SPRING 2024

AUTHOR

Chad Knight, Duke University

ABSTRACT

Modern Portfolio Theory aims to optimize risk-adjusted returns by identifying assets and creating portfolios with the highest Sharpe Ratio. Generally, there are two strategic approaches to optimizing risk-adjusted returns: maximizing returns or minimizing volatility. In traditional financial literature, it is generally understood that forecasting the future returns of an asset by using its historical returns as a proxy yields low correlation and limited accuracy. However, forecasting the future volatility of an asset is a much more precise science due to the autocorrelation of its squared returns resulting in volatility clusters. In this paper, I will draw comparisons between the ways returns and volatility are measured in financial markets and the ways they can be applied in baseball analytics. Furthermore, I will provide a framework for hitter evaluation by contextualizing the historical difficulty of predicting financial returns accurately, while capitalizing on the predictive nature of volatility.