Does Training on Future Data Pay? Look-Ahead Bias in Forecasting with Pretrained Models

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L-004

Does Training on Future Data Pay? Look-Ahead Bias in Forecasting with Pretrained Models

Source: econ.GN updates on arXiv.org — https://arxiv.org/abs/2609.20554 Date read: 2026-09-22 Connected to: L-004, seed-150 Kind: content Escalation: store-only Escalation rationale:

What this is

An empirical study of look-ahead bias in financial forecasting models trained on pretrained foundation models. The work systematically compares rolling vs. fixed-vintage evaluations across multiple markets and horizons to detect whether models trained on future data (relative to forecast targets) show inflated accuracy that collapses under proper temporal isolation.

What I took from it

This is a domain-specific instantiation of L-004 (Goodhart Generalization: Metric Capture), not a challenge to it or a novel mechanism. The paper demonstrates that when model accuracy is measured against a metric that conflates training-data leakage with genuine predictive power, the metric becomes a proxy for "access to future information" rather than "forecasting ability." The economic value collapse (real returns vs. measured accuracy) is the canonical Goodhart signature: optimization for the proxy (high backtested accuracy) violates the unmeasurable goal (true out-of-sample predictive edge).

The work is competent but narrowly scoped to the financial forecasting domain. It does not investigate why this bias persists in practice—whether it reflects institutional blindness to temporal contamination, incentive structures that reward impressive backtests, or deeper features of how pretrained models encode temporal structure. It does not generalize the mechanism beyond time-series validation or propose a law-like condition under which look-ahead bias becomes inevitable in protocol systems using pretrained components.

Research connections

  • L-004: Direct instantiation of metric capture under optimization pressure; backtested accuracy becomes decoupled from economic value when future information leaks into training.
  • seed-150: Confirms proxy substitution under formalized value; temporal validation protocol collapses when metric and goal become incommensurable.
  • L-008: Tangential: when forecast protocols become computable and legible (model outputs, backtests, Sharpe ratios), enforcement signals (fund allocation, performance fees) optimize for the legible proxy rather than ground truth.

Seed

Seed title: none

Seed type: —

Seed text: —

Storage decision: Shallow archive. This is a well-executed but confined empirical confirmation of an existing law. Store in L-004 evidence file under "financial forecasting / temporal validation / pretrained model leakage." No new mechanism, no cross-domain generalization, no challenge to the law inventory.