Information Ratio Calculator

Calculate information ratio, tracking error, and excess returns to measure portfolio manager skill and risk-adjusted performance.

Evaluate investment portfolio performance by calculating the information ratio, which measures excess return per unit of tracking error relative to a benchmark index.

Examples

Click on any example to load it into the calculator.

Equity Fund vs S&P 500

Equity Fund

A well-performing equity fund compared to the S&P 500 benchmark over a 12-month period.

Portfolio Return: 15.8 %

Benchmark Return: 12.3 %

Portfolio Returns: 2.1, -1.5, 3.2, 1.8, -0.5, 2.3, 1.9, -0.8, 2.7, 1.4, 2.1, 1.2 returns

Benchmark Returns: 1.8, -1.2, 2.9, 1.5, -0.3, 2.1, 1.7, -0.6, 2.4, 1.2, 1.9, 1.0 returns

Bond Fund vs Aggregate Bond Index

Bond Fund

A corporate bond fund performance relative to the Bloomberg Aggregate Bond Index.

Portfolio Return: 6.2 %

Benchmark Return: 5.1 %

Portfolio Returns: 0.8, 0.5, -0.2, 0.9, 0.6, 0.4, -0.1, 0.7, 0.5, 0.8, 0.3, 0.6 returns

Benchmark Returns: 0.7, 0.4, -0.3, 0.8, 0.5, 0.3, -0.2, 0.6, 0.4, 0.7, 0.2, 0.5 returns

International Fund vs MSCI EAFE

International Fund

An international equity fund compared to the MSCI EAFE developed markets index.

Portfolio Return: 8.9 %

Benchmark Return: 7.2 %

Portfolio Returns: 1.2, -0.8, 2.1, 0.9, -0.4, 1.8, 1.1, -0.6, 1.5, 0.7, 1.3, 0.5 returns

Benchmark Returns: 1.0, -0.9, 1.9, 0.8, -0.5, 1.6, 1.0, -0.7, 1.3, 0.6, 1.1, 0.4 returns

Underperforming Fund

Underperforming Fund

A fund that underperforms its benchmark, resulting in a negative information ratio.

Portfolio Return: 9.5 %

Benchmark Return: 11.2 %

Portfolio Returns: 1.5, -1.8, 2.8, 1.2, -1.2, 2.1, 1.5, -0.9, 2.3, 1.0, 1.8, 0.8 returns

Benchmark Returns: 1.8, -1.5, 3.1, 1.5, -0.9, 2.4, 1.8, -0.6, 2.6, 1.3, 2.1, 1.1 returns

Other Titles
Understanding Information Ratio Calculator: A Comprehensive Guide
Master the art of risk-adjusted performance measurement. Learn how to calculate, interpret, and use information ratio to evaluate portfolio manager skill and investment strategy effectiveness.

What is the Information Ratio Calculator?

  • Core Concepts and Definitions
  • Why Information Ratio Matters
  • Components of Information Ratio
The Information Ratio Calculator is a sophisticated financial analysis tool that measures the risk-adjusted performance of an investment portfolio relative to a benchmark index. Unlike simple return comparisons, the information ratio accounts for both the excess return generated by active management and the risk (tracking error) taken to achieve that excess return. This calculator transforms raw performance data into a meaningful metric that quantifies portfolio manager skill and strategy effectiveness.
The Strategic Importance of Information Ratio
Information ratio serves as a critical performance evaluation metric in the investment industry, providing insights that simple return comparisons cannot. It measures how much excess return a portfolio generates per unit of risk taken relative to the benchmark. A higher information ratio indicates better risk-adjusted performance and suggests that the portfolio manager possesses genuine skill rather than simply taking on more risk. This metric is particularly valuable for institutional investors, fund selectors, and individual investors evaluating active management strategies.
Key Components: Excess Return and Tracking Error
The information ratio consists of two fundamental components: excess return and tracking error. Excess return represents the difference between portfolio performance and benchmark performance, measuring the value added by active management. Tracking error measures the volatility of these excess returns, indicating the consistency and risk of the active strategy. The ratio of these two components provides a comprehensive view of risk-adjusted performance that accounts for both return generation and risk management capabilities.
Mathematical Foundation and Interpretation
The information ratio formula is: Information Ratio = (Portfolio Return - Benchmark Return) / Tracking Error. This calculation produces a dimensionless number that can be interpreted across different asset classes and time periods. Positive values indicate outperformance, while negative values indicate underperformance. The magnitude of the ratio indicates the efficiency of the active management strategy, with higher values suggesting better risk-adjusted performance.

