Fund Universe

The user wants me to analyze the 13F holdings data for ADAMS DIVERSIFIED EQUITY FUND, INC. for 2026Q1. However, the data only shows one quarter (2026Q1). The instructions say "2026Q1 is left-censored — every position is mechanically 'NEW.' Ignore its action column; use it as baseline only. All inferences start at 2026Q1+1."

But there is no 2026Q2 data provided. The data only contains 2026Q1. The user says "Attached: holdings 2026Q1–2026Q1." So we only have one quarter of data. According to the rules, we cannot make any inferences about flows because we need at least two quarters to see changes. The action column in 2026Q1 is meaningless because it's the first quarter in the dataset.

Thus, I must state that with only one quarter of data, no flow analysis is possible. The baseline is 2026Q1, but we have no subsequent quarter to compute Δshares, turnover, etc. The data provided includes action, weight, prev_weight, delta_shares for 2026Q1. However, the prev_weight_pct is presumably from the previous quarter (2025Q4) but we don't have that quarter's data. The delta_shares is the change from previous quarter to this quarter. But we don't have the previous quarter's holdings to verify. The instructions say "2026Q1 is left-censored — every position is mechanically 'NEW.' Ignore its action column; use it as baseline only." So we should ignore the action column and delta_shares for 2026Q1 because we don't have the prior quarter to confirm splits, etc.

Thus, we cannot compute any QoQ changes, turnover, conviction rank, etc. We can only describe the portfolio composition at 2026Q1: position count, gross value (but we don't have total portfolio value, only weights), top-5/10 weight, HHI, sector buckets, cap/liquidity tilts, etc. But we cannot infer strategy from flows because no flows are observable.

The user asks: "Answer: (1) strategy implied by flows, citing concentration, turnover, sizing dispersion; (2) core vs tactical positions, and which stated convictions the flows contradict; (3) each sub-sector rotation → the macro assumption it requires; (4) cap/liquidity/leverage/geography tilts and drift; (5) divergence from not stated -- infer only what the flows themselves support, and say so/not provided; (6) 3–5 falsifiable predictions and what the next filing would show if wrong."

Since we have no flows, we cannot answer (1), (2), (3) regarding rotations, (4) drift, (5) divergence, (6) predictions based on flows. We can only describe the current snapshot.

We must label claims Verified/Inferred/Speculative. We must flag gaps.

We need to output: bottom line (5 bullets) · tables · top-10 plus material new/exits · one paragraph per sub-sector · contradictions and open questions · data limitations.

Given the data, we can compute: - Position count: number of rows with weight > 0 in 2026Q1. - Gross value: not provided, but weights sum to 100%? Let's check sum of weight_pct. We'll compute. - Top-5/10 weight and HHI. - Sector buckets: we need to derive reasonable sub-sector buckets from tickers. - Cap/liquidity/leverage/geography tilts: we can infer from tickers (large cap, US-listed). - But we cannot compute turnover proxy, conviction rank, new/exited/scaled because we have no prior quarter.

The data includes "prev_weight_pct" and "delta_shares" for 2026Q1, which reflect changes from 2025Q4 to 2026Q1. However, the instructions say to ignore action column and treat 2026Q1 as baseline. But the user might expect us to use the delta_shares and prev_weight as the flow from 2025Q4 to 2026Q1? The instruction: "2026Q1 is left-censored — every position is mechanically 'NEW.' Ignore its action column; use it as baseline only. All inferences start at 2026Q1+1." This suggests we should not use the delta_shares for 2026Q1 because we don't have 2025Q4 data to verify corporate actions. However, the data includes delta_shares for 2026Q1. The user might have provided a single quarter of data but with previous quarter's weights and share changes. The instruction says "Attached: holdings 2026Q1–2026Q1." So only one quarter. But the data includes prev_weight_pct and delta_shares. That implies we have some info about the change from prior quarter. However, the rule says to ignore because it's left-censored. We must follow the rule.

Thus, we cannot use delta_shares for any analysis. We can only use the 2026Q1 weights and tickers.

We need to produce tables: position count and gross value QoQ (but only one quarter, so no QoQ). Top-5/10 weight and HHI. Turnover proxy: cannot compute. Sub-sector buckets with bridge (opening → flow → price → closing; state ticker mapping) - but we have no opening, flow, price, closing because only one quarter. We can maybe use the prev_weight as opening weight, delta_shares as flow, but we don't have prices. The rule says "not provided -- derive reasonable sub-sector buckets from the tickers/issuers below weights with bridge (opening → flow → price → closing; state ticker mapping)". This suggests we should attempt to reconstruct the bridge using the data we have: prev_weight_pct (opening weight), delta_shares (flow), and we need quarter-end price to compute closing weight. But we don't have prices. We have weight_pct (closing weight). We could back out price impact? But we don't have share counts for opening. We have delta_shares, but not opening shares. We could compute opening shares = closing shares - delta_shares. But we don't have closing shares either. We have weight_pct and prev_weight_pct, which are value weights. Without total portfolio value, we cannot get shares. So we cannot compute the bridge.

Thus, we must state data limitations.

We need to output bottom line (5 bullets), tables, top-10 plus material new/exits, one paragraph per sub-sector, contradictions and open questions, data limitations.

Given the constraints, I will produce an analysis based solely on the 2026Q1 snapshot, noting that no flow analysis is possible due to single quarter data. I will compute concentration metrics, sector allocation, and describe the portfolio composition. I will label all claims as Verified (computed from data) or Inferred (from tickers) or Speculative.

Let's compute the necessary metrics from the data.

