How To Think About IPOs in 2026

With several high-profile initial public offerings (IPOs) expected in 2026, many investors may be wondering whether buying a stock on its first day of trading is a smart move. Historically, stock performance in the first year post-IPO has been a mixed bag of volatility, with a large minority delivering excess returns in their first year of trading, while a slight majority deliver negative returns. In today’s blog, we analyze data on 30 years of selected IPOs and highlight some recent changes that will likely impact some of the coming year’s largest expected new issues.

Starting with the data, we pulled IPOs from the last 30 years that have traded for at least one year, thus approximately April 1995 to April 2025. We filtered the dataset to only include issuances on the NYSE or Nasdaq exchanges and set a floor on the capital raised in the IPO to $50 million. Finally, we included only IPOs of common stocks, excluding REITs, Master Limited Partnerships (MLPs), and other non-common equity issues. This left us with about 1,500 IPOs over the sample period (1,494 to be exact) that offered up over $600 billion in equity ownership for new issues.

As for performance, we started with a one-year time horizon from the closing price of the first day of trading. We chose the closing price instead of the offer price because ordinary investors rarely have access to the offer price, as the investment banks often allocate much of the shares they have underwritten to large institutional investors. One can quibble with the rationale here, but given typical volatility on the first day an IPO trades, this approach made the most sense. Getting into the findings, starting with one-year price-based returns from the closing price of the first day of trading, the average return generated was 10.5%. You may be thinking that’s not too bad, about in line with equity market expectations. However, averages in this analysis introduce an upward bias, as hypothetically there is no ceiling on the positive returns, but a floor on the losses, as the most an unleveraged investor can lose on an investment is 100%. The range of outcomes in this dataset is very large, and dispersion (volatility) is high; the standard deviation of the one-year returns is 107%. When we measure the median return of the sample, we get a more realistic picture of the potential outcome, a negative return of -4.7%. Interestingly, the middle 50% of outcomes in the distribution are pretty evenly distributed, with the bottom 25th percentile landing at -38.9%, and the top 25th percentile coming in at 36.2%. In terms of a simple distribution of the percentage of IPOs that generated positive returns in their first year of trading compared to those that produced negative returns, the split was relatively close, with the percent positive (46.1% of the sample, producing an average return of 68.7%) slightly below the percent negative (53.9% of the sample, producing an average return of -39.2%). So slightly skewed to the downside, with a very wide distribution of outcomes in the tails.

The large standard deviation tells the story of the variation of outcomes at the finish line, but what about the journey to get there? For that, we calculated the maximum drawdown experienced in the first year of trading for every IPO in our sample. The average drawdown experienced was -48.9%, and the distribution for this dataset was much less volatile than the average one-year return, with the median drawdown coming in at -48%, with a standard deviation of 22%. Said another way, while the variation of outcomes was wide, the variation in the journey was much more similar; that is, it was typically a pretty volatile ride regardless of the ending outcome.

One final bit of data analysis we took on was comparing the one-year returns of the IPO data set to the returns of the S&P 500 based on each IPO's one-year return window. This allowed us to somewhat normalize the outcomes based on how the broad market performed in the year. We get a slightly more negative outcome, with the average difference in performance coming in at 2.4%, with the median performance difference coming in at -12.5%. The volatility in the distribution was similar, as would be expected. The percentage of IPOs that outperformed the S&P 500 was slightly below the percentage that produced positive returns, with just 40.6% producing one-year returns above the S&P 500 during the first year of trading, while 59.4% underperformed. Finally, in terms of drawdowns, only 6.7% of IPOs in the sample (56 total) experienced a less severe drawdown than the S&P 500 during the one-year period. Diversification does its job yet again.

Understanding why these outcomes occur can help investors avoid common pitfalls and better time entry into newly public companies. IPOs have historically had a few things working against them. First, both management and the investment bank or banks involved in the IPO have a vested interest in maximizing the company’s valuation. Whether driven by hopes and dreams or strong fundamentals, higher valuations imply a higher hurdle for future earnings to keep investors excited and engaged. Taking a private company public also creates a liquidity event for existing shareholders. Insider ownership stakes may be sold on the exchanges after a pre-determined lockup period, which adds to expected selling pressure post-IPO. Compounding all of this from the perspective of retail investors is limited access to the initial offering price. The benefit of any pop in price during the initial hours of trading generally accrues to institutional investors that received allocations from the underwriting investment bank(s).

While these historical patterns are important, they may not be a perfect guide for the upcoming wave of large IPOs. Structural changes in how indexes handle new listings could alter the typical post‑IPO experience.

IPOs also traditionally face hurdles for index inclusion. These requirements vary across indexes but generally include float minimums and a minimum amount of time trading on the exchanges for “seasoning” to ensure stability and liquidity requirements are met. This seasoning period delays the mechanical buying associated with index inclusion when passive funds are forced to buy to mimic the index. In response to heavy lobbying from management teams at large private companies expected to IPO this year, index providers are loosening constraints to speed up index inclusion under the guise of ensuring indexes “are more representative of the U.S. equity market sooner” (FTSE Russell Market Consultation, February 2026).

