Conclusion

The total value of long positions has topped $1 billion for the first time.

I consider it business as usual. Indeed, AMETEK INC (AME)’s position size—about $12 million—is the largest to date, but it still represents only 1% of the long positions. I continue to believe that the long positions show solid diversification and appropriate adjustments to position sizes.


It may be a coincidence, but the turnover rate may be decreasing.







Reference: Box-and-whisker plot of the turnover ratio as of December 31, 2024.


History 13F-HR in the Excel file


Analysis_Numerai_GP.ipynb


The value of long positions (AUM from 13F)

Date                     Long Position
2022-12-31         $0.15
2023-03-31         $0.30
2023-06-30         $0.49
2023-09-30         $0.30
2023-12-31         $0.28
2024-03-31         $0.23
2024-06-30         $0.28
2024-09-30         $0.38
2024-12-31         $0.37
2025-03-31         $0.53
2025-06-30         $0.69
2025-09-30         $0.71
2025-12-31         $0.83
2026-03-31         $1.00
2026-06-30        $1.22

Number of holdings

The holdings count decreased from the previous period. Since similar declines have occurred in the past, I consider this to be coincidental.


Position Size


Extra: Having a Generative AI Break Down a Portfolio Built by Decentralized AI

I had Gork analyze the History 13F-HR, which I converted into a CSV file from the Excel file.

Prompt

-This CSV file summarizes hedge fund 13F-HR filings.
-The columns are as follows:
･nameOfIssuer: Company name of the Long Position
･cusip: CUSIP of the Long Position
･From "2022/12/31" to "2026/6/30": Amount of the Long Position-The rows contain the following:For each nameOfIssuer and cusip, the Long Position amounts from "2022/12/31" to "2026/6/30"
-Please generate a comprehensive report covering:
･Portfolio trends by sector (e.g., Tech, Healthcare, Financials)･Strategic shifts over time and correlation with macroeconomic factors (e.g., interest rate hikes, geopolitical risks)
･Fund characteristics: growth vs. value focus, active vs. passive style, and thematic concentration (e.g., AI, renewable energy)
･Risk management: diversification level and defensive stock ratio
･Forward-looking insights: forecast future portfolio directions based on historical trends, sector rotation patterns, thematic focus, and macro sensitivity

What Grok Said


For clarification, the 13F‑HR CSV file contains no incomplete sections. It seems that Grok mistakenly assumed the data was truncated.

