Pollard Banknote Limited (PBL) Fair Value & Analysis
Consumer Cyclical · CA · Market cap C$428M
Fair value as of: Jun 24, 2026
Analysis
Pollard Banknote Limited (PBL) currently trades at C$17.41, while our model-based Fair Value estimate is C$28.16 — implying the stock looks roughly 61.7% undervalued today. We read business quality at 95/100 (high quality), in the Consumer Cyclical 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
Pollard Banknote Limited, together with its subsidiaries, engages in the manufacture and sale of lottery and charitable gaming products and solutions in the United States, Canada, and internationally. It produces and provides a range of instant tickets and lottery services, including licensed products; distribution; interactive digital internet games, such as eInstants; iLottery services comprising platform and player account management software through Catalyst brand; loyalty and engagement programs; marketing; and instant ticket vending machines. The company also provides easyVEND brand in-lane ticket vending solutions and scanACTIV brand retail solutions for in-lane retail distribution of instant tickets; charitable gaming products, such as pull-tab tickets, bingo paper, bingo daubers, and pull-tab vending machines, as well as ancillary products, including pull-tab counting machines; promotional scratch and win, transit tickets, and parking passes; lottery management services; eT…
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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.