A comparison of nonlinear methods for predicting earnings surprises and returns

We compare four nonlinear methods on their ability to learn models from data. The problem requires predicting whether a company will deliver an earnings surprise a specific number of days prior to announcement. This problem has been well studied in the literature using linear models. A basic question is whether machine learning-based nonlinear models such as tree induction algorithms, neural networks, naive Bayesian learning, and genetic algorithms perform better in terms of predictive accuracy and in uncovering interesting relationships among problem variables. Equally importantly, if these alternative approaches perform better, why? And how do they stack up relative to each other? The answers to these questions are significant for predictive modeling in the financial arena, and in general for problem domains characterized by significant nonlinearities. In this paper, we compare the four above-mentioned nonlinear methods along a number of criteria. The genetic algorithm turns out to have some advantages in finding multiple "small disjunct" patterns that can be accurate and collectively capable of making predictions more often than its competitors. We use some of the nonlinearities we discovered about the problem domain to explain these results.