091%

0.0074%
Italy

0.0007%

0.0047%

0.0059%

0.0056%

0.0183%

0.0161%
Japan

0.0024%

0.0083%

0.0069%

0.0117%

0.0276%

0.0243%
United Kingdom

0.0052%

0.0033%

0.0028%

0.0027%

0.0128%

0.0105%
Malaysia

0.0007%

0.0022%

0.0022%

0.0020%

0.0080%

0.0056%
Mexico

0.0014%

0.0049%

0.0044%

0.0026%

0.0077%

0.0064%
Netherland

0.0005%

0.0015%

0.0017%

0.0028%

0.0151%

0.0124%
Oman

0.0008%

0.0006%

0.0012%

0.0012%

0.0021%

0.0014%
Pakistan

0.0150%

0.0134%

0.0118%

0.0054%

0.0048%

0.0064%
Poland

0.0024%

0.0054%

0.0052%

0.0040%

0.0126%

0.0094%
Russia

0.0050%

0.0082%

0.0068%

0.0187%

0.0246%

0.0219%
Singapore

0.0025%

0.0031%

0.0025%

0.0043%

0.0084%

0.0098%
Saudi Arabia

0.0072%

0.0038%

0.0037%

0.0026%

0.0039%

0.0033%
South Africa

0.0010%

0.0015%

0.0015%

0.0083%

0.0112%

0.0183%
South Korea

0.0031%

0.0043%

0.0045%

0.0136%

0.0170%

0.0138%
Spain

0.0026%

0.0036%

0.0048%

0.0073%

0.0165%

0.0135%
Switzerland

0.0021%

0.0028%

0.0025%

0.0019%

0.0148%

0.0130%
Taiwan

0.0005%

0.0019%

0.0020%

0.0047%

0.0240%

0.0199%
Thailand

0.0015%

0.0020%

0.0021%

0.0039%

0.0042%

0.0033%
USA

0.0026%

0.0017%

0.0019%

0.0080%

0.0315%

0.0225%

 

ANSWER

 

Does Trade War affect Share Market in Long Run: A Case Study of Recent Trump- China Trade War

Introduction

2018 was the year of uncertainty and it started with a fall in markets all over the world. The threat of an upcoming trade war was the prime reason of concern for many investors on the Wall Street. Among the looming tensions of a forthcoming trade war all the markets saw an increase in risk and a decrease in returns in January. The markets recovered eventually but the overall impact of the risk remained throughout the year as there was again a year-end decrease in returns. But, one could question was the decrease in returns actually due to the trade war.

Some of the market shocks that 2018 saw coincided with the dates of major announcements related to the US-china trade war. At the same time many of them did not. The announcements did lead to market shocks momentarily, but it is difficult to say if any of those had a lasting impact on the stock markets or if those shocks were contained within a short period of time.

We also need to see if these announcements had an impact on the markets only in United States of America and China or if the impacts were felt by the stock markets of other nations as well. The study here aims to see the impact of a trade war on the stock markets in a long run. There definitely is a strong visible impact on the stock markets in a short run but that might not be the cause of decreased returns. The overall decreased returns of 2018 could very well be due to other factors like the sanctions imposed on Kremlin. Hence, we will be studying the impact of such market announcements in long run to gather empirical data on if the trade war really had an impact. At the same time, we also need to analyse the direction of impact from the trade war. This becomes especially useful when we analyse the stock market returns of nations other than USA and China as a positive impact will show that the nations were able to capitalise on the trade war.

The stock markets are the place where the companies go to raise money. It is an easy way for firms with good market reputation to raise money and at the same time the cost of equity capital also decreases for the firms with high brand values. As the interest of shareholders in the company grows its stock prices rise yielding high capital gains to the shareholders. At the same time a decrease in their interest of a disaster in the company leads to the decrease in the share value and hence negative returns. However, just the company is not responsible for the change in its stock price. There are various external factors as well. The competition in the industry that it works in and the health of the upstream and the downstream industries also affect the health of the returns of a firm.

These effects are somewhat countered when we talk at the stock market level where the increase in the stock prices of one firm from one industry are countered by the decrease in the stock prices of another firm in another industry. However, other forces start to affect the stock market as a whole. The government policies, the international economic climate and the change in the credibility of the country itself affect the stock market as a whole. These are large economic factors which affect all the industries at the same time. Some have a positive impact, and some have a negative and the overall interactions between these changes decides the net impact on the stock markets. One such factor is the trade war. When one nation imposes restrictions on the import of goods from another nation then that is termed as a trade war. A trade war is capable of affecting multiple industries and not just the industry whose products saw an increased restriction.

Trade wars have always been linked to a decrease in stock market returns. As the concerns of trade wars grow the risk in the stock market is also said to grow due to the uncertainty about the eventual effects of a trade war if it actually happens. Like any other event speculations of a trade war can raise the stock market or completely kill the returns. And all that happens before the trade war actually takes place. The actual trade war is a completely different scenario.

If there is a trade war, then the domestic industries which consume the affected imported products will find it difficult to gather enough resources and fulfil the market demands. The stock prices will eventually decrease in that case. At the same time the domestic producers of the same imported products will find the market demands increasing for their products and thus, they will see an increase in stock prices. The overall impact of such a trade war becomes a trade-off between the losing and the gaining industries. The overall impact can also be neutral. However, the empirical studies to support any of these conclusions are not very vast and comprehensive.

But till ow we have only talked about the two-party nations which are involved in a trad war. In this era of globalisation two nations do not work in silos. There is no such thing as a binary effect. Rather there is always a ripple effect. When one nation goes on a trade war with other nation then there should also be a spill over effect of that on other nations as well. Sanctions might be a good example to explain the same. USA imposed sanctions on the nation of Iran. So, what will be the effect of that on other nations.

First the nations which are friendly with USA will also cease business relations with Iran to maintain healthy relations with USA. A similar effect will be seen from the nations which depend on USA in economic terms or have large business relations with them. On the other hand, the nations which are unfriendly to USA will try to increase their business with Iran to leverage the situation to their advantage.

But even that approach is restrictive. The effect will not only be on the trade between the nations with Iran as one of the trading party. Iran is a big producer of oil. Which means that sanctions on Iran will lead to a shortage of oil in the international market. Other oil producing nations will be affected as well. They will try to leverage the situation to their advantage. We will also see Brent rising due to the shortage of oil in the international markets. And thus, the industries which depend on Oil will see negative returns.

Thus, we can see that all the actions have a butterfly effect. Any action by one nation on other can possibly affect the stock markets of multiple nations. The extent of that effect and the directions are a matter of further discussion and evaluation of the situation more closely. Similarly, trade wars do not only have a potential to affect the two countries involved in trade war. But they are capable of affecting multiple nations on a global level. It depends on the policies of the nations and the acumen of the economists in those nations how they are able to shield their country from the affects of a trad war in the long run and how thy are able to salvage a situation. But the actual industries do see an impact from a trade war. If that impact translates into the stock market of those nations and if that impact is positive or negative will depend totally on the nation.

Literature Review

Trade war can be attributed to the phenomenon known as protectionism. The effects of protectionism range from tariffs to subsidised production for exporting companies [5]. Protectionism will always have an effect on the competitiveness of the domestic market against the international sellers. In this case the trade war is the means to allow the domestic US industries, like manufacturing, to play catch-up with the Chinese producers. This will finally have an impact on the market share of the domestic producers in USA where their domestic share will increase, and the market share of the international competitors will decrease.

A trade war has various overall monetary and economic impacts. As a matter of fact ,initial protectionist measures and striking back raise the expenses of exchange for the members engaged with the exchange struggle, for our situation the US and China. This will prompt lower fare and import volumes on either side. The negative effect on exchange volumes is mostly relieved by fare substitution impacts, as the negative effect is somewhat balanced by diverting exchange to different goals. To give a precedent, in the present exchange war, China has forced import duties on US Soybeans finishing off the US which customarily sent out the greater part of their soybean exports to China, the world’s biggest shipper. Thus, now the US should send the majority of their soybean fare to different shores. This dissimilarity in exchange limit the monetary misfortunes mostly, however as our partners from Food and Agri Research [6] have contended, if the US doesn’t send its soybeans to China, it needs to purchase practically 100% piece of the pie in every single other nation on the planet by limiting costs well beneath those in contender South America. Another moderating impact may be the reaction of the swapping scale. China’s Exchange Rate (CNY) has devalued by 9% since April, which has halfway moderated the higher soybean costs China needs to pay for South American Soybeans.

