Where Cmind Was Most Accurate — and Why AVGO Matters This Week

Cmind Earnings Analysis - Week of June 1–5, 2026

INTERACTIVE HEATMAP

The June 1–6 earnings tape is lighter than the prior mega-cap AI weeks, but it may be more useful for systematic and quantamental investors because it combines two things that matter: model validation and targeted dispersion. Roughly 80+ companies report this week, and Cmind’s active heatmap captures 81 model-tracked names with an average beat probability of approximately 57.3%. About 77.8% of the universe carries a beat label, but only 12.3% screens above the 70% high-conviction threshold, while 8.6% sits below the 40% miss-risk threshold. That makes this a stock-selection week, not a beta week.

The key setup is that Cmind’s latest 2026Q1 accuracy review shows the model performed best in the exact areas that matter most for institutional deployment: Large Caps and Information Technology. Large Caps delivered 77% accuracy in 2026Q1, while Information Technology posted 74% accuracy. That makes this week’s feature name, Broadcom ($AVGO), especially relevant. AVGO sits directly at the intersection of Large Cap, Technology, semiconductors, and AI infrastructure.

AVGO reports fiscal Q2 2026 earnings on June 3 after the close. Cmind assigns Broadcom a 74.5% beat probability, with a forecast EPS estimate of $2.02, and ranks AVGO 5th out of 22 semiconductor peers on near-term beat conviction. The signal is constructive, but not one-dimensional: AVGO’s probability path has been volatile, moving between the low 0.60s and high 0.90s before settling at 74.5%. For event-driven and systematic readers, that creates a clean setup: positive beat risk, but guidance quality and AI revenue visibility matter more than the EPS print alone.

This week’s signal map: Industrials and Health Care lead the beat side, Large Caps show the cleanest signal quality, and the sharpest miss-risk pockets sit in Communication Services, Financials, and small-cap tail-risk names. The most important score movers include IDT +44 pts, NX +29 pts, ULTA -41 pts, COO -33 pts, BRID -32 pts, and MDT -31 pts.

Feature of the Week: Accuracy Check + AVGO

2026Q1 Accuracy: Large Caps and Tech Remain the Cleanest Signal Pools

Cmind’s latest 2026Q1 accuracy table shows that model performance remained strongest in areas that institutional investors care most about: liquid large-cap names and data-rich sectors.

By market cap, Large Caps delivered 77% accuracy in 2026Q1, followed by Mid Caps at 71% and Small Caps at 62%. That spread is important. It reinforces a recurring theme in the Cmind signal: the model is strongest where disclosure quality, analyst coverage, liquidity, and signal density are highest. Small caps still offer dispersion, but the accuracy profile is naturally noisier.

The three-year history tells the same story. Across the 2023Q2–2026Q1 period, Large Caps averaged approximately 75.7% accuracy, Mid Caps averaged 69.1%, and Small Caps averaged 63.8%. The persistence matters more than any single quarter. It suggests that market-cap segmentation should be part of how users size, basket, and risk-control the signal.

Sector results were also useful. In 2026Q1, Information Technology posted 74% accuracy, Industrials 73%, Financials 72%, and Communication Services 70%. Consumer Staples followed at 67%, Consumer Discretionary at 66%, Energy at 65%, Materials at 64%, Utilities at 63%, Real Estate at 61%, and Health Care at 60%.

The three-year averages again show persistence: Information Technology averaged roughly 74.9% accuracy, Industrials 70.3%, Consumer Staples 69.6%, Financials 69.2%, and Real Estate 67.9%. Materials and Utilities were the weakest long-term sectors, averaging 57.8% and 56.6%, respectively.

Cmind’s EPS signal should not be treated as one undifferentiated score. Accuracy varies by market cap and sector. The highest-confidence deployment remains in liquid large caps and historically stronger sectors, while smaller-cap and lower-accuracy sectors may be better used in diversified baskets or relative-value structures.

AVGO: AI Infrastructure, Custom Silicon, and a High-Bar Print

AVGO reports fiscal Q2 2026 earnings on June 3 after the close. Cmind’s EPS-beat model assigns a 74.5% probability of beating consensus, with a forecast EPS estimate of $2.02. AVGO also ranks 5th out of 22 semiconductor peers on near-term beat conviction, behind NVDA, NXPI, ADI, and QCOM.

