Gains to Valuation Accuracy of Direct Valuation Over Industry Multiplier Approaches

Abstract

The primary objective of this paper is to assess the gain in valuation accuracy when the analyst performs an exhaustive pro-forma about the target firm beyond the four-year forecast horizon under the direct method, compared to the alternative of using heuristic industry multiples to compute continuing values if she is uncertain about the firm's post-horizon prospects. We also examine the determinants of the edge to direct valuation vis-a-vis industry multiplier approaches.

Three industry-multiplier approaches are considered, the ETSS, IHP and PE4 models. Given variations in size and growth prospects across firms within an industry, we expect to achieve greater valuation accuracy under the direct method than any of the multiplier models that we explore in the study. Results from the study are consistent with this prediction. In particular, the direct method generates the lowest mean squared errors, tightest inter-percentile ranges and highest regression, when data are either un-scaled or deflated by current book value per share. The direct method loses some of its edge over the other models, however, when current stock price per share is used as a deflator. Results also indicate that the valuation gains to directly forecasting firm-specific continuing values are greatest for small and fast growing target firms from highly heterogeneous industries.

We contribute to the academic literature in three ways: first, we present a benchmark model against which the efficacy of various multiplier approaches may be evaluated; second, we identify firm characteristics and industries under which gains to direct forecasts of continuing value is greatest; third, we show the analyst and students of financial statement analysis how to use reverse engineering techniques to extract from comparable firms inferences about industry average growth prospects at the horizon.

JEL Classification: G12, G29, M41