First, parse the data. I'll write a script in my mind to compute.

List of rows: each with ticker, weight_pct, prev_weight_pct, delta_shares.

We need to filter for weight_pct > 0 for current positions.

Count positions: number of unique tickers with weight_pct > 0.

Sum of weight_pct should be ~100% (maybe not exactly due to rounding). Let's sum.

I'll do approximate calculation.

But better to write a quick mental sum. However, there are many rows. Let's count rows: I see many lines. Let's count programmatically in mind? I'll approximate.

But for the answer, I can present a table with top 10, position count, HHI.

HHI = sum of (weight_pct/100)^2 * 10000? Usually HHI = sum (weight_i)^2 where weight_i is fraction. So if weight_pct is percentage, HHI = sum (weight_pct/100)^2 * 10000 = sum (weight_pct^2) / 100. Or just sum of squared weights as decimals.

We'll compute.

Also, top-5 weight sum, top-10 weight sum.

We need to derive sub-sector buckets. We'll group tickers into sectors: e.g., Technology (AAPL, MSFT, NVDA, ADI, AMD, ANET, CRM, PANW, SNOW, PLTR, etc.), Financials (JPM, BAC, GS, SCHW, MTB, COF, etc.), Healthcare (ABBV, BIIB, GILD, CVS, UNH, MRK, LLY, etc.), Industrials (GE, GEV, LHX, PCAR, JBHT, EME, ROK?, etc.), Consumer Discretionary (AMZN, HD, MCD, TSLA, DECK, W, TKO, etc.), Consumer Staples (KO, PEP, PG, COST, DG, WMT), Energy (CVX, HAL, TRGP, FCX?), Utilities (AEE, DUK, ETR, NEE, DUK exited), Real Estate (PLD, PSA, SPG, VICI exited, IRM), Materials (FCX, NUE, CCK, GLW, APH?), Communication Services (GOOGL, META, NFLX, TMUS, XLC), ETFs (XLC, XLF, XLV, XLK exited), etc.

Also, PEO is Adams Diversified Equity Fund? Actually PEO is a ticker for Adams Natural Resources Fund? Wait, PEO is Adams Natural Resources Fund? But it's in the portfolio. Might be a closed-end fund. We'll treat as Financial/Investment.

We'll need to map each ticker to a sector.

Given time, I'll produce a reasonable grouping.

Now, the user wants "one paragraph per sub-sector". So I'll write a paragraph for each sector describing the allocation.

Also, "contradictions and open questions": since no flows, we can't see contradictions. But we can note that the portfolio holds both XLF (financial sector ETF) and individual financial stocks, and XLV (healthcare ETF) and individual healthcare stocks, suggesting a core-satellite approach.

"Data limitations": only one quarter, no share counts for previous quarter, no prices, cannot verify corporate actions, 45-day lag, etc.

Now, bottom line 5 bullets.

Let's start computing.

I'll write a quick Python-like mental script.

But since I'm an AI, I can simulate computation.

Let's list all tickers with weight_pct > 0:

AAPL 6.8637 ABBV 0.9920 ABNB 0.6899 ADI 0.8859 ADSK 0 (EXIT) AEE 0.5664 AIG 0 AMD 0.9178 AMZN 3.9215 ANET 0.7087 APH 0.5963 APP 0 AVB 0 AVGO 2.8626 BA 0.2645 BAC 1.6027 BIIB 0.6481 BKNG 0 BKR 0 BRKB 1.4161 CBRE 0.4507 CCK 0.4067 CEG 0.2106 CNC 0.2614 COF 0.5811 COIN 0 COR 0 COST 0.4812 CRM 0.4323 CSCO 0.9676 CVS 0.5081 CVX 1.0226 DECK 0.2981 DG 0.5792 DUK 0 ECL 0.3235 EG 0.3110 EME 0.5673 ETR 0.7925 FCX 0.5708 FDX 0.8223 GE 0.4571 GEV 1.3322 GILD 0.8700 GLW 0.1740 GOOGL 5.4276 GS 1.0884 HAL 0.4171 HD 0.8555 IBM 0.5288 IRM 0.2909 JBHT 0.7999 JCI 0.6337 JNJ 0.8857 JPM 2.1240 KO 0.3860 LECO 0.8542 LHX 0.7956 LITE 0.0892 LLY 1.6458 LRCX 1.5190 LW 0 MA 0.6175 MCD 0.8150 MCO 0 MDT 0.5891 META 2.2283 MRK 1.2728 MSCI 0.6573 MSFT 5.3259 MTB 0.8736 MU 0.5930 NEE 0.8492 NFLX 0.7134 NUE 0.4959 NVDA 7.7883 ORCL 0.3873 ORLY 0.6784 PANW 0.6566 PCAR 0.7084 PEO 2.5691 PEP 0.9676 PFG 0.6641 PG 0.3576 PH 0.8804 PLD 0.6616 PLTR 0.6120 PM 1.1663 POST 0 PSA 0.2759 ROK 0 RSG 0.5288 SCHW 1.0693 SNDK 0.1657 SNOW 0.3471 SPG 0.2360 TKO 0.6290 TMUS 0.3924 TRGP 0.4295 TROW 0 TSLA 1.6890 TTWO 0.3703 UBER 0.7968 UNH 0.3910 V 1.2066 VICI 0 VRT 0.4575 VST 0.1001 W 0.3094 WDC 0.1954 WMT 1.3205 XLC 0.3466 XLF 0.4441 XLK 0 XLV 1.4727

Now, count positions: let's count non-zero weight rows.

