account-research
Research a company or person and get actionable sales intel. Works standalone with web search, supercharged when you connect…
\"Apply behavioral finance theory to identify systematic investor biases and their impact on asset prices. Use this skill when the user needs to analyze irrational market behavior, explain pricing anomalies through cognitive biases, diagnose investor decision errors, or when
Claude Code
$ npx -y skills add charlieviettq/awesome-agent-skill --skill grad-behavioral-finance --agent claude-code
Codex
$ npx skills add charlieviettq/awesome-agent-skill --skill grad-behavioral-finance -a codex -y
Cursor
$ npx skills add charlieviettq/awesome-agent-skill --skill grad-behavioral-finance -a cursor -y
Gemini CLI
$ npx skills add charlieviettq/awesome-agent-skill --skill grad-behavioral-finance -a gemini-cli -y
GitHub Copilot
$ npx skills add charlieviettq/awesome-agent-skill --skill grad-behavioral-finance -a github-copilot -y
OpenCode
$ npx skills add charlieviettq/awesome-agent-skill --skill grad-behavioral-finance -a opencode -y
Windsurf
$ npx skills add charlieviettq/awesome-agent-skill --skill grad-behavioral-finance -a windsurf -y
name: "\"grad-behavioral-finance\"" description: "\"Apply behavioral finance theory to identify systematic investor biases and their impact on asset prices. Use this skill when the user needs to analyze irrational market behavior, explain pricing anomalies through cognitive biases, diagnose investor decision errors, or when they ask 'why do investors hold losers too long', 'how does loss aversion affect pricing', or 'what biases drive this market pattern'.\"." allowed-tools: Read, Glob, Grep
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: 1. Cognitive biases are systematic, not random — they create directional price effects 2. Limits to arbitrage prevent rational traders from fully correcting mispricings 3. Reference points and framing significantly affect decisions
Observe the pricing pattern or decision that deviates from rational expectations.
| Bias | Description | Market Effect | |------|-------------|---------------| | Loss aversion | Losses hurt ~2x more than equivalent gains | Disposition effect, equity premium puzzle | | Overconfidence | Overestimate precision of private information | Excessive trading, under-diversification | | Herding | Follow the crowd regardless of private signal | Bubbles, momentum, crashes | | Anchoring | Over-rely on initial reference points | Under-reaction to earnings surprises | | Mental accounting | Treat money differently based on source/label | Portfolio segregation, house-money effect |
## Behavioral Finance Analysis: [Context] ### Observed Anomaly - [Description of pricing pattern or decision error] ### Bias Diagnosis | Bias | Evidence | Severity | |------|----------|----------| | [bias name] | [specific observation] | [High/Medium/Low] | ### Limits to Arbitrage - [Why rational traders cannot fully correct this] ### Recommendations 1. [De-biasing strategy or trading implication] 2. [Process improvement]
Curated skill pack for LLM agents in engineer and science workflow (Cursor & Claude ready).
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