Logista Integral, S.A (CDNIF) Fair Value & Analysis
Industrials · US · Market cap $4.5B
Analysis
Logista Integral, S.A (CDNIF) currently trades at $34.17, while our model-based Fair Value estimate is $45.66 — implying the stock looks roughly 33.6% undervalued today. We read business quality at 95/100 (high quality), in the Industrials sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: high) — always confirm before acting.
About the company
Logista Integral, S.A., through its subsidiaries, operates as a distributor and logistics operator in Spain, France, Italy, Portugal, and Poland. The company distributes a range of products and services, including tobacco products; convenience products; pharmaceutical products; electronic top-ups; books; fiscal stamps, postage stamps, and other official documents; and periodicals, collectibles, and magazines. It also provides parcel and express courier, temperature-controlled capillary transport, and long distance and full load transportation services. Logista Integral, S.A. serves clients in various sectors comprising tobacco, publications, books, e-transactions, transport, pharmaceutical, and public sectors. Logista Integral, S.A. was formerly known as Compañía de Distribución Integral Logista Holdings, S.A. and changed its name to Logista Integral, S.A. in February 2024. The company was incorporated in 2014 and is headquartered in Leganés, Spain. Logista Integral, S.A. operates a…
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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.