Model uncertainty in financial forecasting

TL;DR

This paper explores how incorporating relationships among competing models can reduce uncertainty in financial forecasting, emphasizing the dynamic nature of market participants' expectations.

Contribution

It introduces a method to incorporate relations among models into the estimation process to reduce uncertainty in investment return predictions.

Findings

Incorporating model relations reduces forecast uncertainty.

Model choice changes are unpredictable but can be managed.

Enhanced estimation improves prediction reliability.

Abstract

Models necessarily capture only parts of a reality. Prediction models aim at capturing a future reality. In this paper we address the question of how the future is constructed (or: imagined) in an investment context where market participants form expectations on the returns of a risky investment. We observe that the participants' model choices are subject to unforeseeable change. The objective of the paper is to demonstrate that the resulting uncertainty may be reduced by incorporating relations among competing models in the estimation process.