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📈 Earnings Estimate Revisions Tracker — Upward / Downward Analyst EPS Revisions Ahead of Earnings

The single highest-alpha factor in earnings-momentum quant strategies — sell-side analyst EPS estimate revisions over the trailing 30 / 60 / 90 days. Pulled fresh per run from Yahoo Finance's earningsTrend surface, computed the way a long-short equity PM actually wants to see it: signed % revision per window, positive-revision cluster boolean, strength bucket, and the next earnings date so you can time the entry.

📊 Sample Output

👁 📈 Earnings Estimate Revisions Tracker sample output — 📈 Earnings Estimate Revisions Tracker, premium API, JSON output, NexGenData premium dataset for analysts, hedge funds, compliance, and research te

When a fund's covering analysts revise their next-quarter EPS estimate 4% higher in the trailing 30 days, the stock has — empirically — about a 2.3% statistically significant excess return in the subsequent month (the foundational Estimize / I/B/E/S earnings-momentum literature, replicated across every major decade since the 1980s). The signal is so robust that it survives transaction costs, sector neutralisation, market-cap controls, and post-publication arbitrage. It is the core factor in Two Sigma, AQR, and Renaissance's earnings books. Bloomberg charges $24,000/year/seat to pipe these revisions into your terminal. This actor does it for $0.15 per symbol on demand, no contract, no setup fee.

What This Actor Does

For each US-listed stock symbol you pass in, the actor:

1. Acquires a fresh Yahoo Finance crumb + cookie jar (Yahoo blocks anonymous JSON, requires a one-time auth token per session).
2. Calls the quoteSummary endpoint requesting the earningsTrend, earningsHistory, recommendationTrend, calendarEvents, price, and assetProfile modules in a single round-trip.
3. Parses the per-period EPS trend trajectory — current, 7-day-ago, 30-day-ago, 60-day-ago, 90-day-ago consensus EPS — for the current quarter (0q), next quarter (+1q), current year (0y), and next year (+1y).
4. Computes signed revision percentages for each rolling window: (current - past_value) / abs(past_value) * 100. EPS sign-flips (positive to negative, etc.) are handled correctly so a worsening loss shows up as a DOWN revision.
5. Classifies direction (UP / DOWN / STABLE) and strength (large / medium / small / negligible) based on the user-selected primary window.
6. Flags positive-revision clusters — is_positive_revision_cluster = true when 3+ analysts revised UP in the trailing 30 days (or, when Yahoo doesn't expose the per-analyst granularity, when the 30d aggregate revision is ≥ +1% and 3+ analysts cover the name).
7. Returns the next scheduled earnings date and the most recent earnings surprise (actual EPS vs. estimate) for cross-signal context.

Routed through Apify's RESIDENTIAL proxy group by default — Yahoo Finance throttles datacenter IPs aggressively, and a residential rotation reliably stays under the per-IP threshold.

Output Schema

Each dataset item is one stock-symbol record:

Input Filters

Use Cases

1. Hedge Funds — Long-Short Earnings-Momentum Books

The single most-traded factor in long-short equity. Long the top decile of positive-revision-cluster names, short the bottom decile of negative-revision names — sector-neutralised, market-cap-controlled, rebalanced weekly. This actor is the data-feed input.

2. Earnings-Momentum Traders — Single-Name Plays

Run with revision_direction=UP, min_revision_pct=3, limit=20 two weeks before earnings season. The names that come out are the textbook "post-earnings drift" setups — 3-4% mean reversion to consensus over the 2-month window leading into the print.

3. Sell-Side Research — Competitive Intel

Track when your competitors' notes move the consensus. If 4 firms cut numbers in the last 7 days on a coverage name and you haven't, you're behind the curve. The revision_7d_pct field flags exactly this.

4. Retail Smart-Money — "Pre-Earnings Bullish Setup" Screens

The combination is_positive_revision_cluster = true AND earnings_date_next ≤ 30 days away is the textbook "analysts are raising numbers into the print" setup. Statistically high-probability long.

5. IR Teams — Self-Coverage Tracking

Run weekly on your own ticker plus your 5 closest peers. Spots the moment your name's consensus diverges from the peer-group trajectory.

6. Algorithmic Re-Rating Detectors

Combined with our analyst-price-targets actor, the joint signal (price targets up + EPS estimates up in the same 30-day window) is the strongest sell-side conviction signal that exists outside of paid I/B/E/S data.

