The Prediction of Earnings Using Financial Statement Information: Empirical Evidence With Logit Models and Artificial Neural Networks

Abstract

In the past three decades, earnings have been one of the most researched variables in accounting. Empirical research provided substantial evidence on its usefulness in the capital markets but evidence in predicting earnings has been limited, yielding inconclusive results. The purpose of this study is to validate and extend prior research in predicting earnings by examining aggregate and industry‐specific data. A sample of 10,509 firm‐year observations included in the Compustat database for the period 1982–91 is used in the study. The stepwise logistic regression results of the present study indicated that nine earnings and non‐earnings variables can be used to predict earnings. These predictor variables are not identical to those reported in prior studies. These results are also extended to the manufacturing industry. Two new variables are identified to be significant in this industry. Moreover, an Artificial Neural Network (ANN) approach is employed to complement the logistic regression results. The ANN model's performance is at least as high as the logistic regression model's predictive ability.

Accurate forecasting of the undecided population in a public opinion poll

“…It has been utilized successfully in noisy image recognition Saloma 1995, 1998;Fu et al, 1999) and feature identification in different kinds of signals (Zhang et al, 1998;Chen and Ware, 1999;Hoorn et al, 1999;Huse and Gjosaeter, 1999;Li and Bridgewater, 2000;Mittal and Zhang, 2000;Qureshi et al, 2000;Shimada et al, 2000), stock trading and securities (Anders et al, 1998;Gençay and Stengos, 1998;Moody et al, 1998;Shin and Kil, 1998;Gençay, 2000. Hamm andBrorsen, 2000;Lam and Lam, 2000), financial management fraud (Fanning and Cogger 1998), inflation (Moshiri and Cameron, 2000), risk assessment in auditing (Ramamoorti et al, 1999), and financial earnings (Charitou and Charalambous, 1996). To our knowledge, the NN method has not yet been applied to forecasting the tendencies of the undecided population in a public opinion poll.…”