ISSN- 2394-5125 VOL 7, ISSUE 18, 2020 THE IMPACT OF MACROECONOMIC VARIABLES ON EXCHANGE

Isbat Alam

School of Administration, Liaoning Technical University, Huludao,

E-mail: [email protected]

Received: 14 March 2020 Revised and Accepted: 8 July 2020

ABSTRACT: The determination of this study is to investigate the association between the with macroeconomic factors M2 (Money supply) and BT (Balance of Trade). Utilize annual time series data from 1995 to 2019. Multiple regression technique is utilized to examine the variables significance with stock market. Serial correlation and Heteroskedasity valuation is used to examine the correctness as well as residual normality of data series. The finding of this analysis exposed that M2 is significant negatively at 5% level and BT is also negatively significant at 1% level. It’s indicated that significant negative relation with stock market. These financial analyses motivate shareholders, investors, as well as editor.

KEYWORD: Macroeconomic Factors, Stock Market, Multiple Regressions

I. INTRODUCTION

The stock market plays an important and major role in the financial institutions in both developing and developed countries. The stock market deals all term of capital of the scheduled firm by funds pooling from investors and allowed them to business expand through offering investors for alternative investment possibilities to put the fund surplus. Stock market indexes this regards provided market performances historically. But it is possible for economies to ensure relation commitments in actual capital Rafiq et al., (2019) The macroeconomic variables have the diverse impact across the spectrum of economic. Therefore, the recently innovations in macro fundamentals are lacking of emerging market like china, pointed the improvement of stock performances in 2012. During the third quarter the market rebound the experience largely sorts due to depreciations of currency. However, the inflation due to the exchange and consumer price index by capital authority. Salamat et al., (2019) indicate that hypothetically the market must contain the related information and prices schedules that ease to public access information. Utilization of indexes to evaluate every determinations of the portfolio and delivers the indications forthcoming market tendency according to (Aggarwal and schrim, 1998). Basic of the modifications about the structure of macroeconomic policy and the significant role in finance reliability gaining could be subtle for the growth of country stock market. Exchange market assists the third one party of organized business and functions of transfer of shares from stockholders to investors as an essential part of open market. Mohsin et al., (2019) examined the calls of borrower for the Possess of parallel frames by the protest that will obligatory to compensate the full volume with the financial charges in coming. According to the APT (arbitrage price theory) introduce by (Rahee and Wang, 2009) that macro factors to discuss the specific stock return in the United States markets. When the productions comes in the risk premium, and variations are positively associated to the predictions of stock returns. Therefore, this research study is frequently examine the possible consequence of the variables eithers on the stock market development and understand to how the relations of investors help the capital and best country selections for investment and to increase their return and avoid the risk capital that face the investors in the pervious. It is clearly mentioning that diversification also decrease the risk premium. There is a large gap between the literatures that we are selecting the two different economies in the place of parallel portfolio. The GDP is regarded the most determined components of the stock performances which measure the real activities of economic growth Other side Naseem et al., (2020) the exceptional study to look the significant prospective gains of the relationship, especially in the portfolio preparing when the situations are the crises mood of economic. The cost of the stock prices is fixed of macro variables. The stock prices inverse influences by macroeconomic variables indicate the variations and stock market can expect the future condition of the economy, according to (Mukherjee and Nika, 1995). The Balance of trade or Balance of goods and services, are the difference between exports minus import. The association between Bot and stock prices has also deliberated, and several have inspected the changes of stock return sensitivity in the Balance of trade. The price-volume causality has an important effect on future market research because it is claim that prices changeability may touch the trade volume in the future by (Chappel, 1997). The Turkish emerging stock market and proposed the changes in price and trading volume are co- integrated examined by (Cook, 2007). Naseem et al., (2018) investigated six emerging markets of Latin America use monthly data indicate the volume of trade changes the stock prices. However, the Balance of trade

