LLMs copy biased analyst ratings in investment decisions but a new detection method encourages independent reasoning and can improve stock return predictions beyond human levels.
Lopez-Lira, Y
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Frozen LLM checkpoints serve as time capsules of public text and generate outlook scores that forecast equity returns and analyst actions beyond contemporaneous valuations.
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.
Strat-LLM demonstrates that LLM trading performance varies by reasoning mode and model scale, with strict alignment reducing drawdowns in downtrends and deep reasoning avoiding small-gain traps.
citing papers explorer
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Fin-Bias: Comprehensive Evaluation for LLM Decision-Making under human bias in Finance Domain
LLMs copy biased analyst ratings in investment decisions but a new detection method encourages independent reasoning and can improve stock return predictions beyond human levels.
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ChatGPT as a Time Capsule: The Limits of Price Discovery
Frozen LLM checkpoints serve as time capsules of public text and generate outlook scores that forecast equity returns and analyst actions beyond contemporaneous valuations.
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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.
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Strat-LLM: Stratified Strategy Alignment for LLM-based Stock Trading with Real-time Multi-Source Signals
Strat-LLM demonstrates that LLM trading performance varies by reasoning mode and model scale, with strict alignment reducing drawdowns in downtrends and deep reasoning avoiding small-gain traps.