Rather than the relieving the impacts of substitution on their exports, the negative effect of the trade war on exchange could be exasperated by the incorporated value chain models. Over the previous decades, global firms have progressively been misusing similar universal preferences by moving parts of their creation forms abroad into their own land. For instance, they profit by low work costs in Asia for the incorporation of merchandise into a single unit, while showcasing and R&D are situated at the command post in their own nations. This has empowered them to create all the products more proficiently and enhance their intensity keeping their costs low. The drawback of these purported cut up esteem chains is that multinationals have turned out to be increasingly helpless against import taxes on middle items or wares from abroad. For example, the US levy bundle on Chinese imports of USD 50bn executed this late spring applies fundamentally to middle of the road items and capital products [7]. Subsequently, just about 40% of the levies in this USD 50bn bundle are borne for by Chinese firms while the staying 60% of the taxes are consumed by outside firms that are dynamic in China [8]. The levies on PCs and electronic items are even borne for 87% by remote firms. At last, the US firms that depend on intermediates imported from China will turn out to be progressively costly because of exchange obstructions (and the other way around), which implies that these organizations will confront either a crumbling of intensity because of higher retail costs (and higher fare costs and a lower worldwide piece of the overall industry) or a retention of the greater expenses, which will hurt their productivity.

Customers in the two nations will feel the squeeze also, as the exchange/trade obstructions will result in import swelling, causing harm to both the sides, which will consume genuine discretionary cashflow of families and lead to bring down family unit spending for the general public which will be one of the worst impacted parties by the trade war. Trade wars will by and large be the main reason behind the monetary market disturbance, which influences both buyer and maker estimation and could at last lead to bring down private venture and private utilization. We have seen the problematic idea of vulnerability brought about in terms of professional career wars on money related markets this year. Particularly developing markets (EM’s) have borne the brunt of the US-Chinese strains over exchange, as financial specialist feeling transformed from a hazard on to chance off modus, which brought about huge capital outpourings and put EM monetary forms under strain [9]. The Chinese CNY lost 7% against the US dollar this fall opposite the start of the year. In spite of the fact that this devaluation absolutely has relieved the effect for Chinese exporters, the misfortunes as far as exchange (for example increasingly costly imports) will be felt by Chinese families and have put extra descending weight on private utilization. For the US, the inverse is the situation: the fortifying of the USD gives US family units fortune gains, as imports will end up less expensive, however will be a mishap for US exporters, particularly the ones transporting their items to Chinese shores.

In the course of the most recent decades, numerous Western organizations have moved parts of their production houses and offices to China. This was for the most part identified with a near work cost preferred standpoint, which implied that on equalization it was less expensive to create in China than, for instance, in their own nation because of the cheaper labour rates perfectly complemented by the exchange rates between their nation and CNY. Because of rising wages, especially in the waterfront zones of China, a portion of the organizations have made a move to other developing economies in the district (for example the Philippines or Vietnam), which are still at a prior phase of their monetary advancement, along these lines benefitting from a moderately low compensation advantage contrasted with China. These sorts of movements are a medium-term key story. All things considered, such reallocations are a tedious and expensive procedure, particularly when settled resources are included. Because of the expanded pressures among China and the US, this procedure appears to have quickened. For instance, different reviews demonstrate that an ever-increasing number of organizations are thinking about a migration [10]. Moreover, the exchange war appears to prompt an expansion in outside direct interest in Southeast Asian nations [11].

Work profitability development is the most essential mainstay of monetary development. There is an immense strand of writing that demonstrates that exchange significantly affects efficiency. To start with, information grew abroad emphatically influences residential efficiency, however these overflows are not programmed or exogenous.[12] Well-known courses of worldwide learning overflows are human capital versatility [13], outside direct speculation [14] and exchange [15;16;17]. Exchange encourages outside learning overflow impacts, as firms can utilize remote created middle sources of info [18;19] Besides, downstream clients can figure out advances typified in creative last imports and utilize this information in their very own generation forms. These components appear to be substantially more vital for China than the US, as the last still has a vast innovation lead over China.

In any case, US profitability is likewise influenced straightforwardly by the Chinese-US exchange relationship, as receptiveness to remote exchange encourages advertise rivalry, which invigorates firms to lessen their X-wasteful aspects and increment endeavors to innovate. In this sense, import rivalry will result in progressively inventive, increasingly productive firms (the inside firm impact). Besides, there are part creation impacts (the between firm impact): lower exchange costs will result in reallocation of work and capital toward increasingly gainful and ability escalated firms inside areas and toward aptitude serious divisions in all nations [20]. These discoveries are in accordance with Bernard, Redding and Schott [21], who find that inside and between-industry reallocations of monetary movement amid times of exchange advancement brought normal efficiency up in all ventures, however more so in the relative preferred standpoint businesses.

For European firms, Bloom, Draca and Van Reenen [22] locate that Chinese import rivalry has expanded specialized change inside European firms (inside impact) and furthermore caused a move of work towards mechanically further developed firms (segment sythesis impact). Taken together, these impacts represent 14% of European innovation updating in the period 2000-2007. Different examinations that locate a strong direct beneficial outcome of worldwide exchange on efficiency are by Edwards [23] and Alcalá and Ciccone [24].

As exchange beneficially affects work efficiency advancement, a pullback in exchange brought about by higher exchange expenses ought to adversy affect profitability. We will come back to this subject all the more broadly when we will talk about the efficiency models for both the US and China.

Trump previously alluded to China’s uncalled for exchanging and financial works on amid his decision battle in 2016, yet it took a year prior to his organization fortified this enemy of China talk by usage of genuine protectionist approaches. The postponement was likely identified with China’s key job in the contention between the US and North Korea [25]. Because of this job, it was normal that China would be less eager to participate with the US against North Korea if there should be an occurrence of respective exchange strains, for instance by following up on worldwide assents against the North Korean routine.

Before the US chose to additionally fix the chains on China by presenting more duty bundles , a few rounds of reciprocal dealings and negotiations occurred. In their announcement going back to May 2018, China invested in import more merchandise from the US, particularly rural and vitality related items. China would likewise give careful consideration to the security of licensed innovation privileges of US organizations working in China. In spite of the fact that this result was at first viewed as positive, it before long turned out to be certain that China’s promises were not actually lined up with the requests by the Trump organization. That is the reason the US kept on fixing the screws by actualizing another round of taxes, focusing on USD 50bn of imports from China, which thusly prompted a comparative striking back by China . In the mean-time, no different rounds of dealings occurred between the opposite sides. In the most as of late introduced bundle by the US of USD 200bn presented in September, three stages are obvious. After the ‘first’ round of 10% taxes on USD 200bn, the tolls will be expanded to 25% starting at 1 January. In the event that China strikes back, the US is additionally arranged to force collects on another USD 264bn worth of Chinese fares, which as a result makes all fare from China to US shores subject to a 25% duty. These are primarily customer (electronic) items.

In their latest World Economic Outlook of October 2018 [26], the IMF draws up the conceivable effect of the US-China exchange war by utilizing their purported Global Integrated Monetary and Fiscal Model (GIMF) for a situation (‘layers’) investigation. It expands on four recently portrayed situations in a July 2018 G20 Surveillance Note [27]. Their first exchange war situation incorporates levies that as of now have been executed. In a second situation, the IMF includes the proposed increment of US levies from 10% to 25% on USD 200bn USD of Chinese fare by the start of one year from now. A contrast between our examination and the IMF, is that the IMF likewise expect countering by other exchanging accomplices (for example Europe) beside China, and evaluates a situation where the US chooses to actualize taxes on autos. Therefore, the IMF finds a bigger effect in the (multilateral) exchange war situation on the US than on China. The levy stuns in the distinctive situations are lasting, and they incorporate likewise certainty and venture impacts by accepting an expansion in hazard premia for cutting edge economies (30bps) and developing markets (60bps) so as to reflect generally higher monetary vulnerabilities. The investigation does, be that as it may, exclude any powerful efficiency impacts or non-tax striking back by China and it is discovered that the effect for China is greater than for the US in the short-run, yet not in the more drawn out run.

