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Conflicting History in Foundation Model Return Forecasts

Levent Bulut · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.25822 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

古い観測は、予測者に予測関係を学ぶのに役立ちますが、その関係が変化するときに間違っているときに、この緊張を研究します。我々は、古い観測で異なる並びに最近の回収と次の回収の条件分布を共有する並びに異なる並びの回復履歴を使用して、この緊張を研究します。5つの基礎モデル予測手順と3つのシミュレートプロセス、それぞれ1024の独立した複製で評価され、矛盾した履歴は、30の高い信号比較でゼロで改善する確率を減らします。この有用性の損失は、安定した条件下で信号回復が成功したにもかかわらず発生します。 56の資産についての別々の探査比較では、256から64の

Machine translation (Meta M2M100); the English original follows.

Abstract

Older observations can help a forecaster learn a predictive relationship while becoming misleading when that relationship changes. We study this tension using paired return histories that differ in older observations but share recent returns and the conditional distribution of the next return. Across five foundation-model forecasting procedures and three simulated processes, each evaluated on 1,024 independent replications, conflicting history reduces the probability of improving on zero in all 30 higher-signal comparisons. This loss of usefulness occurs despite successful signal recovery under stable conditions. A separate exploratory comparison on 56 assets finds that shortening inputs from 256 to 64 returns increases mean absolute error for every model by 1.77-4.58 basis points; neither length improves observed overall error over zero. The evidence distinguishes the ability to extract a signal from the ability to withstand conflicting history, while showing why context sensitivity alone does not justify discarding older data.

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Topics: Econometrics, Context (archaeology), Economics, Actuarial science, Forecast error, Statistics

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