A practical guide for business and technical professionals comparing variance, entropy, and mutual information in financial analysis
Guide
September 27, 2026
by Sandeep Khatri
Variance gives a baseline view, but can oversimplify.
Variance is the first stop for most teams. It measures the spread of returns and gives a quick read on uncertainty. But in Indian markets, with their unpredictable swings and non-normal distributions, variance can oversimplify. It can miss the nuances of tail risks or nonlinear dependencies, making it just one part of a comprehensive toolkit.
Entropy measures total unpredictability across outcomes.
Entropy quantifies unpredictability across all outcomes—not just the width of a distribution. For portfolios holding a mix of Indian asset types, entropy clarifies how much is genuinely uncertain. The calculation is more demanding, requiring robust probability estimates, but the payoff is a clearer risk picture.
Mutual information detects hidden dependencies.
Mutual information reveals when assets share hidden relationships. Even uncorrelated pairs can hold surprises—mutual information captures those links. For technical teams, it’s a powerful way to check that diversification isn’t just an illusion.