💡 What I Learned from Reverse Engineering IPO Performance
Over the past few days, I tackled an IPO analysis assignment that turned into a pretty eye-opening deep dive into market behavior, risk, and technical indicators. Here's a breakdown of what I learned from it — and why you might want to replicate some of this logic in your own financial models 👇
📊 Withdrawn IPOs Can Reveal Market Trends
Using scraped data from stockanalysis.com, I categorized 100 withdrawn IPOs by company type. Surprisingly, Acquisition Corporations had the highest total withdrawal value — possibly hinting at cooling SPAC enthusiasm.
📉 Most IPOs Underperform After 1 Year
From 75 IPOs that launched in the first five months of 2024, I computed one-year returns and Sharpe ratios.
➡️ The median Sharpe ratio was only 0.08, well below the risk-free rate of 4.5%.
It turns out, buying IPOs blindly isn’t alpha-generating — volatility-adjusted returns are often negative.
📈 Holding for 2 Months Yields the Best Growth
I simulated a simple fixed-holding strategy — buying IPOs on the first day and selling them after n months.
Result? Holding for 2 months showed the highest average growth (0.94), slightly outperforming other holding windows up to 12 months.
🧠 RSI < 25 = Consistent Alpha?
I also tested an RSI-based rule: buy whenever a stock is oversold (RSI < 25).
Running this over a dataset from 2000–2025 produced a $42K return from 1568 trades of $1000 each.
🔍 Bonus Idea: Filtering for Profitable IPOs
To improve IPO strategies, I’d experiment with:
Filtering by sectors with proven resilience
Avoiding companies without clear IPO pricing data
Using momentum or macro indicators post-IPO before investing
I’ll be publishing the full notebook on GitHub shortly. DM or comment if you want the link or want to fork the analysis.
📌 Takeaway: Even with simple logic, public market data can surface patterns worth exploring — and a lot of traditional IPO hype doesn’t translate into long-term gains.