I'll count: 1 AAPL 2 ABBV 3 ABNB 4 ADI 5 AEE 6 AMD 7 AMZN 8 ANET 9 APH 10 AVGO 11 BA 12 BAC 13 BIIB 14 BRKB 15 CBRE 16 CCK 17 CEG 18 CNC 19 COF 20 COST 21 CRM 22 CSCO 23 CVS 24 CVX 25 DECK 26 DG 27 ECL 28 EG 29 EME 30 ETR 31 FCX 32 FDX 33 GE 34 GEV 35 GILD 36 GLW 37 GOOGL 38 GS 39 HAL 40 HD 41 IBM 42 IRM 43 JBHT 44 JCI 45 JNJ 46 JPM 47 KO 48 LECO 49 LHX 50 LITE 51 LLY 52 LRCX 53 MA 54 MCD 55 MDT 56 META 57 MRK 58 MSCI 59 MSFT 60 MTB 61 MU 62 NEE 63 NFLX 64 NUE 65 NVDA 66 ORCL 67 ORLY 68 PANW 69 PCAR 70 PEO 71 PEP 72 PFG 73 PG 74 PH 75 PLD 76 PLTR 77 PM 78 PSA 79 RSG 80 SCHW 81 SNDK 82 SNOW 83 SPG 84 TKO 85 TMUS 86 TRGP 87 TSLA 88 TTWO 89 UBER 90 UNH 91 V 92 VRT 93 VST 94 W 95 WDC 96 WMT 97 XLC 98 XLF 99 XLV

That's 99 positions? Let's count again: I numbered up to 99. But some might be missing. Let's count rows in data: The data has many lines. I'll trust around 99 positions.

Sum of weights: Let's approximate sum of weight_pct. Should be near 100%. We'll compute roughly.

Top 10 by weight: 1 NVDA 7.7883 2 AAPL 6.8637 3 MSFT 5.3259 4 GOOGL 5.4276 (actually GOOGL 5.4276 > MSFT 5.3259, so order: NVDA, AAPL, GOOGL, MSFT) 5 AMZN 3.9215 6 AVGO 2.8626 7 PEO 2.5691 8 META 2.2283 9 JPM 2.1240 10 TSLA 1.6890 (but LLY 1.6458, BRKB 1.4161, XLV 1.4727, WMT 1.3205, V 1.2066, PM 1.1663, MRK 1.2728, GEV 1.3322) Let's sort properly.

Let's list top 15 weights:

NVDA 7.7883 AAPL 6.8637 GOOGL 5.4276 MSFT 5.3259 AMZN 3.9215 AVGO 2.8626 PEO 2.5691 META 2.2283 JPM 2.1240 TSLA 1.6890 LLY 1.6458 BAC 1.6027 GEV 1.3322 WMT 1.3205 MRK 1.2728 V 1.2066 PM 1.1663 XLV 1.4727 BRKB 1.4161 SCHW 1.0693 GS 1.0884 CVX 1.0226 ABBV 0.9920 PEP 0.9676 CSCO 0.9676 AMD 0.9178 ADI 0.8859 JNJ 0.8857 MTB 0.8736 PH 0.8804 NEE 0.8492 HD 0.8555 LECO 0.8542 FDX 0.8223 MCD 0.8150 UBER 0.7968 JBHT 0.7999 ETR 0.7925 LHX 0.7956 ANET 0.7087 PCAR 0.7084 ORLY 0.6784 PFG 0.6641 MSCI 0.6573 PANW 0.6566 PLD 0.6616 TKO 0.6290 MA 0.6175 PLTR 0.6120 COF 0.5811 DG 0.5792 FCX 0.5708 EME 0.5673 AEE 0.5664 APH 0.5963 MU 0.5930 MDT 0.5891 RSG 0.5288 IBM 0.5288 CVS 0.5081 NUE 0.4959 COST 0.4812 VRT 0.4575 GE 0.4571 XLF 0.4441 CBRE 0.4507 CRM 0.4323 TRGP 0.4295 HAL 0.4171 CCK 0.4067 TMUS 0.3924 UNH 0.3910 ORCL 0.3873 KO 0.3860 TTWO 0.3703 PG 0.3576 XLC 0.3466 SNOW 0.3471 ECL 0.3235 EG 0.3110 W 0.3094 DECK 0.2981 IRM 0.2909 PSA 0.2759 BA 0.2645 CNC 0.2614 SPG 0.2360 CEG 0.2106 WDC 0.1954 GLW 0.1740 SNDK 0.1657 LITE 0.0892 VST 0.1001

Now, sum of all weights: Let's approximate by summing top 20 and rest.

But we can compute HHI: sum of (weight_pct)^2 / 10000? Actually HHI = sum (weight_i)^2 where weight_i is fraction. So if weight_pct is percentage, weight_i = weight_pct/100. HHI = sum (weight_pct/100)^2 * 10000 = sum (weight_pct^2) / 100. So we can compute sum of squared weight_pct.

But for the answer, we can provide a table with top 10 weights, HHI, position count.

We also need to derive sub-sector buckets. Let's assign sectors.

I'll create a mapping:

Technology: AAPL, MSFT, NVDA, ADI, AMD, ANET, CRM, PANW, SNOW, PLTR, LRCX, MU, SNOW, ORCL, CSCO, IBM, ADI, AMD, etc. Also XLK (exited), XLC (Communication Services ETF), but XLC is Communication Services. We'll separate.

Better to use GICS sectors.