Portfolio Analysis Report: Hedge Fund 13F-HR Holdings (Q4 2022 – Q2 2026)The dataset reflects long-only equity positions from institutional 13F-HR filings. Position values (in USD) are reported quarterly across roughly 15 periods. Many holdings are intermittent or concentrated in mid/small-cap names, with notable entries in technology, financials (especially regional banks), industrials, healthcare, consumer discretionary, and materials/energy. Duplicate CUSIPs and name variants (e.g., GOLD/A-MARK, various bank name iterations) appear and require careful aggregation in practice.Data limitations: The provided extract is truncated and contains sparse early-period values for many names. Full quantitative sector aggregates, exact AUM, or precise turnover metrics cannot be computed from the incomplete file. Analysis relies on visible patterns, large positions, and entry/exit timing.1. Portfolio Trends by SectorVisible holdings span multiple sectors, with rotation visible in both presence and size of positions:
  • Technology / Information Technology: Persistent and growing presence. Examples include Adobe (ADBE), AMD, Arista Networks (ANET – multiple share classes), Cloudflare (NET), CrowdStrike (CRWD), Datadog (DDOG), Axon Enterprise (AXON), Box, Amplitude, AppLovin, C3.ai, and later-period strength in semiconductor-related and software names (e.g., Advanced Energy, Arteris, Astera Labs). Cloud, cybersecurity, AI-enabling, and networking names feature repeatedly. Large recent values appear in names such as AMETEK (industrial tech/electronics) and various software platforms.
  • Financials: Heavy emphasis on regional and community banks plus some larger financials. Notable: Bank OZK (very large late-period positions exceeding $10M in one quarter), Commerce Bancshares, Banc of California, BancFirst, Cullen/Frost, various Canadian banks (BMO, BNS, TD), American Express, Ally, and asset managers. Insurance and specialty finance also appear (Aflac, Assurant, Bread Financial). Bank holdings show expansion in 2024–2026 periods.
  • Healthcare / Life Sciences: Biopharma, devices, and services. Examples: Amneal Pharmaceuticals (large mid-period), Boston Scientific, Align Technology, Agilent, Bio-Techne, various specialty pharma (Amylyx, ANI, Amphastar), and services (AMN Healthcare, Acadia, Centene). Positions are more episodic than tech.
  • Industrials / Materials / Energy: Equipment, transportation, and resources. Examples: API Group (growing sharply late-period), Albany International, Alamo Group, Astec Industries, Boise Cascade, Clean Harbors, various mining/energy names (Alcoa, Antero, Baytex, Cameco, Canadian Natural Resources, Barrick). Construction and infrastructure-related names (Apogee, Construction Partners) expand later.
  • Consumer Discretionary / Staples: Retail, leisure, and restaurants. Examples: Abercrombie & Fitch, American Eagle, AutoNation, Autozone, Burlington, Cheesecake Factory, Chipotle (smaller), Costco (very large late positions), Celsius, Deckers. Consumer strength appears more pronounced in 2024–2026.
  • Other: Utilities/REITs sparse; some gold/precious metals and shipping/transport names appear intermittently.
Trend summary: Early periods (2022–early 2023) show more scattered small/mid-cap positions. Mid-to-late periods exhibit larger absolute values in select tech, banks, industrials, and Costco-style consumer defensives. Tech and financials appear as core ongoing allocations; energy/materials and certain industrials show more tactical sizing.2. Strategic Shifts Over Time and Macro Correlation
  • 2022–2023: Positions more fragmented. Some growth-oriented tech (early Affirm, AppLovin, Celsius peaks) and healthcare appear alongside materials (Alcoa large in mid-2023). This aligns with post-COVID recovery, inflation peaking, and the start of the Federal Reserve rate-hike cycle (funds rate rising aggressively through mid-2023). Higher rates pressure growth stocks and rate-sensitive sectors; visible bank and industrial exposure may reflect value/cyclical rotation.
  • 2024: Increased concentration in larger or growing positions (Arista, Cloudflare, regional banks, API Group, Autozone, Boise Cascade). Timing coincides with expected rate cuts (Fed began easing later in 2024 in historical context), soft-landing narratives, and AI-driven tech enthusiasm. Bank holdings expand amid improving net-interest-margin expectations and reduced recession fears. Geopolitical risks (ongoing Ukraine/Russia, Middle East tensions) may explain intermittent energy and gold/mining positions (precious metals, Canadian energy/miners).
  • 2025–mid-2026: Further growth in select large positions (Bank OZK, Commerce Bancshares, Costco, AMETEK, API Group, certain tech). This period would correlate with potential continued rate normalization, AI infrastructure spend, and any residual inflation or fiscal stimulus effects. Defensive consumer (Costco) and industrial automation/tech names strengthen, consistent with late-cycle or soft-landing positioning.
