- Top hedge funds are rotating toward energy, defense/aerospace, select industrials, and still‑dominant AI/semiconductor names, while trimming some mega‑cap tech, consumer, and rate‑sensitive growth.hedgevision.
- Across recent 13F rundowns, new positions and largest adds cluster in technology platforms (GOOGL, META, ASML, TSLA), but there is a noticeable pickup in energy and resource names like XOM and MTDR, and industrial/financial infrastructure plays.
- Several marquee funds fully or partially exited prior winners (NVDA, NFLX, MSFT, AMZN, META in some portfolios), suggesting more selective, valuation‑sensitive exposure to big tech rather than blanket overweighting.
- Investors can mirror these tilts with sector and factor ETFs such as XLK (tech), XLE (energy), XLI (industrials), XLU (defensives), SMH or SOXX (semiconductors), and XAR or ITA (aerospace/defense).
- Tickeron’s AI trading bots are built to respond to exactly this kind of rotation, reallocating among sectors and names as hedge‑fund‑style factors—momentum, earnings revisions, volatility regimes—shift over time.
What the latest 13Fs say about “smart money” rotation
Recent 13F roundups for major hedge funds and “superinvestors” show a market that is no longer blindly piling into the same handful of mega‑cap tech names. Instead, portfolios are becoming more bar‑belled: concentrated bets on AI and platforms on one side, and increased exposure to energy, industrials, and select financials on the other.
A Q4 2025 review of Tiger Global, Whale Rock, Pershing Square, Lone Pine, TCI, SRS, and others highlights top buys in companies such as Coupang (CPNG), Wealthfront (WLTH), ServiceNow (NOW), Alphabet (GOOGL), Carvana (CVNA), Frog (FROG), Meta Platforms (META), Amazon (AMZN), ASML (ASML), DoorDash (DASH), Tesla (TSLA), and Airbnb (ABNB). At the same time, these funds trimmed or sold down positions in Microsoft (MSFT), Apple‑like software names (APP), Taiwan Semiconductor (TSM), Amazon (AMZN) in some portfolios, Nvidia (NVDA), Netflix (NFLX), Starbucks (SBUX), and others, indicating profit‑taking and factor rebalancing rather than outright tech abandonment.
Dataroma’s superinvestor activity reinforces this picture: long‑term managers are adding to high‑quality compounders such as Fair Isaac (FICO), S&P Global (SPGI), Moody’s (MCO), Visa (V), and Monolithic Power Systems (MPWR), while fresh buys in Exxon Mobil (XOM) and resource names like Matador Resources (MTDR) point to a quiet but real tilt into energy.
Where the top 10 funds are adding vs. cutting
New entries and biggest adds
From the consolidated 13F summaries, several themes stand out.dataroma+3
- AI platforms and semis: GOOGL, META, ASML, TSLA, NVDA, and MSFT are still core holdings for many top funds, with new or increased positions reported by Whale Rock (GOOGL, SNDK, CVNA, FROG), Pershing Square (META, AMZN), Lone Pine (ASML, DASH), SCGE (GOOGL, TSLA, CRDO), and others.
- Quality financial/analytics moats: FICO, SPGI, MCO, V, and MA saw meaningful additions at Valley Forge and other value‑oriented funds, signaling confidence in durable pricing power and data monopolies.
- Energy and resources: Dataroma’s aggregated insider/superinvestor list shows new or growing positions in XOM and MTDR, alongside other hard‑asset plays like Alpha Metallurgical Resources (AMR) and Black Stone Minerals (BSM).
- Travel, platforms, and consumer tech: ABNB, RBLX, DASH, and CPNG feature among the top new buys at SRS and other growth funds, indicating a willingness to bet on normalized travel/experience spending and platform economics.
Full exits and major trims
On the flip side, some previous darlings have been reduced or exited.
