Paper Contents
Abstract
Bayesian estimation does provide a flexible framework for updating beliefs about the parameters of a model as new data becomes available. Unlike frequent approaches, which depend on fixedpoint estimates, the Bayesian approach provides a probabilistic interpretation of uncertainty, enabling more robust inferences, especially in situations with limited noisy data. In this paper, we consider applied repeated Bayesian estimation in modeling governance scores for mutual funds and ETFs. We update successive stages of the validation process, showing Bayesian model refining prediction as new data come aboard bringing an adaptive approach to financial modeling. Our results show that repeated Bayesian updates significantly outperform the best static models, particularly in finance regimes where regulatory and market conditions are changing. This work illustrates the value of Bayesian estimation in better decision-making by financial analysts and portfolio managers.
Copyright
Copyright © 2024 NISHANT KUMBHAR. This is an open access article distributed under the Creative Commons Attribution License.
