Correlation & Causation

Lesson Content

In the world of quantitative trading, distinguishing between correlation and causation is paramount. Correlation measures the statistical relationship between two variables, while causation indicates that one variable directly influences another. In Indian markets, we often observe false correlations that can mislead trading strategies. For instance, Nifty 50 and Bank Nifty indices show high correlation during market-wide movements, but this doesn't mean one causes the other. Similarly, certain stocks may move together due to sectoral factors rather than direct causation. Backtesting strategies requires identifying true causal relationships to avoid spurious correlations. Statistical methods like Granger causality tests can help determine if past values of one variable predict another. In Indian markets, consider how changes in RBI policy rates might cause movements in banking stocks, which could then correlate with broader market movements. Always question why variables move together and whether the relationship is fundamental or coincidental.