The purpose of the eVestment Guide to Investment Statistics is to assist you in gaining a better understanding of how to derive meaningful conclusions from investment statistics. It is not only a glossary of investment statistics, but it is also manual on the usage of key investment statistics.
Learning Objectives
- Explain how to use statistics to predict future investment returns.
- Interpret the different investment risk statistics and risk-adjusted statistics.
- Explain the concepts of correlation and regression analysis for investment analysis.
- Describe the key characteristics of peer group analysis and its usefulness in the search process.
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Using Statistics to Understand Return Characteristics
- Using Standard Deviation
- Assessing Skewness and Kurtosis in the Return Distribution
- Predicting Returns using Monte Carlo Simulation
Risk Statistics and Risk-adjusted Statistics
- Standard Deviation
- Sharpe Ratio
- Sortino Ratio
- Omega Ratio
- Drawdown Analysis
- Calmar Ratio
- Sterling Ratio
- Comparing Risk Statistics and Risk-Adjusted Statistics
Correlation and Regression Analysis
Composite Returns: Portfolio Construction, Optimization, Simulation
Fat Tail Analysis, Risk Budgeting, Factor Analysis & Stress Testing
- Fat Tail Analysis
- VaR
- The Differences between Normal VaR, Modified VaR and “Fat-Tailed” VaR
- ETL (Expected Tail Loss)
- ETR (Expected Tail Return)
- STARR Performance
- Rachev Ratio
- Marginal Contribution to Risk (MCTR) / Marginal Contribution to Expected Tail Loss (MCETL)
- Percentage Contribution to Risk (PCTR) / Percentage Contribution to Expected Tail Loss (PCETL)
- Skew
- Excess Kurtosis
- Implied Return
- Risk Budgeting
- Factor Analysis & Factor Contribution to Risk
- Stress Testing
Investors who are required to select and monitor investment managers should develop a basic understanding of investment statistics. Quantitative tools can provide you with good insight that you can use in your qualitative interviews with managers and when monitoring your investments.