Changepoint Detection in the Cross-Section of Stock Returns

We investigate changepoints in the cross-section of stock returns using an ensemble of dedicated unsupervised learning methods. Our large-scale study reveals a sustained incidence of changepoints in the mean, variance, and distribution. This finding is robust to the choice of the changepoint detection method. We also find that a six-factor empirical asset pricing model partially explained these results until the mid-1990s, but its explanatory power has weakened considerably. Moreover, GARCH models do not account for changepoints in the variance of the residuals. This striking finding indicates that conditional heteroskedasticity is not connected to changepoints, at least not in a way standard econometric models capture. Thus, our research highlights the contribution of unsupervised learning changepoint detection algorithms to financial time series analysis.Online appendix available at https://ssrn.com/abstract=4404234