Earnings Season Setup: 3,000 Beats vs. 1,200 Misses | Feature: Sector Analysis | Score Movers

Hi to everyone—

This week is the “earnings-season on-ramp.” The calendar is still light, but the signal map is not. As of the Jan 5 snapshot, Cmind is tracking 4,226 upcoming reports across the Q4 2025 / early-Q1 2026 season. The model currently classifies 2,998 as likely EPS beats vs. 1,228 as likely misses—an overall 71% / 29% split that’s constructive, but still dispersion-led by sector and cap tier.

The heatmaps say it in one glance: green is broad, but the red tail is real—especially in commodity-sensitive pockets and in the small-cap long tail where financing and operating leverage matter more than macro.

This week’s slate (Jan 6–9): quiet tape, live signals

The near-term schedule in our dataset includes 35 names, skewing heavily to Industrials and Health Care. Even here, the bias is positive: 26 beats vs. 9 misses (74% / 26%). Industrials is notable: 10/10 scheduled Industrials names screen as beats this week—an early tell that backlog/pricing power still carries an execution premium.

At the same time, don’t get lulled by “soft-green.” The 55-65% probability band is where guidance—not the headline EPS print—typically decides the reaction. In this regime, size your mid-band exposure to confirmation risk, not conviction.

New: We’re now posting our score movers (±9 points) each week as this signals pre-positioning.

Feature of the Week — Sector Setup: what investors will watch vs. what Cmind is implying

(Street focus areas summarized from the sector notes; Cmind counts/ratios computed from this week’s full prediction universe.)

Below is the playbook we’re seeing from the Street going into the heart of earnings season—paired with what our model is signaling today.

Health Care — “High hit-rate tape, reaction driven by forward math”

Health Care is the cleanest regime in the current heatmap: 888 beats / 65 misses (93% beat rate). That’s not just a green skew; it’s a compressed left tail, which matters for quant/hedge fund positioning because it implies fewer “landmine” prints relative to other sectors. In this setup, the risk shifts from whether companies beat to how they beat—and what they imply about 2026.

For systematic strategies, Health Care tends to behave like a high base-rate carry bucket: better probability-of-profit, lower tail risk, but often lower convexity unless the name sits in a true idiosyncratic catalyst zone (trial readouts, reimbursement shifts, product launch inflections). Practically, this is a sector where the post-earnings move can be dominated by guidance, mix, and cadence rather than headline EPS: commentary around payer behavior, utilization trends, margins (especially labor/input cost normalization), and the durability of recent performance. The heatmap suggests Health Care remains a “quality of earnings” trade—stocks with tight probabilities can still gap if management introduces uncertainty on reimbursement, pipeline timing, or margin trajectory.

Lean into Health Care for stability and signal clarity, but avoid treating it as “no risk.” The asymmetry comes from expectations reset, not the beat itself.

Industrials — Execution premium and widest stock-level dispersion

Industrials is where the heatmap looks most “tradeable.” The sector is constructive—384 beats / 144 misses (73% beat rate)—but what matters is the distribution: real right tail (strong greens) and a visible left tail. That combination typically shows up when outcomes are highly sensitive to operational execution: backlog conversion, pricing realization, productivity, project timing, and cost control.

For quant/hedge funds, this is classic alpha-density: the sector can support both high-conviction longs (clean execution, improving probability, strong second-derivative momentum) and shorts (deteriorating probabilities, margin pressure, end-market softening). It is also a sector where “beat” doesn’t always translate to “up”—if the print is strong but the guide implies decelerating orders or weaker pricing, the market can fade it. Conversely, a modest beat with a better-than-feared backlog/booking narrative can re-rate.

The model’s dispersion profile is telling to focus on idiosyncratic execution winners vs. margin/volume losers.

Information Technology — Constructive, but the market punishes narrative slippage.

Tech is positive but more mixed: 382 beats / 235 misses (62% beat rate). The heatmap shows a meaningful orange/red band, which is consistent with a regime where the market is hyper-sensitive to AI monetization vs. spend and to the quality of growth (cloud, enterprise demand, datacenter/semis). In other words, Tech isn’t a simple “beat/miss” sector right now—it’s a narrative validation sector.

For systematic investors, that means dispersion is driven by guidance precision and second-order metrics: margin durability, operating leverage, capex intensity, backlog/remaining performance obligations, and customer concentration signals. Tech names can beat EPS and still sell off if the market infers the beat was cost-driven rather than demand-driven, or if forward commentary introduces uncertainty (pipeline, pricing, seat growth, consumption patterns). Conversely, a mid-band probability name can gap up on clean forward visibility even if the beat is modest.

Focus research and sizing on names near threshold crossings (roughly 55-65%) where small informational changes can drive large reactions.