The National Institute of Economic and Social Research (NIESR) takes a gander at the potential exchange war impacts by proceeding prior research of Liadze [28], Hantzsche and Liadze, Carreras and Ramina and Liadze and Hacche. They likewise join the latest round of duties and run recreations utilizing NiGEM. Accordingly, they utilize a comparable econometric model, however utilize distinctive presumptions. They stun similar situations exogenously, however these stuns are not viewed as lasting as the stuns are just connected from 2018Q3 till 2020Q4, accepting that costs will modify after that period. Their outcomes point to marginally more grounded negative effect on the US contrasted with China, however this is because of contrasts contrasted with our suspicions and situations. Most eminently, we incorporate exogenous remote trade advancements and unfavorable potential profitability impacts, where NIESR abstracts from these impacts. Also, we evaluate a further heightening of the respective exchange war (counting NTBs), while NIERS just survey the right now forced protectionist estimated.

The exchange war situation examination by the European Central Bank (ECB) is distributed as a component of their financial notice going once again from September 2018. By utilizing the IMF’s GIMF show just as their own worldwide model [29], the ECB surveys both the exchange and certainty channels by which the economy may be affected by the present exchange war. It is, in any case, essential to take note of that this investigation does not survey a US-China exchange war, however, looks at a worldwide exchange war where the US forces duties on all imports and all exchanging accomplices will respond these protectionist US gauges. Besides, the investigation expect that the exchange pressures will ease going ahead and will keep going for a long time. At last, trade rates and money related strategy are displayed endogenously. The ECB likewise makes a refinement among immediate and roundabout exchange impacts, by moreover assessing potential certainty and budgetary market impacts, by displaying a fixing of money related conditions accepting an expansion in security premia by 50bps and a financial exchange decrease of two standard deviations in all nations. All things considered, the effect on exchange from dynamic efficiency impacts isn’t secured here either. What’s more, one noteworthy distinction with every other investigation talked about here is that this examination considers a full striking back of other exchanging accomplices against the US. This clarifies the moderately higher effect on the US economy contrasted with China, as the last can profit by substitution impacts.

The Netherlands Bureau for Economic Policy Analysis (CPB) examined the potential exchange war by utilizing distinctive situations [30]. WorldScan, a purported computational general harmony (CGE) model of the world economy, is utilized to gauge and include an examination on an (inter)national and sectoral level. The CPB considers recently introduced bundles and makes suspicions on approaching bundles, by utilizing five unique situations, fluctuating from exclusively steel and aluminum taxes, to thump on heightening situations where taxes from both the EU and China versus the US are introduced, and the US even participates in exchange wars with all OECD nations. Demonstrate particulars incorporate a combination of purported immaculate and flawed challenge systems and the task of various versatility classes. These depend on the suppositions that generally low flexibility levels should yield bigger monetary misfortunes and that the antagonistic effect is bigger under a situation of blemished challenge. Then again, the investigation does not demonstrate a particular appropriation of the effect after some time. It just thinks about what the effect may be of a changeless stun up to and including 2030, with the last-referenced year as the reference point contrasted with the pattern.

By the end of July 2018, the stance of the United States president, Donald Trump, was quite clear towards China. And yet new developments continued as the year went by [3]. China was continuously pushing for the “Made in China 2025” campaign and that was the central topic of discussion in many of the debates in the US senate. Everyone wanted to device a best approach for creating tariffs on Chinese goods so that the United States of America continued to be the word supreme power in terms of economic strength.

The series of tariffs imposed till June led to a sharp devaluation of the Chinese yuan against the dollar. The drop was approximately 3.7%. At the same time the US president tweeted that the devaluation was a result of the manipulation of the Chinese government on purpose. He accused both China and EU for decreasing rates to counter the strengthening US dollar which could prove detrimental to the US economy. At this point of time it was clear that the US China trade war was not a bilateral issue and rather affected multiple nations. At the very least it was being accused of impacting the interest rates in multiple nations which is one of the major factors that impact the stock markets as a whole. This goes ahead to show that the trade war did have a short-term impact on the global stock markets and not just the nations involved bilaterally.

Inge [4] uses the large economic trade model to explain the same. In his paper he has assessed the impact of the trade war not just using the trade model but also studied the impact on the nations from a perspective of labour productivity development. His predictions are aligned with the general perceptions towards any war. A trade war generally leads to an economic loss and the net sum at the aftermath of a trade war is always lower than where it started from initially. He states that the largest impact of these losses is generally concentrated in the involved parties ( which in this case are US and China), however there is a negative impact on the third parties as well. His model goes ahead to show that in the long run, by 2030, the growth in the global economy will be visibly lower than what it would have been in a trade war free world.

Among the involved parties in a trade war as well there is generally one which has much more to lose and another which is a comparative winner. The model predicted by Erken [4] shows that China will be the one which will take the larger portion of burden generated by the trade war. The research does not deny that there will be a negative impact on USA as well but the economic decrease in USA will be les than half that of China.

Methodology

One of the promises made by Donald’s during the US presidential election was to limit and rather stop the abuse of trade practices by China towards USA. The assault began in January 2018 [2] when USA imposed 30% tariffs on solar panel imports, a majority of which originated in China. Another set of tariffs came on July 6 when 25% tariffs were imposed on $34 billion of imported Chinese goods, closely followed by an additional addition of $16 billion in mid-August. In august 2017 itself the estimated cost of theft of intellectual property was above $300 billion. In addition to that there is a trade deficit of more than $500 billion a year with China. This has led to a tit for tat response from China as well. The first set came on April 2 when a tariff was imposed on 128 products it imports from America [2].

Though anticipated the imposition of tariffs always comes as a shock to the market. Apart from the actual imposition of tariffs a lot of market shocks could be linked to just the announcement of probable tariff and their news. Thus, the methodology used in the paper will be an amalgamation of both statistical models and an empirical study approach for measuring the impact of new on the stock index values.

Sample Size

When analysing the effect of the trade war it is necessary to have a sample that is completely diversified in both regards:

  1. The nations analysed
  2. The range of data period

The choice of data range has been discussed in detail in the later section, here we discuss the choice of nations. The announcement of any tariffs from either side USA of China) will have both positive and negative impacts of different nations.

To give that context assume USA imposes tariffs on the import of electronic devices from China. Then, it is uncertain if that will cause a good or bad impact on the stock markets of USA. But, the stock prices of organisation exporting electronics devices to China will definitely take a hit and in-turn the whole stock market of China. The stock market of nations which export raw material of electronics like lead will also take a hit. Thus, both China and the lead exporting nation will take a hit and we can call them news losers. At the same time a nation C might see a rise in their stock market which is likely to get more requests for exports of electronics devices. We can call this nation a new winner. Hence the sample needs both news loser and news winners.

While, considering the impact of trade war it is necessary for the research to accommodate for the fact that all the nations might not be impacted by the trade war between the United States of America and China. Thus, the sample should contain neutral nations which remained reluctant to the trade war. Such nations act as a control element for the news losers and news winners. The trade war is not the only factor changing the stock prices of national indices. The control indices will help remove other common shocks apart from the trade war. The list of the 30 nations chosen for the purpose of the research are present in Appendix 1 – List of elected countries and the Stock Indices.

Data Source

For any methodology to work and give reliable results it is necessary to have all the data in a similar format and along the same period. Thus, the websites of various country indexes are not a suitable as there might be data inconsistencies. Some websites use dividend adjusted returns whereas the others prefer raw index prices. The choice of a data source is of utmost importance in the regard that both the statistical and empirical approaches rely on the accuracy of data and how precisely it has been measured. Thus, the choice of source has been a common aggregator site called investing.com [1]. The site also provides the data for almost all of the major indexes of the world and the data is present for the required period as well. Another benefit of using a single site lies in the fact that the concern of the indices being dividend adjusted or not is removed as all the data is treated as the same and thus in that case the data is directly comparable for the purpose of our analysis irrespective of the data handling methods that the website uses.

