I Built a Tool to Track Dividend Ex-Date Patterns and Added a Bottom Detector. Here's What 17 Years of Data Shows.
What 17 years of data across 2,428 dividend-paying securities shows about ex-date dip patterns, VIX regime performance, and a new multi-signal bottom detection system.
Everything in this article is for research and educational purposes only. Nothing here is investment advice, a recommendation to buy or sell any security, or a suggestion that any pattern will repeat in the future. All investing involves risk. Do your own research and consult a qualified financial professional before making any investment decision.
I am the founder of DivDip, a platform that tracks dividend-paying securities and analyzes ex-date price patterns. I am sharing this data because I think it is genuinely useful to the broader dividend investing community, the same community that taught me most of what I know about income investing.
How this started
A few years ago I read Steve Selengut's Retirement Money Secrets. If you are a dividend investor and you have not read it, stop here and go get it. The framework he lays out, income-focused retirement investing built around quality closed-end funds, systematic profit-taking, and the discipline to let compounding do its work, fundamentally changed how I thought about my portfolio.
What the book does not give you is a systematic way to see ex-date patterns across the entire dividend-paying universe. Which securities drop consistently before their ex-date? How fast do they recover? Does the pattern hold across different market environments? Is there a way to identify when entry conditions are statistically favorable?
Those questions sent me down a research rabbit hole that eventually became DivDip. Along the way I collected 17 years of data across 2,428 dividend-paying securities, including CEFs, ETFs, REITs, BDCs, and dividend stocks, and I want to share some of what that data shows.
What the ex-date data actually shows
The conventional wisdom is that a stock or fund drops by roughly the dividend amount on its ex-date as the market adjusts for the distribution. The reality is more interesting.
Across our database, ex-date price behavior varies significantly by security type. CEFs, particularly those in fixed income and multi-sector categories, tend to show more predictable dip-and-recovery patterns than stocks or ETFs. This makes intuitive sense: CEF distributions are more consistent and predictable, so the ex-date becomes a more reliable calendar event. But the pattern shows up across all security types to varying degrees, which is why we track the full universe rather than CEFs alone.
More interesting is the recovery data. The average days to recover from an ex-date dip varies widely, from under 2 days for some of the most liquid and widely-held funds, to 20 or more days for less liquid securities or those with wider discounts to NAV. That spread is the signal. A security that historically recovers in 4 days looks very different from one that takes 28 days, even if their stated yields are similar.
The regime question
One of the things we wanted to understand is whether these patterns hold across different market environments. Using 9,171 days of VIX history going back to 2008, we tagged each historical signal by the volatility regime at entry: Low VIX below 15, Normal between 15 and 20, Elevated between 20 and 30, and High above 30.
The finding surprised me. Signals entered during elevated and high VIX environments actually showed higher recovery rates than those entered during calm markets. The average hit rate across all securities at a 2.5% target within 3 weeks was roughly 90% in high-VIX environments versus 83% in low-VIX periods.
This is counterintuitive but makes sense on reflection. When the whole market is selling off, discounts widen faster than fundamentals change. The rubber band gets stretched further, and when it snaps back it snaps harder. Market volatility is not the enemy of dividend investors. It can create the entry conditions worth watching.
The hardest part: knowing when a security has stopped falling
Identifying that a security is statistically cheap is one problem. Knowing when it has actually stopped dropping is a different one entirely, and it is where most investors either get in too early or miss the move entirely.
To try to solve this we built what we call Bottom Detection. It does not predict bottoms. Nothing does reliably. What it does is watch every active signal in real time and count how many independent confirmation signals are pointing in the same direction at once.
For stocks and ETFs we use three signals. First, the z-score, which measures how far the price has fallen below its own 2-year rolling average, starts improving day over day while the price closes higher than the prior day. Second, volume is running above the 20-day average, meaning real buyers are showing up rather than just a low-volume bounce. Third, that improvement holds for a second consecutive day rather than reversing. One signal alone is noise. Two or more agreeing at the same time starts to mean something.
For CEFs, REITs, and BDCs we add a fourth signal that does not apply to stocks or ETFs: whether the discount to net asset value is narrowing. When a fund's price is recovering faster than its underlying assets, it means buyers are specifically coming back for the income, not just riding a broad market bounce. That is the most reliable recovery indicator we found for these asset classes.
When two signals agree the system shows an upward arrow labeled Possible in amber. When three or more agree it shows Confirmed in green. Neither is a buy signal. Both are a prompt to look closer at data you might otherwise miss.
What this means for dividend investors of all types
Whether you are building a retirement income portfolio around CEFs, running a dividend growth strategy with common stocks, using ETFs for broad income exposure, or mixing REITs and BDCs into the picture, the underlying question is the same: when a security you want to own goes on sale, how do you know when the sale is actually over?
That is the question this data tries to answer. Not perfectly, not with guarantees, but with more information than a gut feeling and a price chart.
Having 17 years of ex-date history, recovery timelines, pattern consistency scores, and real-time turning signal detection organized in one place changes how you can approach the research process. Instead of manually tracking a handful of securities and guessing at the pattern, you can see the full picture across the universe and let the data tell you when conditions historically worth watching are starting to line up.
That is what we built DivDip to do, and it is built for any dividend investor, not just CEF specialists. If you want to explore the data or the platform, we are happy to answer questions.
For absolute clarity: this is research. Not advice. Not a recommendation. The patterns described here are historical observations only. Markets change, patterns break, and past performance tells you nothing guaranteed about the future. Please do your own research.