Bayesian ML – Dynamic Sharpe Ratios and Pairs Trading
In this chapter, we will introduce Bayesian approaches to machine learning (ML) and how their different perspective on uncertainty adds value when developing and evaluating trading strategies.
Bayesian statistics allows us to quantify uncertainty about future events and refine our estimates in a principled way as new information arrives. This dynamic approach adapts well to the evolving nature of financial markets. It is particularly useful when there are fewer relevant data and we require methods that systematically integrate prior knowledge or assumptions.
We will see that Bayesian approaches to machine learning allow for richer insights into the uncertainty around statistical metrics, parameter estimates, and predictions. The applications range from more granular risk management to dynamic updates of predictive models that incorporate changes in the market environment. The Black-Litterman approach to asset...