Let's assign each ticker to a sector:

• AAPL: Technology
• ABBV: Healthcare
• ABNB: Consumer Discretionary (Travel)
• ADI: Technology (Semiconductors)
• AEE: Utilities
• AMD: Technology (Semiconductors)
• AMZN: Consumer Discretionary (E-commerce)
• ANET: Technology (Networking)
• APH: Technology (Electronic Components)
• AVGO: Technology (Semiconductors)
• BA: Industrials (Aerospace)
• BAC: Financials (Banks)
• BIIB: Healthcare (Biotech)
• BRKB: Financials (Insurance/Diversified)
• CBRE: Real Estate
• CCK: Materials (Packaging)
• CEG: Utilities (Energy)
• CNC: Healthcare (Managed Care)
• COF: Financials (Consumer Finance)
• COST: Consumer Staples (Retail)
• CRM: Technology (Software)
• CSCO: Technology (Networking)
• CVS: Healthcare (Pharmacy Retail)
• CVX: Energy (Integrated Oil)
• DECK: Consumer Discretionary (Apparel)
• DG: Consumer Staples (Discount Retail)
• ECL: Materials (Chemicals)
• EG: Energy (Midstream) - Actually EG is Everest Group? Reinsurance? Wait, EG is Everest Group, reinsurance, so Financials.
• EME: Industrials (Construction)
• ETR: Utilities
• FCX: Materials (Mining)
• FDX: Industrials (Logistics)
• GE: Industrials (Conglomerate)
• GEV: Industrials (Energy Equipment)
• GILD: Healthcare (Biotech)
• GLW: Technology (Electronic Components)
• GOOGL: Communication Services (Interactive Media)
• GS: Financials (Investment Banking)
• HAL: Energy (Oilfield Services)
• HD: Consumer Discretionary (Home Improvement)
• IBM: Technology (IT Services)
• IRM: Real Estate (Data Centers)
• JBHT: Industrials (Trucking)
• JCI: Industrials (Building Products)
• JNJ: Healthcare (Pharma)
• JPM: Financials (Banks)
• KO: Consumer Staples (Beverages)
• LECO: Industrials (Electronic Instruments) - Actually Lincoln Electric, industrials.
• LHX: Industrials (Aerospace/Defense)
• LITE: Technology (Semiconductors) - Lumentum
• LLY: Healthcare (Pharma)
• LRCX: Technology (Semiconductor Equipment)
• MA: Financials (Payment Processing)
• MCD: Consumer Discretionary (Restaurants)
• MDT: Healthcare (Medical Devices)
• META: Communication Services (Interactive Media)
• MRK: Healthcare (Pharma)
• MSCI: Financials (Financial Data)
• MSFT: Technology (Software)
• MTB: Financials (Banks)
• MU: Technology (Semiconductors)
• NEE: Utilities (Renewable Energy)
• NFLX: Communication Services (Streaming)
• NUE: Materials (Steel)
• NVDA: Technology (Semiconductors)
• ORCL: Technology (Software)
• ORLY: Consumer Discretionary (Auto Parts)
• PANW: Technology (Cybersecurity)
• PCAR: Industrials (Trucks)
• PEO: Financials (Closed-end Fund) - but it's an investment fund, maybe Financials.
• PEP: Consumer Staples (Beverages/Snacks)
• PFG: Financials (Insurance/Retirement)
• PG: Consumer Staples (Household Products)
• PH: Industrials (Motion Control) - Parker Hannifin
• PLD: Real Estate (Logistics REIT)
• PLTR: Technology (Software)
• PM: Consumer Staples (Tobacco)
• PSA: Real Estate (Self Storage REIT)
• RSG: Industrials (Waste Management)
• SCHW: Financials (Brokerage)
• SNDK: Technology (Semiconductors) - Sandisk? Actually SNDK is Western Digital? Wait, SNDK is Sandisk, now part of Western Digital. But it's technology.
• SNOW: Technology (Software)
• SPG: Real Estate (Retail REIT)
• TKO: Consumer Discretionary (Entertainment) - TKO Group (UFC/WWE)
• TMUS: Communication Services (Wireless)
• TRGP: Energy (Midstream)
• TSLA: Consumer Discretionary (Auto)
• TTWO: Communication Services (Gaming)
• UBER: Industrials (Ride-sharing) or Technology? Usually Industrials.
• UNH: Healthcare (Managed Care)
• V: Financials (Payment Processing)
• VRT: Technology (Data Center Infrastructure) - Vertiv
• VST: Utilities (Independent Power)
• W: Consumer Discretionary (Home Goods) - Wayfair
• WDC: Technology (Data Storage)
• WMT: Consumer Staples (Retail)
• XLC: Communication Services ETF
• XLF: Financials ETF
• XLV: Healthcare ETF

Now, we can aggregate weights by sector.

We'll compute sector weights.

Let's sum weights for each sector.

I'll create a dictionary.

Technology: AAPL 6.8637, ADI 0.8859, AMD 0.9178, ANET 0.7087, APH 0.5963, AVGO 2.8626, CRM 0.4323, CSCO 0.9676, GLW 0.1740, LRCX 1.5190, MSFT 5.3259, MU 0.5930, NVDA 7.7883, ORCL 0.3873, PANW 0.6566, PLTR 0.6120, SNOW 0.3471, VRT 0.4575, WDC 0.1954, LITE 0.0892, SNDK 0.1657, IBM 0.5288. Also maybe ADSK exited, XLK exited. Also XLC is Comm Services. So Technology sum = let's add.

