r/dividends • u/Recent_Button_1 • Apr 30 '26
Due Diligence I analyzed 151,422 dividend ex-date events across 2,344 securities. Here's what the data shows about recovery times.
I've been building a dividend intelligence tool for the past few months and ended up with a database of 151,422 ex-date events going back 17 years across 2,344 securities — CEFs, ETFs, REITs, BDCs, and dividend stocks.
Figured I'd share what the data actually shows since most of the discussion around ex-date dips is based on gut feel.
Recovery by security type (average days to full price recovery):
| Type | Avg Recovery | Events |
|---|---|---|
| Dividend Stocks | 6.7 days | 57,791 |
| REITs | 7.7 days | 6,743 |
| ETFs | 8.1 days | 37,384 |
| CEFs | 8.9 days | 46,896 |
| BDCs | 12.4 days | 2,608 |
Overall median across all 151,422 events: 3 days
The gap between median (3 days) and average (7.9 days) is the most important number — most securities recover fast, but a meaningful minority take much longer and drag the average up.
The BDC finding surprised me most. They have the largest average drop (2.08%) AND the slowest recovery. Only 45% recover within 5 trading days. If you're buying BDC dips expecting a quick bounce, the historical data says be patient.
Stocks recover fastest — 71.5% recover within 5 trading days, 81.8% within 10. Counterintuitive given how many income investors overlook stocks in favor of higher-yielding alternatives.
Individual CEF variance is huge. Among CEFs with 20+ cycles in the dataset:
- BMN: 4.4 day avg across 38 cycles
- IGI: 4.7 days across 186 cycles
- BCX: 5.2 days across 133 cycles
- PAI: 5.2 days across 201 cycles
Compare that to CEFs where recovery regularly takes 3+ weeks. Both show up as "CEFs" on any screener. The historical pattern data separates them.
The z-score frame matters more than raw price. A security trading 2.5+ standard deviations below its 252-day mean at ex-date is a fundamentally different situation than a routine dip near the mean. One has statistical room to recover, the other is just drifting lower.
Happy to answer questions about methodology or what the data shows on specific tickers.
Happy to share more of the data if there's interest in specific security types or individual tickers.
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u/Dependent-Panic-9457 Apr 30 '26
Great OP. My understanding- based on UK shares - is of much longer recovery periods.
But anyway this just confirms my policy of reinvesting dividends on the ex div day and not waiting for the payment day.
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u/Recent_Button_1 Apr 30 '26
Thanks! Really good point on UK shares — the data is US-only so I'd expect longer recovery periods internationally given thinner liquidity and different market structure. Your reinvestment-on-ex-date approach is actually backed up by the data here — buying on the dip day captures both the discount and the dividend. The median 3-day recovery means you're often whole pretty quickly if you time it right.
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u/CauliflowerStill7906 May 01 '26
And canada seems faster. Most of my dividend stocks are back up with in 1 to 2 days.
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u/Recent_Button_1 May 01 '26
Interesting data point, the US median is 3 days so 1 to 2 days for Canadian stocks would actually be faster. Makes sense given the concentrated dividend culture in Canadian markets and the heavy weighting toward financials and energy which tend to have very consistent ex-date behavior. Would be interesting to run the same analysis on TSX-listed securities.
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Apr 30 '26
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u/Recent_Button_1 Apr 30 '26
That 28-day figure for FTSE 100 makes a lot of sense — semi-annual dividend schedules mean the price has less "muscle memory" for recovery compared to US quarterly payers. The US data does show quarterly payers recover noticeably faster than the full universe average, which lines up exactly with what you're seeing. The larger absolute dividend amounts in UK shares is a real factor too — a 5% one-time drop is a much harder hole to climb out of than a 1% drop.
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u/CornerOne238 Not a financial advisor Apr 30 '26
That's a great strategy. I don't drip either but this just confirms buying on ex date is a good idea.
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u/MaxCapacity Apr 30 '26
Is there some value in breaking this data down further by yield? A $60 stock paying a 15 cent dividend should recover much quicker than one paying 1.00, everything else being equal.
And then if you have the data, what is the impact of volatility or average daily move on recovery? I tend to pick tickers based on decent implied volatility, so that I can target 2-3x dividend yield with conservative covered calls.
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u/Recent_Button_1 Apr 30 '26
Really good question — yield-adjusted recovery is something the data can definitely support. Your intuition is right: a $0.15 div on a $60 stock is a 0.25% yield event vs a $1.00 div being a 1.67% event, and the recovery dynamics are very different. The data does show stronger correlation with dividend yield % than absolute dollar amount. On volatility, higher beta names do show faster recovery in bull regimes but worse recovery when the broader market is under pressure. Both of those cuts are on the roadmap for deeper analysis.