Comparison vs Bloomberg / FactSet / Refinitiv I/B/E/S / Zacks Premium

Where this actor wins: ad-hoc revision sweeps, R&D / backtesting on 50-500 names, SaaS products that need a revisions data source without negotiating a $20K+ data contract, retail-facing dashboards, single-PM family offices, sell-side associates pricing what their seniors get from Bloomberg. Where the paid feeds win: realtime tick-by-tick streaming revisions, regulated trade execution, broad universe coverage (every global ADR), and audit-trail-required compliance shops.

Cross-Signal: Combine With Our Sister Actors

The Earnings Revisions feed is the first leg of every serious sell-side-signal quant strategy. Layer it with:

• Analyst Price Targets — when price targets go up in lockstep with EPS estimates over the same 30-day window, it's a "conviction stack" signal. Cross-tabulate revision_30d_pct > 3 AND upside_pct > 15 for the textbook bullish-pre-earnings setup.
• Earnings Calendar — without the next-print date you can't time the entry. Use earnings_date_next ≤ 21 days to filter for "imminent print" setups.
• Earnings Transcripts — read management's guidance commentary from the last call, then check if analysts revised UP in the trailing 30 days. Tight feedback loop.
• Short Interest Tracker — names with high short interest AND positive revision clusters are the textbook squeeze setup (Tesla 2020, GameStop 2021, NVIDIA Q3 2023). Layer the two feeds.
• 13F Holdings Delta Tracker — when a top whale (Tiger, Coatue, Lone Pine) increases a position AND analysts revise UP in the same quarter, that's the "smart-money-plus-sell-side" double signal.
• Finance MCP Server — expose this actor (and all the sister feeds) directly to Claude / GPT / Gemini agents over MCP. Build an LLM-driven earnings-revision research workflow in 10 minutes.

Pricing

• $0.01 flat per actor start
• $0.15 per symbol record returned

Examples:

• 5-symbol smoke test (AAPL, NVDA, TSLA, MSFT, AMZN) → $0.01 + 5 × $0.15 = $0.76
• 50-symbol institutional screen → $0.01 + 50 × $0.15 = $7.51
• 200-symbol Russell 1000 sweep → $0.01 + 200 × $0.15 = $30.01
• 1000-symbol full universe → $0.01 + 1000 × $0.15 = $150.01

Premium tier — hedge-fund alpha source. The first 1000 symbols of an I/B/E/S subscription cost ≈ $22,000/year fully loaded. This actor delivers the same primary signal (revisions, strength, direction, clusters) for 150x less.

Anti-Bot / Rate Limit Notes

• Yahoo Finance throttles datacenter IPs aggressively from a single egress. The actor defaults to Apify RESIDENTIAL proxy group, which rotates residential IPs per session and reliably stays under the throttle. Free-tier accounts may downgrade to auto but expect dropped symbols on bursts > 5.
• The actor uses a single-in-flight semaphore + 1.2s per-symbol stagger with 6-attempt exponential-backoff retries on HTTP 429. Crumb refresh is automatic if the session token expires mid-run.
• Stockanalysis.com and Zacks URLs are emitted as stockanalysis_url / zacks_url in every record — useful for click-through verification. We do not scrape them in the current build (they would require a separate Playwright pass with deeper anti-bot evasion); they're reserved for a future "data_source=stockanalysis" fallback when Yahoo is completely down.

Roadmap

• v0.1 — Add Stockanalysis.com as fallback when Yahoo returns empty earningsTrend (most common for small-caps and non-US-domiciled ADRs).
• v0.2 — Add Zacks Rank cross-reference (proprietary 1-5 earnings revision rank — paid Zacks Premium feed, but we can scrape the free preview).
• v0.3 — Optional Estimize crowd-sourced consensus comparison.

Built and maintained by NexGenData — Apify's most-downloaded finance fleet. Sister actors share schema conventions for trivial cross-actor joins.

About NexGenData

NexGenData publishes 220+ buyer-intent actors covering SEC filings, YC alumni, Delaware DOC, global stock screeners across 30+ exchanges, IPO calendars, IP and patent intelligence, FDA approvals, B2B lead generation, and more. Every actor is pay-per-result with no seat licensing.

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