# THE EFFECT OF SOME MACROECONOMIC VARIABLES ON STOCK MARKET MOVEMENT IN IRAQ

Muhammed Al-Kassab, Mowafaq (2022) THE EFFECT OF SOME MACROECONOMIC VARIABLES ON STOCK MARKET MOVEMENT IN IRAQ. Advances and Applications in Statistics, 73. pp. 1-16.

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## Abstract

Regarding the effect of macroeconomic variables such as money supply and interest rate on stock prices, the efficient market hypothesis suggests that competition among the profit-maximizing investors in an efficient market will ensure that all the relevant information currently known about changes in macroeconomic variables are fully reflected in current stock prices, some of these relevant information (explanatory variables) may have interrelation (collinearity). In regression analysis, multicollinearity refers to a high strength correlation among the explanatory variables. Thus, the ordinary least squares (OLS) are unreliable because of the violation of the independence assumption of the explanatory variables. The high dependency in explanatory variables will cause huge value in the standard errors of parameter estimates and thus, OLS is no longer appropriate to be employed in modeling the data. The most popular method used to overcome this problem is ridge regression. A numerical example of stock market price and macroeconomic variables in Iraq is employed using both methods to investigate the relationship of the variables in the presence of multicollinearity in the data set. The variables of interest are consumer price index (CPI), gross domestic product (GDP), base lending rate (BLR), and money supply (M1). The obtained findings show that the ridge regression method can estimate the model and produce reliable results by reducing the effect of multicollinearity in the data set. It is concluded that there is a relationship between macroeconomic variables (CPI, GDP, BLR and M1) and stock prices in the Iraqi stock market ISE, and that these variables account 98.5% of the change in ISE.

Item Type: Article macroeconomic variables, mean squares error, ordinary least squares, multicollinearity, ridge regression. Q Science > QA Mathematics ePrints deposit 09 Feb 2022 13:00 07 Nov 2022 06:50 http://eprints.tiu.edu.iq/id/eprint/777

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