Forecast loss differentials are reframed as returns and assessed with risk-adjusted finance metrics, showing professional forecasters are harder to beat on risk-adjusted performance than on raw accuracy in US macro forecasting.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Supervised fine-tuning with LoRA on rational benchmark forecasts corrects extrapolation bias out-of-sample in LLM predictions for controlled experiments and cross-sectional stock returns.
citing papers explorer
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Quantifying the Risk-Return Tradeoff in Forecasting
Forecast loss differentials are reframed as returns and assessed with risk-adjusted finance metrics, showing professional forecasters are harder to beat on risk-adjusted performance than on raw accuracy in US macro forecasting.
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Debiasing LLMs by Fine-tuning
Supervised fine-tuning with LoRA on rational benchmark forecasts corrects extrapolation bias out-of-sample in LLM predictions for controlled experiments and cross-sectional stock returns.