Information Coefficient

The Information Coefficient (IC) measures how good you are at predicting which stocks will outperform. It’s the correlation between your predictions and actual outcomes - the purest measure of investment skill.

Beginner

What It Means

IC answers: “When I predict a stock will do well, how often is that prediction correct?” It’s measured as a correlation, ranging from -1 to +1.

Portfolio Example

At the start of each quarter, you predict expected returns for 100 stocks. At quarter end, you compare predictions to actual returns.

IC Value	Interpretation
0.00	No predictive ability (random guessing)
0.03	Weak but positive skill
0.05	Typical skilled manager
0.10	Strong skill (rare)

Why It Matters

IC directly measures forecasting skill - the core of active management value. If you can’t predict which stocks will outperform, you can’t add value. IC tells you if your predictions have any merit.

Advanced

Mathematical Definition

IC = Correlation(Forecast Returns, Realized Returns)

IC = Cov(f, r) / (σf × σr)

Where:
- f = Forecasted returns
- r = Realized returns
- Range: -1.0 to +1.0

Realistic IC Values

Most investors overestimate achievable IC:

Manager Type	Typical IC
Bottom Decile	-0.02 to +0.01
Median Manager	0.02 - 0.04
Top Quartile	0.05 - 0.08
Top Decile	0.08 - 0.12

An IC of 0.10 is exceptional. Claims of IC above 0.15 should be viewed with extreme skepticism.

The Fundamental Law of Active Management

IC connects to expected performance through:

E(IR) = IC × √BR

Where:
- IR = Information Ratio
- IC = Information Coefficient
- BR = Breadth (independent bets per year)

Example:

IC = 0.05, BR = 100 independent bets
E(IR) = 0.05 × √100 = 0.05 × 10 = 0.50

Why Small IC Matters

Even tiny IC creates value with enough breadth:

IC	Breadth	Expected IR
0.02	100	0.20
0.05	100	0.50
0.05	400	1.00
0.10	100	1.00

The law shows two paths to high IR: better skill (higher IC) or more independent bets (higher breadth). Most quant strategies focus on breadth since IC is hard to improve.

IC Stability

IC is not constant:

Factor	Effect on IC
Market Volatility	Higher dispersion = higher achievable IC
Regime Changes	IC varies across bull/bear markets
Strategy Crowding	More users = lower IC
Information Decay	Signals lose power over time

Measuring IC

Approach	Description
Cross-Sectional	Rank correlation each period across all stocks
Time-Series	Track individual stock forecast accuracy over time
Quintile Spreads	Top quintile return minus bottom quintile

Data Requirements

Requirement	Details
Observations	100+ independent forecasts minimum
Preferred	500+ forecasts for stable IC estimate
Time Coverage	Multiple periods to confirm consistency
Cross-Sectional	50-100 stocks per period typical

Limitations

Limitation	Description
Hard to Measure	Requires detailed forecast data
Time-Varying	IC changes across market conditions
Implementation Gap	Forecast IC differs from realized portfolio IC
Correlation Effects	High correlations reduce effective breadth

IC vs. Hit Ratio

Metric	Measures	Considers Magnitude?
Hit Ratio	Frequency of correct direction	No
IC	Correlation with outcomes	Yes

IC is more comprehensive because it accounts for both direction and magnitude of predictions.

Information Ratio

IC helps determine IR

Hit Ratio

Related but simpler measure

Alpha

What IC enables

​Beginner

​What It Means

​Portfolio Example

​Why It Matters

​Advanced

​Mathematical Definition

​Realistic IC Values

​The Fundamental Law of Active Management

​Why Small IC Matters

​IC Stability

​Measuring IC

​Data Requirements

​Limitations

​IC vs. Hit Ratio

​Related Terms

Information Ratio

Hit Ratio

Alpha

Beginner

What It Means

Portfolio Example

Why It Matters

Advanced

Mathematical Definition

Realistic IC Values

The Fundamental Law of Active Management

Why Small IC Matters

IC Stability

Measuring IC

Data Requirements

Limitations

IC vs. Hit Ratio

Related Terms