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ISSN- 2394-5125 VOL 7, ISSUE 18, 2020 has also influence the stock prices, with the increase in Balance of trade being inflation and leading to a reaction from the monetary authority through a high rate of interest, which causes to affect stock prices negatively Naseem et al., (2019), There are two primary channels exist which affect the real stock prices through the Balance of trade, namely wealth effect channel and some to extend, by the exchange rate channel. Rafiq et al., (2019), according to the wealth channel, the general fundamental logic is that when stock prices rise, specifically if it considers being perpetual, increases household income expected. Hence, in consumption, however, it also made it easier for the firm to capital investment opportunity, hence with a decline in a particular country’s Balance of trade. Supply of money (M2) is the merger of M1 and less liquid in natures. Also contained with saving, times deposit, market money funds, and deposit certificate, of money market. It signifies the broad money supply in economy. As upraised in the supply of money causes of liquidity and increased the citizen purchasing power. It means that much money will be available for investment but not just for consumption. Therefore, the relationship is positively expected. Will be assumed correspondingly as the assertion of supply of money has a negligible influence on the stock price. Although, influences of monetary policy on stock prices also be influenced by on environment of the economy. Explain by Naseem et al., (2019) that increasing the stock prices is responsive to the monetary extension. Conservative tools of monetary policy are proficient to reason greater variation in prices paralleled to the policy tools for minor certain economy. However, the excessive money supply would originate the possibility increased in inflation, which is harmful for stock markets.

II. LITERATURE REVIEW

The theories of literature is the most significant clearly that explanation the causality between the macro variables and stock market. The macro factors and stock market relations are connected with areas of research. This is pointed by May researcher nationally and globally. The outcomes of the calculated work indicate the existence of short and long run connections between the macroeconomic variables and stock market. Equally, there is no connection exist in the exchange rate and stock market. King et al., (1990) considers the threshold adjustment in the association between macro variables and economic activities in the UK. The previously disclose work un-detected abnormality in the long run association between the economic activity and stock market by utilizing the momentum threshold autoregressive Co-integration test. Naseem et al., (2019) examined the relationships among the set of macro variables containing interest rate, inflation, exchange rate, industrial production, supply of money, and stock market of China. They found an indication of co-integration of association between macro variables and the stock market. Furthermore, the position of the macroeconomic variable are originated to be a positive and relation influence on the performance of stock market. Naseem et al., (2019) inspect the association between a set of macroeconomic variables and the Japanese stock market. They originate positive relationships among the share prices and money supply escorted by exchange rate and industrial production. Moreover, (Granger, 1986) attempt to describe the presence equilibrium utilizing the Co-integration exploration, which has now; come to the preferred method and association between for the study of macroeconomic variables and stock market. Mohsin et al., (2019) investigate the dynamic association between the financial factors and equity prices in Pakistan employing the Granger Causality and Multivariate Co-integration technique. Their research result exposed the presence of the connotation between financial factors and equity prices, such as supply of money, consumer price index foreign exchange and treasury bills rate. Naiwen et al., (2020) inspected the dynamic association between the four macro variables and stock prices for the stock market of Malaysian utilizing the vector autoregressive and co-integration. They suggest their results indicate the presence of linkage amongst the variables and stock prices with considerable connections amongst them. Mohsin et al., (2019) inspected a time series linkage among seven macro variables and New Zealand stock indexes applied co-integration, Granger causality, and Johansen maximum likelihood tests to define whether the stock indexes redirect the variations in the analysed macro variables. The response was no. Majeed et al., (2020) utilizing the Johansson’s co-integration test and found that the macro variables are co- integrates with Indian stock markets, the stock prices behave to be positive correlated to industrial production and interest, while exchange and inflation rates are negative correlated to the stock market. However, the results is fail to originate from the short run association between the macro variables and the Indian stock market. Majeed et al., (2019) examine the relations between the macroeconomic variables and the Lahore stock market of Pakistan. Utilizing monthly data for the period 2002 to 2008, they perceive a negatively influence of consumer’s price index on the stock return; however, the exchange rate, production indexes, and supply of money behave a significant positively influences stock return in the long term. Azam et al., (2020), utilizing the Vector Error Correction technique ensured that the inflation positively affects the stock market, Treasury bill, and exchange rate negative effect through crude oil. However, in short-run inflation behave negatively and variation with stock returns, and treasury bill positively

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Variables Description Variables Description Unit SSE Shanghai stock exchange Independent M2 Broad money RMB/Rupees BT Balance of trade %of GDP Collection of Data

For this research study we collect the time series data which cover the gap from 1995 to 2019. This data are collect from world development indicators, business recorder and yahoo finance. The total observation are become 25.

HYPOTHESIS

H1: there is relationship between stock market and macro variables

H2: there is no relationship between stock market and macro variables

III. ECONOMETRICK MODEL

The analysis are empirically conducted though the statistical technique for the testing of hypothesis. We utilized here the multiple regressions, Pearson’s correlations movement coefficient model to determine the objectives of the study.