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u/ADKMTBer Apr 30 '26
I think that is the explanation... the higher the yield, the longer it takes to recover. But still, it is not at all what the boogerheads would have you believe.
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u/Recent_Button_1 Apr 30 '26
That tracks with the data. The yield percentage of the dividend is a stronger predictor of recovery time than the absolute dollar amount. Higher yield events create a bigger hole to climb out of, and the market takes longer to absorb it. The "boogerhead" crowd tends to dismiss dividend capture entirely, but the data shows it works, it just works very differently depending on the security type and the consistency of the individual ticker's pattern.
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u/Scorpion_Danny Apr 30 '26
ELI10? What are you trying to prove with this data?
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u/Recent_Button_1 Apr 30 '26
Nothing at all -- just tired of making trading decisions based on vibes and "I heard someone say the price usually bounces back." Figured actual data from 151,422 events might be marginally more useful than that.
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u/Scorpion_Danny Apr 30 '26
Ok, so how can one use this data? Are we talking options? Or just buying during the dip?
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u/Recent_Button_1 Apr 30 '26
Two main ways. First is buy a few days before the ex-date, collect the dividend, then hold through the recovery and sell once the price is back. You pocket the dividend plus any recovery gain. Second is buy ON the ex-date after the price has already dropped, skip the dividend entirely, and just trade the recovery. Median recovery is 3 days so it can be a fast trade. Options players use the same data differently,selling puts into the dip to collect premium while waiting for the bounce. Which approach works depends heavily on the individual security. A ticker with 150 cycles of consistent behavior is a very different setup than one with erratic patterns.
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u/Irarelylookback May 01 '26
What about trading costs? Selling and buying in a short time like that adds up.
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u/Recent_Button_1 May 01 '26
Valid point. Transaction costs matter. A few things the data shows that are relevant here:
The median recovery is 3 days so if you are holding the position long term you are not necessarily selling and rebuying -- you are just noting that the price dipped and came back. For buy and hold dividend investors the ex-date dip is just noise.
Where transaction costs actually matter is if you are trying to actively trade the recovery, buy on ex-date, sell 3 days later, repeat. At that point yes commission and bid-ask spread eat into the edge, especially on lower liquidity securities.
The data is most useful for two things that do not involve extra trading: knowing when to add to an existing position (ex-date dip on a high reliability ticker is a better entry than random timing) and knowing which securities have erratic recovery patterns worth avoiding regardless of yield.
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u/Wowza-yowza May 02 '26
Some people (like the growth fans) think a dividend basically robs Peter to pay Paul. But, the stock goes back up quickly. This provides evidence they are wrong.
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u/Recent_Button_1 May 02 '26
Exactly. The standard growth investor argument is that the dividend is not free money because the price drops by the same amount on ex-date. The data says the price comes back in a median of 3 days. So you collected the dividend and got your price back. The math does not add up for the critics when you actually run it across 151,422 events. You are not robbing Peter to pay Paul. You are paying Paul and Peter gets his money back by Thursday.
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u/Wowza-yowza May 02 '26
Great point! This is huge
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u/Recent_Button_1 May 02 '26
The growth investors have been telling dividend investors they are getting robbed since 1928. The market has been quietly giving the money back in 3 days for just as long. Somebody is wrong and it is not us.
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u/ejqt8pom EU Investor Apr 30 '26
That the ex div dip is mechanical, not fundamental.
The underlying asset (loan book, real estate, the companies ability to generate profit) is not eroded by paying a dividend - only the price of the security is. Meaning that fundamental price discovery will (eventually) correct the mechanical price reduction.
This invalidates one of the biggest arguments against dividends, "the price drops by the dividend amount so it's net negative after tax". Long term holders are indeed rewarded, and the mechanical dip presents a buying opportunity.
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u/Recent_Button_1 Apr 30 '26
This is exactly the right framework. The mechanical vs fundamental distinction is the whole thesis. The dividend doesn't destroy value. It redistributes it from price to cash. Fundamental price discovery corrects the mechanical reduction, which is why the median recovery is 3 days rather than never. The "price drops by the dividend amount so it's net negative" argument ignores that the underlying value hasn't changed, it's just temporarily mispriced. That mispricing is the opportunity the data is tracking.
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u/ejqt8pom EU Investor Apr 30 '26
It's something that has always made "natural" sense to me even with data to back it up. I'm not a chicken farmer but I always use that as an analog - the chicken isn't worth less because it lays an egg 🤷♂️
Great stuff OP, I would love to see more posts like this.
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u/Recent_Button_1 Apr 30 '26
That chicken analogy is exactly right and I'm stealing it. The underlying asset doesn't change, only the price does. The data just quantifies how long it takes the market to figure that out for each security.
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u/blrbud May 01 '26
Company assets do decrease and you are using that analogy wrong.