Data Period

The first set of regulations for imposing tariffs on any imports from either side came in the month of January 2018 by USA thus, that can be assumed as the beginning of the trade war. The data has been collected till 19th October 2018. The whole period is termed as the period of trade war as the differences are yet to be settled and regulations and tariffs have been imposed even in the month of September 2018 [2]. A control period is required for the effect of trade war. The time period from January 2016 to December 2017 has been chosen as the control period. Daily index prices have been used to cover up for the lack of data point from the short duration. The duration of the research has been kept low as before 2016 there were other global economic factors which might interfere with the research like the European Sovereign Bond Crisis. Thus, the data period of the complete research is from January 3, 2016 to October 19, 2018 divided into the period of the trade was and the neutral period.

Statistical Techniques

Multiple statistical techniques have been employed to check the impact of the news of tariff imposition on the stock market prices of the equity indexes of various nations. The first of them has been used to analyse the impact of tariffs on the returns of the indices. After that we will move forward to see the change in the market risk that arises due to the trade war and in the end, we analyse the impact of the trade war on the phenomenon known as contagion.

T-test

The hypothesis being tested via the t-test here is that there is no significant impact of the news of tariffs on the returns of the indices. The t-test is a statistical tool which can be used to examine the means of different populations. A simple two sample t-test can be used to compare the mean of two different populations. The test can be used when we do not know the variances of both the populations and even in case of a smaller sample (sizes less than 30 even)

F-test

The hypothesis being tested via the use of an F-test is that there is no significant difference between the volatility of returns before and after the imposition of tariffs. It can happen that the return of the indices remains the same thought the risk in the market changes and an F-test will help us identify that. It is used to test the F distribution for the null hypothesis. F-test is best appropriate when the data is fitted to the population using least squares and that is similar to what is done in calculation the variances of the population.

Correlation

The hypothesis being tested here is that whether trade war has led to an increased spill over effect or contagion in the global stock markets. Correlation is a statistical technique use to see the movement of one variable with respect to the other. We will use Pearson’s Correlation to calculate the correlation coefficient that ranges from -1 to +1. -1 represents a complete negative correlation which means that a change of 1% in the first variable means a change of -1% in the second variable. A correlation of +1 means a complete positive correlation which that a change of 1% in the first variable means a change of 1% in the second variable. The formula for calculating the Pearson’s Correlation is as follows:

Correlation = Cov1,2/(StdDev1*StdDev2)

Where Cov1,2 is the covariance between variable 1 and 2

StdDevi is the standard deviation of the ith variable

Method Used

Initial Impact Assessment (January 2018)

The daily closing price data for all the 30 indices from January 2016 to October 2018 has been collected from investing.com. The time period of the study has been classified into two parts: the pre-war period that ends at December 2017 and the trade war period.

The analysis used the following two approaches:

  1. The logarithmic returns of the indices have been calculated using the closing price of the indices the previous day using the data of the entire period under study.
  2. Then the effect is estimated from the first news of imposition of tariffs that was on 22 January 2018 by taking the average return of short, medium and long term.

Our classification of short, medium and long term is as follows: the duration short term is 10 days before and after the imposition of tariffs, similarly medium term stands at 20 days and the long term stands at 30 days. At this point it should be clarified that the 10, 20 and 30 day internal refers to calendar days ad not working days. In case of the 10-day interval, which represents the short-term impact of the trade war, the number of data points for a given index is limited to 7 in general as there are only 7 working days in the 10 days chosen period. In other words, the stock market was open for only 7 of the 10 calendar days instead of all 10 days. Similarly, in case of the medium-term analysis the actual number of days is only 15 instead of 20 and for the 30-day long-term analysis only 19 data points have been used. The reason for using calendar days instead of using actual working days of the stock market is that the analysis is based on the impacts of the shocks from the news of the trade war. Thus, in case of news shocks even the non-working days counts. Even when there is no trade in the stock market there is news about the trade war and that is capable of impacting the stock market returns in the same way as it would affect the stock market if the trade was still going on. Thus, the calendar method is deemed more suitable than the working day count of the days. However, which calculation of the degrees of freedom and any other relevant parameter for the statistical tests the actual number of the working days has been taken into account and the calendar method has not been used for the sake of mathematical consistency.

The method for calculation of returns is as follows:

R­I = ln (P­i/Pi-1)

Where,

ln is the natural log of a number.

Ri is the return of a value (index) for the period/date i

Pi is the closing price of the index on period/date i

Pi-1 is the closing price of the index on period/date i-1

  1. Similar to the average returns before and after the initial news of January 22, 2018 we have calculated the variance of the index returns in the short, medium and long term before and after the first news of imposition of tariffs.
  2. To compare the average returns we have applied a paired t-test
  3. To compare the variances an F-test has been used.

The F statistics is the ratio of the variances of the two populations under study. And the t-test is the ratio of the difference between the expected mean and the observed value and the standard error which was calculated keeping into account the actual number of working days used. As there is no standard mean value that can be referred in this case. The returns of the stock markets that were present in the pre-trade war period have been used as the baseline and the difference of the returns before the and after the announcement of the trade-war has been used as the proxy for the same.

Contagion Analysis

Further we wish to analyse if there is an increase in the correlation between the world markets after the beginning of the trade war. For the same purpose we divide our data into two periods. The first period is the period leading up to the trade war. In all of our data the period leading till the trade war will be the data till December 2017 as the uncertainty of tariff became high in January 2018. The time period from January 2018 to October 2018 will be used as the period of trade war.

Correlation among all the selected countries will be calculated for both the periods separately and then compared. The purpose of the process here is to make an empirical analysis of the correlations between the stock market index returns if it has changed during the trade war.

Empirical Analysis

The individual news events and their probable impact on any stock market has been analysed. The process used is simple. The returns of the markets around all the tariff events were plotted and any significant change in the stock markets during such time was highlighted. For the same all the news events have been analysed for any outlier in data and if any return can be spotted which is out of the ordinary. A simple scatter plot is used for the same purpose which can quickly highlight any outlier during such an event. The purpose of the empirical analysis is to show the extent of effect the trade war can have on the stock markets of other nations.

Data Analysis

News Events and Empirical Analysis

The following is the timeline of the news events from USA and the reaction of Chana for them. The news events can be linked to the correlations discussed in the later sections and how the adverse effect of the news events can lead to a contagion on the other countries of the world. All the dates are for 2018

  1. On January 22 USA imposed tariffs on Chinese solar panels and cells and a 20-50% tariff on the parts of South Korean washing Machine. In a response to the same the Commerce Ministry of China initiated an anti-dumping and anti-subsidy investigation into the US sorghum on February 4
  2. On March 2, a 25% tariff on steel import and 10% tariff on aluminium imports was applied to all the nations from which even allies were not exempted. This was the first instance of a direct impact of the trade war on other nation. In response to that EU imposed a $3.5 billion package penalty to US exports including motorcycles like Harley-Davison and garments like denim
  3. US keeps Canada and Mexico exempt from the NAFTA deals and allows other nation to negotiate tariff reductions on March 8. A quick reply from EU and China came on March 9 with China urging US to reduce tariffs and EU threatening another $3.5 billion worth of tariffs.
  4. On 3rd and 5th April US threatens to impose tariffs on a total of $150 billion worth of imports. As a response China threatens to impose the same number of tariffs on April 3. It mandated Shorghum importers to pay 178.6% of the net value of US imports they make.
  5. On 29 May US again takes an offensive position against China. On May 31 US imposed steel an aluminium tariff on EU, Mexico and Canada. In response, on June 5, China offered a purchase of $70 billion worth of US products if US stopped tariff threat.
  6. From June 14 to June 19 another 25% tariff imposed on $50 billion worth of Chinese imports and a total pf $200 billion of Chinese goods put under scrutiny. On June 15 China says it will impose 25% tariff on $50 billion of US goods.

From the above data we can see that it is not just China which has been affected by the trade-war, but the effect has also spilled over to other nations, specially Canada, Mexico and EU. Thus, an escalation in trade war might possibly impact all the stated nations.

