TOWARDS LONG-RUN INTEGRATION BETWEEN FIRM-SPECIFIC AND MACROECONOMIC VARIABLES IN PAKISTAN, CHINA, AND INDIA
The security price movements are closely related to the economic activity level. According to the Efficient Market Hypothesis (EMH), an efficient capital market is one in which stock prices change rapidly as new information becomes available. Several studies have found a relationship between changes in the economic world and macroeconomic variables. Moreover, previous studies also provide evidence of a significant relationship between firm-specific variables and stock prices. Therefore, this study was conducted to test the long-run and short-run relationship between firm-specific & macroeconomic indicators and stock prices for Pakistan, China, and India. The selected firm-specific and macroeconomic variables including Assets, Inflation, Exchange Rate, Interest Rate, National outcome (IPI), Money Supply M2, Taxes paid by firms, and Stock Prices. Quarterly data from 2000Q1 to 2016Q4 of firm-specific and macroeconomic variables of Pakistan, China, and India was used in this study. Moreover, quarterly data of firm-specific variables were collected from Data Stream (data source of Thomson Reuters) and quarterly data of macroeconomic variables were collected from the website of IMF. Panel Cointegration tests including Kao Residual Cointegration Test andthe Augmented Dickey-Fuller Test were employed. Vector Error Correction Model (VECM) and Vector Autoregressive (VAR) Models were also employed after implication of Cointegration tests. The present study finds that there is a long-run relationship among variables (Assets, Inflation, Exchange Rate, Interest Rate, National outcome (IPI), Money Supply M2, Taxes paid by firms, and Stock Prices) in the case of Pakistan and China; therefore, VECM was employed on data of Pakistan and China. However, in the case of India, the present study is unable to confirm a long-run relationship among variables; therefore Vector Autoregressive (VAR) was employed. The Co-integration test is also applied for data on all three countries including Pakistan, China, and India. In the present study, Vector Error Correction Model (VECM) is applied to test redundancy of variables for China and Pakistan while VAR is applied in the case of India. The ordinary least squares (OLS) method is used for estimating the unknown parameters in a linear regression model, to minimize the sum of the squares of the differences between the observed responses in the given dataset and those predicted by a linear function of a set of explanatory variables.
Total Assets; Inflation; Exchange Rate; Interest Rate; National outcome (IPI); Money Supply M2; Taxes paid by firms; Stock Prices; Cointegration; VAR; VECM; OLS