asgard-ai-platform/grad-emh
Apply the Efficient Market Hypothesis (Fama, 1970) to evaluate information incorporation in asset prices across weak, semi-strong, and strong forms. Use this skill when the user needs to assess market efficiency, determine if a trading strategy can generate abnormal returns, evaluate event studies, or when they ask 'can technical analysis work', 'does the market already know this', or 'is this anomaly exploitable'.
npx skills add https://github.com/asgard-ai-platform/skills --skill grad-emh
The Efficient Market Hypothesis (Fama, 1970) posits that asset prices fully reflect available information, making it impossible to consistently earn abnormal returns. EMH is organized into three forms — weak, semi-strong, and strong — each defined by the information set reflected in prices.
IRON LAW: In an efficient market, prices reflect available information —
beating the market consistently requires either superior information
or accepting more risk. No free lunch.
Key assumptions:
| Form | Information Reflected | Implication |
|------|----------------------|-------------|
| Weak | Historical prices | Technical analysis cannot earn excess returns |
| Semi-strong | All public info | Fundamental analysis cannot earn excess returns |
| Strong | All info (public + private) | Even insiders cannot earn excess returns |
Any test of efficiency is simultaneously a test of the asset pricing model used to define "abnormal" return.
## EMH Assessment: [Market / Strategy]
### Efficiency Form Tested
- Form: [weak / semi-strong / strong]
- Information set: [description]
### Evidence
| Test | Result | Supports Efficiency? |
|------|--------|---------------------|
| [test name] | [finding] | [Yes/No/Ambiguous] |
### Known Anomalies in This Context
- [List relevant anomalies and their current status]
### Conclusion
- [Efficiency assessment with caveats]
- [Joint-hypothesis caveat]
Take asgard-ai-platform/grad-emh from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.