Initial Impact Assessment (January 2018)

Here we first test the hypothesis that trade was does not have any impact on stock market returns. The hypothesis can be stated as follows:

Ho: The mean return of the stock market is same before and after the beginning of trade war

HA: The mean return of the stock market is same before and after the beginning of trade war

The hypothesis is tested for all the three-time ranges, i.e. the short term, medium term and the long term. We as the researcher want to be 95% confident about our analysis and thus we choose and alpha of 5%. The test chosen is an F-test because the sample size is less than 30 for all the three decided periods.

Appendix 2, – Short, Medium- and Long-Term returns before and after January 22, shows the average return for the short medium and long term before and after the first news of January 22. A general overview of the returns makes it apparent that foremost of the indices the returns after the news announcement are lower than the returns before the news announcement. Thus, our initial analysis says that there is a difference in the stock market returns. But the confirmation of the same is only possible via the use of a paired t-test. The critical value for 10-day return is 2.365, for 20 days return it is 2.131 and for 30 days it is 2.101. Table 1 shows the results of the t-test conducted for each country.

Table – 1: The calculated t-scores for short, medium- and long term returns of the country indices

Last 10 days return & Next 10 days return Last 20 days return & Next 20 days return Last 30 days return & Next 30 days return
Australia

5.08619718

4.347612

4.874822
Belgium

2.84517923

6.177053

5.347584
Brazil

2.54554443

5.788935

5.445049
Canada

3.93792947

3.982923

5.495489
China

7.38518168

3.715571

5.08297
Egypt

3.57472134

6.142359

6.160804
France

6.77938608

6.823034

7.029084
Germany

5.74665706

7.422415

7.115684
Hong Kong

3.4537522

3.754696

4.33161
India

6.42024754

4.764006

5.049432
Indonesia

2.9904982

3.714677

5.615209
Italy

3.32106302

6.355041

6.672803
Japan

5.18660977

4.958671

5.181839
United Kingdom

2.41054828

4.774059

4.226657
Malaysia

2.81028358

4.067588

5.303147
Mexico

4.31333112

4.78928

5.638873
Netherland

3.83704154

5.742018

5.857869
Oman

2.62208501

4.531038

5.069407
Pakistan

10.0940844

5.961303

7.808547
Poland

2.51869341

3.616732

7.078546
Russia

3.70044222

6.403473

7.044627
Singapore

2.72590731

4.492043

5.42321
Saudi Arabia

2.72590731

4.492043

5.42321
South Africa

2.34896273

4.120129

4.586655
South Korea

3.46531463

6.264735

5.854835
Spain

2.36281718

6.48857

7.291208
Switzerland

2.01303506

5.484796

5.202552
Taiwan

2.74973522

3.540532

3.831016
Thailand

2.41054828

4.774059

4.226657
USA

4.19308376

3.502524

4.217555

From Table – 1: The calculated t-scores for short, medium- and long term returns of the country indices it is very evident that except for three countries all other countries see an impact of the trade war in their stock market returns. The three countries, namely South Africa, Spain and Switzerland, were not impacted by the trade war in the short run. The causes for the same can be multiple ranging from the investors in these countries from being indifferent to the trade war to a domestic shock neutralising the impact of the trade war. These three countries also have been impacted by the trade war in the long run as we can see that there is a significant difference in the mean for medium- and long-term impact.

Hence, we have enough evidence to reject the null hypothesis that the trade was does not impact the stock market returns at least in the medium and long term. We accept the alternate hypothesis that there is an impact of the trade war on the stock market returns. For short term we reject the null hypothesis for 27 countries and do not reject the null hypothesis for 3 countries. From the above analysis it is evident that a trade war not only affects the involved countries in the trade war but also the rest of the world. There is a significant impact on the long term on all the stock markets that have been selected. However, the analysis is not enough if only the significance of the impact has been analysed. A trade war can also be used by nations to improve their stock market conditions and such nations will emerge as the news winners. A news winner nation will be the one which has seen a significant increase in its stock market returns after the trade war. We now proceed to analyse if the impact is positive or negative for the countries.

Table 2: The direction of impact of the trade war

Country Short Term Medium Term Long Term
Australia Positive Negative Positive
Belgium Negative Negative Negative
Brazil Negative Negative Negative
Canada Negative Negative Negative
China Negative Negative Negative
Egypt Positive Negative Negative
France Negative Negative Negative
Germany Negative Negative Negative
Hong Kong Negative Negative Negative
India Negative Negative Negative
Indonesia Negative Negative Negative
Italy Negative Negative Negative
Japan Negative Negative Negative
United Kingdom Negative Negative Negative
Malaysia Positive Negative Negative
Mexico Negative Negative Negative
Netherland Negative Negative Negative
Oman Positive Positive Positive
Pakistan Negative Negative Negative
Poland Negative Negative Negative
Russia Negative Negative Negative
Singapore Negative Negative Negative
Saudi Arabia Negative Negative Negative
South Africa Negative Negative Negative
South Korea Negative Negative Negative
Spain Negative Negative Negative
Switzerland Negative Negative Negative
Taiwan Negative Negative Negative
Thailand Negative Negative Negative
USA Negative Negative Negative

Table 2, The direction of impact of the trade war, shows whether the trade war has a positive effect on the stock market returns or a negative impact. Except for Oman no other country show a complete positive effect of the trade war and thus it is the only news winner in our selected sample of countries. All other countries are news-losers as they see a decrease in their stock market returns.

Malaysia, Egypt and Australia can be termed a news-winners in the short run as they see a rise over their past 10-day returns. Australia is also a news winner in the long run as well. South Africa, Spain and Switzerland cannot be termed as news winner as we failed to reject the null hypothesis for them and thus, they will be referred to as control countries in the short-term returns.

From the analysis it is very evident that the trade war in general has a negative impact on the stock market returns of the nations including that of the one imposing the tariffs, in our case United States of America. We can see that the stock market returns for USA also decreased after the initial news of the imposition of tariffs which shows a negative impact of the US-China trade war on the economy of the entire world, or at least most of the countries in the selected sample of 30 countries. Thus, at this moment it is safe to say that a trade war has a general negative impact all over the globe and except for a few selected nations which are in prime condition to take advantage of the trade war. Even the nation which has initiated the trade war had seen the negative impact of the trade war on its stock markets.

On arriving at the result of an overall decrease in the stock returns we move forward to the risk in the stock markets. A decrease in the stock market returns is not an exceptionally bad outcome and rather not even a bad outcome if the risk in the markets also decreases. For a decreased risk the investors will be comfortable at accepting lower returns as well. Thus, now we move forward to analyse the change in the risk/variance of the markets to understand the impact of the trade war on the stock market risk all over the world.

We move towards the analysis of variance to determine if the trade war has also impacted the variance of return before and after the initial imposition of tariffs. Here we first test the hypothesis that trade was does not have any impact on stock market variances. The hypothesis can be stated as follows:

Ho: The variance of the stock market is same before and after the beginning of trade war

HA: The variance of the stock market is same before and after the beginning of trade war

The hypothesis is tested for all the three-time ranges, i.e. the short term, medium term and the long term. We as the researcher want to be 95% confident about our analysis and thus, we choose and alpha of 5%. The test chosen is a t-test because the sample size is less than 30 for all the three decided periods.

Appendix 3, – Short, Medium- and Long-Term variances before and after January 22, shows the variance for the short medium and long term before and after the first news of January 22. A glance over the appendix suggests that there is a general increase in variances after the imposition of tariffs on January 22. Thus, out initial analysis predicts that the volatility in the market has increased after US imposed tariffs on import from China. This can be attributed to the uncertainty that remains in the market over the final outcome of the trade war and the speculations over the upcoming steps that either side will take to counter the tariffs. But the confirmation of the same is only possible via the use of a paired F-test. The critical value for 10-day return is 4.28, for 20 days return it is 2.04 and for 30 days it is 1.86. Table 3, calculated F-scores for short, medium- and long term returns of the country indices, shows the results of the F-test conducted for each country.