Where stock market is dependent, GDP (gross domestic product) and FDI (foreign direct investment) are independent variables the stock market intercept is “a” of , indicate the coefficient and “ε” is the “error” standard Empirical result

Descriptive Statistics LNSSE LNM2 LN_BT Mean 7.565041 5.047482 1.082208 Median 7.647128 5.02594 0.947536 Maximum 8.568183 5.344491 2.160588 Minimum 6.31949 4.5954 -0.24395 Std. Dev. 0.518582 0.207613 0.547575 Skewness -0.321953 -0.459186 0.129175 Kurtosis 2.786319 2.476394 3.283905 Jarque-Bera 0.460274 1.117572 0.147348 Probability 0.794425 0.571903 0.928975 Sum Sq. Dev. 6.185321 0.991372 6.896285 Observations 24 24 24

The descriptive statistics result of the all variables annual average SSE-100 return (7.565041), M2 (5.047482) and BT (1.082208) and positively standard deviation. The skewness value of SSE and M2 are positive and BT is positively which shows long right rear. The kurtosis values are between 0-3 of both variables, which behave playto-kurtic and normally distributed. All of the variables are indicated that’s values nears to means and respited of high peaked leptokurtic. This type of similarity is also found with Alam et al., (2020). The jarque- bera is statistically confirming the standard distribution of variables

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Correlation Matrix LNSSE LNM2 LN_BT LNSSE 1 LNM2 0.79872 1 LN_BT 0.194933 -0.14471 1

The above result shows the correlations among the three variables. The SSE has the positive causality with all of the variables. M2 has the positive strong correlation other variables. BT has also positive correlation with SSE but have strong positive relationship with BT. that shows the moderated positive and negative correlation which is according to Fuzzy firm linear

Multiple Regression Variable Coefficient Std. Error t-Statistic Prob. LNM2 2.10971 0.282808 7.459881 0 LN_BT 0.300367 0.107226 2.801245 0.0107 C -3.408743 1.449931 -2.350969 0.0286 R-squared 0.736437 Mean dependent var 7.565041 Adjusted R-squared 0.711336 S.D. dependent var 0.518582 S.E. of regression 0.278621 Akaike info criterion 0.398538 Sum squared resid 1.630219 Schwarz criterion 0.545795 Log likelihood -1.782453 F-statistic 29.33873 Durbin-Watson stat 1.916648 Prob(F-statistic) 0.000001 Annotation: dependent variable is KSE (least squares estimation) *** significant 10% ** significance 5% * significance 1% The presented results of the above table of the multiple regressions indicate that all of the variables are significant statistically with M2 and BT. The coefficient of M2 is negatively significant at 1% level with SSE and accepted H1. The coefficient of BT are negative significant at 5% level and reject the H0. The assessment of the coefficient determined (R2) is 0.736437 which shows the value of 73% of variations in SSE which is causes of M2 and BT. F statistic value is 29.33873 which are significant at 1% level. All of the result indicated that model is good fit. This similarity is also found with Hashim et al., (2020).

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 1.009391 Prob. F(2,19) 0.3832

Obs*R-squared 2.305117 Prob. Chi-Square(2) 0.3158

The table result shows that the P-value 0.3832 (38%) which is greater than 5% it’s mean that can’t accept H1 hypothesis that there is no serial correlation. H0 = there is no serial correlations. H1 = there is serial correlations.

Heteroskedasticity Test: ARCH

F-statistic 0.085478 Prob. F(1,21) 0.7729

Obs*R-squared 0.093239 Prob. Chi-Square(1) 0.7601

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H1= heteroskedasticity is present H0= heteroskedasticity is no present The above result indicate that Heteroskedasticity ARCH pointed the F-statistic value is 0.7729 with 77% of probability which is clearly mentions that heteroskedasticity is not present and reject the H0. The Normality test for Residuals

The Graphs represent the normality Residuals test. The Jarque-Bera value is 5.2684200, with probability of 71%, which is greater than 5% at level significance. The distributions of normality of null hypothesis are not to be excluded the statement. The Jarque-Bera outcomes shows that error term are distributed thoroughly. This similarity is also found with Alam et al., (2020)

IV. CONCLUSION

This study scrutinized the essentials of macro variables impact on stock market of Pakistan. The main objective of this study is to investigate the associations between the stock market and variables. Utilize annually time series data from 1995 to 2019, with nominated variables of M2 and BT with SSE. Serial correlation and heteroskedasity with normality residual test are used. The M2 and BT are negative significant with 5% and 1% respectively. The relationships among both variables are negatively significant. The awareness of this type of information the investor can invest their capital with right place with right time.

V. REFERENCES

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