SpaceX IPO Hype vs. RealityE-mini S&P 500 FuturesCME_MINI_DL:ES1!EdgeToolsWith SpaceX preparing for what could become the largest tech IPO in history, the usual predictions are circulating: "this one is different," "guaranteed to double," "don't miss it." Before committing capital based on hype, it is worth looking at what actually happened to investors who bought mega-IPOs on or near their listing date. We analyzed 27 of the largest tech and growth IPOs from the past two decades, from Google (2004) to Reddit (2024), and measured their performance at standardized intervals against the S&P 500. The results are not what most people expect. 1. The IPO universe We selected IPOs that were widely anticipated and heavily covered at the time of listing. The dataset includes companies across tech, fintech, electric vehicles, semiconductors, and consumer sectors: Google, Baidu, VMware, Visa, LinkedIn, Yelp, Facebook, Twitter, Alibaba, Shopify, Snap, Spotify, DocuSign, Zoom, Uber, Lyft, CrowdStrike, Pinterest, Palantir, Snowflake, Airbnb, DoorDash, Coinbase, Rivian, ARM, Birkenstock, Reddit. Returns are measured from the first available closing price after the IPO date, not the offering price. This reflects what a retail investor buying on day one actually received. 2. The first-day pop is a myth for public buyers Fig. 1: First-day returns for all 27 mega-IPOs, sorted. Green bars indicate positive returns, red bars negative. The "IPO pop" that gets reported in headlines refers to the gain from the offering price to the first-day close. That gain goes to institutional investors and insiders who received shares at the offering price. For anyone buying at the opening price on the exchange, the picture is different. Across our 27 IPOs, the average first-day return from close of day one is -0.8%. Only 30% of IPOs were positive on day one. The median is -2.8%, meaning more than two thirds of mega-IPOs closed below where retail investors could first buy. This is not statistically different from zero (t = -0.46, p = 0.65). The biggest day-one winners were Rivian (+22.1%), CrowdStrike (+16.5%), and Visa (+13.9%). The biggest losers: Yelp (-14.6%), Lyft (-11.9%), and Facebook (-11.0%). 3. Returns by horizon Fig. 2: Average cumulative returns at standard horizons for IPOs (blue) vs S&P 500 (gray). Fig. 3: Average excess return vs S&P 500 at each horizon. Asterisks mark statistical significance at p < 0.05. The average picture looks deceptively positive. At the one-year mark, the average IPO returned +17.8% vs the S&P 500's +10.0%, an apparent excess of +7.8 percentage points. But this average is driven almost entirely by a handful of extreme outliers: Google (+179%), Palantir (+153%), Reddit (+149%), Zoom (+140%), and ARM (+118%). Remove the top five performers and the average excess turns negative. The more telling pattern is the non-monotonic shape: excess returns spike at month three (+9.5%), collapse to -6.4% at month six, then recover to +7.8% at year one. This volatility makes the average unreliable as a predictor for any individual IPO. 4. The paths diverge enormously Fig. 4: Post-IPO price paths for eight selected IPOs (indexed to 100 at listing). Dashed gray line is S&P 500. Fig. 5: Median mega-IPO path (blue) with interquartile range (shaded) vs S&P 500 (dashed gray). First two years after listing. The individual paths show why averages are misleading. Some IPOs like Zoom and CrowdStrike delivered spectacular returns. Others like Rivian, Coinbase, and Lyft lost 50-80% of their value within the first year. The median path (Fig. 5) is the more representative view: it tracks roughly in line with the S&P 500 for the first 100 trading days, then underperforms for months 5 through 12 before recovering. The interquartile range spans from roughly 70 to 250 at the one-year mark. This enormous dispersion is the key finding: buying a mega-IPO is not a bet on IPOs as a category, it is a bet on a single company with extremely high variance. 5. Drawdowns are severe Fig. 6: Maximum drawdown within the first two years after IPO for all companies in the dataset. Every single IPO in the dataset experienced a drawdown of at least 29% within two years of listing. The median max drawdown is -54%. Several companies saw drawdowns exceeding 80%: Rivian (-93%), Coinbase (-91%), VMware (-86%), Palantir (-83%), DoorDash and Snap (-82%). This is structurally different from the broad market. The S&P 500's worst drawdown over any two-year window in this period was roughly 34% (the Covid crash). IPO buyers face two to three times that downside risk as a baseline. Even the best-performing IPO in the dataset, Google, experienced a -29% drawdown within its first two years. There is no such thing as a smooth ride. 6. First-day returns do not predict long-term performance Fig. 7: Scatter plot of first-day return vs one-year return. Green dots beat the market at one year, red dots underperformed. A strong first day tells you nothing about where the stock will be 12 months later. The scatter plot shows no meaningful correlation between first-day returns and one-year returns. Rivian had the biggest first-day pop (+22.1%) and lost 67% over the next year. Reddit dropped 8.8% on day one and gained 149% over the following year. Facebook fell 11% at open and went on to become one of the largest companies in the world (though it took more than a year to recover its IPO price). This matters because first-day price action is what drives the media narrative around an IPO. "Doubled on its first day" generates headlines but does not predict returns. 