The investment question is broader than EPS. Broadcom is one of the cleanest tests of whether AI infrastructure demand is broadening beyond GPUs into custom ASICs, networking silicon, switching, connectivity, and hyperscaler-specific architectures. After several weeks focused on Nvidia, AI software monetization, and SaaS quality, AVGO becomes the next read-through on whether AI capex is still converting into revenue, margin, and backlog across the supply chain.

Investors will likely focus on six variables:

1. AI revenue outlook: whether management raises or reaffirms the fiscal 2026 AI revenue trajectory.

2. Custom silicon demand: whether hyperscaler ASIC programs are expanding or becoming more concentrated.

3. Networking and switching: whether AI cluster buildout is driving sustained silicon demand.

4. VMware integration: whether the acquisition continues to support margin expansion and free cash flow.

5. Non-AI cyclicality: whether broadband, wireless, enterprise, and storage semis are stabilizing.

6. Gross margin and cash conversion: whether AVGO can preserve premium economics as AI scales.

The bull case is supported by AVGO’s profitability profile. The company’s 33.3% EBIT margin, 35.2% operating margin, and 64.1% gross margin place it near the top of the semiconductor peer group. Its 126-day cash conversion cycle is also materially better than the peer average of 417 days, pointing to strong working-capital efficiency. The last analyzed earnings call scored 0.80 overall sentiment and 0.90 management sentiment, supported by record revenue and an upgraded fiscal 2026 AI outlook.

But this is not a low-risk setup. AVGO carries a Medium Risk C-Score of 0.609, with 15 accounting red flags across three categories. The standalone research report frames this as acquisition-related complexity rather than a disclosure red flag, with NLP red flags at zero. That distinction is important: the risk is structural/accounting complexity from the VMware and prior acquisition base, not an obvious negative language signal.

The probability path also argues for discipline. AVGO’s beat probability has been highly dynamic across the forecast window. It began near 0.87 in mid-March, dipped into the mid-0.60s, repeatedly moved into the 0.90s, peaked near 0.975 on May 9, and then settled at 0.745 by May 30. The model has remained beat-biased throughout, but the late pullback suggests expectations have normalized into the print.

For systematic investors, that is the actionable read: AVGO is a constructive beat signal in a historically strong Cmind cohort, but not a complacent one. The EPS signal supports a positive setup, while the stock reaction likely depends on AI revenue guidance, custom silicon visibility, VMware margin capture, and evidence that non-AI semis are no longer dragging the mix.

AVGO is not just this week’s semiconductor print. It is a live test of Cmind’s strongest historical signal pools — Large Cap and Information Technology — applied to one of the market’s most important AI infrastructure names.

Heatmap Read: Constructive, but Selective

INTERACTIVE HEATMAP

This week’s heatmap is constructive, but not aggressively green. The signal is strongest in Health Care and Industrials, while the largest risk pockets sit in Communication Services, Financials, and small-cap miss-risk names.

Market Cap Exposure

Mega / Large Caps

Mega/Large Caps: Large caps show the strongest signal quality, with an average beat probability of approximately 61.8%. About 94.1% of large caps carry a beat label, and none fall below the 40% miss-risk threshold. The key large-cap signals include HCM, TCOM, AVGO, CRDO, HPE, MDT, COO, ULTA, and DG. The group is not full of very high-conviction scores, but it is broadly constructive.

Mid-Caps

Small Caps

Small Caps: Small caps are the widest dispersion group. The average beat probability is 54.3%, with 67.4% beat-labeled, but 15.2% below the 40% miss-risk threshold. Small-cap upside includes NX, CMCO, AVXL, BBCP, BNR, VLGEA, TLYS, and FIVE. The downside tail includes JFIN, BRID, HURC, TIGR, HUIZ, OESX, and RICK.

Sector Exposure

Health Care is the strongest sector by average probability, with an average near 69.8% and no miss-risk names below 40%. HCM, AVXL, BNR, MDT, VEEV, and COO keep the sector solidly green.

