Good Example Of Macroeconomic Variable And The Stock Market Term Paper

Type of paper: Term Paper

Topic: Investment, Market, Education, Economics, Stock Market, Study, Economy, Macroeconomics

Pages: 5

Words: 1375

Published: 2021/01/04

Overview and Research question.

The past few decades have had a growing interest among practitioners and scholars and about the relationship existing between macroeconomic variables and mainly stocks, house prices, and asset prices. In an expanding economy, stocks prices of are expected to increase owing to an expectation of future large cash flows and profits for businesses and various players within an economy. On the same note, a downward spiraling or bad economy is marked by expectations of future low cash flows as well as profits decrease and consequently a dercre4ase in socks prices. Stock markets are representative of a country’s economy and the investors’ belief (Pal & Mittal, 2011).
Thus, they are capable of capturing macroeconomic movements in an economy and the idiosyncratic factors that are related to each business or industry. Since Stock prices are more frequent compared o macroeconomic releases and are also real time, they are a better reflector of domestic as well as a global economy changes hence can predict macroeconomic indicators movement. Thus, stock markets are expected o be leading indicators of an economy as he markets respond to various macroeconomic indicators in various ways. Such response by stock markets to macroeconomic events and changes is dependent on how the events and changes influence other variables. (Yadav, 2012)
In that view, this analysis seeks to identify the long-run relationship between capital markets and key macroeconomic variables such as an inflation rate, interest rates, gross domestic savings and exchange rates.

Literature review

Indian economy has been one of the global star economies being among the fastest growing as well as fourth largest in terms of purchasing power parity. The capital investment boom experienced in the global economy drives the growth phase currently experienced in the Indian economy. Markets react to news, including but not limited to political tensions. Other variables such as population, money supply growth, movements in global markets, aggregate deposits of scheduled banks and, the manufacturing sector growth affect capital markets (Pal & Mittal, 2011).
Thus, conducive economic environment enhances businesses profitability which in turn propels them to a level where they can have access to securities markets for sustained growth. Generally, the barometers used for measuring an economy’s performance include macroeconomic variables such as real GDP growth rate, the exchange rate, rate of inflation, the debt position and fiscal position, and among other factors. Those macroeconomic factors are the key determinants of an economy’s growth. Further, since stock prices accurately reflect underlying fundamentals, they should be employed as he crucial indicators of economies future activities (Yadav, 2012).
Different researchers have tried to identify factors that explain stock returns. The famous of all models and the earliest to be applied is the Capital Asset Pricing Model (CAPM); ha was developed by Sharpe, Lintner, Moss, and Black. The single factor model’s concept is developed through diversification that was introduced by Markowitz in 1952. In the CAPM model, the expected returns on stock returns are explained with the help of the one risk factor market and the Risk-free rate (Yadav, 2012).
In their study, Pal & Mittal, (2011) tested the relations existing between the Indian stock market that is represented by BSE and the domestic as well as global macroeconomic variables. The study concluded that India stock markets are mostly driven by the influence of global macro factors and domestic demand. They also tested for Granger’s causality between IP and BSE finding that BSE is a significant indicator of an economy’s Industrial production thus can be useful n predicting the Indian industrial climate.

Main methodology and conclusions of the two papers

The study by Yadav (2012) used Quarterly time series data that covered the period from January 1995 to December 2008. There was an application of unit root test, error correction mechanism (ECM) and the co-integration test to derive both long and short-term statistical dynamics. For the empirical analysis, the study used a regression model for conducting various econometrics tests. The study’s model was based on the model in which the two popular India stock indices BSE Sensex and the S&P CNX Nifty (50 shares) taking hem as the dependent variables.
The first step in constructing econometric models entails constructing time series which are all in same units. Most of the time series applied in their analysis were in different formats. For instance CPI, BSE Index, PPI, and SP500 were in levels. The M1 money supply and USDINR exchange rate are in the current format while the Industrial production is taken as being in absolute production levels. Thus, they first converted all of given time series to levels. The manner in which they constructed he time series in levels by firstly treating the initial data point for each time series as base 100 (Yadav, 2012).
In its conclusion, the study was insightful for investors as well as for professionals seeking investment opportunities for risk diversification. Since he Indian stock markets were been identified as being more dependent on the domestic factors, investors can invest in the Indian indices as well as stocks to diversify the risks they are exposed to by investing in other markets such as U.S. and European markets (Yadav, 2012).
On the other hand, the period used by Pal & Mittal, (2011) study ranged from 1990 to 2011 and was chosen as it represented big regulatory as well as structural changes in the Indian economy. The analysis of that period could be expected to provide with insights as to how regulatory as well as structural changes impacted the asset prices and the economy in that country. In the study, they used unit root tests as well as cointegration and Ljung-Box Q tests in addition to multivariate VAR analysis for evaluating individual macroeconomic, as well as assets prices time series individually. They then built a model that can analyze the impact of one variable on the other. Also, they conducted Granger's Causality test as well as analyzing the impulse response between macroeconomic indicators and the stock market. In addition, it was used in analyzing the impact of macroeconomic shocks on the India Stock index (BSE). Finally, the study also applied unit root test to find whether the series was stationary or non-stationary.
The Pal & Mittal, (2011) study’s findings established that there was co-integration between the macroeconomic variables and the Indian stock indices that was indicative of a long-run relationship. The study indicated that the inflation rate had a significant impact on both the S&P CNX Nifty and BSE Sensex. In addition, Interest rates had a significantly impacted he S&P CNX Nifty but not BSE. However, foreign exchange rate’s significant impact was only on BSE Sensex. Further, changing GDS was observed as being insignificantly related to both the S&P CNX Nifty and the BSE Sensex. In conclusion, the study established that capital markets indices were dependent on macroeconomic factors although the same may not have been statistically significant for all cases.

Main lessons learned from the papers and additional research plan based on the readings.

The main lesson learned from the two studies is that macroeconomic factors have an impact on the stock market. That has been confirmed by the two studies indicating that although various factors had varying impact on stock prices and market’s performance, they all had an impact. Thus, there a need for investors to consider both local and global macro factors in their decision-making.
The studies also opened doors for future research in the field as variance decomposition technique can be used to identify the level of variance of the BSE that can be explained my different global and domestic macro factors. Also various and different global factors can be used such as T-Bill rates and sovereign CDS spreads as composite indicators of the global economy for further studies on the interaction existing between the Indian stock market and the global economy.


In conclusion, the two studies had relatively similar approach with the use of time series data. In addition, they both used the unit root test to establish whether the data was stationary or non-stationary. It is also notable that the two studies had a common finding based on the Indian stock market and the economy concluding that the economy and macroeconomic factors impacted on the stock market. In that respect, investors need to consider local and global economic environment in their decision-making regarding the stock market.


Pal K. & Mittal, R. (2011). Impact of macroeconomic indicators in the Indian capital markets. The Journal of Risk Finance. 12(2), pp. 84 – 9
Yadav, S. (2012). An Empirical Study of Macroeconomic Variables and Stock Market: An Indian Perspective. EDHEC Business School

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