How do you backtest a quantitative equity screen and interpret the results?

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AcadiFi · Accepted Answer

Backtesting a quantitative equity screen involves applying historical data to see how the screened portfolio would have performed versus a benchmark. This is fundamental to quantitative equity investing. **Backtesting framework:** 1. **Define the screen rules** -- Factor thresholds, rebalancing frequency, universe 2. **Apply rules at each historical rebalance date** using only information available at that time (no look-ahead) 3. **Construct equal-weighted or cap-weighted portfolios** from passing stocks 4. **Calculate period returns** including transaction costs and slippage 5. **Compare against benchmark** and compute risk-adjusted metrics **Key statistics to evaluate:** | Metric | Interpretation | |---|---| | Annualized return | Absolute performance of screened portfolio | | Information ratio | Excess return per unit of tracking error (>0.5 is good) | | Max drawdown | Worst peak-to-trough loss | | Hit rate | % of periods outperforming benchmark | | Turnover | Annual portfolio turnover (higher = more costly) | | Decay analysis | How quickly alpha decays after rebalancing | **Example -- Evergreen Capital Quality Screen (fictional):** Screen: ROE > 18%, D/E < 0.4, 5-year earnings stability > 80%, P/E < industry median | Metric | Screen Portfolio | Benchmark (S&P 500) | |---|---|---| | 10-year CAGR | 12.8% | 10.2% | | Information Ratio | 0.62 | -- | | Max Drawdown | -28% | -34% | | Annual Turnover | 45% | -- | | Hit Rate | 64% of years | -- | **Critical warnings:** Out-of-sample testing is essential. Split your data into training (build the screen) and testing (validate) periods. A screen that works in-sample but fails out-of-sample is likely overfit. Also account for transaction costs -- a screen with 100% annual turnover needs much higher gross alpha to deliver net value.

How do you backtest a quantitative equity screen and interpret the results?

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