Relationship Between Macroeconomic Variables and Stock Market Index: A Comparative Study Between India and United Kingdom

The stock market plays a crucial role in the context of economic development of any country. To examine the impact of various independent factors such as currency fluctuation (in context with USD), Interest rate and Inflation on the Stock Indices of India and UK. The study has been conducted for a period of 9 years i.e., April 2014 to March 2023. Techniques like Unit Root test, VAR, Cointegration test, Granger Causality test, Correlation and Regression are used to study the causality relationship and impact of macroeconomic variables between two nations. The results show that there is a unidirectional relationship between Nifty 50 and exchange rate, interest rates of India; FTSE 100 and interest rates of UK. And also, it is concluded that there is an impact of inflation rate and exchange rate of India on FTSE 100. On other hand, it is concluded that there is an impact of inflation rate and interest rate of UK on Nifty 50. Hence, investors are suggested to consider inflation rate, exchange rate and interest rate before investing in stock market.


Introduction
Stock market is being affected by many external factors like inflation rate, currency exchange rate, money supply, GDP, interest rate, exports etc. This study mainly focuses on three macroeconomic variables that affect stock market namely… • Inflation rate • Exchange rate • Interest rate People who live in countries with low interest rates borrow more money from banks and save less. Both the supply and demand of money in the economy rise. The rising commodity prices leads to inflation. Higher inflation generally has a depressing effect on a nation's currency's value. This is so because rising inflation devalues a currency's purchasing power, which makes it less valuable in relation to other currencies. Increased inflation typically has a negative effect on currency conversion rates. Stock market return is also influenced by inflation. Many emerging economies are struggling to control inflation because it tends to have a negative impact on stock market performance. Economic stability is measured in terms of price stability of a country. High inflation creates uncertainty in an economy and it will make domestic and foreign investors not to invest in stock market.

Inflation:
Inflation refers to increase in the overall average price level of the products or services. The most widely reported measure of inflation in India after 2014 is the consumer price index (CPI) which measures the changes in the average prices of consumer goods and services. The increase in price of products and services cause market uncertainty. Corporation's profit and growth margins may be affected by rising inflation, which could have an impact on investor decision.

Exchange Rate:
Exchange rate is the rate at which one nation's currency will be exchanged for another nation currency and affects the trade movement between the countries. The movements in stock market influence exchange rate to appreciate or depreciate against foreign currencies. This study is to observe the impact of Indian and UK currency fluctuation in context with Dollar on stock market.

Interest Rate:
One of the factors that affect stock prices is expected earnings which in turn, is affected by interest rates. If the repo rate continues to go up, banks will raise loan rates. This will lead to higher loan repayment costs for corporates. Rising costs reduce the net profit, which reflects in stock prices. Interest rate are a reflection of economic conditions. Central banks raise and lower interest rates to encourage economic growth and manage inflation. If inflation is high, they raise interest rates to control it. If inflation is low, they lower interest rates to encourage consumers to spend and borrow money.

Indian Stock Exchange:
Indian stock exchange refers to 7 official operating stock and commodity exchanges by SEBI. The two principal large stock exchange sin India are Bombay Stock Exchange (BSE) and National Stock Exchange (NSE). This study is mainly concentrated on impact of Interest rate, Exchange rate and Inflation rate on NSE Index. National Stock Exchange (NSE) is one of the leading stock exchanges of India, based in Mumbai. NSE is under the ownership of various financial institutions such as banks and insurance companies. It is the world's largest derivatives exchange by number of contracts traded and the third largest in cash equities by number of trades. It is one of the largest stock exchanges in the world by market capitalization. NSE's flagship index is NIFTY 50, a 50 stock index is used extensively by investors in India and around the world as a barometer of the Indian capital market. The NIFTY 50 is a benchmark of Indian stock market index that represents the weighted average of 50 of the largest Indian companies listed on the National Stock Exchange. The NIFTY 50 index was launched in 1996 by NSE. The study also analyzed the investor's perception on the relationship between interest rates, inflation and stock market volatility. It found that there is a positive relationship between inflation and stock market volatility both in short run and long run. It also found that there is a negative relationship between interest rates and stock market volatility both in short run and long run. Through primary data from investor's, it is analyzed that 69% respondents agreed that a change in inflation will cause a change in stock market. And 75% respondents agreed that a change in interest rates will affect the stock market.