Key Metrics Explained:

  • Information Ratio > 1.0: Excellent risk-adjusted performance, indicating strong manager skill
  • Information Ratio 0.5-1.0: Good performance with reasonable risk-adjusted returns
  • Information Ratio 0.0-0.5: Marginal performance, may not justify active management fees
  • Information Ratio < 0.0: Underperformance relative to benchmark on a risk-adjusted basis

Step-by-Step Guide to Using the Information Ratio Calculator

  • Data Collection and Preparation
  • Input Methodology
  • Result Interpretation and Analysis
Maximizing the value of the Information Ratio Calculator requires careful data preparation, accurate input, and thoughtful interpretation of results. Follow this comprehensive methodology to ensure your analysis provides actionable insights for investment decision-making.
1. Define Your Analysis Period and Benchmark
Establish clear parameters for your analysis, including the time period and appropriate benchmark selection. Common periods include 1-year, 3-year, and 5-year analyses, though shorter periods (monthly or quarterly) can provide more granular insights. Choose a benchmark that accurately represents your portfolio's investment universe and risk profile. For equity portfolios, consider market indices like S&P 500, MSCI World, or Russell 2000. For bond portfolios, use indices like Bloomberg Aggregate Bond Index or Barclays Corporate Bond Index.
2. Gather Accurate Performance Data
Collect comprehensive performance data from reliable sources such as fund fact sheets, custodial statements, or financial databases. Ensure you have both total return data (including dividends and interest) and periodic return data for tracking error calculation. The periodic returns should be consistently measured (monthly, quarterly, or annually) and should cover the same time periods for both portfolio and benchmark. Include sufficient data points (typically 12-60 periods) to calculate meaningful tracking error statistics.
3. Input Data with Precision
Enter your portfolio and benchmark total returns as percentages, ensuring you're using the same time period for both. Input the periodic returns as comma-separated values, maintaining chronological order and ensuring both portfolio and benchmark return series have the same number of data points. Double-check your data for accuracy, as small input errors can significantly affect tracking error calculations and final information ratio results.
4. Analyze Results in Context
Interpret your information ratio results against relevant benchmarks and industry standards. Information ratios above 0.5 are generally considered good, while ratios above 1.0 indicate excellent performance. Consider the time period analyzed, as shorter periods may show higher volatility and less reliable results. Compare your results to peer group averages and consider the consistency of performance over multiple periods to assess whether the results represent genuine skill or statistical noise.

Industry Information Ratio Benchmarks:

  • Large Cap Equity Funds: 0.3-0.8 average information ratio
  • Small Cap Equity Funds: 0.4-1.0 average information ratio
  • International Equity Funds: 0.2-0.6 average information ratio
  • Fixed Income Funds: 0.1-0.5 average information ratio
  • Alternative Investment Funds: 0.5-1.2 average information ratio

Real-World Applications and Investment Decision Making

  • Fund Selection and Due Diligence
  • Performance Attribution Analysis
  • Portfolio Construction and Optimization
The Information Ratio Calculator transforms from a simple computational tool into a strategic investment decision-making asset when applied thoughtfully across various investment contexts and portfolio management scenarios.
Fund Selection and Manager Due Diligence
Institutional investors and fund selectors use information ratio analysis to evaluate potential investment managers and existing fund relationships. The metric helps distinguish between managers who generate genuine alpha through skill and those who simply take on additional risk. Fund selectors typically establish minimum information ratio thresholds (often 0.3-0.5) for manager selection and use the metric to rank managers within peer groups. The analysis also supports ongoing monitoring and termination decisions, as declining information ratios may indicate deteriorating manager skill or strategy effectiveness.
Performance Attribution and Strategy Analysis
Portfolio managers and investment teams use information ratio analysis to evaluate the effectiveness of their investment strategies and identify areas for improvement. The metric helps attribute performance to specific factors, sectors, or investment decisions. Managers can analyze how changes in portfolio construction, sector allocation, or security selection affect their information ratio over time. This analysis supports strategy refinement, risk management improvements, and communication with clients about performance drivers and expectations.
Portfolio Construction and Risk Management
Advanced portfolio construction techniques incorporate information ratio analysis to optimize risk-adjusted returns. Multi-asset portfolio managers use information ratios to determine optimal allocations to different asset classes and strategies. The metric helps balance the trade-off between active risk and expected excess return, supporting decisions about how much active management to employ in different market segments. Risk managers use tracking error analysis to monitor portfolio risk levels and ensure they remain within acceptable parameters.

