Exploring Breaks in the Distribution of Stock Returns: Empirical Evidence from Apple Inc.

Abstract

We implement and test four leading families of unsupervised learning changepoint detection models to investigate the incidence, origins, and effects of breaks in the mean and variance of Apple’s stock returns distribution. These models reveal a sustained incidence of breaks, mainly in the variance. Empirical asset pricing models do not explain this result, even allowing for time-varying coefficients. The breaks occur in response to corporate events, particularly earnings releases and stock-related news. These findings have general implications beyond Apple. Estimation procedures for asset pricing models must address these breaks. Our findings also open event studies to new types of inquiry.

Keywords: changepoint methods, regime switching models, machine learning, factor models, empirical asset pricing, event studies

JEL Classification: C1, C22, C44,G12, G14, C32