Compute: AAPL 6.8637 MSFT 5.3259 NVDA 7.7883 AVGO 2.8626 LRCX 1.5190 ADI 0.8859 AMD 0.9178 ANET 0.7087 APH 0.5963 MU 0.5930 CSCO 0.9676 CRM 0.4323 PANW 0.6566 PLTR 0.6120 ORCL 0.3873 IBM 0.5288 GLW 0.1740 SNOW 0.3471 VRT 0.4575 WDC 0.1954 LITE 0.0892 SNDK 0.1657

Sum = let's add stepwise.

6.8637 + 5.3259 = 12.1896 +7.7883 = 19.9779 +2.8626 = 22.8405 +1.5190 = 24.3595 +0.8859 = 25.2454 +0.9178 = 26.1632 +0.7087 = 26.8719 +0.5963 = 27.4682 +0.5930 = 28.0612 +0.9676 = 29.0288 +0.4323 = 29.4611 +0.6566 = 30.1177 +0.6120 = 30.7297 +0.3873 = 31.1170 +0.5288 = 31.6458 +0.1740 = 31.8198 +0.3471 = 32.1669 +0.4575 = 32.6244 +0.1954 = 32.8198 +0.0892 = 32.9090 +0.1657 = 33.0747

So Technology ~33.07%

Communication Services: GOOGL 5.4276, META 2.2283, NFLX 0.7134, TMUS 0.3924, TTWO 0.3703, XLC 0.3466. Also maybe UBER? Usually Industrials. So sum: 5.4276+2.2283=7.6559; +0.7134=8.3693; +0.3924=8.7617; +0.3703=9.1320; +0.3466=9.4786. ~9.48%

Financials: BAC 1.6027, BRKB 1.4161, JPM 2.1240, GS 1.0884, SCHW 1.0693, MA 0.6175, V 1.2066, MSCI 0.6573, MTB 0.8736, COF 0.5811, PFG 0.6641, PEO 2.5691, EG 0.3110 (Everest Re). Also XLF 0.4441. Sum: JPM 2.1240 BAC 1.6027 PEO 2.5691 BRKB 1.4161 V 1.2066 GS 1.0884 SCHW 1.0693 MTB 0.8736 MA 0.6175 MSCI 0.6573 PFG 0.6641 COF 0.5811 EG 0.3110 XLF 0.4441

Add: 2.1240+1.6027=3.7267; +2.5691=6.2958; +1.4161=7.7119; +1.2066=8.9185; +1.0884=10.0069; +1.0693=11.0762; +0.8736=11.9498; +0.6175=12.5673; +0.6573=13.2246; +0.6641=13.8887; +0.5811=14.4698; +0.3110=14.7808; +0.4441=15.2249. ~15.22%

Healthcare: ABBV 0.9920, BIIB 0.6481, CNC 0.2614, CVS 0.5081, GILD 0.8700, JNJ 0.8857, LLY 1.6458, MDT 0.5891, MRK 1.2728, UNH 0.3910, XLV 1.4727. Also maybe others? ABNB? No. Sum: LLY 1.6458 XLV 1.4727 MRK 1.2728 ABBV 0.9920 JNJ 0.8857 GILD 0.8700 BIIB 0.6481 CVS 0.5081 MDT 0.5891 CNC 0.2614 UNH 0.3910

Add: 1.6458+1.4727=3.1185; +1.2728=4.3913; +0.9920=5.3833; +0.8857=6.2690; +0.8700=7.1390; +0.6481=7.7871; +0.5891=8.3762; +0.5081=8.8843; +0.3910=9.2753; +0.2614=9.5367. ~9.54%

Consumer Discretionary: AMZN 3.9215, HD 0.8555, MCD 0.8150, TSLA 1.6890, DECK 0.2981, ORLY 0.6784, TKO 0.6290, W 0.3094, ABNB 0.6899, UBER 0.7968, PCAR? Actually PCAR is Industrials (trucks). JBHT Industrials. So Consumer Discretionary: AMZN, HD, MCD, TSLA, DECK, ORLY, TKO, W, ABNB, UBER. Also maybe FDX? Industrials. Sum: AMZN 3.9215 TSLA 1.6890 HD 0.8555 MCD 0.8150 UBER 0.7968 ABNB 0.6899 ORLY 0.6784 TKO 0.6290 W 0.3094 DECK 0.2981

Sum: 3.9215+1.6890=5.6105; +0.8555=6.4660; +0.8150=7.2810; +0.7968=8.0778; +0.6899=8.7677; +0.6784=9.4461; +0.6290=10.0751; +0.3094=10.3845; +0.2981=10.6826. ~10.68%

Consumer Staples: KO 0.3860, PEP 0.9676, PG 0.3576, COST 0.4812, DG 0.5792, WMT 1.3205, PM 1.1663. Also maybe CVS? Healthcare. Sum: WMT 1.3205 PM 1.1663 PEP 0.9676 DG 0.5792 COST 0.4812 KO 0.3860 PG 0.3576

Sum: 1.3205+1.1663=2.4868; +0.9676=3.4544; +0.5792=4.0336; +0.4812=4.5148; +0.3860=4.9008; +0.3576=5.2584. ~5.26%

Industrials: BA 0.2645, EME 0.5673, ETR? Utilities. FDX 0.8223, GE 0.4571, GEV 1.3322, JBHT 0.7999, JCI 0.6337, LHX 0.7956, LECO 0.8542, PCAR 0.7084, PH 0.8804, RSG 0.5288, UBER? already in Cons Disc. Also maybe CAT? not present. Sum: GEV 1.3322 FDX 0.8223 LECO 0.8542 PH 0.8804 LHX 0.7956 JBHT 0.7999 PCAR 0.7084 JCI 0.6337 EME 0.5673 RSG 0.5288 GE 0.4571 BA 0.2645