The takeaway for investors is not to avoid IPOs altogether, but to approach them thoughtfully. As seen in the analysis of 30 years of IPO data, there have been a wide range of outcomes, with some massive home runs driving up the average returns, and many strike outs pulling the median measure of one-year returns lower. We suggest investors proceed with any IPO investment with caution and expect to experience a great deal of volatility.

LPL Financial prohibits the purchase of equity IPOs within client accounts.

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Mid-Year Outlook 2026: Key Investor Lessons for the Second Half of the Year

July 9, 2026

Melanie Weischwill | Partner & Financial Advisor July 8, 2026

Don’t rely on AI for personal finance advice, study finds

July 9, 2026

Greg Iacurci@GregIacurci | Personal Finance Reporter Published Tue, Jul 7 202612:21 PM EDT Key Points A new study in the Journal of Financial Planning found that artificial intelligence programs can provide inconsistent, inaccurate or biased recommendations when it comes to personal finance. Researchers prompted seven AI programs — ChatGPT, Claude, Copilot, DeepSeek, Gemini, Meta AI and Perplexity — with questions about emergency savings, asset allocation and withdrawals from a retirement portfolio. The findings align with those of other experts, who recommend using AI as a starting point for financial questions but not as a final authority. When it comes to personal finance, artificial intelligence gives advice that can be inaccurate or demographically biased, and can range widely depending on the particular program that consumers use, according to a new academic research study. The research — which studied seven “widely available” generative AI platforms — found “significant variation” in how GenAI answered prompts about emergency savings, asset allocation and withdrawals from a retirement portfolio. Researchers examined free-access versions of ChatGPT, Claude, Copilot, DeepSeek, Gemini, Meta AI and Perplexity. “GenAI-driven responses may sound confident but can still be incomplete, misleading, or incorrect,” according to the paper, published last month in the Journal of Financial Planning and authored by finance professors at the University of Georgia and University of Rome Tor Vergata in Italy. Its “suboptimal” or biased outputs raise questions “about the consistency and fairness of GenAI-driven recommendations,” according to authors Swarn Chatterjee, Brenda Cude and Gianni Nicolini. The findings come as a large share of Americans are turning to AI to help manage their money. Two out of three Americans — 66% — who have used GenAI said they’ve leveraged it for financial advice, according to an Intuit Credit Karma survey published in September. The share is higher for Gen Z and millennials, at 82% for each cohort. Experts said that AI is generally good at providing high-level overviews of financial topics: For example, why it’s important to diversify investments, or why exchange-traded funds may be better than mutual funds in some cases but not others. However, it has limitations that mean users shouldn’t trust its output blindly, they said. For one, the programs can also provide wrong answers due to so-called “hallucination” of the algorithm, experts said. “One of the things about LLMs that I find particularly concerning is that no matter what you ask it, it’ll always come back with an answer that sounds authoritative, even if it’s not,” Andrew Lo, director of MIT’s Laboratory for Financial Engineering and principal investigator at its Computer Science and Artificial Intelligence Lab, told CNBC in an interview in March. “When it comes to very, very specific calculations of your own personal situation, that’s where you have to be very, very careful,” Lo said. In addition, AI is sensitive to how users write their prompts, meaning small differences in input can lead to variation in its recommendations. AI also doesn’t owe a fiduciary duty to users, meaning it doesn’t legally need to provide financial advice in users’ best interests. Other research studies have also pointed to the limitations of AI for personal finance. In one 2024 study, for example, researchers examined ChatGPT’s ability to provide financial advice. They found it could be a “first stop” for households seeking financial advice, but ultimately found its recommendations to be “generic,” often overlooking certain pertinent information. “We believe that ChatGPT can serve as a starting point in giving and finding financial advice, but its recommendations should be carefully scrutinized and assessed,” according to the study, published in the Journal of Risk and Financial Management. The latest study, in the Journal of Financial Planning, queried the seven GenAI platforms in August 2025 with the same set of prompts. Researchers prompted the platforms with three identical financial scenarios, related to emergency savings, the optimal withdrawal rate from retirement savings and the recommended composition of an investment portfolio. They then used the same prompts, but changed the race and gender of the hypothetical individual to learn if the GenAI recommendations would change. They found “substantial variation in guidance” across platforms relative to emergency savings and asset allocation. “Although the tools often produced recommendations that broadly aligned with generic financial planning principles, such as the 4 percent retirement withdrawal rule, there were significant differences across platforms in suggested emergency savings and portfolio allocations,” researchers wrote. “The findings suggest that GenAl may serve as a helpful starting point for consumers but should complement, not replace, professional financial advice,” they said. Of course, GenAI tools are “still evolving,” and future studies may find different results, they said. And, outputs from the paid GenAI models may differ from those of the free versions that were assessed. Securities and advisory services offered through LPL Financial, a registered investment advisor. Member FINRA/SIPC. CNBC, South Star Wealth Management and LPL Financial are separate entities.

How To Think About IPOs in 2026

Additional content provided by Tucker Beale, Sr. Analyst, Research.

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How To Think About IPOs in 2026

Additional content provided by Tucker Beale, Sr. Analyst, Research.﻿

Additional content provided by Tucker Beale, Sr. Analyst, Research.