7. The IPO ETF tells the same story Fig. 8: Renaissance IPO ETF (blue) vs S&P 500 (dashed gray), indexed to 100 at inception (October 2013). The Renaissance IPO ETF (ticker: IPO) holds a basket of recently listed companies and removes them after two years. It provides a diversified way to measure IPO performance as an asset class. Since inception in 2013, the ETF has substantially underperformed the S&P 500. As of May 2026, the S&P 500 sits near 500 (indexed), while the IPO ETF is around 230. This gap widened dramatically after 2021, when the post-Covid IPO bubble burst and many recent listings collapsed. Even including the strong 2020 run-up, the long-term record is clear: as an asset class, IPOs do not compensate for the additional risk. 8. Win rates decline over time Fig. 9: Percentage of mega-IPOs that beat the S&P 500 at each horizon. The 50% line represents a coin flip. On day one, only 30% of IPOs beat the market. At one month and three months, the win rate briefly rises above 50% (56% and 59%), likely reflecting the short-term momentum that attracts buyers. By month six, it drops to 37%. At the one-year mark, only 37% of mega-IPOs have beaten the S&P 500. This is below a coin flip. The average excess return is positive (+7.8%) despite a win rate below 50% because a small number of extreme winners (Google, Palantir, Zoom) pull the mean upward while the majority underperform. This is a classic skewed distribution: most IPOs lose, a few win big. 9. Performance by era Fig. 11: Average one-year excess return vs S&P 500, grouped by IPO vintage. The era matters more than the company. The 2004-2009 cohort (Google, Baidu, VMware, Visa) shows +30.2% average excess return, but this is a tiny sample (n=4) that includes Google's extraordinary run. The 2010-2015 cohort (LinkedIn, Yelp, Facebook, Twitter, Alibaba, Shopify) shows -22.8%, with Facebook's disastrous first year and Alibaba's decline pulling the average down. The 2016-2019 group (Snap through CrowdStrike) shows a moderate +7.1%. The 2020-2021 cohort (Snowflake through Rivian) shows -7.8%, reflecting the speculative excess of the zero-rate era. The 2022-2026 group (ARM, Birkenstock, Reddit) shows +72.6%, but this is only three IPOs and ARM's +118% and Reddit's +137% dominate. Three data points do not make a pattern. 10. What this means for SpaceX SpaceX is a fundamentally different company from most IPOs in this dataset. It has real revenue (Starlink), government contracts, and a near-monopoly in launch services. None of that changes the structural dynamics of IPO pricing: The offering price will be set by bankers to generate a first-day pop for institutional buyers. Public investors buy at the inflated opening price. The first six months after listing will likely see high volatility as the lock-up period expires and insiders begin selling. In our data, the six-month mark shows the worst excess return (-6.4%). The base rate for mega-IPOs beating the S&P 500 at one year is 37%. SpaceX may be the exception, but exceptions are not a strategy. Every single mega-IPO in our dataset, including Google, experienced at least a 29% drawdown within two years. There will almost certainly be a better entry point than day one. 11. Summary Fig. 10: Complete performance table for all 27 mega-IPOs. Across 27 mega-IPOs spanning two decades: The average first-day return for public buyers is -0.8%, not statistically different from zero. 70% of mega-IPOs close below where retail investors could first buy. The average 1-year excess return is +7.8%, but this is entirely driven by a few extreme winners. The median tells a different story. Only 37% of mega-IPOs beat the S&P 500 at the one-year mark. This is worse than a coin flip. The median max drawdown within two years is -54%, roughly two to three times the worst S&P 500 drawdown in the same period. First-day returns have zero predictive value for long-term performance. As an asset class, IPOs have substantially underperformed the S&P 500 since 2013 (Renaissance IPO ETF). For investors considering SpaceX or any future mega-IPO: the data suggests waiting 6-12 months after listing. The best companies eventually prove themselves, but historically they do so at lower prices than day-one buyers paid. References Ritter, J.R. (1991) 'The Long-Run Performance of Initial Public Offerings', Journal of Finance, 46(1) Loughran, T. and Ritter, J.R. (1995) 'The New Issues Puzzle', Journal of Finance Ljungqvist, A. (2007) 'IPO Underpricing', Handbook of Empirical Corporate Finance Ritter, J.R. (2024) 'Initial Public Offerings: Updated Statistics', University of Florida. Available at: https://site.warrington.ufl.edu/ritter/ipo-data/