AI Replicates Human Investor Biases
A study of 48 models found framing and sunk-cost effects distort AI investment decisions.
AI hallucinations get a lot of attention. But another risk is bias.
The way you write a prompt can steer a model toward different answers, even when the underlying question is exactly the same. I wrote in November:
If you asked a (human) financial analyst whether Microsoft or Apple is the better investment, the answer wouldn’t depend on whether you said Microsoft or Apple or Apple or Microsoft. For LLMs, that word order matters, according to new research.
This risk is harder to detect since an answer isn’t necessarily wrong, the model just chooses to highlight a different point.
Individual investors and, I suspect, some institutional ones as well, are likely falling for this risk by asking AI for research ideas and stock-picking guidance.
More and more retail investors are relying on AI tools, and almost three quarters of millennials do so, according to an October eToro survey.
To investigate how widespread this issue is, researchers at Auburn University and the University of Tulsa evaluated 48 large language models across investment-style decision tasks.
They presented identical financial scenarios twice, changing only how the information was framed, such as wording risk as a gain versus a loss, adding a prestigious source, or mentioning prior spending. Many of the same biases have long been documented in human investors. The difference is that AI systems can reproduce them consistently and at scale.
What they did
Showed each model the same scenario twice: once neutral, once with a subtle wording or context change
Tested 11 well-known investor errors, including framing, anchoring, herding, narrative appeal, and sunk costs
Ran 25 scenario pairs per error across all 48 models
Evaluated mitigation methods such as debiasing instructions and prompt rewriting
Results
Framing alone moved ratings by 1.62 points on a 10-point scale, enough to flip decisions around common thresholds
Narrative cues dominated fundamentals: describing founders as fitting a familiar archetype raised ratings by 65% or when attributing the analysis to a Nobel Laureate.
