Summary: The scale of the upcoming mega IPOs is unlike anything we’ve seen before. Some index providers have rewritten rules to fast-track their inclusion. In contrast, certain systematic investment strategies intentionally exclude recent IPOs until lockup periods have expired and data is reliable; this approach helps avoid a drag on returns as newly listed stocks have historically underperformed the market.
The financial press is transfixed. On the horizon looms a trio of historic corporate debuts: SpaceX, OpenAI, and Anthropic. These are not ordinary initial public offerings; together, their valuations rival the entire UK stock market. Largely, they represent a bet on the most ambitious promises of artificial intelligence. The capital they seek to raise measures in the hundreds of billions of dollars, more than all capital raised in IPOs globally in 2025.
For the retail investor, the narrative spun by Wall Street is intoxicating: a once-in-a-lifetime opportunity to buy a piece of the future on day one. The story feels equally urgent even for the “passive” investor, as benchmark index providers scramble to rewrite long-standing rules to ensure these corporate behemoths find a nearly immediate home in major indices.
Here at Geometric, these events have generated a handful of questions from our clients, some eager to get in on these Mega IPOs, others asking how to avoid them. This divergence of sentiment highlights why investing is inherently difficult: differences in opinions are what make markets, and the resulting prices reflect an equilibrium where intelligent people, by definition, stand on both sides of a trade. This is precisely why we rely on empirical data and economic theory to guide our decision-making, rather than being swayed by personal opinion or popular narratives.
The Great Index Rewrite: Engineering Acceleration
Historically, index providers balanced market representation with structural execution by enforcing “seasoning periods.” These periods required newly public companies to trade on secondary markets for three to twelve months before becoming eligible for index inclusion. This allowed the market time for price discovery, established liquidity, and absorbed early insider volatility.
But the sheer scale of SpaceX, OpenAI, and Anthropic has triggered a shift. They will enter the public markets as some of the largest market capitalization companies around. If an index provider delays their inclusion, the index risks failing to accurately reflect the overall stock market, potentially resulting in tracking discrepancy.
Consequently, index providers are altering their rulebooks to facilitate engineered acceleration:
● FTSE Russell: Following market consultations, they shortened the post-IPO seasoning window to just five trading days for its Russell Index family. Companies starting with a market cap higher than the smallest company in the Russell Top 500 now qualify for “fast entry.”
● Nasdaq (Nasdaq-100): Introduced a fast-entry rule allowing large, highly liquid IPOs to join the benchmark within approximately 15 trading days (shortened from at least three months).
● S&P Dow Jones (S&P 500): In a surprise move on Thursday, June 4th, S&P Dow Jones announced that it will not change its rules to fast-track companies for inclusion in the S&P 500. Inclusion in the index will still require GAAP profitability and a seasoning period of 12 months.
While index providers view these changes as administrative necessities to maintain market representation, they introduce hidden risks for passive index investors. Fast-tracking a multi-billion-dollar stock means that index funds must purchase tens of billions of dollars of shares within a compressed event window. This pulls passive demand forward, forcing index funds to transact precisely when liquidity may be artificially restricted, and price volatility is highest. Far from a harmless logistical adjustment, this shifts early-life volatility, trading spreads, and structural implementation costs directly onto the passive investor.
The Empirical Reality: IPOs Underperform on Average
When a highly anticipated company goes public, some investors assume that spectacular technological success translates directly to spectacular stock returns. The academic literature, however, soundly refutes this assumption.
The seminal research in this domain was conducted by Professor Jay Ritter of the University of Florida. In his landmark 1991 paper, “The Long-Run Performance of Initial Public Offerings,” Ritter documented a persistent market anomaly: newly public firms systematically underperform the broader market over long horizons.
