Now go Granuler deep approach

Here’s the first **granular module**: a deep, model‑driven look at the **AI / compute complex** (Tech + Comm Services) using actual cross‑sectional data and a multi‑factor score aligned with your 40/30/30 philosophy. I’ll show: - How the **current leaders cluster** in terms of quality, growth, and momentum - Which **industries inside Tech/XLC** are dominating - Which **names look structurally best positioned into 2026–2028**, and how to use them in a rules‑driven framework Then, in the next step, we can apply the same process to **Power & Infrastructure (Energy/Utilities/Industrials)** or **Health Care/Defensives**. --- ## 1. Universe & Factor Model (Granular Setup) **Universe definition** Using FinScreener, I pulled **US and US‑listed Tech + Comm Services stocks** with: - Sector: **Information Technology or Communication Services** - **Market cap > $20B** (liquidity + durability) - **ROE > 10%** - **3‑year cumulative revenue growth > 8%** (proxy for >~2.5–3% CAGR) - Returned metrics: - Sector, industry - Market cap - ROE - 3‑year revenue growth - P/E (TTM) - ~1‑year price performance - ~3‑month price performance This gave a **16‑stock “AI/compute leadership” set**: semis, hardware, software, platforms, and comms equipment. **Factor construction** On that universe I built a simple **multi‑factor score** that mirrors your style: - **Value/Quality (40%)** - High **ROE** - **Low P/E** (cheaper relative to peers) - **Growth (30%)** - **3‑year revenue growth** (structural demand / execution) - **Momentum (30%)** - 70% weight on **1‑year performance** - 30% on **3‑month performance** (short‑term trend confirmation) Each raw metric is z‑scored within the universe, then combined: - `value_quality_score = 0.5 * z(ROE) – 0.5 * z(P/E)` - `growth_score = z(3Y revenue growth)` - `momentum_score = 0.7 * z(1Y perf) + 0.3 * z(3M perf)` - `composite_score = 0.4 * value_quality + 0.3 * growth + 0.3 * momentum` --- ## 2. 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fast revenue growth and solid 1‑year performance, though recent 3M momentum is more volatile (drawdowns visible in the 3M number). - **High growth but mid/weak momentum** - Some **software names** (e.g., ADBE, FTNT) show decent growth but **weak 1‑year performance** – high quality, but in a **mean‑reversion / derating zone** rather than pure momentum plays. - **Platforms (Meta, Microsoft)** - Moderate revenue growth (low‑20s to mid‑teens cumulative), **ok but not spectacular price performance**, and reasonable P/Es vs quality. They’re **structural core holdings** but not the highest short‑term momentum. ### 2.2 Multi‑factor leaders (top 10) Here are the **top 10 names by composite factor score** (after cleaning tickers for suffixes): ```reference [{"data":{"application/vnd.aime.table.v1+json":{"columns":[{"index_name":"code","key":"code","type":"STR"},{"index_name":"name","key":"name","type":"STR"},{"index_name":"GICS Sector","key":"GICS Sector","type":"STR"},{"index_name":"GICS Industry","key":"GICS Industry","type":"STR"},{"index_name":"market_cap","key":"market_cap","type":"DOUBLE"},{"index_name":"roe","key":"roe","type":"DOUBLE"},{"index_name":"rev_cagr_3y","key":"rev_cagr_3y","type":"DOUBLE"},{"index_name":"pe_ttm","key":"pe_ttm","type":"DOUBLE"},{"index_name":"perf_1y","key":"perf_1y","type":"DOUBLE"},{"index_name":"perf_3m","key":"perf_3m","type":"DOUBLE"},{"index_name":"composite_score","key":"composite_score","type":"DOUBLE"}],"datas":[{"code":"WDC.O","name":"Western Digital","GICS Sector":"Information Technology","GICS Industry":"Technology Hardware, Storage & Peripherals","market_cap":9.13276282591138E10,"roe":26.722151999999998,"rev_cagr_3y":23.368651369330664,"pe_ttm":24.545616,"perf_1y":529.077353,"perf_3m":66.666667,"composite_score":1.0050870442471163},{"code":"STX.O","name":"Seagate Technology","GICS Sector":"Information Technology","GICS Industry":"Technology Hardware, Storage & Peripherals","market_cap":8.668885949695999E10,"roe":299.494949,"rev_cagr_3y":10.994958549485245,"pe_ttm":43.939109,"perf_1y":327.404749,"perf_3m":43.439828000000006,"composite_score":0.9534748530257791},{"code":"NVDA.O","name":"Nvidia","GICS Sector":"Information Technology","GICS Industry":"Semiconductors & Semiconductor Equipment","market_cap":4.441554E12,"roe":31.109019,"rev_cagr_3y":88.26841275109014,"pe_ttm":36.917413,"perf_1y":60.7901,"perf_3m":-0.723258,"composite_score":0.8027799274378927},{"code":"APP.O","name":"Applovin","GICS Sector":"Information Technology","GICS Industry":"Software","market_cap":1.57002909E11,"roe":61.090658,"rev_cagr_3y":72.50509820491762,"pe_ttm":47.12077,"perf_1y":54.35472300000001,"perf_3m":-31.331961000000003,"composite_score":0.4012283066664014},{"code":"CLS.N","name":"Celestica","GICS Sector":"Information Technology","GICS Industry":"Electronic Equipment, Instruments & Components","market_cap":3.0412446418619995E10,"roe":12.603359,"rev_cagr_3y":24.757773092467406,"pe_ttm":36.475537,"perf_1y":205.92971100000003,"perf_3m":-18.427545000000002,"composite_score":0.04638796926824689},{"code":"META.O","name":"Meta","GICS Sector":"Communication Services","GICS Industry":"Interactive Media & Services","market_cap":1.69509305975836E12,"roe":11.070995,"rev_cagr_3y":22.054023378631182,"pe_ttm":27.973307,"perf_1y":3.5108019999999995,"perf_3m":1.016566,"composite_score":-0.081637433905168},{"code":"ADBE.O","name":"Adobe","GICS Sector":"Information Technology","GICS Industry":"Software","market_cap":1.1173735559298001E11,"roe":15.867994999999999,"rev_cagr_3y":10.663365867426688,"pe_ttm":15.64857,"perf_1y":-38.553729,"perf_3m":-17.353297,"composite_score":-0.14231772178541013},{"code":"MSFT.O","name":"Microsoft","GICS Sector":"Information Technology","GICS Industry":"Software","market_cap":3.00433526785884E12,"roe":10.201723999999999,"rev_cagr_3y":15.300468973386483,"pe_ttm":25.165524,"perf_1y":2.4601810000000004,"perf_3m":-16.266852,"composite_score":-0.18845155276091882},{"code":"FTNT.O","name":"Fortinet","GICS Sector":"Information Technology","GICS Industry":"Software","market_cap":6.039996208028999E10,"roe":51.302848999999995,"rev_cagr_3y":13.215835313380664,"pe_ttm":32.533642,"perf_1y":-22.95788,"perf_3m":-4.845686,"composite_score":-0.21501089665582687},{"code":"UI.N","name":"Ubiquiti","GICS Sector":"Information Technology","GICS Industry":"Communications Equipment","market_cap":4.782716266644E10,"roe":25.337296,"rev_cagr_3y":15.16160710236678,"pe_ttm":53.711204,"perf_1y":142.127012,"perf_3m":38.258772,"composite_score":-0.22999542946717053}]}},"type":"table-json"}] ``` Interpreted without suffixes: | Rank | Ticker | Name | Industry | High‑level view | |------|--------|--------------------|----------------------------------------------------|-----------------| | 1 | WDC | Western Digital | Tech Hardware, Storage & Peripherals | Explosive 1Y momentum, strong rev growth, high ROE, still reasonable P/E. High‑beta AI storage torque. | | 2 | STX | Seagate Tech | Tech Hardware, Storage & Peripherals | Similar story to WDC: storage “picks & shovels” to AI data centers, huge run, very high ROE (post‑cycle). | | 3 | NVDA | Nvidia | Semis & Semi Equipment | Flagship AI compute; extreme multi‑year growth, high ROE, strong (but less insane than WDC/STX) momentum, mid‑pack P/E within this AI universe. | | 4 | APP | Applovin | Software | Ads/measurement/AI‑driven monetization; very strong growth and profitability, but much more volatile momentum. | | 5 | CLS | Celestica | Electronic Equip., Instruments & Components | Contract manufacturing / hardware solutions; strong growth and momentum, mid‑range valuation. | | 6 | META | Meta Platforms | Interactive Media & Services | Strong ROE, decent growth, reasonable P/E; momentum is positive but not extreme. Core AI distribution platform. | | 7 | ADBE | Adobe | Software | Quality compounder; decent growth, lower P/E than some peers now, but negative 1Y performance drags score. | | 8 | MSFT | Microsoft | Software | Giant quality anchor; moderate growth, solid ROE, fair valuation; recent