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Earnings Revision

OrganizationPopular
HKUDS
earnings-revision

Earnings estimate revisions, guidance analysis, and post-earnings drift (PEAD) — track analyst consensus changes, earnings surprise patterns, and management guidance shifts for US/HK equities.

Overview

PublisherHKUDS
RepositoryVibe-Trading
Skill nameearnings-revision
Stars
33.6K
Forks
5.5K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by HKUDS on GitHub. Read the source before you install it.

Installation

Install the Earnings Revision AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git /tmp/Vibe-Trading
mkdir -p .claude/skills
cp -r /tmp/Vibe-Trading/agent/src/skills/earnings-revision .claude/skills/earnings-revision
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Earnings Revision in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Earnings Revision on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Earnings Revision is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Earnings Revision & Guidance Analysis

Overview

Track sell-side analyst estimate revisions, management guidance changes, and post-earnings price drift to generate investment signals. Earnings revisions are among the most persistent and well-documented alpha factors in equity markets — stocks with upward revisions tend to continue outperforming, and vice versa.

Core Concepts

1. Earnings Revision Momentum

The revision signal hierarchy (strongest to weakest):

Signal TypeDescriptionTypical Alpha (annualized)Persistence
Earnings surprise (beat/miss)Actual EPS vs consensus3-8% post-event drift60-90 days (PEAD)
Consensus revision breadth% of analysts revising up vs down4-6% long-short spread3-6 months
Estimate magnitude changeSize of revision relative to prior estimate3-5%1-3 months
Guidance revisionManagement guidance change vs prior5-10% on eventImmediate + 30-60 day drift
Whisper number miss/beatActual vs buy-side whisper (not official consensus)2-4%1-2 weeks

2. Key Metrics

Earnings Surprise:

python
# Standardized Unexpected Earnings (SUE)
sue = (actual_eps - consensus_eps) / std_of_forecast_errors

# Interpretation
# SUE > +2: large positive surprise → strong PEAD signal
# SUE < -2: large negative surprise → strong negative PEAD
# |SUE| < 0.5: in-line with expectations → weak signal

Revision Breadth:

python
# Revision breadth ratio
breadth = (num_upgrades - num_downgrades) / total_analysts

# Interpretation
# breadth > +0.5: strong consensus upgrade momentum
# breadth < -0.5: strong consensus downgrade momentum
# |breadth| < 0.2: mixed / no clear direction

Estimate Dispersion:

python
# Analyst disagreement
dispersion = std_of_estimates / abs(mean_estimate)

# High dispersion (>15%): high uncertainty → larger potential surprise
# Low dispersion (<5%): tight consensus → smaller surprise but higher confidence

3. Post-Earnings Announcement Drift (PEAD)

The most robust anomaly in equity markets: prices continue to drift in the direction of the earnings surprise for 60-90 days after the announcement.

PEAD trading rules:

Surprise QuintileAverage 60-day DriftStrategy
Q5 (top surprise)+4 to +8%Go long, hold 60-90 days
Q4+1 to +3%Mild long
Q3 (in-line)~0%No action
Q2-1 to -3%Mild short / underweight
Q1 (worst surprise)-4 to -8%Go short / avoid, hold 60-90 days

PEAD enhancement filters:

  • Small/mid cap > large cap (less analyst coverage = slower price discovery)
  • Low institutional ownership > high ownership (slower information diffusion)
  • First surprise in a direction > consecutive same-direction surprises
  • Revenue surprise + EPS surprise together > EPS surprise alone

4. Management Guidance Analysis

Guidance types:

TypeWhat It CoversSignal Weight
Revenue guidanceTop-line outlookHigh (harder to manipulate)
EPS guidanceBottom-line outlookMedium (can be managed via buybacks, tax rate)
Margin guidanceProfitability trajectoryHigh (reflects pricing power and cost control)
CapEx guidanceInvestment intentionsMedium (forward-looking growth signal)
Segment guidanceDivision-level detailHigh (reveals where growth is coming from)

Guidance change signals:

python
# Guidance revision scoring
def score_guidance_change(current_guide, prior_guide, consensus):
    # Guide above consensus = positive signal
    if current_guide.midpoint > consensus * 1.02:
        guide_vs_consensus = "above"
    elif current_guide.midpoint < consensus * 0.98:
        guide_vs_consensus = "below"
    else:
        guide_vs_consensus = "inline"

    # Guide raised vs lowered vs maintained
    if prior_guide:
        if current_guide.midpoint > prior_guide.midpoint * 1.01:
            guide_revision = "raised"
        elif current_guide.midpoint < prior_guide.midpoint * 0.99:
            guide_revision = "lowered"
        else:
            guide_revision = "maintained"

    # Strongest signal: raised guidance above consensus
    # Weakest signal: lowered guidance below consensus

Guidance language analysis:

Language PatternInterpretationSignal
"Raising full-year outlook"Confidence in accelerationStrong positive
"Reaffirming guidance"No change, on-trackMild positive (met expectations)
"Narrowing guidance range to upper half"Soft raise without formal revisionPositive
"Updating guidance to reflect..."Euphemism for guidance cutNegative
"Withdrawing guidance"High uncertainty, loss of visibilityStrong negative
"Providing preliminary results"Pre-announcement, usually bad newsNegative (if below consensus)

5. Earnings Quality Indicators

Red flags in earnings reports:

  1. Revenue growing but cash flow declining → accrual manipulation
  2. Changing revenue recognition policy → inflating top line
  3. Declining DSO (Days Sales Outstanding) story contradicted by rising AR → channel stuffing
  4. Beating EPS via lower tax rate or share buyback, not operating improvement
  5. Frequent "non-GAAP adjustments" that always add back expenses → questionable earnings quality
  6. Inventory build outpacing revenue growth → future write-down risk

Quality scoring:

python
earnings_quality = {
    "fcf_conversion": fcf / net_income,           # >0.8 = good, <0.5 = poor
    "accrual_ratio": (net_income - ocf) / avg_assets,  # <5% = good, >10% = concern
    "revenue_cash_alignment": revenue_growth - ocf_growth,  # small gap = good
    "non_gaap_gap": non_gaap_eps - gaap_eps,      # large gap = red flag
    "buyback_eps_boost": eps_growth - net_income_growth,  # large = artificial
}

Earnings Calendar and Workflow

Pre-Earnings Analysis Checklist

  1. Consensus snapshot: current EPS/revenue consensus, revision trend (30d/60d/90d)
  2. Historical surprise pattern: has the company consistently beat/missed? By how much?
  3. Guidance comparison: last quarter's guidance vs current consensus
  4. Whisper number: buy-side expectations (often 1-3% above street consensus for serial beaters)
  5. Options market pricing: implied move from at-the-money straddle
  6. Sector peers already reported: read-through signals from competitors

Post-Earnings Analysis Checklist

  1. Headline numbers: EPS surprise %, revenue surprise %
  2. Quality of beat/miss: operating income-driven or one-time items?
  3. Guidance change: raised / maintained / lowered / withdrawn
  4. Call tone: management confidence level, Q&A defensiveness
  5. Analyst reaction: immediate revision direction (first 24-48 hours)
  6. Price/volume reaction: gap up/down, reversal, volume multiple vs average

Multi-Market Considerations

US Equities

  • Earnings season: Jan/Apr/Jul/Oct (roughly 2-6 weeks after quarter-end)
  • Data: SEC filings (10-Q within 40 days, 10-K within 60 days for large accelerated filers)
  • Consensus: Bloomberg, Refinitiv, FactSet, Visible Alpha
  • Via yfinance: ticker.earnings_dates, ticker.earnings_history

Hong Kong Equities

  • Earnings season: Mar-Apr (annual), Aug-Sep (interim)
  • Many HK-listed companies report semi-annually, not quarterly
  • Dual-listed (A+H): compare A-share analyst estimates vs HK analyst estimates for arbitrage
  • Via yfinance: yf.Ticker("0700.HK").financials

Key Differences

DimensionUSHK
Reporting frequencyQuarterlySemi-annual (most)
Guidance practiceCommonRare
Analyst coverageDeep (>20 for large caps)Thinner (5-15 for large caps)
Pre-announcementRegulated (Reg FD)Less regulated
Earnings callStandardLess common for mid/small caps

Output Format

## Earnings Revision Analysis — [Ticker]

### Consensus Snapshot
- **Current FY EPS consensus**: $X.XX (N analysts)
- **30-day revision**: [up/down X%] — [N upgrades / N downgrades]
- **60-day revision**: [up/down X%]
- **Estimate dispersion**: [low/medium/high] (CV = X%)

### Last Earnings Event
- **Date**: YYYY-MM-DD | **Quarter**: FY25Q3
- **EPS**: $X.XX actual vs $X.XX consensus (surprise: +X%)
- **Revenue**: $X.XB actual vs $X.XB consensus (surprise: +X%)
- **Guidance**: [raised / maintained / lowered] — FY25 EPS guide: $X.XX-$X.XX
- **Price reaction**: [+X% on day, +X% over 5 days]

### Revision Momentum
- **Breadth**: [+0.6 → strong upgrade momentum]
- **Magnitude**: [average revision +X% over 30 days]
- **PEAD status**: [still within 60-day drift window / drift exhausted]

### Earnings Quality
- **FCF conversion**: X% [strong/adequate/weak]
- **Accrual ratio**: X% [clean/moderate/concern]
- **Non-GAAP gap**: $X.XX [small/large]

### Signal
- **Direction**: [bullish / neutral / bearish]
- **Catalyst**: [next earnings date: YYYY-MM-DD]
- **Confidence**: [high / medium / low]

Notes

  • Earnings revision data requires real-time consensus feeds (Bloomberg, Refinitiv) for professional-grade signals; yfinance provides historical actuals but not real-time consensus
  • PEAD is strongest in the first 30 days post-announcement; signal decays significantly after 60 days
  • Guidance withdrawals are almost always negative — companies rarely withdraw guidance when business is going well
  • Beware of "beat and lower" — beating current quarter but lowering forward guidance is often net negative
  • This framework is for research purposes only and does not constitute investment advice

Frequently asked questions

What does the Earnings Revision AI skill do?

Earnings estimate revisions, guidance analysis, and post-earnings drift (PEAD) — track analyst consensus changes, earnings surprise patterns, and management guidance shifts for US/HK equities.

Why use Earnings Revision on TypingMind?

Because you install it once and use it with any model. Earnings Revision is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Earnings Revision in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/earnings-revision. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Earnings Revision?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Earnings Revision?

As many as you like. As long as a model supports skills, you can use Earnings Revision with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Earnings Revision AI skill free?

Yes. It is published on GitHub by HKUDS under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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