Ritter’s continuously updated dataset—now spanning 9,253 U.S. IPOs from 1980 through 2024 (with returns tracked through December 31, 2025)—presents an unvarnished view of the post-issue lifecycle. On average, IPOs underperformed established companies of a similar size by 3.6% per year during the five years following their debut. When adjusted for both size and style (book-to-market ratio), the underperformance still averaged 2.1% per year.
This systemic underperformance could be driven in part by behavioral biases. Academic research reveals that young, high-profile companies attract overly optimistic investors who treat them like lottery tickets. Investors willingly overpay for the slim probability of owning the next generational “star performer” (e.g., the next Microsoft or Amazon). Because the distribution of IPO returns is heavily skewed, the average investor overpays for this lottery feature, leading to poor realized returns across the broader cohort of new listings.
This empirical underperformance is further supported by internal research from Dimensional Fund Advisors. Dimensional evaluated the secondary market performance of 6,362 U.S. IPOs from 1991 to 2018. To replicate realistic trading conditions, they constructed a hypothetical market-cap-weighted portfolio of IPOs issued over the preceding 12 months, intentionally excluding the first-day “pop” to account for the fact that institutional and retail investors rarely access the primary offering price.
The results were striking: over the full sample period, the simulated IPO portfolio delivered an annualized compound return of 6.93%, compared to 9.13% for the Russell 3000 Index. This significant drag of 220 basis points per year highlights the structural headwind investors face when allocating dollars to unseasoned equities.
Anatomy of the IPO Drag: Lockups, Adverse Selection, and Price Discovery
To understand why companies like SpaceX or OpenAI may face a challenging transition to the public markets, one must look at the mechanics of an IPO. There are three key factors that can create a disadvantage for public buyers:
1. Adverse Selection and Insider Timing
An IPO is not an act of corporate charity; it is a liquidity event. The insiders, founders, early employees, and venture capitalists, possess a massive informational advantage over public buyers. They control the clock. Insiders choose to go public when they believe the market environment is optimal, the corporate narrative is most compelling, and valuations are maxed out. This asymmetry creates an adverse selection problem: public investors are buying precisely when private insiders have concluded that public capital is willing to pay peak dollar.
2. The Cliff of Insider Lockup Expirations
To stabilize a new listing, underwriters implement “lockup periods,” which typically prohibit insiders from selling their shares for 180 days (or 6 to 12 months) post-IPO. During this initial phase, the tradable “free float” is a small fraction of the company’s true economic size. However, when the lockup period expires, a massive wave of restricted supply suddenly floods the secondary market. Academic data show that this structural supply shock frequently exerts strong downward pressure on the stock price, penalizing early public buyers who bought during the initial float restriction.
3. Intractable Price Discovery
True price discovery requires deep, liquid secondary markets, short-selling availability, and time. In the weeks following an IPO, trading is dominated by momentum, media coverage, and behavioral sentiment. Bid-ask spreads are wide, and trading volume is highly volatile. For systematic managers, trading during this unseasoned phase represents an inefficient expenditure of transaction costs for an asset whose fundamental valuation has not yet been stress-tested by the market.
Evergreen Investing Principles: Factors Still Rule
Some argue that historical data is irrelevant because artificial intelligence represents a paradigm shift that invalidates past economic rules. This argument conflates technological progress with investment returns. History is replete with revolutionary innovations – railroads, automobiles, the internet – that fundamentally transformed human productivity while destroying vast swaths of investor capital due to high starting valuations.
The drivers of expected stock returns are grounded in financial economic theory and remain evergreen. As established by the Fama-French multi-factor models and utilized by Dimensional and Avantis Investments, expected returns are a direct function of the price paid relative to fundamentals. The cost of capital is driven by three primary factors:
Expected Return = f(Market Cap, Valuation, Profitability)
● Size: Smaller companies carry higher risk premiums and higher long-term expected returns than mega-cap giants.
● Valuation (Value vs. Growth): Companies trading at low prices relative to their book value or cash flows (Value) offer higher expected returns than companies trading at premium multiples (Growth), because a higher current price compresses future returns.