Industrials are the best high-conviction sector, with NX, AGX, CMCO, BBCP, and ABM all screening well. The sector average is approximately 63.8%, but the tail risk is real, with HURC and OESX in miss-risk territory.

Information Technology is important but more moderate. The sector average is roughly 57.2%, and none of the IT names clear the 70% threshold in the weekly heatmap. CRDO, ODD, AVGO, DSGX, HPE, GWRE, PANW, CIEN, and DOCU are positive-to-neutral, while OCC and CRWD screen weaker.

Consumer Discretionary is broad but mixed. The sector average is about 56.8%. TCOM, AMWD, CAL, TLYS, FIVE, LE, and LULU are constructive, while RICK, PLCE, TOUR, CHPT, VIR, and ULTA are lower-confidence or de-risked.

Financials and Communication Services carry the clearest risk pockets. Financials average only 47.1%, weighed down by TIGR and HUIZ. Communication Services averages 48.9%, with JFIN as the lowest-probability name in the full heatmap.

Top 6 Beats/Miss-Risk Signals This Week

Top Beats

Top Miss-Risks

Top Movers

Upward Movers

• IDT: 12.1% → 56.1% /+44.0 pts. 

Large positive reset, though still marginal rather than high conviction.

• NX: 57.0% → 86.0% /+29.0 pts. 

Now the top beat signal in the weekly heatmap.

• TOUR: 18.9% → 44.3% /+25.4 pts. 

De-risked meaningfully, but still not a clean beat signal.

• BBCP: 50.8% → 75.0% /+24.2 pts. 

Small-cap Industrials moved into a high-conviction beat profile.

• HCM: 56.9% → 78.7% /+21.8 pts. 

Strong large-cap Health Care improvement.

• WDH: 44.8% → 66.4% /+21.6 pts. 

Financials signal improved sharply, though still below 70%.

Downward Movers

• ULTA: 94.0% → 52.7% /-41.4 pts. 

The largest large-cap de-risking; now closer to marginal.

• COO: 95.1% → 61.6% /-33.5 pts. 

Still positive, but no longer high conviction.

• BRID: 51.2% → 18.7% /-32.5 pts. 

Moved into clear miss-risk territory.

• MDT: 96.9% → 65.4% /-31.5 pts. 

Large-cap Health Care probability reset lower but remains constructive.

• CRDO: 94.6% → 66.4% /-28.2 pts. 

The IT signal de-risked but remains above neutral.

• HURC: 49.9% → 22.3% /-27.6 pts. 

Now one of the clearest miss-risk names this week.

30-Day Setup

The next 30 days should be treated as a model-validation and second-order AI dispersion window. The prior few weeks focused on mega-cap AI infrastructure and software monetization. This week brings the performance lens back to the model itself: Cmind’s historical accuracy remains strongest in large caps and Information Technology, and AVGO is a direct test of that setup.

For systematic investors, the practical framework is: use high-accuracy cohorts for conviction and sizing, use small-cap signals in diversified baskets, and pay attention to score revisions when probabilities change by 20–40 points before the print.

Recap

This week is not a broad market week. It is a model discipline and dispersion week.

Cmind’s 2026Q1 accuracy remained strongest in Large Caps and Information Technology, with three-year averages confirming that the model’s signal quality is persistent across market-cap and sector buckets. AVGO is the key feature name because it sits at the intersection of AI infrastructure, semiconductors, Large Cap, and Technology — all areas where investors are searching for durable earnings confirmation.

The heatmap is constructive but selective. Industrials and Health Care lead the beat side. Financials, Communication Services, and small-cap tail-risk names carry the largest downside signals. The key alpha opportunity is not simply finding who may beat. It is identifying where Cmind’s probability revisions, accuracy profile, and sector/cap segmentation create better baskets, hedges, and sizing decisions into catalyst windows.

About the Model

Cmind AI’s EPS predictions are powered by a machine learning model built for accuracy, objectivity, transparency, and daily updates with the 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.

Our EPS signals are updated daily across 4,400+ U.S. stocks using a multi-input ML model (filings, transcripts, price-to-earnings dynamics, governance, and peer signals). The goal isn’t to predict headlines—it’s to quantify where dispersion is most likely so you can build better baskets, hedges, and sizing into catalyst windows.

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