TIP780: Top Stocks for 2026 w/ Shawn O'Malley, Daniel Mahncke, & Clay Finck

What It Covers

Three TIP analysts pitch their top 2026 stock picks: Sean O'Malley presents Exor NV as a discounted Ferrari proxy trading at 60% below net asset value, Daniel Mahncke pitches MercadoLibre's 27-quarter streak of 30%+ growth, and Clay Finck advocates for Meta's AI-driven advertising dominance.

Key Questions Answered

• •Holding Company Discount Strategy: Exor NV trades at 60% discount to net asset value versus historical 25-30% discount, with Ferrari stake alone worth more than Exor's entire market cap. Investors effectively buy Ferrari at half price plus get Stellantis, CNH Industrial, and other assets free, creating asymmetric upside even if NAV grows only 5% annually.
• •Latin American E-Commerce Runway: MercadoLibre maintains 27 consecutive quarters of 30%+ revenue growth with e-commerce penetration at just 14-15% in Latin America versus 24% in US and 30% in UK. The company operates hybrid logistics model owning fulfillment centers and fleet while partnering with local delivery services for last-mile, achieving Amazon-like reliability without full capital intensity.
• •Meta's AI Monetization Advantage: Meta generates $50 billion annual run rate from Reels while ad pricing increased 10% year-over-year through AI-enhanced targeting and recommendation algorithms. The company trades at 22x adjusted PE despite 20%+ revenue growth and deploys $40 billion in buybacks, with WhatsApp's 3 billion users representing untapped monetization opportunity ahead.
• •Ferrari's Luxury Business Model: Ferrari compounds earnings per share at 18% annually for a decade with 20%+ returns on invested capital, driven by 8-10% annual price increases that customers accept without resistance. Approximately 80% of sales come from repeat customers, and vehicles appreciate over time rather than depreciate like traditional automobiles.
• •MercadoPago's Data Advantage: MercadoPago achieves 20%+ risk-adjusted margins on lending, double Nubank's rate, by leveraging first-party behavioral data from marketplace transactions including purchase history, return rates, and payment patterns. This proprietary data enables more aggressive but informed credit decisions with NPLs managed through reserves exceeding expected losses across major markets.