Equity Valuation Using Price Multiples: A Comparative Study for BRICKS

Abstract

In this paper, we evaluate the efficacy of three value drivers namely, earnings per share, book value and sales for developing stock price forecasts using two performance evaluation criteria: 1) Root Mean Squared Error and 2) Thail Inequality Coefficient. We employ data for BRICKS economies excluding Russia from 1993-2007. We conduct our analysis in three phases. In phase one we find that price to book value is the best standalone price multiple for the Asian economies (India, China and South Korea) while price to earnings does a better job for equity valuation in case of Brazil and South Africa. In the next phase we show that combination of value drivers do not significantly improve price forecast vis-à-vis standalone multiples. Our findings are in contrast with those for developed markets as shown by Penman (1996). We also find that in Indian context market regression is a better tool for price forecasting compared to sector regression as larger number of observations result in better estimator for our forecast equation. Our findings are extremely relevant for equity analysts and portfolio managers who are continuously involved in equity evaluation and developing global asset allocation strategies.