Investment Decision Framework:

  • Information Ratio > 1.0: Consider increasing allocation or expanding strategy
  • Information Ratio 0.5-1.0: Maintain current allocation, monitor for consistency
  • Information Ratio 0.0-0.5: Reduce allocation or implement strategy improvements
  • Information Ratio < 0.0: Consider strategy termination or significant changes

Common Misconceptions and Best Practices

  • Myth vs Reality in Performance Measurement
  • Statistical Considerations and Limitations
  • Implementation Best Practices
Effective information ratio analysis requires understanding common pitfalls and implementing evidence-based best practices that account for statistical limitations and real-world investment constraints.
Myth: Higher Information Ratio Always Means Better Performance
This misconception leads to oversimplified manager selection and can result in poor investment decisions. Reality: Information ratio must be interpreted in context, considering factors such as time period, market conditions, and strategy characteristics. Short-term high information ratios may reflect luck rather than skill, while low ratios during difficult market periods may not indicate poor management. Additionally, different asset classes and strategies have different expected information ratios, making cross-category comparisons misleading.
Statistical Considerations and Measurement Limitations
Information ratio analysis has important statistical limitations that must be understood. The metric assumes normal distribution of returns, which may not hold true for all strategies, particularly those involving options or alternative investments. Small sample sizes can produce unreliable results, and the metric is sensitive to outliers. Additionally, information ratio doesn't account for transaction costs, taxes, or other real-world constraints that affect actual investor returns. These limitations should be considered when interpreting results.
Implementation Best Practices and Risk Management
Successful implementation of information ratio analysis requires systematic approaches and robust risk management. Establish clear benchmarks and measurement periods, and maintain consistency in data collection and analysis. Consider using rolling information ratios to identify trends and changes in manager skill over time. Implement appropriate risk controls, including maximum tracking error limits and regular performance reviews. Remember that information ratio is one tool among many, and should be used in conjunction with other performance metrics and qualitative analysis.

Best Practice Principles:

  • Consistent Measurement: Use the same benchmark and time period for all comparisons
  • Statistical Significance: Ensure sufficient data points for reliable tracking error calculation
  • Contextual Analysis: Consider market conditions and strategy characteristics when interpreting results
  • Regular Monitoring: Track information ratio trends over time to identify changes in manager skill

Mathematical Derivation and Advanced Applications

  • Formula Variations and Calculations
  • Statistical Analysis and Confidence Intervals
  • Multi-Period and Attribution Analysis
While basic information ratio calculations are straightforward, advanced applications involve statistical analysis, confidence testing, and multi-period attribution that provide deeper insights into portfolio performance and manager skill.
Core Mathematical Framework and Variations
The fundamental information ratio formula can be enhanced with various statistical measures and adjustments. Annualized information ratios account for different measurement periods, while rolling information ratios provide trend analysis. Risk-adjusted information ratios incorporate additional risk factors beyond tracking error. More sophisticated calculations might include conditional information ratios that measure performance during specific market conditions, or downside information ratios that focus on performance during negative market periods. These variations provide more nuanced insights into manager skill and strategy effectiveness.
Statistical Analysis and Confidence Testing
Advanced information ratio analysis incorporates statistical testing to determine whether observed performance represents genuine skill or random variation. T-tests can assess the statistical significance of information ratios, while confidence intervals provide ranges for expected performance. Monte Carlo simulations can model the probability of achieving certain information ratios by chance, helping distinguish between skill and luck. These statistical tools are particularly important for evaluating managers with limited track records or during periods of unusual market conditions.
Multi-Period Analysis and Performance Attribution
Sophisticated portfolio analysis extends beyond single-period information ratios to examine performance consistency and attribution over multiple periods. Rolling information ratios reveal trends in manager skill and strategy effectiveness. Attribution analysis breaks down excess returns into components such as sector allocation, security selection, and timing decisions. Multi-factor models can isolate the contribution of different risk factors to overall performance. This comprehensive analysis supports more informed investment decisions and better risk management.

Advanced Calculation Examples:

  • Annualized Information Ratio: Adjusting for different measurement periods to enable comparisons
  • Rolling Information Ratio: Calculating moving averages to identify trends in manager skill
  • Conditional Information Ratio: Measuring performance during specific market conditions (bull/bear markets)
  • Downside Information Ratio: Focusing on performance during negative market periods to assess risk management