In the last article we explained the downfalls of relying on the Central Limit Theorem (CLT) and using the mean and standard deviation to calculate a point estimate of the Sharpe Ratio.
Today, we’ll again be disussing the revered Sharpe Ratio (SR), the metric that has been widely used for decades to measure investment performance relative to risk.
But as with any tried-and-true method, it’s crucial to question its efficacy and explore innovative alternatives. That’s where the Probabilistic Sharpe Ratio (PSR), developed by Bailey and López de Prado, comes into play¹.