Santoshi Gande (2016) in her article "Impact of Select Macro Economic Variables on BSE Sensex
Returns" published in "Indian Streams Research Journal" studied the impact of selected Macroeconomic variables on BSE Sensex returns. It used correlation to analyze the relationship between 5 macroeconomic variables and BSE Sensex returns. It is concluded that there is a positive relation between variables like Reverse repo rate, Bank rate, inflation, Industrial growth rate, Gross domestic product, Exchange rates on BSE Sensex returns. The study revealed that repo rate is not influencing the BSE Sensex returns. It also revealed that when compared to other macroeconomic variables inflation and exchange rates are more influencing the BSE Sensex returns. It suggested that effect of Dow Jones industrial average on Indian stock market can also be analyzed.

Research Methodology 3.1 Statement of the Problem
Inflation rates are closely related to interest rates, which can influence exchange rates. During high inflation foreign goods and services become more attractive as they are cheaper. This means imports will increase and cause an increase in currency supply in forex markets which results in depreciated currency. The higher inflation results in higher interest rates in order to control the cash flow in the society. And higher interest rates attract more foreign investment which leads to increase in exchange rates.
• How higher inflation will affect the interest rates and exchange rates?
• How does inflation devalue a currency?
The problem of this study is how inflation rate, exchange rate and interest rate will affect the stock market.

Need of the study
The stock prices of securities are affected by the sectoral Indices of National Stock Exchange (NSE) & London Stock exchange (LSE). The volatility in the sectoral indices is due to various micro and macroeconomic variables. An investor has to concentrate on these micro and macroeconomic variables before taking any investment decisions. This study focuses on the impact of selected macroeconomic variables namely interest rates, inflation and exchange rates on stock market index of India and UK.

Objectives of the Study
• To identify whether there is a causal relationship between macroeconomic variables and SMI of India and UK. • To see the interrelationship and impact of selected macroeconomic variables between the two nations.

Scope of the Study
The study focuses on the relationship and impact of three macroeconomic variables namely interest rates, exchange rates and inflation on stock market index of India and UK over a period of 9 years i.e., April 2014 to March 2023.

Data Collection
This study is fully based on secondary data. Monthly data of Stock market indices are collected from yahoo.finance website for a period of 9 years from April 2014 to March 2023. Inflation rate, exchange rate and interest rate data are collected from Handbook of statistics on Indian economy from RBI portal and Trading Economics.

Limitations of the Study
• This study considers only three macroeconomic variables that affect Indian stock market and UK stock market. • CPI measure is considered while collecting the data of inflation rates.
• The study is performed only for 9 years period.

Statistical tools 3.7.1 Unit Root Test
Unit root test is used to test whether the time series data of a variable is non-stationary and possesses a unit root. The first step, the study employed Augmented Dickey Fuller (ADF) test to check the stationarity of the time series data of selected variables, because mostly the time series data of macroeconomic variables are often assumed to be non-stationary.

Vector Auto Regression (VAR)
Vector autoregression (VAR) is a statistical model used to capture the relationship between multiple quantities as they change over time and also to determine the optimum lag length.

Johansen Test of Cointegration
After ensuring that the data series is stationary at 1 st difference, we test the possibility of a long run equilibrium relationship between variables using Johansen test of cointegration. Johansen's test comes in two main forms i.e., Trace tests and Maximum Eigenvalue test. Johansen test suggests the trace and the maximum eigenvalue tests in testing the statistical significance.

Granger Causality Test
Granger causality is an econometric test used to verify the usefulness of one variable to forecast another variable. A variable is said to Granger-cause another variable if it is helpful for forecasting the other variable. Fail to Granger-cause if it is not helpful for forecasting the other variable. According to Granger's definition of causal relationships: H0: Macroeconomic variables does not Granger cause SMI. H01: SMI does not Granger cause Macroeconomic variables.

Correlation
Correlation is a statistical technique that expresses how two variables are linearly related. It's a common tool used for describing the relationship between two variables.

Regression
A regression is a statistical technique that relates a dependent variable to one or more independent variables. A regression model is able to show whether changes observed in the dependent variable are associated with changes in explanatory variables. Simply, it shows the impact of one or more independent variables on dependent variable.