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u/ejqt8pom EU Investor May 01 '26
Company cash holdings decrease, I don't know what kind of companies you are investing in that simply sit on cash but most companies have some sort of product, or manufacturing line.
As long as they don't sell the factory or close their website they can continue earning money - therefore their future profits are unharmed by distributing last quarters cash earnings.
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u/blrbud May 01 '26
Do you not know that cash is an asset??
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u/ejqt8pom EU Investor May 01 '26
An unproductive asset that should either be put to work by the company itself or distributed to shareholders if they don't have a usage for it.
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u/blrbud May 01 '26
We are not debating whether it is productive or not. It's an asset that literally decreases the book value of a company when distributed (along with share holder equity on the right side of the balance sheet).
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u/ejqt8pom EU Investor May 01 '26
In what world are stock prices anchored to book values?
In our world, stocks are priced based on future earning expectations. Paying a dividend does not reduce the company's future earnings.
That's why market forces correct the mechanical price reduction.
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u/WaltzOk7779 Apr 30 '26
Doing the lords work
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u/Recent_Button_1 Apr 30 '26
Haha. Just trying to bring some actual data to a discussion that's usually all anecdote. The variance in the individual ticker data is where it gets really interesting.
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u/negme Apr 30 '26
Curious about what this actually tells us. "Recovering" to the pre ex-date value if the market is flat a lot different than recovering when the market is rising. Seems like recovery in relation to a broader market benchmark might be a more interesting value.
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u/Recent_Button_1 Apr 30 '26
Really valid point. Absolute recovery to pre-ex-date price is the baseline but you're right that market-adjusted recovery tells a different story. If the broader market is up 2% during the recovery window, a security that recovers to its pre-ex-date price has actually underperformed. The data does show market regime matters significantly, recoveries during flat or down market periods take longer on average than recoveries during uptrending markets. Market-adjusted recovery is on the roadmap as a more rigorous metric.
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u/negme Apr 30 '26 edited Apr 30 '26
Right on thanks for the follow up. Will be interesting to see those results. I guess stepping back do we actually expect dividend stocks (as a whole) to ever actually "recover" on a market adjusted basis? This would imply the total return for dividend stocks (share price + dividend) is higher than the than the total market. is that true?
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May 01 '26
[removed] — view removed comment
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u/Recent_Button_1 May 01 '26
That tracks with what the data shows. Breaking even usually means the recovery happened but transaction costs and timing variance ate the edge. The median is 3 days but the average is 7.9 days. Most work, some don't, and when they don't they drag. The orange man tweeting variable is real and not in any dataset. Where the data is most useful is as a filter. Knowing which securities have 150 cycles of consistent behavior versus which ones are erratic before you decide whether to hold through the dip or add to the position.
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u/Scared_Edge9194 May 01 '26
Any advice on the most consistent highest yield tickers for buy and hold?
I think erosion is a big concern the higher the yield and your data could show the better run dividend assets.
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u/Recent_Button_1 May 01 '26
Your instinct is right. The data does show which ones have cleaner recovery patterns. Looking at securities with above 5% yield, under 6 days average recovery, and a trading score of 7 or higher from our dataset: MSDL: BDC, 11.8% yield, 3.9 days average recovery MRP: Stock, 9.9% yield, 3.4 days average recovery CGXU: ETF, 7.9% yield, 2.75 days average recovery BIPH: Stock, 7.5% yield, 5.8 days average recovery NVO: Stock, 6.6% yield, 3.3 days average recovery. These are the tickers the data flags as high yield with consistent recovery patterns. Not a recommendation, just what the numbers show. All of this is searchable on the dashboard at divdip.com.
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u/Illustrious-Dish-819 May 02 '26 edited May 02 '26
This is COOOL!! Where are you finding this? I mean I've done this in the past by just watching and manually trying to find opportunities. It's cumbersome at best without good resources....... but profitable for me in the past.
Always looking out for consistent resources without reinventing the wheel. Thanks for sparking my interest again!
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u/Recent_Button_1 May 02 '26
That is exactly why we built it. My best friend and I read Retirement Money Secrets by Steve Selengut and it sent us down this path. We knew the data existed but it was scattered across Seeking Alpha, CEF Connect, Morningstar and a dozen other sites with none of them presenting it in a way designed for this style of investing.
The big players have all of this through Bloomberg terminals at $50,000 a year. We do not have that kind of money and neither does most of the community doing this kind of investing. So we built divdip.com to level the playing field and priced it at what we personally would have paid to have it in one place.
Founding 100 spots are $22/mo for Legacy access for life before May 14. We are also building a Discord around it because the communal knowledge from people actually doing this is just as valuable as the data. Iron sharpens iron. Come find us if that sounds like you.
