Fairvalue-Calculator Fairvalue-Calculator
EN DE

Unusual Machines, Inc (UMAC) Fair Value & Analysis

Technology · US · Market cap $933M

Price$19.52
Fair Value$10.98
Upside-43.8%
Quality95/100
Evidence: Low Range $8.24 – $13.73

Analysis

Unusual Machines, Inc (UMAC) currently trades at $19.52, while our model-based Fair Value estimate is $10.98 — implying the stock looks roughly 43.8% overvalued today. We read business quality at 95/100 (high quality), in the Technology sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: low).

About the company

Unusual Machines, Inc. engages in the commercial drone industry. The company offers small drones and essential components. It distributes their products through B2B sale, e-commerce site, and retail channel. The company has a strategic collaboration with Lantronix Inc. for the development of autonomous drone components integrating edge AI compute with flight control systems. The company was formerly known as AerocarveUS Corporation and changed its name to Unusual Machines, Inc. in July 2022. Unusual Machines, Inc. was incorporated in 2019 and is headquartered in Orlando, Florida.

Open the full interactive analysis →

Similar stocks

Compal Electronics, Inc (CEIR) +105% Dell Technologies Inc (DELL) -15% Arista Networks, Inc (ANET) -55% Western Digital Corporation (WDC) -90% Quanta Computer Inc (2382) -31% Hangzhou Hikvision Digital Technology Co (002415) -8%

How we calculate Fair Value

Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.

Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.