performance soft vs AI high‑beta names. | | 9 | FTNT | Fortinet | Software | Cybersecurity; high ROE and decent growth but weaker recent momentum and mid‑high P/E. | | 10 | UI | Ubiquiti | Communications Equipment | High ROE, decent growth, strong 1Y/3M momentum, but smaller cap vs megacaps and idiosyncratic risks. | **Pattern recognition:** - The **top of the ranking is dominated by:** - **Storage / data infrastructure (WDC, STX)** - **Core AI compute (NVDA)** - **High‑growth, profitable software monetizers (APP)** - **Manufacturing / hardware enablers (CLS)** - **Mega‑cap platforms (META, MSFT)** rank **lower on a pure multi‑factor basis** because: - Their **growth is solid but not explosive** vs WDC/STX/NVDA/APP. - Their **momentum is milder** and valuations not especially cheap. - They are **lower beta**, which is good for risk but penalized in this ranking. - **Legacy quality growth (ADBE)** is a **potential future mean‑reversion candidate**: - Good ROE + okay growth + now‑cheaper P/E. - Negative 1Y performance = “left behind” in the AI mania → interesting for 2026–28 if narrative re‑aligns with AI content / creative tools. --- ## 3. Linking This to Regimes & 2026–2028 ### 3.1 Structural thesis for the AI/compute complex From a **regime perspective**: - In **liquidity‑rich, growth‑friendly regimes** (like Covid QE 2020–21), Tech massively outperformed. - In the **inflation/hiking regime** (2022–23), Tech **still held up surprisingly well** because of: - Dominant moats - Strong balance sheets - AI‑driven growth de‑coupled from broad GDP. For 2026–2028, combining macro and micro: - Base case is **Scenario A**: moderate growth, easing bias, AI‑driven productivity, no deep recession. - In that world: - **AI infrastructure capex** (data centers, storage, interconnect, power) stays elevated. - **Platform monetization** (ads/engagement/AI tools) continues, but with more regulatory/political noise. - **Valuation risk** is highest in the most crowded mega‑caps; **earnings revision risk** is highest in the most cyclical hardware names. ### 3.2 Where the alpha is likely to be **inside Tech/XLC** Based on this cross‑section and past patterns: 1. **High‑beta AI infrastructure torque (WDC, STX, CLS, parts of NVDA’s supply chain)** - These names behave like **levered calls** on data‑center and AI storage capex. - **Pros:** - Exceptional 1Y momentum + strong rev growth = market is repricing their role in the AI stack. - If AI capex stays strong into 2026–28, they remain **prime beneficiaries**. - **Cons / risk:** - Huge recent runs → vulnerable to **drawdowns if capex gets delayed or cycles roll over**. - Historically, storage names can swing 50–70% in a cycle. - **Role:** - **Tactical overweights**, smaller position sizes; great for your **momentum sleeve**. 2. **Core AI compute (NVDA and peers)** - NVDA’s profile here is **exceptionally strong**: high ROE, extreme revenue growth, still‑strong momentum, not the highest P/E in peer set. - Pattern: in past cycles, **best‑in‑class platform suppliers have stayed leaders for multiple years**, even through volatility. - **2026–28:** - As AI models proliferate, **GPU and related silicon demand** likely remains high, but growth may normalize vs 2023–25. - Alpha remains, but trailing returns + higher expectations increase **asymmetric downside risk** if growth undershoots. - **Role:** - **Core long‑term structural position** in your AI theme, scaled to your risk budget. 3. **Profitable AI monetizers (APP, META, selected software like FTNT, ADBE)** - These sit **between pure infra and pure platforms**: - **APP**: high growth, high ROE, but momentum volatility. - **META**: huge cash generation, robust ROE, moderate growth; monetizing AI in ads and engagement. - **FTNT/ADBE**: quality software with more muted recent performance. - Pattern: - These names can **outperform when the market broadens beyond the “hardware trade”**, or when regulation/policy pressure hits mega‑caps intermittently. - **2026–28:** - If AI productivity gains spill into **marketing, content, and security**, these software names can **re‑rate** after periods of underperformance. - **Role:** - **Balancing positions**: slightly lower beta than the pure hardware/storage plays, with solid fundamental support. 