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The rank correlation between a forecast (signal) and the realized outcome across a cross-section of assets.
The rank correlation between a forecast (signal) and the realized outcome across a cross-section of assets.
The IC is the core evaluation metric for alpha signals in quantitative equity research. It is typically computed as the Spearman rank correlation between predicted and realized returns at each rebalance. The Information Ratio of a strategy is approximately IC × sqrt(Breadth) under standard assumptions, connecting signal quality to portfolio performance.
Breadth is the number of independent bets per year; IR ≈ IC × sqrt(Breadth).
IC decay over the holding horizon is a key signal characteristic.
IC time-series statistics (mean, std, t-stat) evaluate signal stability and significance.
Quantitative research is the disciplined process of turning a financial hypothesis into a validated, deployable trading model. It spans data engineering, signal design, backtesting with proper out-of-sample validation, and ongoing production monitoring. The discipline's central problem is overfitting: most strategies that pass backtests fail in production.
Financial machine learning is the application of supervised, unsupervised, and reinforcement learning methods to financial prediction, classification, and decision problems. The defining methodological constraint is that financial data are serially correlated, not independently and identically distributed, which means that the standard machine learning toolkit must be substantially adapted. The dominant practitioner reference is López de Prado (2018).