Paper: arXiv 2604.18821

Authors: Chang Liu

Abstract

Institutional allocators often evaluate structured strategies on the basis of marketed backtests – hypothetical track records constructed by applying a strategy’s rules to historical data prior to any live trading, also referred to as pro-forma performance. It is unclear how much of that signal survives once the strategy is actually traded. Using 1,726 commercially distributed structured strategies from ten global institutions, this paper shows that raw pro-forma performance has only limited portability into the live period and weakens sharply once live outcomes are measured relative to peer and external benchmarks. The evidence indicates that marketed backtests predominantly reflect the common factor regime present before launch rather than strategy-specific skill. Strategies launched after unusually strong bucket-factor conditions experience materially worse subsequent deterioration. For allocators, the implication is practical: backtests should be judged relative to appropriate peer benchmarks, and the discount applied to them should increase when launch occurs after an extreme factor run.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 6.0/10 (capped from 9.0: transaction costs not modelled; see penalty flags)
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper relies on a massive proprietary dataset and rigorous statistical testing, such as leave-one-out benchmarks and wild-cluster bootstrapping, rather than complex theoretical modeling or dense mathematical derivations.

Research Flowchart

  flowchart TD
    A[<b>Research Goal:</b> Evaluate Portability of Pro-forma Backtests] --> B[<b>Data:</b> 1,726 Commercially Distributed Strategies]
    B --> C[<b>Methodology:</b> Compare Pro-forma vs. Live Performance]
    C --> D[<b>Analysis:</b> Adjust for Peer & Factor Regime Benchmarks]
    D --> E[<b>Key Findings:</b> Limited Signal Portability & Factor Bias]
    E --> F[<b>Recommendation:</b> Apply Discount Based on Pre-launch Factor Extremes]