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Traderoid

InformationRatio Node

Risk-adjusted alpha per tracking error unit

StatisticalPerformanceRisk

Overview

Information Ratio (IR) measures how effectively a strategy generates alpha relative to benchmark while managing tracking error. It specifically quantifies alpha per unit of active risk taken, making it ideal for evaluating active strategies. IR=1.0 is considered excellent; IR>2.0 is exceptional.

Unlike Sharpe Ratio which measures return per total risk, Information Ratio focuses on excess return (alpha) per tracking error (active risk). This makes it perfect for assessing portfolio managers, algorithmic strategies, and factor exposure. A strategy with high IR generates consistent outperformance with minimal deviation from benchmark.

Formula & Calculation

Information Ratio
IR = (Portfolio Return - Benchmark Return) / Tracking Error
Annualized: IR = Annual Alpha / Annual Tracking Error
Tracking Error
TE = StdDev(Portfolio Return - Benchmark Return)
Measures volatility of excess returns (active risk)
Interpretation
IR > 1.0: Excellent (outperforming well)
IR = 0.5-1.0: Good (solid alpha generation)
IR < 0.5: Poor (insufficient alpha)
IR < 0: Negative (underperforming benchmark)

Parameters

ParameterDefaultDescription
period252Trading days for annualization
benchmarkSPYBenchmark ticker for comparison

Common Use Cases

1. Strategy Evaluation

Compare actively managed strategies: IR>1.0 strategies are worth trading. IR<0.5 strategies need refinement or abandonment.

2. Portfolio Manager Assessment

Evaluate fund managers: Long-term IR>1.0 indicates genuine skill. Volatility of IR over time matters (consistency).

3. Factor Exposure Analysis

Assess factor premiums: If factor IR is 1.5+, it's reliable. If IR declining, factor premium may be eroding.

4. Capital Allocation

Allocate capital proportionally to IR: Higher IR strategies deserve more capital (Optimal Kelly-like sizing).

Advantages & Limitations

Advantages

  • Focuses on alpha (excess return)
  • Controls for active risk taken
  • Perfect for active managers
  • Penalizes unnecessary volatility
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Limitations

  • Benchmark selection critical
  • Zero/negative alpha is problematic
  • Requires long history for stability
  • Sensitive to data frequency

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