Data Analysis & Interpretation 4.1 Unit root test:
The results of unit root test for the variables (inflation rate, exchange rate, interest rate, stock market indices) of India and UK are shown in below table 4.1 Null Hypothesis H0: Variables have a unit root. and stock market index of both India and UK are greater than the critical value (-2.88) at 5% level and the p-values of all variables are greater than the level of significance (0.05). So, the null hypothesis H0 cannot be rejected at 5% significance level. Hence all variables have a unit root. We concluded that, all series of variables are non-stationary at level. In order to make the series stationary we take first difference. At 1 st Difference & Intercept, except for the interest rates of UK the t-Statistic value of all variables of India and UK are less than the critical value (-2.88) at 5% level and p-values of all variables are less than the level of significance (0.05). So, the null hypothesis is rejected at 5% significance level. Hence, all series of variables except interest rates of UK are stationary at first difference and variables are integrated of order I ~ (1). We concluded that, all series of variables are stationary at first difference except interest rates of UK. In order to make the interest rates data series stationary we take second difference. At 2 nd Difference & Intercept, the t-Statistic value of UK interest rates is less than the critical value (-2.88) at 5% level and p-value of UK interest rates are less than the level of significance (0.05). So, the null hypothesis is rejected at 5% significance level. Hence, the interest rate data series of UK is stationary at second difference and the variable is integrated of order I ~ (2).

VAR Lag Order Selection Criteria
We found that all the variables and SMI of India and UK are non-stationary at level but stationary at first difference, whereas UK interest rate data series is stationary at second difference. The next step is selecting the optimum lags used in the Johansen Test of Cointegration.

Interpretation:
The results show the t-Statistics values, for selecting the optimum lags by using VAR model, the lag selection criteria consisting of sequential modified LR test, Akaike information criteria (AIC), Final prediction error (FPE), Hannan-Quinn information criteria (HQ) and Schwarz information criteria (SC). The lower value of each criteria, the better will be the model.

India:
The results indicate that the optimum lags for LR criteria is 3, FPF & AIC is 2, SC & HQ is 1. Among all the models the best model is Akaike information criteria (AIC). Hence, we consider the optimum lags as 2.

UK:
The results indicate that the optimum lags for LR, FPF & AIC is 4, SC & HQ is 1. Among all the models the best model is Akaike information criteria (AIC). Hence, we consider the optimum lags as 4.

Johansen Test of Cointegration Results
The next step is to determine the long-term relationship between macroeconomic variables and stock market index. We consider all the variables and SMI that are stationary at first difference. The results of the Johansen Cointegration test for the variables and stock market index of India and UK are shown in below table 4.3 H0: There is no cointegration between inflation, exchange rates, interest rates and Nifty 50. H01: There is no cointegration between inflation, exchange rates and FTSE 100. The results shows that the Trace Statistic (35.52177) and Max-Eigen Statistic (15.54910) are less than 0.05 critical value of Trace (47.85613) and Max-Eigen (27.58434) tests. Also, the P-value of Trace (0.4208) and Max-Eigen (0.7037) tests are greater than the level of significance (0.05). So, the null hypothesis H0 of cointegration relation (None) cannot be rejected at 5% significance level. Hence, there is no long-term relationship between macroeconomic variables and stock market index.

UK:
The results shows that the Trace Statistic (17.43881) and Max-Eigen Statistic (9.583135) are less than 0.05 critical value of Trace (29.79707) and Max-Eigen (21.13162) tests. Also, the P-value of Trace (0.6076) and Max-Eigen (0.7828) tests are greater than the level of significance (0.05). So, the null hypothesis H01 of cointegration relation (None) cannot be rejected at 5% significance level. Hence, there is no long-term relationship between macroeconomic variables and stock market index.

Granger Causality Results
The Granger Causality test is used to determine whether one time series data is useful for forecasting another time series data. The results of Granger causality test between macroeconomic variables and SMI are shown in below table 4.4 Interpretation: • The Granger Causality between inflation and Nifty 50 has a probability value 0.85 for H0 and 0.09 for H01 are greater than the level of significance (0.05). So, the null hypotheses H0 and H01 cannot be rejected at 5% significance level. Hence, inflation data cannot be used to forecast the Nifty 50 and vice-versa. • The Granger Causality between exchange rate and Nifty 50 has a probability value 0.28 for H02 is greater than the level of significance (0.05) and probability value 0.000 for H03 is less than the level of significance (0.05). So, the null hypotheses H02 cannot be rejected and H03 is rejected at 5% significance level. Hence, exchange rate data cannot be used to forecast the Nifty 50 but we can use Nifty 50 data to forecast exchange rate. • The Granger Causality between interest rate and Nifty 50 has a probability value 0.29 for H04 is greater than the level of significance (0.05) and probability value 0.00 for H05 is less than the level of significance (0.05). So, the null hypotheses H04 cannot be rejected and H05 is rejected at 5% significance level. Hence, interest rate data cannot be used to forecast the Nifty 50 but we can use Nifty 50 data to forecast interest rate. • The Granger Causality between inflation and FTSE 100 has a probability value 0.38 for H0 and 0.23 for H01 are greater than the level of significance (0.05). So, the null hypotheses H0 and H01 cannot be rejected at 5% significance level. Hence, inflation data cannot be used to forecast the FTSE 100 and vice-versa. • The Granger Causality between exchange rate and FTSE 100 has a probability value 0.19 for H02 and 0.41 for H03 are greater than the level of significance (0.05). So, the null hypotheses H02 and H03 cannot be rejected at 5% significance level. Hence, exchange rate data cannot be used to forecast the FTSE 100 and vice-versa. • The Granger Causality between interest rate and FTSE 100 has a probability value 0.45 for H04 is greater than the level of significance (0.05) and probability value 0.01 for H05 is less than the level of significance (0.05). So, the null hypotheses H04 cannot be rejected and H05 is rejected at 5% significance level. Hence, interest rate data cannot be used to forecast the FTSE 100 but we can use FTSE 100 data to forecast interest rate.