Sum: 1.3322+0.8223=2.1545; +0.8542=3.0087; +0.8804=3.8891; +0.7956=4.6847; +0.7999=5.4846; +0.7084=6.1930; +0.6337=6.8267; +0.5673=7.3940; +0.5288=7.9228; +0.4571=8.3799; +0.2645=8.6444. ~8.64%

Energy: CVX 1.0226, HAL 0.4171, TRGP 0.4295, VST 0.1001, FCX? Materials. Also maybe OXY? not present. Sum: CVX 1.0226 HAL 0.4171 TRGP 0.4295 VST 0.1001

Sum = 1.9693. ~1.97%

Utilities: AEE 0.5664, ETR 0.7925, NEE 0.8492, VST? already energy. CEG 0.2106. DUK exited. Sum: NEE 0.8492 ETR 0.7925 AEE 0.5664 CEG 0.2106

Sum = 2.4187. ~2.42%

Real Estate: CBRE 0.4507, IRM 0.2909, PLD 0.6616, PSA 0.2759, SPG 0.2360, VICI exited. Sum: PLD 0.6616 CBRE 0.4507 IRM 0.2909 PSA 0.2759 SPG 0.2360

Sum = 1.9151. ~1.92%

Materials: CCK 0.4067, ECL 0.3235, FCX 0.5708, NUE 0.4959, GLW? Technology. APH? Technology. Sum: FCX 0.5708 NUE 0.4959 CCK 0.4067 ECL 0.3235

Sum = 1.7969. ~1.80%

Other: maybe PEO is Financials, included. XLF, XLV, XLC are ETFs, we assigned to sectors.

Now, check total sum of sector weights: 33.07 + 9.48 + 15.22 + 9.54 + 10.68 + 5.26 + 8.64 + 1.97 + 2.42 + 1.92 + 1.80 = let's add: 33.07+9.48=42.55; +15.22=57.77; +9.54=67.31; +10.68=77.99; +5.26=83.25; +8.64=91.89; +1.97=93.86; +2.42=96.28; +1.92=98.20; +1.80=100.00. Good, sums to ~100%.

Now, top 10 positions by weight: 1 NVDA 7.79% 2 AAPL 6.86% 3 GOOGL 5.43% 4 MSFT 5.33% 5 AMZN 3.92% 6 AVGO 2.86% 7 PEO 2.57% 8 META 2.23% 9 JPM 2.12% 10 TSLA 1.69% (or LLY 1.65%, but TSLA slightly higher)

Top 10 weight sum = 7.79+6.86+5.43+5.33+3.92+2.86+2.57+2.23+2.12+1.69 = 40.8% approx.

HHI: sum of squared weights (as fractions). Compute sum of (weight_pct)^2.

We'll compute approximate HHI.

We have many positions. The top positions dominate.

We can compute HHI = sum (w_i^2) where w_i = weight_pct/100.

But we can compute sum of weight_pct^2 / 10000.

Let's compute sum of weight_pct^2 for top few and approximate rest.

But for the answer, we can provide a table with exact numbers if we compute precisely. Since I'm an AI, I can compute precisely by iterating through the list. But I'll approximate.

Given the instructions, we need to show formulas. We'll present the formulas and computed values.

Now, the user wants "position count and gross value QoQ" but only one quarter, so we can only report current position count and gross value (not provided, but we can assume total portfolio value = 100% of reportable sleeve). We can say gross value not provided.

"Turnover proxy = Σ|Δshares|×quarter-end price ÷ avg value (lower bound)" - cannot compute because no quarter-end prices, no previous quarter shares.

"Conviction rank by cumulative Δshares" - cannot compute.

"New / exited / ±25% scaled" - we have action column for 2026Q1 but it's left-censored. However, the data includes NEW, EXIT, ADD, TRIM, HOLD. But we are told to ignore action column. However, we could mention that the data flags new positions (NEW) and exits (EXIT) for 2026Q1 relative to prior quarter, but we cannot verify due to left-censoring.

We must flag gaps.

Now, for the answer structure:

Bottom line (5 bullets) Tables: - Table 1: Portfolio Summary (Position count, Gross Value (NA), Top 5 Weight, Top 10 Weight, HHI) - Table 2: Top 10 Holdings - Table 3: Sector Allocation (with weights) - Table 4: New Positions in 2026Q1 (from action NEW) and Exited Positions (EXIT) - but with caveat. - Table 5: Sub-sector bridge (if possible) - but we lack data.

One paragraph per sub-sector (maybe 11 sectors). Contradictions and open questions. Data limitations.

We must label claims: Verified (computed from data), Inferred (from ticker mapping), Speculative.

Now, let's write.

We'll start with bottom line.

Bottom line: - The 2026Q1 reportable sleeve holds 99 positions across 11 sectors, heavily concentrated in Technology (33.1%) and Financials (15.2%), with top 10 positions accounting for ~40.8% of sleeve value (HHI ~ 0.18? Actually compute HHI: sum of squared weights. Let's compute HHI properly.

Compute HHI = sum (weight_pct/100)^2. Equivalent to sum(weight_pct^2)/10000.

We'll compute sum of weight_pct^2 for all positions.

We have the list of weights. Let's compute squares.

I'll write a quick mental script but maybe approximate.

Given many small positions, HHI will be driven by top holdings.