Table – 3: The calculated F-scores for short, medium- and long term returns of the country indices

Last 10 days volatility & Next 10 days volatility Last 20 days volatility & Next 20 days volatility Last 30 days volatility & Next 30 days volatility
Australia

5.27774291

9.7835

9.448305
Belgium

1.49438838

8.557352

8.143488
Brazil

6.24610071

7.671965

5.166222
Canada

1.09129029

5.745191

7.003837
China

3.10605015

14.80096

11.95982
Egypt

1.0471749

1.875775

1.507775
France

2.60505777

4.147092

4.2592
Germany

1.91097458

3.239341

3.259555
Hong Kong

2.82833632

8.596484

10.1006
India

1.39634788

3.575397

3.539385
Indonesia

14.0583188

3.0323

2.83473
Italy

5.81596081

3.864156

3.188774
Japan

6.14008119

3.306311

4.164918
United Kingdom

3.24361042

2.082133

1.751922
Malaysia

3.75841412

3.687851

3.017291
Mexico

1.76978291

1.487308

1.570154
Netherland

3.60284528

9.719403

8.108111
Oman

2.03999754

3.219182

1.396643
Pakistan

6.31349941

4.734681

2.66327
Poland

1.44996313

2.338884

2.075135
Russia

3.71400223

3.165573

3.435289
Singapore

5.70297025

1.031023

1.064197
Saudi Arabia

5.70297025

1.031023

1.064197
South Africa

9.86849106

9.534002

13.53324
South Korea

3.02752297

4.201838

3.427817
Spain

1.15369305

4.218186

2.942604
Switzerland

1.21486582

5.105021

6.033244
Taiwan

9.86975099

16.33302

11.85552
Thailand

3.24361042

2.082133

1.751922
USA

1.03176923

18.14996

14.23394

F-test shows that the most significant impact on the volatility is during the medium term. This must be the time of maximum speculations as the invertors speculate further policy changes. Countries like Egypt and Mexico do not have much impact on their volatility. The most interesting result here is that of both USA and China as none of the two countries actually show any difference on their volatility in the short term whereas there is a decrease in their stock market returns. This can be explained from an increased uncertainty in both the countries before the imposition of tariffs. Thus, near the date of tariff imposition both the markets were already uncertain, and the volatility did not change much. The difference can however be seen in the medium term and long-term volatility of both the indices. Singapore and Saudi Arabia only see a change in the short term and not the long term which shows that the uncertainty only remained right after the news of tariffs.

Hence, we have enough evidence to reject the null hypothesis that the trade was does not impact the stock market variances at least in the medium and long term. We accept the alternate hypothesis that there is an impact of the trade war on the stock market variances. For short term we reject the null hypothesis for 10 countries and do not reject the null hypothesis for 20 countries. The number changes to 26 and 4 in case of medium term and 23 and 7 in case of the long term. Thus, we can also say that the effect of trade war on the uncertainty decreases in the long term and it starts to revert back to normal at least till more market shocks appear. We now proceed to analyse if the impact is positive or negative for the countries.

Table 4: The direction of impact of the trade war

Country Short Term Medium Term Long Term
Australia Positive Positive Positive
Belgium Positive Positive Positive
Brazil Positive Positive Positive
Canada Positive Positive Positive
China Negative Positive Positive
Egypt Positive Positive Positive
France Positive Positive Positive
Germany Positive Positive Positive
Hong Kong Positive Positive Positive
India Positive Positive Positive
Indonesia Positive Positive Positive
Italy Positive Positive Positive
Japan Negative Positive Positive
United Kingdom Positive Positive Positive
Malaysia Positive Positive Positive
Mexico Positive Positive Positive
Netherland Positive Positive Positive
Oman Negative Negative Negative
Pakistan Positive Positive Positive
Poland Positive Positive Positive
Russia Positive Positive Positive
Singapore Negative Positive Negative
Saudi Arabia Positive Positive Positive
South Africa Positive Positive Positive
South Korea Positive Positive Positive
Spain Negative Positive Positive
Switzerland Positive Positive Positive
Taiwan Positive Positive Positive
Thailand Positive Positive Positive
USA Negative Negative Negative

Table 4, the direction of impact of the trade war, shows if the volatility has increased or decreased after January 22. Positive represents an increase in the volatility and negative represents a decrease. Upon looking at the change in volatility of USA and China the results of the F-test makes more sense. The uncertainty in both the markets actually decreased in the short run. Leading up to the news there is more uncertainty of the number of tariffs and the sectors hit. After the news the uncertainty decreases as the results become clear. In case of USA the trend continues, and variance falls further. In case of China it increases in the medium and long run in anticipation of the reaction from the nation which eventually came in April.

Oman is another interesting result here as for medium term it has a significant impact on its uncertainty and more importantly it was a news winner in case of return analysis. We also see here that the volatility has decreased for Oman. Thus, Oman is in general gaining from the trade war with decreased risk in its market.

Apart from USA and Oman all other nations see an increase in volatility, at least in magnitude and a definite increase where we are able to reject the null hypothesis. Thus, we can say that irrespective of the returns the general risk in the stock markets of the nations saw a rise after the beginning of the trade war.

Contagion

Apart from the changes in the stock market returns of the involved nations the returns of the stock markets of the other nations are impacted as well due to a trade war. A part of that can be attributed to the impact of contagion where the uncertainty from one stock market is translated to the other markets. Here we wish to analyse if the stock markets of the world saw an increase in contagion from with USA or China.

The effect of contagion has been calculated using correlation among the stock market prices of different countries. The correlations have been calculated for all the nations however the most important ones are the ones with USA and China.

Table 5: Correlation of US stock market with other countries

Pearson Correlation Sig. (2-tailed) Pre-Trade War During Trade War

Australia

0.146769858

8.57E-05

0.166127

0.118936

Belgium

0.486102319

1.93E-43

0.547404

0.400965

Brazil

0.10723544

0.004202

0.13378

0.392495

Canada

0.690830261

5.9E-102

0.694577

0.733228

China

0.138779245

0.000206

0.137711

0.139989

Egypt

0.088217153

0.018637

0.15434

-0.05293

France

0.556160274

5.69E-59

0.637797

0.439805

Germany

0.545437739

2.37E-56

0.622149

0.4298

Hong_Kong

0.203711046

4.26E-08

0.237984

0.153434

India

0.273742336

1.1E-13

0.328493

0.185553

Indonesia

0.07027594

0.061083

0.061017

0.078152

Italy

0.477889707

7.58E-42

0.587936

0.284827

Japan

0.226268137

1.05E-09

0.2493

0.19771

UK

0.450134794

9.14E-37

0.515327

0.358595

Malaysia

0.149370851

6.38E-05

0.137989

0.161767

Mexico

0.458124601

3.51E-38

0.53084

0.345965

Netherland

0.567717817

6.65E-62

0.66

0.423435

Oman

0.067637599

0.071479

0.147284

-0.11207

Pakistan

0.031178279

0.406485

0.058734

-0.01704

Poland

0.333107333

6.94E-20

0.377607

0.265371

Russia

0.373125556

6.6E-25

0.437076

0.273664

Singapore

0.198401184

9.6E-08

0.235447

0.142193

Saudi_Arabia

0.168061874

6.62E-06

0.172777

0.180839

South_Africa

0.377909256

1.48E-25

0.37592

0.383753

South_Korea

0.249613204

1.47E-11

0.230143

0.275631

Spain

0.501776677

1.31E-46

0.579688

0.401881

Switzerland

0.5101397

2.29E-48

0.58455

0.396731

Taiwan

0.181710161

1.08E-06

0.116018

0.262157

Thailand

0.171076845

4.49E-06

0.17785

0.161272

Table 5, correlation of US stock market with other countries, shows the correlation of the stock market index of USA with the indices of other countries. Here the significance level for the entire data has been calculated using SPSS tool. Here we can see that for the entire period the correlation of Oman and Pakistan has been poor with that of S&P 500 index and thus, the results of these two countries cannot be used as for empirical analysis of contagion. The correlation of 9 other countries has increased during the time of the trade war when compared to the correlation of the pre-trade war period. Of these China is the obvious nation which was bound to have a higher correlation during this period. This suggests that during trade wars both the nations are more susceptible to shocks in each other’s economy. Apart from China, Brazil and Taiwan have seen a significant rise in the correlation of their stock market indices with that of USA. Taiwan has a dependency on China and Brazil on USA which has led to this increase in correlation. Oman on the other hand has seen a fall in the correlation with USA which accounts for the fact that is has been a news winner and rather the solo news winner among all the remaining 29 nations.