Additional Sectors:

Energy | Materials: lowest beat-skew and most expansive dispersion translates to hedge pocket and tail-risk basket

Financials: net-positive but large miss count, which highlights guide sensitivity (NIM/credit/opex)

REITs: better skew than expected; risk is maturity-wall and name-specific

Staples | Communication Services | Utilities | Discretionary: the mid-band is heavy, which translates into guidance decides reaction

Importantly, the heatmap reflects expectation drift, not just reported fundamentals. These probabilities have been updating daily—meaning they already embed post-guidance digestion, sector read-throughs, and late-December positioning behavior.

Market Cap Breakdown — broadening is real, but Large still drives the “easy beats”

Cmind’s cap-tier classification reinforces a key positioning theme: earnings strength is broadening beyond mega-cap, but the quality/visibility premium is still most pronounced in Large.

• Large Cap: 651 beats / 105 misses (86% beat rate; 6.2:1 beat-to-miss)

• Mid Cap: 726 beats / 194 misses (79% beat rate; 3.7:1)

• Small Cap: 1,621 beats / 929 misses (64% beat rate; 1.7:1)

The takeaway: Mid/Small offer the richest spread capture (and the most convex outcomes), but sizing and liquidity discipline matter more. Large still contributes disproportionately to aggregate earnings growth—but likely be paid for selection, not blanket exposure.

🔝 Top 6 Predicted Beats This Week (Jan 5-9, 2026)

LNN (BMO, Jan 8) — 94% — Industrials — Small Cap

GBX (AMC, Jan 8) — 94% — Industrials — Small Cap

UNF (Jan 7) — 91% — Industrials — MidCap

WDFC (Jan 8) — 90% — Materials — MidCap

AZZ (Jan 7) — 87% — Industrials — MidCap

RGP (Jan 7) — 87% — Industrials — Small Cap

🔻 Top 6 Predicted Misses This Week (Jan 5–9, 2026)

NTIC (Jan 8) — 9% — Materials — Small Cap

APLD (Jan 7) — 25% — Financials — MidCap

LEDS (Jan 9) — 29% — Information Technology — Small Cap

HELE (Jan 8) — 35% — Consumer Staples — Small Cap

SAR (Jan 7) — 37% — Financials — Small Cap

CMC (Jan 8) — 42% — Materials — MidCap

Score Movers — the Second Derivative: tickers with 9+ point probability shifts over the last week

“Score Movers” are names with 9+ point week-over-week shifts in beat probability. In a light week, these moves often capture pre-positioning, that is, new information absorbed into the tape via peer read-throughs, pricing/demand signals, tone/filing changes, and factor re-weighting as the model refreshes daily.

Biggest Up-Shifts

• ENVX +54 → 95% (Industrials, Mid; Feb 18)

• GP +53 → 80% (Industrials, Small; Feb 13)

• CRGO +46 → 92% (Industrials, Small; Feb 23)

• AWR +46 → 61% (Utilities, Mid; Feb 18)

• ULCC +45 → 83% (Industrials, Small; Feb 6)

Biggest Down-Shifts

• HCC −55 → 22% (Materials, Mid; Feb 12)

• GPRE −54 → 5% (Materials, Small; Feb 6)

• SXC −45 → 19% (Materials, Small; Jan 29)

• MARA −42 → 12% (Financials, Mid; Feb 25)

• NTIC −37 → 9% (Materials, Small; this week, Jan 8)
  

How to use this week:

• Improving into the print tends to screen as cleaner follow-through (probability + momentum aligned).

• Late downside movers are where “beat but guide down” risk lives—especially in Materials/Energy/levered small caps where the distribution is naturally fat-tailed.
  

• In a quiet week, movers can dominate attention; treat them as priority watchlist inputs for pairs/baskets, avoiding “soft-green” complacency.
  

📅 Looking Ahead: Earnings Setup for Jan 12–16, 2026

The calendar meaningfully ramps with big-bank earnings. Watch the usual trio—NIM + credit + expenses—but also the “confidence channel”: how management teams frame loan demand, deposit mix, and capital-markets momentum. Early in the season, the market tends to reward clean guidance and punish even small uncertainty premiums. 

About the Model

Cmind AI’s EPS predictions are powered by a machine learning model built for accuracy, objectivity, transparency, and daily updates with latest market information. We ingest over 150 variables across five data modalities—including real-time 10-Q filings, earnings transcripts, governance metrics, and peer signals—to provide early, company-specific EPS forecasts.

Updated daily, our model covers 4,400+ public companies with proven backtests demonstrating improvements in Sharpe and Sortino ratios across portfolios.

 Cmind content is provided for informational purposes only and does not constitute investment research or advice.

📩 To learn more, contact us at [email protected]