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Grad Emh Agent Skill

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'.

2k tokens
context cost
the whole folder, loaded on every use
2
files
instructions only
0
copies elsewhere
how many repositories repackaged it
223
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/asgard-ai-platform/skills --skill grad-emh

What comes with it

5 036 bytes besides the instruction
examples/sample_scenario.md

The instruction itself

13 sections, as written by the author

Efficient Market Hypothesis (EMH)

Overview

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.

When to Use

  • Evaluating whether a trading strategy exploits genuine inefficiency
  • Designing event studies (semi-strong form test)
  • Assessing if active management adds value over passive indexing
  • Debating the validity of technical or fundamental analysis

When NOT to Use

  • As justification to ignore all market anomalies without investigation
  • When markets are clearly illiquid or informationally segmented
  • For normative claims — EMH describes price behavior, not what prices "should" be

Assumptions

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:

  • Large number of rational, profit-maximizing participants
  • Information is costless and available simultaneously to all participants
  • Transaction costs do not prevent trading on information
  • Investors react quickly and unbiasedly to new information

Methodology

Step 1 — Identify the Information Set

  • Weak form: past prices and trading volume only
  • Semi-strong form: all publicly available information
  • Strong form: all information including private/insider information

Step 2 — Determine the Testable Implication

| 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 |

Step 3 — Select Appropriate Test

  • Weak: autocorrelation tests, runs tests, filter rules
  • Semi-strong: event studies (abnormal returns around announcements)
  • Strong: insider trading profitability studies

Step 4 — Interpret Results with Joint-Hypothesis Awareness

Any test of efficiency is simultaneously a test of the asset pricing model used to define "abnormal" return.

Output Format

## 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]

Gotchas

  • Joint-hypothesis problem: you cannot test efficiency without assuming an equilibrium model
  • Grossman-Stiglitz paradox (1980): if markets are perfectly efficient, no one has incentive to gather information
  • Anomalies (momentum, value, size) persist but may reflect risk or data mining
  • EMH does not claim prices are always "correct" — only that mispricings are not systematically exploitable
  • Market efficiency varies by market segment; large-cap equities are more efficient than micro-caps
  • Behavioral finance provides systematic counterexamples but does not necessarily invalidate EMH

References

  • Fama, E. (1970). Efficient capital markets: a review of theory and empirical work. *Journal of Finance*, 25(2), 383-417.
  • Grossman, S. & Stiglitz, J. (1980). On the impossibility of informationally efficient markets. *American Economic Review*, 70(3), 393-408.
  • Malkiel, B. (2003). The efficient market hypothesis and its critics. *Journal of Economic Perspectives*, 17(1), 59-82.

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How to use it

Copy the folder

Take asgard-ai-platform/grad-emh from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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.