BCA Research on Monday flagged a potential headwind for equity markets as a new surge of large initial public offerings approaches. In a note, Chief Strategist Noah Weisberger said the firm’s review of about 40 years of market history and roughly 12,000 IPOs shows that waves of sizeable listings have historically been followed by weaker forward returns and diminished multiple expansion.
"The coming IPO wave may dampen forward market returns, mute further multiple expansion, and possibly interrupt sector trends," Weisberger wrote, summarizing the firm’s observation that heavy issuance tends to coincide with softer subsequent performance.
Despite the warning, BCA urged investors to treat the pattern as a cautionary signal rather than a trigger to exit markets. The firm noted that only about 20% of mega-IPOs align with market peaks, indicating that most large listings do not mark the onset of prolonged market downturns.
BCA pointed to a set of conditions that are contributing to an increase in IPO activity: strong prior returns, expanding valuation multiples, more accommodative financial conditions, and a firmer phase of the economic cycle. Together, these factors are said to point toward a meaningful uptick in the volume of public offerings.
The research note singled out the technology sector as a particular area of concern. BCA warned that new listings tied to artificial intelligence could reduce the scarcity premium currently enjoyed by established AI beneficiaries. By introducing additional choices for investors, AI-related IPOs could dilute the uniqueness of existing winners and siphon capital toward newly public companies.
Weisberger emphasized that the main threat is not the IPO wave itself but the potential for leadership rotation within AI: "The bigger risk is AI leadership rotation as new listings dilute scarcity premia in existing winners," he concluded.
Contextual note: The firm’s historical analysis covers approximately 40 years and about 12,000 IPOs; its conclusions are based on patterns observed across that dataset.