● Profitability: Companies with high current operating profits relative to book equity outperform companies with weak or negative profitability.
Where will SpaceX, OpenAI, and Anthropic land within this factor framework? They will enter public markets as ultra-large, hyper-growth companies trading at astronomical valuation multiples. Furthermore, in the case of OpenAI and Anthropic, their business models require massive, capital-intensive investments in computational infrastructure, which suppresses profitability.
Therefore, a factor-based framework classifies these Mega IPOs as Large-Cap Growth, Low-Profitability stocks. Historically, this specific quadrant of the market has exhibited some of the lowest expected returns in equity markets. No matter how brilliant the technology, an investor cannot escape the mathematical reality that paying an extreme premium multiple for low-profitability equity diminishes long-term expected outcomes.
The Systematic Approach: How New Listings Are Treated
Recognizing these structural headwinds, systematic managers like Dimensional do not buy newly IPO’d stocks on day one, nor do they rush to follow index providers down the path of accelerated inclusion.
Dimensional enforces a strict seasoning period, typically excluding newly public companies from its eligible investment universe for the first 12 months of public trading. They do this for several explicit reasons:
1. To Avoid Transaction Costs: The early days of an IPO feature thin liquidity, wide bid-ask spreads, and heightened market impact. Avoiding day-one trading protects portfolios from unnecessary execution drag.
2. To Await Price Discovery and Lockup Expiration: By waiting a full year, systematic portfolios allow the initial hype to dissipate, the market to organically establish equilibrium pricing, and the insider lockup expiration to be fully absorbed.
3. To Gather Reliable Fundamentals: A factor-driven portfolio requires reliable data to accurately measure valuation multiples and operating profitability. A seasoning period allows for consecutive quarters of public financial reporting, ensuring that asset allocation is based on actual financial data rather than pro-forma underwriting projections.
By remaining disciplined, systematic managers shield client capital from the structural disadvantages of the IPO lifecycle while ensuring that when a stock is eventually added, it is done at an efficient price with clear factor characteristics.
This is one of several examples where a “passive but not indexed” approach seeks to improve the odds of success.
The Bottom Line
As SpaceX, OpenAI, and Anthropic prepare to cross the threshold into public markets, the noise will become deafening. Wall Street will urge you to buy the hype. Index providers will mechanically alter their rules to force these stocks into passive benchmarks, exposing index investors to accelerated volatility.
An evidence-based approach filters out the noise and executes on data. While the technological achievements of these companies are highly admirable, a great company and a great investment are two entirely different concepts.
Wealth is unlikely to be built by winning an IPO lottery. Systematically capturing the evergreen dimensions of the global markets, diversifying broadly, minimizing transaction friction, and tilting toward the drivers of expected returns may not generate as many headlines but it can put the odds in the investor’s favor. Let the market chase the hype; a systematic strategy remains focused on the data.
This material is for informational purposes only and does not constitute an investment recommendation or advice regarding any specific security. All investing involves risk, and past performance is no guarantee of future results. The hypothetical and index data cited from academic research is for illustrative purposes only. It does not reflect the deduction of any transaction costs or fees and cannot be invested in directly.
References & Data Sources
Ritter, Jay R., “The Long-Run Performance of Initial Public Offerings.” Journal of Finance, 1991.
Ritter, Jay R., “Initial Public Offerings: Updated Long-run Statistics.” University of Florida Working Data (Data through Dec. 31, 2025).
Dimensional Fund Advisors, “IPOs: Profiles Are High. What About Returns?” Academic Research & Insights.
FTSE Russell, “Consultation on Fast Entry and Seasoning Requirements for Sizable US Equity IPOs” (Official Rule Revisions, May 2026).
Ang, Andrew, Hochberg, Yael V., and Gu, Li, “Is IPO Underperformance a Peso Problem?” National Bureau of Economic Research (NBER).