Three persistent myths about entropy and mutual information in financial analysis
Most people get entropy wrong. Here’s why it matters.
Too many portfolio analysts misunderstand entropy and mutual information. We address common myths, reveal where conventional wisdom fails, and show how proper application changes decisions. No shortcuts—just clarity.
Misunderstanding entropy’s meaning
Entropy is not just another word for risk. Risk, in the standard sense, often refers to variance or volatility. Entropy captures the unpredictability of an entire distribution, going beyond variance. If you conflate the two, you miss key nuances. Entropy’s role is to clarify, not to replace or repeat conventional risk metrics. Recognize the difference and you sharpen your analysis.
Overreliance on correlation
It’s easy to assume that zero correlation means two assets are independent. Not so. Mutual information reveals hidden dependencies even when correlation is zero. Ignoring this leads to a false sense of diversification. Our fieldwork with Indian asset classes repeatedly finds overlooked links missed by correlation. Use mutual information as a diagnostic—never as a standalone solution, but as a complement to existing analysis.
The data quality trap
Quantity does not trump quality. Using more data without considering relevance or data integrity can actually worsen entropy and mutual information estimates. Indian market data, with its irregularities, teaches us this lesson often. Filter and validate your data before jumping into complex calculations. Quality input is essential for meaningful results.