Top 10 weights squared: NVDA: 7.7883^2 = 60.66 AAPL: 6.8637^2 = 47.11 GOOGL: 5.4276^2 = 29.46 MSFT: 5.3259^2 = 28.36 AMZN: 3.9215^2 = 15.38 AVGO: 2.8626^2 = 8.19 PEO: 2.5691^2 = 6.60 META: 2.2283^2 = 4.97 JPM: 2.1240^2 = 4.51 TSLA: 1.6890^2 = 2.85

Sum top10 squares = 60.66+47.11=107.77; +29.46=137.23; +28.36=165.59; +15.38=180.97; +8.19=189.16; +6.60=195.76; +4.97=200.73; +4.51=205.24; +2.85=208.09.

Now other positions: we have many with weights between 1.6 and 0.1. Let's approximate sum of squares for rest.

We can compute roughly: The total sum of weights is 100. The sum of squares for a diversified portfolio with many small positions. But we can compute more accurately by summing squares of all weights I listed.

But maybe we can provide a formula and say "computed HHI = X". Since I cannot run code, I'll approximate.

Let's sum squares of all weights I have in the list above (99 positions). I'll do a rough calculation by grouping.

We have weights for each ticker. I'll compute squares for each and sum.

But that's tedious. However, for the answer, we can present the HHI as a computed number. I'll do a more systematic approximate calculation.

I'll list all weights again with squares:

I'll create a table in mind.

But maybe we can compute HHI using the fact that the portfolio is fairly concentrated. Top 10 ~40.8%, top 20 ~? Let's compute top 20 weights.

Top 20 weights (from earlier): 1 NVDA 7.7883 2 AAPL 6.8637 3 GOOGL 5.4276 4 MSFT 5.3259 5 AMZN 3.9215 6 AVGO 2.8626 7 PEO 2.5691 8 META 2.2283 9 JPM 2.1240 10 TSLA 1.6890 11 LLY 1.6458 12 BAC 1.6027 13 GEV 1.3322 14 WMT 1.3205 15 MRK 1.2728 16 V 1.2066 17 PM 1.1663 18 XLV 1.4727 19 BRKB 1.4161 20 SCHW 1.0693

Sum top20 = 40.8 + (1.6458+1.6027+1.3322+1.3205+1.2728+1.2066+1.1663+1.4727+1.4161+1.0693) = 40.8 + 13.105 = 53.905%.

Top 20 squares: we have top10 squares 208.09. Add squares for 11-20: LLY 1.6458^2 = 2.708 BAC 1.6027^2 = 2.569 GEV 1.3322^2 = 1.775 WMT 1.3205^2 = 1.744 MRK 1.2728^2 = 1.620 V 1.2066^2 = 1.456 PM 1.1663^2 = 1.360 XLV 1.4727^2 = 2.169 BRKB 1.4161^2 = 2.005 SCHW 1.0693^2 = 1.143

Sum = 2.708+2.569=5.277; +1.775=7.052; +1.744=8.796; +1.620=10.416; +1.456=11.872; +1.360=13.232; +2.169=15.401; +2.005=17.406; +1.143=18.549.

Total top20 squares = 208.09 + 18.55 = 226.64.

Remaining 79 positions sum of weights = 100 - 53.905 = 46.095%. If they were equally weighted, each ~0.583%, square ~0.34, times 79 = 26.9. But they are not equal; some are larger. The next weights: GS 1.0884, CVX 1.0226, ABBV 0.9920, PEP 0.9676, CSCO 0.9676, AMD 0.9178, ADI 0.8859, JNJ 0.8857, MTB 0.8736, PH 0.8804, NEE 0.8492, HD 0.8555, LECO 0.8542, FDX 0.8223, MCD 0.8150, UBER 0.7968, JBHT 0.7999, ETR 0.7925, LHX 0.7956, ANET 0.7087, PCAR 0.7084, ORLY 0.6784, PFG 0.6641, MSCI 0.6573, PANW 0.6566, PLD 0.6616, TKO 0.6290, MA 0.6175, PLTR 0.6120, COF 0.5811, DG 0.5792, FCX 0.5708, EME 0.5673, AEE 0.5664, APH 0.5963, MU 0.5930, MDT 0.5891, RSG 0.5288, IBM 0.5288, CVS 0.5081, NUE 0.4959, COST 0.4812, VRT 0.4575, GE 0.4571, XLF 0.4441, CBRE 0.4507, CRM 0.4323, TRGP 0.4295, HAL 0.4171, CCK 0.4067, TMUS 0.3924, UNH 0.3910, ORCL 0.3873, KO 0.3860, TTWO 0.3703, PG 0.3576, XLC 0.3466, SNOW 0.3471, ECL 0.3235, EG 0.3110, W 0.3094, DECK 0.2981, IRM 0.2909, PSA 0.2759, BA 0.2645, CNC 0.2614, SPG 0.2360, CEG 0.2106, WDC 0.1954, GLW 0.1740, SNDK 0.1657, LITE 0.0892, VST 0.1001.

That's about 60 positions. Sum of their weights = 46.095%. Let's approximate sum of squares for these.

We can compute squares for each, but maybe we can approximate HHI by using the formula: HHI = sum(w_i^2). Since we have the data, we can claim "Verified: HHI = 0.182" or something. But we need to compute accurately.

Given the constraints, I'll compute HHI precisely by summing squares of all weight_pct values and dividing by 10000.

I'll do a more thorough calculation using a systematic approach. Since I'm an AI, I can simulate a loop. But manually, I'll sum squares for all 99 positions.

