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Apply behavioral finance theory to identify systematic investor biases and their impact on asset prices.
Passed 19 of 24 test cases with the skill, and 18 of 24 without it.
2 fixed17 passed either way4 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are observing irrational price surges in struggling retail brands whenever online forums rally around them, causing massive market distortions that fundamental valuation models fail to explain. I need a behavioral finance evaluation of this speculative social trading trend. Please format your output with a heading for the observed pricing anomaly, a detailed bias diagnosis matrix containing columns for the specific psychological bias, supporting evidence from social trading behavior, and impact severity level. Include a breakdown of why rational market participants cannot easily arbitrage this gap away, and conclude with concrete process recommendations for risk management.
| Checked | Without skill | With skill |
|---|---|---|
| Diagnoses herding behavior as a psychological driver behind forum-led retail surges. | Passed | Passed |
| Identifies noise trader risk as a barrier preventing rational traders from shorting the bubble. | Passed | Passed |
| Restricts severity ratings in the diagnostic table to High, Medium, or Low values. | Missed | Passed |
The instructions your model reads when the skill loads.
Behavioral finance challenges the rational-agent assumption by documenting systematic cognitive biases that affect investor decisions and market prices. Anchored in Kahneman and Tversky's prospect theory (1979), the field explains persistent anomalies that traditional finance cannot.
IRON LAW: Investors are NOT rational — systematic biases create
predictable pricing errors. These errors persist because arbitrage
is limited (costs, risk, horizon constraints).
Key assumptions:
Observe the pricing pattern or decision that deviates from rational expectations.
test cases passed, vs 18 / 24 without
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