A study of determinants of predictive accuracy of analysts? estimates of earnings in Indian markets

Abstract

Researchers in the past have tried to explain the predictive accuracy of analysts? estimates of earnings (EPS) by analyzing variables like size, age, disclosures and number of analysts? following the firm. In this study, we examine 54 variables from three broad categories: financial, ownership and demographic, with an objective to explain the predictive accuracy of analysts? estimates of earnings. To achieve this objective we use a regression model with percentage in prediction error as the dependent variable and the categorized variables as the explanatory variables. The initial sample for this study consist of analysts? estimates of quarterly earnings for firms listed in CNX 200 Index (NSE India) for quarterly results arriving during 2008-15 (28 quarters). The final sample comprises of 1148 firm quarters over the stated period. Results suggest that out of the three broad categories, variables belonging to the Financial and Ownership category have a significant impact on the predictive accuracy of analysts? estimates. A significant implication of the study is that by giving more importance to the variables in the Financial and Ownership categories analysts? are likely to improve the predictive accuracy of their estimates. Our results may further improve the market efficiency by limiting the market surprises at the time of the announcement of EPS estimates.