LaunchTower Factor Screen — 2026-09-14: AMD Leads, META Trails

Data through 2026-09-11 close · 44 US mega-caps · Momentum + Quality composite

---

What we did

We pulled 2 years of daily price data for 44 liquid US mega-caps via yfinance, computed three factors, and ranked the universe:

Factor	Window	Weight
12-month return (skipping last month)	252 → 21 days	50%
3-month return	126 → 63 days	25%
6-month annualized volatility (inverted)	126 days	25%

All ranks are percentile ranks within the universe. Higher score = stronger momentum + lower volatility.

Top 10

Rank	Ticker	12-1 Ret	3m Ret	6m Vol	Score
1	AMD	+210.3%	+164.5%	74.8%	0.7614
2	CAT	+99.9%	+31.5%	42.9%	0.7557
3	CRWD	+108.2%	+54.6%	61.8%	0.7500
4	PANW	+99.7%	+67.4%	54.9%	0.7443
5	JNJ	+49.4%	+0.3%	22.0%	0.7273
6	BAC	+28.3%	+20.5%	19.3%	0.7216
7	LLY	+60.6%	+15.2%	35.4%	0.7216
8	GS	+34.8%	+36.5%	33.4%	0.7159
9	KO	+32.9%	+6.8%	20.7%	0.6989
10	SLB	+47.3%	+26.3%	37.3%	0.6989

Bottom 5

Rank	Ticker	12-1 Ret	3m Ret	6m Vol	Score
40	TSLA	−7.8%	+3.9%	50.7%	0.2159
41	PLTR	+8.9%	−15.2%	69.4%	0.1989
42	NFLX	−35.0%	−15.7%	34.5%	0.1307
43	SMCI	−10.9%	−0.9%	112.3%	0.1307
44	META	−20.5%	−7.5%	44.7%	0.1080

Key takeaways

• AMD is the clear momentum leader: +210% over 12 months, +165% over 3 months.
• Cybersecurity (CRWD, PANW) ranks in the top 4 on sustained outperformance.
• Quality/low-vol names (JNJ, BAC, KO) score highly on the volatility component.
• META is the weakest signal: negative 12-month and 3-month returns.
• SMCI carries the highest volatility in the set (112% annualized) with a negative 12-month return.

Reproduce it yourself

import yfinance as yf, pandas as pd, numpy as np

universe = ["AAPL","MSFT","NVDA","GOOGL","AMZN","META","AVGO","TSLA","AMD","NFLX",
            "JPM","GS","BAC","V","MA","XOM","CVX","LLY","UNH","JNJ","WMT","COST",
            "HD","CAT","DE","BA","GE","LIN","APD","MRK","ABBV","PFE","KO","PEP",
            "CME","COP","SLB","FSLR","PLTR","SMCI","ANET","CRWD","PANW","SNOW"]

data = yf.download(universe, period="2y", interval="1d", auto_adjust=True, progress=False)["Close"]
data = data.dropna(axis=1, how="any")
px = data.pct_change()

ret_12_1 = (data.iloc[-21]/data.iloc[-252] - 1)
ret_3m   = (data.iloc[-63]/data.iloc[-126] - 1)
vol_6m   = px.iloc[-126:].std() * np.sqrt(252)
rank = lambda s: s.rank(pct=True)
score = 0.5*rank(ret_12_1) + 0.25*rank(ret_3m) + 0.25*(1-rank(vol_6m))

out = pd.DataFrame({"ret_12_1": ret_12_1, "ret_3m": ret_3m,
                    "vol_6m": vol_6m, "score": score})
out = out.sort_values("score", ascending=False).round(4)
print(out)

Get the full pack

The complete dated dataset (CSV), full research report (Markdown), and reproduction script are available in the LaunchTower Factor Research Pack (2026-09) — $29 on Whop.

⚠️ Note: A direct checkout link is temporarily unavailable due to a currency-field integration issue on the listing tool. You can browse the store above in the meantime.

Disclaimer

This is research data, not investment advice. Past factor performance does not guarantee future results. Do your own due diligence.

LaunchTower — independent market-data research.