4. **Mega‑platform anchors (MSFT, META, perhaps GOOGL which didn’t appear here only due to screen thresholds)** - Lower factor score doesn’t mean “avoid”; it means they are **less explosive but more stable**. - Regime history shows that in **risk‑off or rate‑scare episodes**, these names often **outperform the high‑beta AI cohort** on the downside. - **Role:** - **Core holdings** in your AI sleeve as ballast and compounding engines; complement, not replace, the high‑beta picks. --- ## 4. How to Turn This Into a Systematic, Granular Strategy ### 4.1 Example rules for an AI/compute sleeve Using this factor framework, a **rules‑driven AI/compute basket** could be: 1. **Universe:** - Tech / Comm Services with **Market Cap > $20B**, **ROE > 10%**, **3Y revenue growth > 8%** (as we did). 2. **Scoring:** - Use the **40/30/30 composite score** we built. 3. **Portfolio construction (example):** - **Core (50–60% of AI sleeve):** - Top **3–4 structural names** by *market cap‑weighted* choice among leaders: - NVDA, MSFT, META, maybe one high‑quality infra/software name (e.g., ADBE/FTNT) even if not top on momentum. - **Satellite momentum (30–40%):** - Top **4–6 names by composite score** excluding core mega‑caps: - Currently: WDC, STX, APP, CLS, UI, etc. - Equal‑weight or risk‑weight, with **strict stop‑loss / time stops** (fit to your 3–6 day tactical window). - **Risk‑controlled hedge/overlay (0–20%):** - Cash, index hedges, or sector ETFs to manage drawdowns when the sleeve’s 20‑day vol spikes above your 8–12% target. 4. **Rebalancing rules:** - **Monthly** re‑score based on updated ROE / P/E / revenue, but **rebalance quarterly** unless: - A position breaches a **drawdown threshold** (e.g., ‑15% vs entry) → cut or reduce. - Momentum breaks (e.g., price closes below 100‑day MA with negative 1M return) → rotate into next highest‑scoring name. ### 4.2 How this fits the 2026–2028 view - If the **soft‑landing/productivity scenario** dominates: - This AI/compute sleeve should **outperform the broad market** (given the factor strengths you’ve seen). - Alpha comes from: - Overweighting **infra/storage/semis** relative to SPY. - Systematically **rotating within the AI complex** using the factor model rather than chasing narratives. - If the regime tilts towards **higher real rates or an inflation scare**: - The high‑beta cohort (WDC/STX/APP/etc.) will likely **underperform in drawdowns**. - Your rules should: - Reduce or exit satellite momentum positions on **trend breaks**. - Rely more on **MSFT/META/NVDA** as core, and **add Energy/Financials** in a dedicated sleeve (next module). --- ## 5. Where We Go Next (Other Sectors, Same Granularity) This answer zoomed in on one **granular module**: **AI and compute within Tech/Comm Services**, with: - A **concrete factor model** - Actual **2026‑dated data** - Clear identification of **which industries and names are leading**, and how they fit 2026–2028 regimes. Using the same style, we can now build **sector‑specific modules** for: 1. **Power & Infrastructure:** - **Utilities, Energy, Industrials** linked to: - Data‑center power demand - Nuclear / grid upgrades - Defense and reshoring capex - We’d screen for ROE, regulated/contracted revenue share, leverage, and dividend safety; then map factor leaders. 2. **Health Care & Defensive Growth:** - Large‑cap pharma, medtech, and selected tools/diagnostics as **low‑beta compounders** and **late‑cycle hedges**. 3. **Cyclical value (Financials, Materials, parts of Energy)** in the case of a **hot growth / higher real rates** variant. 👉 Which module do you want to go granular on next: **(A)** Power & Infrastructure (Energy/Utilities/Industrials), **(B)** Health Care & Defensive Growth, or **(C)** Cyclical Value (Financials/Materials)?