Although such income discounting methods as discounted cash flow receive more emphasis in the literature, sell-side analysts prefer to use such relative valuation methods as the P/E multiple. Empirical research shows a strong correlation between the P/E multiple an analyst applies and the quality of the assets being valued. The authors examine how analysts determine the forward P/E multiple for valuation. Their findings will be helpful to investors attempting to make sound investment decisions.

Although long-term investors have criticized the P/E multiples–based valuation method because of a lack of theoretical literature, the authors find that target P/E multiples applied by analysts are based on careful examination of nonfinancial fundamental factors. The data reveal that the majority of firms for which analysts use the P/E multiple (about 87%) are in the consumer discretionary, consumer staples, health care, information technology, and industrial sectors. The authors analyze the ways sell-side analysts apply the forward P/E multiple.

Factors that influence the P/E multiple can be categorized into two groups: measurable financial factors and intangible factors related to the industry, management, and so forth. The latter group requires analysts’ subjectivity, and the P/E multiple will be applied based on analysts’ expectations about industry growth, future earnings, and the like. The authors suggest that analysts apply higher P/E multiples to firms with higher expected earnings growth, higher profitability, and more stable earnings and assign lower multiples to firms with higher risk.

Thus, it can be concluded that valuation of a firm depends not only on the P/E multiple assigned by analysts but also on the accurate forecast of future earnings by analysts.

Using Thomson Research’s Investext, the authors identify 321 sell-side research reports released between October 2010 and March 2012 that reveal the P/E multiple as the only valuation method in the report. Data collected from these reports include the P/E multiple, EPS (earnings per share) forecasts for the next two years, long-term growth, and forecast dividend yield. The authors use such quantitative data as stock price volatility, logarithmic values of market capitalization, book-to-market ratio, gross margin ratio of the last reported year, historical sales growth rate for the last five years, and standard deviation of earnings for 260 firms covered by these reports.

They propose the hypothesis that P/E multiples assigned by sell-side analysts are positively related to a firm’s near-term and long-term earnings growth forecasts and inversely proportional to a firm’s stock price volatility, market capitalization, financial leverage, and earnings volatility. The authors test the hypothesis by regressing the target P/E multiples on these factors. The regression results suggest that analysts assign high P/E multiples to firms with high long-term growth forecasts; these results are statistically significant at a 1% significance level.

Although the research outcomes are in line with the historical empirical results, the authors retest the correlation between P/E multiples and fundamental firm factors in current market conditions. They rely too much, however, on the assumption that forecasts of analysts are accurate enough and that these forecasts are absent of any positive/negative earnings surprises.