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Data Perturb/Replay to Test Strategy Sensitivities

| | Posted in: Big Ideas

How can investment advisors apply historical asset performance data to address client views regarding future market/economic conditions? In their February 2018 paper entitled “Matching Market Views and Strategies: A New Risk Framework for Optimal Selection”, Adil Reghai and Gaël Riboulet present an approach for quantitatively relating historical asset return statistics to investor views. They intend this approach to address the widespread problem of backtest overfitting, whereby researchers discover good performance by fitting strategy features to noise in an historical dataset. Specifically, they:

  1. Collect historical return data for assets of interest and run backtests of alternative strategies on these data.
  2. Perturb historical average return, volatility, skewness and pairwise correlations up or down for these assets and rerun backtests of alternative strategies on multiple perturbations.
  3. Analyze relationships between directions of these perturbations and performance of alternative strategies.
  4. Match investor views first to directions of perturbations and then to strategies responding favorably (or least unfavorably) to these directions.

They apply this approach to generic algorithmic strategies (equal weight, momentum, mean reversion and carry). Based on mathematical derivations and examples, they conclude that:

  • Sensitivities to return characteristics across generic strategies include (positive good and negative bad):
    • Equal weight relates negatively to volatility and positively to skewness.
    • Momentum (trend following) relates negatively to volatility and positively to skewness.
    • Mean reversion relates positively to volatility and negatively to skewness.
    • Carry relates negatively to average return.
  • While diversification tends to suppress portfolio volatility, it has less effect on portfolio skewness.
  • Combining investor views of future market conditions with generic strategy sensitivities helps match investors to strategies.

In summary, sensitivity analysis based on perturbations of historical asset return statistics to determine sensitivities of alternative strategies may help investors decide what strategies best fit their views on future market conditions.

Cautions regarding conclusions include:

  • Investor views may be wrong, such that the proposed approach helps them pick poor-performing strategies.
  • The approach is beyond the reach of most investors, who would bear fees for delegating to an investment manager.
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