Themes · general research

Robotics and autonomy

Surgical and warehouse robots, chip testing and cobots, autonomy and humanoid robots.

Can lose most

Can lose most of the money. Typical volatility 77 %, typical worst fall −72 %; 2 of 5 companies are more than half below their high.

Mostly profitable large companies (Intuitive Surgical, Teradyne, Tesla); the pure plays are micro-caps that fell 85 % from their high.

Typical volatilityMedian 1-year volatility of the theme's companies (annualised daily moves).
77 %
Typical worst fallMedian of each company's worst peak-to-trough fall in five years (or since listing).
−72 %
Typical distance from the high
−31 %
Typical 1-year return
−16 %

How to hold it in the EU

US-listed ETFs have no KID and cannot be bought by EU retail investors; these UCITS lines can. One fund line spreads the single-company risk.

iShares Automation & Robotics UCITS ETF
ISIN IE00BYZK4552TER 0,40 %Assets 4 979 M € Volatility 1y 22 %Worst fall 1y −13 %Since 2016-09-08
LineWhere
2B76 Xetra · EURFreedom24IBKR
RBOT London Stock Exchange · USDFreedom24?IBKR
Global X Robotics & AI UCITS ETF Acc
ISIN IE00BLCHJB90TER 0,50 %Assets 133 M € Volatility 1y 22 %Worst fall 1y −16 %Since 2021-11-16
LineWhere
XB0T Xetra · EURFreedom24IBKR
BOTZ London Stock Exchange · USDFreedom24?IBKR

Green: available; dashed with “?”: the venue is offered, check the line in the terminal; crossed out: not available.

US theme ETFs (BOTZ) are used only to measure the theme's risk: not buyable by EU retail.

The companies

Research data of 2026-10-15; where we score the company, its live Smart Score and red flags are shown.

Volatility: 1 year; worst fall and best/worst: 5 years or since listing; “12 m” = the share of rolling 12-month windows that at least doubled / at least halved.
TickerMarket capRevenue (y/y)P/SVolatility 1yWorst fallFrom high1 year12 m: ×2 / −50 %WhereOur rating
TSLA
Tesla: EVs, autonomy and humanoid robots
1,45 T$103,6 B$ (+12 %)14×45 %−74 %−25 %−16 %3 % / 4 %Freedom24IBKR2/10
ISRG
Intuitive Surgical: surgical robots
148,5 B$11,0 B$ (+21 %)13×37 %−50 %−31 %−4 %0 % / 0 %Freedom24IBKR5/10
TER
Teradyne: chip testing and collaborative robots
56,2 B$4,5 B$ (+58 %)13×77 %−60 %−26 %+158 %18 % / 0 %Freedom24IBKR5/10Caution. Accruals or the Beneish M-score are in the watch zone. More in the glossary →
SYM
Symbotic: warehouse robotics
22,9 B$2,6 B$ (+21 %)9×79 %−72 %−57 %−46 %42 % / 6 %Freedom24IBKRSerious warning. Share count growing more than 10 % a year over three years. More in the glossary →
SERV
Serve Robotics: delivery robots
298 M$8 M$ (+426 %)38×80 %−85 %−85 %−79 %17 % / 16 %Freedom24IBKR1/10Caution. Negative free cash flow in at least two of the last three years. More in the glossary →

Survivorship: the list is of companies known in 2026; earlier names that went bankrupt or were taken private are missing, so the “×2” share is an upper bound.

Base rates: how such bets usually end

Research results to keep in mind before any upside.

IPOs, 3 years38,5 % of IPOs lost more than half in 3 years; 16,1 % doubled, 0,5 % grew 11× or more.

Jay Ritter, IPO statistics, Table 16e (9,195 IPOs 1975–2021), 23.03.2026

SPAC mergers−51 % average 1-year return after a SPAC merger (2012–2024).

Ritter / Gahng–Ritter–Zhang, Table 15c (625 de-SPACs 2012–2024), 05.10.2026

Thematic ETFs−30 % risk-adjusted over the first 5 years of specialised (thematic) ETFs.

Ben-David, Franzoni, Kim & Moussawi, RFS 36(3), 2023

Lottery stocks> 1 % a month: how much “lottery” stocks (the biggest one-day jumps) underperform.

Bali, Cakici & Whitelaw, JFE 99(2), 2011

Where the gains come from4 % of US stocks created all the net wealth since 1926.

Bessembinder, JFE 129(3), 2018

Volatility drag≈ 46 % a year is lost to volatility at 96 % volatility: +40 % a year on average compounds to about −6 %.

standard approximation: geometric mean ≈ arithmetic mean − σ²/2 (our arithmetic)

Source: Nasdaq historical closes 15.10.2021–14.10.2026; stockanalysis.com fundamentals 15.10.2026; justETF profiles and quotes 15.10.2026 · Research data of 2026-10-15 · Methodology and sources