The Marketing Alliance, Inc (MAAL) Fair Value & Analysis
Financial Services · US · Market cap $12.2M
Fair value as of: Jun 26, 2026
Analysis
The Marketing Alliance, Inc (MAAL) currently trades at $1.72, while our model-based Fair Value estimate is $0.8500 — implying the stock looks roughly 50.6% overvalued today. We read business quality at 95/100 (high quality), in the Financial Services 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: high).
About the company
The Marketing Alliance, Inc. distributes life insurance, annuities, long-term care, and financial products in the United States. It offers various services, including expert tele-viewing, risk communication, application efficiency, case fulfillment, negotiation advocacy, virtual expansion, case rescues, exclusive benefits, underwriting simplification, annuity access, turn-key program, support program, competitive contracts, carrier advocacy, professional network, in-person conferences, leadership access, and quick quote system. The company distributes its products through a network of carriers. It serves life insurance agents, brokers, and agencies. The company was founded in 1996 and is headquartered in Saint Louis, Missouri.
Open the full interactive analysis →
Similar stocks
Frequently asked questions
Is The Marketing Alliance, Inc (MAAL) undervalued?
What is the fair value of MAAL?
What is the quality score of MAAL?
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.