Overall style: Tactical rather than purely buy-and-hold. Many names enter and exit or scale dramatically across quarters, consistent with active management responding to rate path, growth vs. recession signals, and sector rotations (tech leadership + financial/industrial cyclical recovery).3. Fund Characteristics
  • Growth vs. Value: Hybrid with growth tilt in technology (software, semiconductors, AI-adjacent: Cloudflare, CrowdStrike, Arista, AMD-related, C3.ai) and consumer (Celsius, certain retail). Value/cyclical elements appear in regional banks, materials, energy, and industrials. Not pure growth or pure value.
  • Active vs. Passive: Clearly active. High turnover implied by intermittent holdings, large scaling of positions, and presence of many mid/small-cap and less-liquid names atypical of broad-index passive strategies. No evidence of pure ETF or index-tracking concentration.
  • Thematic Concentration:
    • AI / Digital Transformation: Strong (Cloudflare, CrowdStrike, Datadog, Arista, software platforms, semiconductors, Axon, C3.ai).
    • Financials / Regional Banking: Pronounced late-period focus.
    • Infrastructure / Industrials / Construction: Growing (API Group, equipment, materials).
    • Consumer Recovery / Defensives: Costco and select retail.
    • Renewables / Clean Energy: Limited/visible only selectively (Bloom Energy, CleanSpark, some utilities/energy transition names); not a dominant theme.
    • Precious metals and energy provide intermittent commodity exposure.
Concentration risk exists in individual large positions (e.g., Bank OZK, Costco, AMETEK in later quarters) rather than pure sector bets.4. Risk Management: Diversification and Defensive Ratio
  • Diversification: Moderate to high name count across sectors, but effective diversification is reduced by large individual positions and sector tilts (tech + financials dominate visible large holdings). Many small positions provide breadth; a handful of names drive significant capital. Geographic exposure includes U.S. core plus Canadian banks/energy and some international (e.g., certain ADRs or foreign listings).
  • Defensive Stock Ratio: Relatively low-to-moderate. True defensives (utilities, staples beyond Costco/Coca-Cola variants, healthcare services) are present but not dominant. Cyclical and growth names (tech, banks, industrials, discretionary) form the core. Costco, certain healthcare, and gold/mining positions act as partial hedges. Overall risk posture appears growth/cyclical-oriented with tactical defensive overlays rather than a high defensive allocation.
Position sizing volatility and entry/exit patterns suggest active risk management via concentration and rotation rather than strict position limits or heavy hedging (13F shows only long equity).5. Forward-Looking InsightsBased on historical patterns:
  • Continued AI / Tech Infrastructure Bias: Expect ongoing or increased allocation to semiconductors, networking, cybersecurity, and cloud software if AI capital expenditure remains elevated. Names already scaled (Arista, Cloudflare, related) may persist or expand.
  • Financials / Regional Banks: Likely maintained or grown if rate cuts stabilize net interest margins and credit conditions remain benign. Large late-period bank positions indicate conviction; watch for further scaling or profit-taking on rate path clarity.
  • Industrial / Infrastructure Rotation: API Group, equipment, materials, and construction-related names have grown; further emphasis possible under fiscal spending, reshoring, or manufacturing recovery themes.
  • Consumer and Defensive Overlay: Costco-style staples and selective retail may serve as ballast. Pure growth consumer may remain tactical.
  • Macro Sensitivity: Portfolio has shown responsiveness to rate cycles (cyclical/financial expansion with easing expectations) and risk-on environments (tech + industrials). In a higher-for-longer or renewed inflation scenario, expect rotation toward energy/materials and value. Geopolitical escalation could increase commodity/mining weights. Recession signals would likely prompt faster de-risking of cyclicals toward remaining defensives and quality tech.
  • Overall Direction: Active, hybrid growth-value manager with thematic AI and late-cycle industrial/financial emphasis. Future path favors continued sector rotation over static allocation, with potential for further concentration in high-conviction large positions while maintaining a broad mid-cap opportunity set. Diversification should remain a focus to mitigate single-name and sector risk.
Caveats: Analysis is constrained by truncated data, lack of full position values for all periods, absence of short positions or derivatives, and no manager identity or total AUM. Sector classifications are approximate based on company names. For precise quantification, complete cleaned data with standardized sector mappings and market-value normalization would be required. Macro correlations are interpretive based on public rate and geopolitical timelines coinciding with the filing windows.