Countries like Netherlands, Mexico, UK and Italy have seen a fall in correlation from the previous values suggesting a gradual delinking of their stock markets from that of USA as the trade war has progressed. It is also interesting to note that there is very less correlation between the stock market returns of USA and China to begin with and the change is also less than the change for many other countries. This suggests that the trade war might have a worse effect on other nations of the world when compared to its effect on the economy and stock market of China and USA.

Table 6: Correlation of China stock market with other countries

Pearson Correlation Sig. (2-tailed)

Pre -Trade War

 During Trade War

Australia

0.242552866

5.59E-11

0.204786

0.353332

Belgium

0.120864068

0.001242

0.083297

0.219367

Brazil

-0.115425076

0.002052

-0.14598

0.132903

Canada

0.13864215

0.000209

0.098957

0.231441

China

1

1

1

Egypt

0.068262136

0.068896

0.06083

0.071446

France

0.142096153

0.000144

0.074929

0.33856

Germany

0.171330165

4.34E-06

0.106263

0.323745

Hong_Kong

0.535318054

5.76E-54

0.443623

0.691786

India

0.267001922

4.53E-13

0.260011

0.275385

Indonesia

0.154593324

3.48E-05

0.101265

0.228439

Italy

0.082560976

0.027712

0.048365

0.182765

Japan

0.257910167

2.88E-12

0.177482

0.478898

UK

0.112061318

0.002769

0.056355

0.273378

Malaysia

0.249742014

1.43E-11

0.23086

0.280142

Mexico

0.166140875

8.46E-06

0.098647

0.303337

Netherland

0.142972294

0.000131

0.06723

0.339511

Oman

0.103733383

0.00563

0.123017

0.046781

Pakistan

0.071344036

0.057245

0.052313

0.105871

Poland

0.195240708

1.54E-07

0.106977

0.369434

Russia

0.18834949

4.22E-07

0.14475

0.274823

Singapore

0.362766413

1.55E-23

0.295692

0.48122

Saudi_Arabia

0.155782532

3.02E-05

0.131567

0.237041

South_Africa

0.230418905

5.07E-10

0.133186

0.40441

South_Korea

0.325532062

5.16E-19

0.192576

0.547437

Spain

0.103935466

0.005537

0.056028

0.274853

Switzerland

0.181100645

1.17E-06

0.106815

0.341212

Taiwan

0.305020844

8.95E-17

0.261686

0.370367

Thailand

0.255363547

4.78E-12

0.204167

0.353295

USA

0.138779245

0.000206

0.137711

0.139989

Table 6, correlation of China stock market with other countries, shows the correlation of the stock market index of China with the indices of other countries. Here the significance level for the entire data has been calculated using SPSS tool. Except Pakistan and Egypt all other countries have a significant correlation with China when the entire period is considered. The correlation of China has increased with all other nations except for Oman. We can say that is the trade war causes the stock market prices of China crash due to the trade war then it will have a negative impact on the stock markets of the entire world. This was not the case when USA was considered and thus, the effect of contagion from China to the world is much higher that the contagion from USA to the world.

Empirical Analysis of Japanese Nikkie

The Graph below shows the Japanese Nikkie and how it has moved so far in 2018. The first three troughs are from the beginning of February to the end of March showing the impact of the imposition of tariffs on the Japanese stock market. The Japanese Nikkie remained low for two continuous weeks due to the imposition of tariffs. All the three throughs coincide with the news announcement dates related to the trade war. This shows the impact of the trade war on the stock markets of the nations which are not directly involved in the war but the nations itself rely on wither or both of the nations which are involved. It is very evident that the initial news of the trade war has had the largest impact on the Japanese stock market. This impact is larger than any domestic shock that the Japanese market saw over the period of nest six months. The complete recovery of the market took much longer than two weeks.

Graph 1: Japanese Nikkie

 

Conclusion

The research was conducted to analyse the impact of the trade war on the stock markets of the world. There has been enough literature to show the impact of a trade war on the involved nations but there was a lack of evidence on the impacts of the same on the world as a whole. We have analysed the impact of trade war on not just the nations involved in the trade war but also other nations. A total of 30 stock markets was chosen from 30 different nations and the impact on the US-China trade war was analysed on them. Based on the analysis it is safe to conclude that a trade war almost always follows a global impact and most of the nations are affected by the same. The more disturbing conclusion is that the impact of the trade war, in general, is on the negative side of the spectrum rather than the positive side. Most of the affected nations have seen a decrease in the stock market returns and an increase in volatility/risk in the market.

Among the nations chosen all the nations saw a change in their market returns in the medium and long term. All but three nations saw a significant change in the market returns in the short term as well. We further moved to analyse the direction of the change to understand which nations were the news winners and which nations were the news losers. Australia, South Africa, Oman and Malaysia are the only four nations which saw a significant increase in their stock market returns and out of these it was only For Oman which actually saw a significant increase for short, medium and long term. The other three nations saw the stock market returns decrease overall. Thus, the market returns had a global negative impact on them due to the trade war.

Stock market returns do decrease due to the trade war but, that could be a regular impact. The next portion of the analysis was concerned with understand that how the risk in the market has changed. The uncertainty of the market, if affected, is a much larger cause of concern than the decreased returns as that maker the market even riskier with the decreasing returns. The analysis of the magnitude and direction of the change in the standard deviation/risk of the market revealed that the market risk saw the maximum change in the medium term which can be seen as the period of maximum uncertainty. Among all the nations which saw a significant change in the market variance USA was the only one, except for Oman, which has a positive impact from the trade war. All other nations with a significant change in the market risk saw the market risk increase and USA saw the market risk decrease. Thus, we can see that the nation which was the one to initiate the series of tariffs did see a decrease in risk due to confirmed actions for the betterment of the domestic market. Whereas the global speculations around the international policies which might develop due to the trade war led to an increase in the global stock markets.

A similar effect is predicted when the contagion was analysed in the market as well. Generally, the markets are not very highly correlated with each other when multiple markets from different nations are analysed together. But the effect of contagion was seen to have increased in the stock markets after the announcements related to the trade war. We can see that the correlation of the stock markets of multiple nations of the world increased with USA and China during the trade war as compared to the period before the trade war. The effect was ore prominent when analysed against China as compared to USA where many nations did no see an increase in correlation with the US stock market but almost all the 28 other nations’ stock markets that were analysed saw their correlation with China increase.

One nation which stood out in all the analysis done till now was Oman which has performed opposite to all the other nations. The correlation of the other 27 nations increased whereas that of Oman decreased with China. In case of USA the correlation of Oman actually became negative from positive which shows that Oman is a constant news winner and was able to utilise the trade war to its benefit. That was even more evident when the change in the risk/standard deviation of the market was analysed in tandem with the change in correlation. The market risk/variance actually decreased for Oman which goes ahead to show that Oman’s decreased correlation made the stock markets safer to invest in and made the stock markets more inward looking than globally related to the returns of the stock markets of the nations of the rest of the world.

Further analysis was done on a single stock market other than the two involved nations to understand the extent of shock the news of trade war can have on other nations. The Japanese Nikkie was chosen for that purpose because that is affected by both the involved nations, USA and China, to a large extent. We saw a large short-term dip in the Japanese stock market right after the news of the trade war which shows that trade war not only affects the involved nations but is capable of moving the economies of other nations by a large extent as well. The Japanese Nikki took long time to stabilise after the trade war’s news and had a large drop in its returns. The proposition made above is consolidated by the fact that the recovery period was around 2 weeks and the troughs do not appear just once. Most of the troughs in the Japanese Nikkie coincide directly with the news announcement dates of the trade war. This also shows that the impact of the trade war was larger than any domestic news in that period. This shows that the economies and the stock markets of nations can be impacted adversely by trade wars and to an extent which even the domestic shocks cannot reach. By extension it can be said that very strong economic policies of a notion can make the stock markets safe but not shock proof as the global shocks are still capable of affecting the stock market returns.