Correlation Between Variables
Correlation is used to find the relationship between variables and it also shows how much of one variable is explained by the other variable, its value lies between -1 to +1. The results show the relationship between macroeconomic variables of India and FTSE 100 and also show the relationship between macroeconomic variables of UK and Nifty 50 in table 4.5

Interpretation:
The probability value of interest rate (0.013) and exchange rate (0.144) shows a significant positive relationship between variables of India and FTSE 100. While probability value of inflation (-0.237) shows a negative significant relationship between Indian inflation and FTSE 100. The probability value of inflation (0.778), exchange rate (0.509) and interest rate (0.461) shows a positive significant relationship between macroeconomic variables of UK and Nifty 50.

Regression Analysis
Regression is used to find the impact of independent variable on dependent variable. It also indicates how much of independent variable explains the dependent variable.

Findings
All macroeconomic variables and SMI of India and UK are stationary at first difference and the variables are integrated of order I ~ (1) except interest rates of UK. UK interest rate series are stationary at second difference and the variable is integrated of order I ~ (2). The results indicates that for Indian variables the selected optimum lag length is 2 and for variables of UK the selected optimum lag length is 4. From cointegration test, it is concluded that there is no long-term relationship between macroeconomic variables and SMI of India and UK. By employing Granger causality test, we found that there is no causality relationship between inflation rate and SMI of India. On other hand, we can conclude that there is a unidirectional causality relationship between macroeconomic variables (exchange rate, interest rate) and SMI of India. And also, there is no causality relationship between inflation rate, exchange rate and SMI of UK. But there is a unidirectional causality relationship between interest rate and SMI of UK. From correlation test, we found that there is a significant positive correlation between macroeconomic variables and SMI between the two nations except for inflation rates of India. It is found that there is a significant negative correlation between inflation rate of India and FTSE 100. By using Regression, we found that there is a significant impact of inflation rate, exchange rate of India on FTSE 100 but there is no significant impact of interest rates of India on FTSE 100. And also, there is a significant impact of inflation rate, interest rate of UK on Nifty 50. On other hand, we conclude that there is no significant impact of exchange rates of UK on Nifty 50.

Suggestions
Before investing in stock market, investors have to consider macroeconomic variables like inflation rate, exchange rate and interest rate. Due to increasing inflation rate of UK, investors have to consider inflation rate before investing in stock market. Currently, the inflation rate of India is under control which benefits fixed rate, low interest mortgages and stock market investors. The interest rates of India and UK are increasing to control inflation, which results in decreasing the stock prices of Nifty 50 and FTSE 100. So, investors have to consider interest rates before investing in stock market.

Conclusion
The main purpose of this research is to know the causality relationship between macroeconomic variables and stock market index as well as to know the interrelationship and impact of macroeconomic variables on SMI between the two nations. From the results we conclude that Nifty 50 data series is used in forecasting exchange rate and interest rates of India. On other hand, it is concluded that FTSE 100 data series is used to forecast interest rates of UK. By performing Correlation analysis, we can conclude that there is a significant positive interrelationship between two nations but Indian inflation rate has a significant negative relationship with FTSE 100. In India, if exchange rate and interest rate increase then there will be an increase in the stock prices of FTSE 100 index. On other hand, if inflation rate of India increases then there will be a decrease in the stock prices of FTSE 100 index. In UK, if there is an increase in selected macroeconomic variables then the stock prices of Nifty 50 index will increase. It is also concluded that there is an impact of inflation rate,