A study of the reliability of cross-sectional earnings forecasting models for estimating IPO firms’ implied cost of capital

Abstract

Purpose

This study aims to evaluate the earnings forecasting models of Hou et al. (J Account Econ, 53:504–526, 2012) and Li and Mohanram (Rev Account Stud, 19:1152–1185, 2014) in terms of bias and accuracy and validity of the implied cost of capital (ICC) estimates for a sample of initial public offerings (IPOs).

Design/methodology/approach

The authors use a sample of 1,657 NYSE, Amex and Nasdaq IPOs from 1972 to 2013.

Findings

The models of Hou et al. and Li and Mohanram produce relatively inaccurate and biased earnings forecasts, leading to unreliable ICC estimates, particularly for small and loss-making IPOs that constitute the bulk of new listings. As a remedy, the authors propose a new earnings forecasting model, a combination of Hou et al.’s and Li and Mohanram’s earnings persistence models, and show that it produces more accurate and less biased earnings forecasts and more valid ICC estimates.

Originality/value

The study contributes novel results to the literature on the validity of cross-sectional earnings models in forecasting IPO firm earnings and estimating the ICC. The findings are directly relevant for practitioners, who can improve their earnings forecasting accuracy for IPO firms and related ICC estimates. The insights can be extended to other settings where investors have limited access to financial information, such as acquisitions of private targets.

Citation

Schreder, M. and Bilinski, P. (2022), "A study of the reliability of cross-sectional earnings forecasting models for estimating IPO firms’ implied cost of capital", Accounting Research Journal, Vol. 35 No. 2, pp. 219-237. https://doi.org/10.1108/ARJ-10-2020-0329