Let's list all weights with 4 decimals:

1. AAPL 6.8637
2. ABBV 0.9920
3. ABNB 0.6899
4. ADI 0.8859
5. AEE 0.5664
6. AMD 0.9178
7. AMZN 3.9215
8. ANET 0.7087
9. APH 0.5963
10. AVGO 2.8626
11. BA 0.2645
12. BAC 1.6027
13. BIIB 0.6481
14. BRKB 1.4161
15. CBRE 0.4507
16. CCK 0.4067
17. CEG 0.2106
18. CNC 0.2614
19. COF 0.5811
20. COST 0.4812
21. CRM 0.4323
22. CSCO 0.9676
23. CVS 0.5081
24. CVX 1.0226
25. DECK 0.2981
26. DG 0.5792
27. ECL 0.3235
28. EG 0.3110
29. EME 0.5673
30. ETR 0.7925
31. FCX 0.5708
32. FDX 0.8223
33. GE 0.4571
34. GEV 1.3322
35. GILD 0.8700
36. GLW 0.1740
37. GOOGL 5.4276
38. GS 1.0884
39. HAL 0.4171
40. HD 0.8555
41. IBM 0.5288
42. IRM 0.2909
43. JBHT 0.7999
44. JCI 0.6337
45. JNJ 0.8857
46. JPM 2.1240
47. KO 0.3860
48. LECO 0.8542
49. LHX 0.7956
50. LITE 0.0892
51. LLY 1.6458
52. LRCX 1.5190
53. MA 0.6175
54. MCD 0.8150
55. MDT 0.5891
56. META 2.2283
57. MRK 1.2728
58. MSCI 0.6573
59. MSFT 5.3259
60. MTB 0.8736
61. MU 0.5930
62. NEE 0.8492
63. NFLX 0.7134
64. NUE 0.4959
65. NVDA 7.7883
66. ORCL 0.3873
67. ORLY 0.6784
68. PANW 0.6566
69. PCAR 0.7084
70. PEO 2.5691
71. PEP 0.9676
72. PFG 0.6641
73. PG 0.3576
74. PH 0.8804
75. PLD 0.6616
76. PLTR 0.6120
77. PM 1.1663
78. PSA 0.2759
79. RSG 0.5288
80. SCHW 1.0693
81. SNDK 0.1657
82. SNOW 0.3471
83. SPG 0.2360
84. TKO 0.6290
85. TMUS 0.3924
86. TRGP 0.4295
87. TSLA 1.6890
88. TTWO 0.3703
89. UBER 0.7968
90. UNH 0.3910
91. V 1.2066
92. VRT 0.4575
93. VST 0.1001
94. W 0.3094
95. WDC 0.1954
96. WMT 1.3205
97. XLC 0.3466
98. XLF 0.4441
99. XLV 1.4727

Now, compute sum of squares.

I'll write a quick script in my head? Better to approximate but we can do it manually with careful addition.

Let's group by weight ranges.

But perhaps we can use the fact that the sum of weights is 100. The HHI is sum(w_i^2). For a portfolio of 99 stocks, if equally weighted, HHI = 99*(1/99)^2 = 1/99 ≈ 0.0101. But this portfolio is concentrated, so HHI will be higher.

We can compute using the top weights.

We already have top20 squares sum = 226.64 (in units of weight_pct^2). Now compute squares for the rest.

I'll compute squares for each of the remaining 79 positions.

We'll go through the list from 21 to 99 (but top20 includes up to SCHW? Actually top20 includes up to SCHW (1.0693). So positions 21-99 are the rest.

Let's list weights for positions 21-99 (excluding top20). But careful: top20 I defined as the 20 largest weights. Let's verify the 20 largest:

Sorted descending: 1 NVDA 7.7883 2 AAPL 6.8637 3 GOOGL 5.4276 4 MSFT 5.3259 5 AMZN 3.9215 6 AVGO 2.8626 7 PEO 2.5691 8 META 2.2283 9 JPM 2.1240 10 TSLA 1.6890 11 LLY 1.6458 12 BAC 1.6027 13 GEV 1.3322 14 WMT 1.3205 15 MRK 1.2728 16 V 1.2066 17 PM 1.1663 18 XLV 1.4727 19 BRKB 1.4161 20 SCHW 1.0693

Yes, that's top20. Next is GS 1.0884? Wait, GS 1.0884 > SCHW 1.0693. So GS should be in top20. Let's re-sort.

Weights above 1%: NVDA 7.7883 AAPL 6.8637 GOOGL 5.4276 MSFT 5.3259 AMZN 3.9215 AVGO 2.8626 PEO 2.5691 META 2.2283 JPM 2.1240 XLV 1.4727 BRKB 1.4161 GEV 1.3322 WMT 1.3205 LLY 1.6458 BAC 1.6027 V 1.2066 PM 1.1663 MRK 1.2728 GS 1.0884 SCHW 1.0693 CVX 1.0226 ABBV 0.9920 PEP 0.9676 CSCO 0.9676 AMD 0.9178 ADI 0.8859 JNJ 0.8857 PH 0.8804 MTB 0.8736 GILD 0.8700 HD 0.8555 LECO 0.8542 NEE 0.8492 FDX 0.8223 MCD 0.8150 UBER 0.7968 JBHT 0.7999 ETR 0.7925 LHX 0.7956 ANET 0.7087 PCAR 0.7084 ORLY 0.6784 PFG 0.6641 MSCI 0.6573 PANW 0.6566 PLD 0.6616 TKO 0.6290 MA 0.6175 PLTR 0.6120 COF 0.5811 DG 0.5792 FCX 0.5708 EME 0.5673 AEE 0.5664 APH 0.5963 MU 0.5930 MDT 0.5891 RSG 0.5288 IBM 0.5288 CVS 0.5081 NUE 0.4959