Overparametrized models with posterior drift

TL;DR

This paper examines how posterior drift affects the forecasting accuracy of overparametrized models, highlighting the risks of regime changes in financial markets and the sensitivity of market timing strategies.

Contribution

It provides empirical evidence on the impact of posterior drift in overparametrized models, especially in financial forecasting, and discusses implications for model complexity and investment strategies.

Findings

Posterior drift causes significant performance loss in out-of-sample forecasts.

Market timing strategies are highly sensitive to regime changes and model bandwidth.

Large bandwidth models are more stable but less optimal for risk-adjusted returns.

Abstract

This paper investigates the impact of posterior drift on out-of-sample forecasting accuracy in overparametrized machine learning models. We document the loss in performance when the loadings of the data generating process change between the training and testing samples. This matters crucially in settings in which regime changes are likely to occur, for instance, in financial markets. Applied to equity premium forecasting, our results underline the sensitivity of a market timing strategy to sub-periods and to the bandwidth parameters that control the complexity of the model. For the average investor, we find that focusing on holding periods of 15 years can generate very heterogeneous returns, especially for small bandwidths. Large bandwidths yield much more consistent outcomes, but are far less appealing from a risk-adjusted return standpoint. All in all, our findings tend to recommend cautiousness when resorting to large linear models for stock market predictions.