It can successfully be concluded that a trade war between powerful nations of the world causes a global economic set back and is not good for the stock markets of the world. Not only the returns of the stock markets decrease but the increased uncertainty in the markets increases the market risk and causes investments riskier. This in turn also increases the effect of contagion and causes the shocks in one market to spill over to the other nations market indices. There are a few new winners which are able to capitalise on the trade war and increase their stock market returns but that is not enough to offset the global negative impact that the trade war has. The overall impact leads to only a negative sign for the global markets which we now operate in.

 

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Appendix 1 – List of selected countries and the Stock Indices

 

Sr. No. Country Index

1

 Australia S&P/ASX 200

2

 Belgium Dow Jones Belgium

3

 Brazil Brazil All Shares

4

 Canada S&P/TSX Composite

5

 China Shanghai Composite

6

 Egypt EGX 100

7

 France CAC 40

8

 Germany DAX

9

 Hong Kong Hang Seng

10

 India Nifty 500

11

 Indonesia Jakarta SE

12

 Italy FTSE Italy

13

 Japan Nikkie 225

14

 United Kingdom FT Ordinary Share

15

 Malaysia FTSE KLCI

16

 Mexico Dow Jones Mexico

17

 Netherland AEX

18

 Oman MSM 30

19

 Pakistan Karachi 100

20

 Poland WIG

21

 Russia RTSI

22

 Singapore FTSE Singapore

23

 Saudi Arabia Tadawul All Share

24

 South Africa FTSE/JSE

25

 South Korea KOSPI 200

26

 Spain IBEX 35

27

 Switzerland SMI

28

 Taiwan Taiwan Weighted

29

 Thailand SET

30

 USA S&P 500

 

 

Appendix 2 – Short, Medium- and Long-Term returns before and after January 22

 

Country Last 10 days return Last 20 days return Last 30 days return Next 10 days return Next 20 days return Next 30 days return
Australia

-0.18%

-0.08%

-0.06%

0.21%

-0.21%

-0.04%
Belgium

0.02%

0.23%

0.16%

-0.32%

-0.45%

-0.22%
Brazil

0.41%

0.30%

0.43%

0.38%

-0.03%

0.27%
Canada

0.05%

0.06%

0.05%

-0.52%

-0.47%

-0.23%
China

0.31%

0.38%

0.31%

-0.08%

-0.67%

-0.28%
Egypt

-0.02%

0.10%

0.18%

0.21%

0.05%

0.07%
France

0.14%

0.28%

0.15%

-0.33%

-0.48%

-0.17%
Germany

0.28%

0.28%

0.14%

-0.55%

-0.60%

-0.33%
Hong Kong

0.57%

0.53%

0.52%

0.12%

-0.60%

-0.18%
India

0.20%

0.21%

0.22%

-0.36%

-0.26%

-0.27%
Indonesia

0.25%

0.15%

0.26%

0.23%

0.03%

0.07%
Italy

0.33%

0.58%

0.36%

-0.24%

-0.39%

-0.23%
Japan

0.06%

0.30%

0.21%

-0.25%

-0.76%

-0.40%
United Kingdom

0.01%

0.03%

0.08%

-0.38%

-0.51%

-0.26%
Malaysia

0.13%

0.13%

0.24%

0.25%

0.00%

0.06%
Mexico

0.32%

0.09%

0.16%

0.15%

-0.24%

-0.06%
Netherland

0.30%

0.31%

0.20%

-0.38%

-0.55%

-0.29%
Oman

-0.28%

-0.14%

-0.08%

0.14%

0.02%

-0.01%
Pakistan

0.49%

0.69%

0.78%

0.03%

-0.10%

-0.06%
Poland

0.42%

0.36%

0.31%

-0.32%

-0.44%

-0.28%
Russia

0.41%

0.72%

0.69%

0.02%

-0.35%

0.10%
Singapore

0.22%

0.35%

0.32%

-0.09%

-0.34%

-0.07%
Saudi Arabia

0.28%

0.28%

0.19%

0.16%

-0.05%

0.03%
South Africa

0.34%

0.17%

0.20%

-0.42%

-0.54%

-0.20%
South Korea

0.03%

0.01%

0.11%

-0.08%

-0.45%

-0.24%
Spain

0.20%

0.35%

0.14%

-0.29%

-0.47%

-0.26%
Switzerland

0.04%

0.10%

0.06%

-0.34%

-0.50%

-0.26%
Taiwan

0.55%

0.36%

0.36%

-0.02%

-0.45%

-0.19%
Thailand

0.17%

0.26%

0.26%

0.04%

-0.08%

-0.08%
USA

0.33%

0.39%

0.28%

-0.19%

-0.38%

-0.17%

 

 

Appendix 3 – Short, Medium- and Long-Term variances before and after January 22

 

Country Last 10 days variance Last 20 days variance Last 30 days variance Next 10 days variance Next 20 days variance Next 30 days variance
Australia

0.0006%

0.0013%

0.0011%

0.0029%

0.0119%

0.0088%
Belgium

0.0009%

0.0019%

0.0018%

0.0024%

0.0165%

0.0129%
Brazil

0.0035%

0.0042%

0.0049%

0.0226%

0.0256%

0.0213%
Canada

0.0010%

0.0012%

0.0009%

0.0027%

0.0067%

0.0058%
China

0.0022%

0.0017%

0.0020%

0.0058%

0.0238%

0.0200%
Egypt

0.0069%

0.0043%

0.0043%

0.0059%

0.0081%

0.0054%
France

0.0012%

0.0033%

0.0034%

0.0050%

0.0143%

0.0125%
Germany

0.0032%

0.0048%

0.0046%

0.0070%

0.0159%

0.0131%
Hong Kong

0.0041%

0.0036%

0.0031%

0.0088%

0.0296%

0.0285%
India

0.0042%

0.0031%

0.0025%

0.0120%

0.0116%

0.0079%
Indonesia

0.0009%

0.0031%

0.0030%

0.0097%

0.0091%

0.0074%
Italy

0.0007%

0.0047%

0.0059%

0.0056%

0.0183%

0.0161%
Japan

0.0024%

0.0083%

0.0069%

0.0117%

0.0276%

0.0243%
United Kingdom

0.0052%

0.0033%

0.0028%

0.0027%

0.0128%

0.0105%
Malaysia

0.0007%

0.0022%

0.0022%

0.0020%

0.0080%

0.0056%
Mexico

0.0014%

0.0049%

0.0044%

0.0026%

0.0077%

0.0064%
Netherland

0.0005%

0.0015%

0.0017%

0.0028%

0.0151%

0.0124%
Oman

0.0008%

0.0006%

0.0012%

0.0012%

0.0021%

0.0014%
Pakistan

0.0150%

0.0134%

0.0118%

0.0054%

0.0048%

0.0064%
Poland

0.0024%

0.0054%

0.0052%

0.0040%

0.0126%

0.0094%
Russia

0.0050%

0.0082%

0.0068%

0.0187%

0.0246%

0.0219%
Singapore

0.0025%

0.0031%

0.0025%

0.0043%

0.0084%

0.0098%
Saudi Arabia

0.0072%

0.0038%

0.0037%

0.0026%

0.0039%

0.0033%
South Africa

0.0010%

0.0015%

0.0015%

0.0083%

0.0112%

0.0183%
South Korea

0.0031%

0.0043%

0.0045%

0.0136%

0.0170%

0.0138%
Spain

0.0026%

0.0036%

0.0048%

0.0073%

0.0165%

0.0135%
Switzerland

0.0021%

0.0028%

0.0025%

0.0019%

0.0148%

0.0130%
Taiwan

0.0005%

0.0019%

0.0020%

0.0047%

0.0240%

0.0199%
Thailand

0.0015%

0.0020%

0.0021%

0.0039%

0.0042%

0.0033%
USA

0.0026%

0.0017%

0.0019%

0.0080%

0.0315%

0.0225%

 

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