- Big tech trims: MSFT, NVDA, NFLX, and even AMZN and META appear on several “top sales” lists, as funds take profits or rebalance away from crowded winners while still keeping core exposure through other vehicles.
- Selective China and EM de‑risking: Multiple managers cut stakes in PDD, SE, TSM, and other Chinese or emerging‑market tech names, reflecting geopolitical and regulatory concerns.
- Legacy consumer and fintech: Names like SBUX, PYPL, and certain discretionary holdings show up on sale lists, implying a shift toward higher‑quality growth and away from more cyclical or margin‑pressured stories.
The net effect is a rotation toward AI‑levered tech, high‑moat financials, incremental energy/resource exposure, and selective cyclicals—while de‑emphasizing more vulnerable growth and over‑owned consumer names.
Stocks and ETFs that fit the hedge‑fund playbook
Given these filings, the following companies and ETFs capture the core themes top funds are leaning into (examples, not recommendations).finance.
Representative stocks and tickers
- AI & platforms: Alphabet (GOOGL), Meta Platforms (META), Amazon (AMZN), ServiceNow (NOW), Tesla (TSLA), ASML (ASML), Nvidia (NVDA).
- Semiconductors & hardware enablers: ASML (ASML), Taiwan Semiconductor (TSM), Monolithic Power Systems (MPWR), Lam Research (LRCX).
- Quality financial/data moats: Fair Isaac (FICO), S&P Global (SPGI), Moody’s (MCO), Visa (V), Mastercard (MA).
- Energy and resources: Exxon Mobil (XOM), Matador Resources (MTDR), Alpha Metallurgical Resources (AMR), Black Stone Minerals (BSM).
- Cyclical platforms and travel: Airbnb (ABNB), DoorDash (DASH), Coupang (CPNG), Roblox (RBLX).
ETFs that mirror these sector tilts
| Theme | Example ETFs | What they capture |
| Big tech & platforms | XLK, QQQ, VUG | Overweight to mega‑cap tech and growth leaders. |
| Semiconductors & AI hardware | SMH, SOXX | Concentrated exposure to NVDA, TSM, ASML, LRCX, and peers. |
| Energy & resources | XLE, XOP, VDE | U.S. majors and exploration/production names, aligning with XOM and MTDR buys. |
| Industrials & defense | XLI, XAR, ITA | Capital‑equipment, infrastructure, and aerospace/defense plays benefiting from capex and war‑related spending. |
| Quality financials | XLF, IYF | Broad financials including payments and data/ratings firms. |
An individual investor looking to “shadow” these trends might use ETFs to approximate sector weights, then selectively add or underweight single names where conviction differs.
Tickeron describes a suite of AI trading bots—powered by Financial Learning Models—that are expressly designed to navigate sector rotations similar to those revealed in 13F filings. These models track factors that hedge funds also care about, such as earnings revisions, momentum, volatility clustering, and capital‑allocation signals like buyback cuts and capex surges.
For example, Tickeron has showcased bots that:
- Rotate between energy, industrials, and semiconductors, achieving triple‑digit annualized returns in backtests by shifting exposure as leadership changes.
- Focus on AI and semiconductor stocks such as NVDA, AMD, and TSM, with reported win rates above 80% on short‑interval strategies.
- Use regime‑based allocation, classifying markets into phases like expansion, investment‑heavy transition, and margin compression, and adjusting sector bets accordingly.
In practice, this means that when 13F flows and price action both point toward increased hedge‑fund interest in sectors like energy or AI hardware, the bots tend to pick that up via improving trend strength, breakout patterns, and cross‑sector relative momentum. Conversely, when prior favorites like NVDA or MSFT show persistent distribution or volatility spikes, the models can scale back exposure faster than a typical quarterly 13F‑driven strategy.
If you’d like, I can next turn this into a shorter, newsletter‑style piece aimed at retail investors who want to “follow the smart money” without reading raw 13Fs.
Tickeron AI Perspective
Disclaimers and Limitations