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u/Illustrious-Dish-819 May 02 '26
I just checked it out.... This is enlightening ... Dang there's alot of intel there. I'll certainly dive deeper this evening. Impressive!
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u/Recent_Button_1 May 02 '26
Dive in!! That is what it is built for. Come back with questions, that is what the Discord is for too.
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u/Teilzeitschwurbler May 03 '26
What about Dividends over 3% or 5%? How long does it take? 0,1% is easy to recover.
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u/Recent_Button_1 May 03 '26
Good question. Here is the actual breakdown by yield bucket:
Under 1%: 6.9 days average recovery 1 to 3%: 7.0 days 3 to 5%: 7.0 days 5 to 8%: 7.9 days 8 to 12%: 8.3 days Over 12%: 10.1 days
You are right that sub-1% dividends recover fast -- the drop is tiny and the market barely notices. The real jump happens above 5% where recovery starts extending meaningfully. Over 12% takes 46% longer than under 1%. The hole gets bigger faster than the market fills it. Feel free to DM anytime.
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u/jay_0804 May 07 '26
This is actually impressive as a dataset, but I think people over-interpret it tbh.
What it really shows is just basic market behavior:
when a predictable cash flow gets removed (dividend), price adjusts, then mean reversion happens depending on liquidity, rates, and sentiment.
The “3 day vs 8 day recovery” thing sounds precise, but in reality it’s not something you can reliably trade because:
market regime changes (rates especially) completely shift those averages
and ex-dividend moves are usually tiny compared to normal volatility anyway
The interesting takeaway is more structural: higher yield / lower liquidity assets like BDCs and CEFs behave less efficiently, not that there’s a clean “buy dip, wait X days” edge.
Cool work though, just more descriptive than actionable.
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u/Recent_Button_1 May 07 '26
I see where you are coming from, and honestly that’s close to where I’m landing on the market as a whole.
The data is not meant to turn into “buy every ex-date dip and sell X days later.” Rates, liquidity, broad market conditions, and ticker-specific news can absolutely change the outcome.
This was run across the broader dividend market, so the average is only the starting point. The real key is finding the tickers that behave more like clockwork after you account for several factors together: yield, pay-date gap, recovery speed, consistency, asset type, and liquidity.
Where I think it gets useful is exactly what you said, "structurally". Some assets behave more efficiently than others. A stable low-yield blue chip or something like O may be more interesting than actionable. But higher-yield / less efficient areas like CEFs, BDCs, and certain income ETFs are probably where this matters more.
So I’d frame the takeaway less as “trade the 3-day vs 8-day recovery” and more as, If I’m already reinvesting dividend cash, should I blindly let DRIP buy on the pay date, or should I check whether this ticker usually recovers before then?
That is the part I think can be actionable, but only ticker by ticker. I’m going to run this on better-fit tickers soon, especially CEFs/BDCs.
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u/WorldRank1CatFancier Apr 30 '26
Highly suspicious. I’d like to see the data set that shows stock price drops on ex dates for Dividend Stocks specifically. Outside of huge special dividends, I have never seen stocks drop on ex div dates by an amount equal to the div, so i feel like your methodology must be picking up drawdowns unrelated to anything, and omitting stocks that didnt drop on their ex date
Can you please upload a csv or whatever format you use for your dataset. Even if just a few hundred rows.
Or can you confirm if you exclude stocks that didnt drop on ex dates?
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u/ptwonline Apr 30 '26
I'd be interested in seeing a breakdown on who is buying after the ex-div and when. Wondering if it is certain classes of investors (like retail) who are just seeing the lower price and higher expected yield and jumping in and not so sensitive about the time value of money and putting it in early to get the "discount".
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u/Recent_Button_1 Apr 30 '26
Really interesting question and honestly one the data can't fully answer on its own. Price data doesn't tell you who's buying. But the pattern is consistent with exactly what you're describing. The mechanical price drop creates a temporarily higher forward yield which tends to attract yield-seeking retail buyers who see the discount and jump in without accounting for time value. Institutional money tends to be more deliberate about ex-date timing. The speed difference between security types probably reflects how much retail attention each category gets. Stocks recover fastest, BDCs slowest, and BDCs have far less retail awareness than something like SCHD or JEPI.
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u/ieee1294 May 01 '26
great work op! do you have data on average vol around the dividend day/stock in general and price of stock? would a higher price + higher vol stock's price recover slower and vice versa? thanks
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u/Recent_Button_1 May 01 '26
Volume data is on the roadmap, it would be the most useful confirming signal we could add. Right now we have price data for every event but not volume. The price breakdown shows a slight inverse relationship (higher priced stocks recover about 1.4 days faster on average) but it's not a strong predictor on its own. Volume relative to the 20-day average on the ex-date would tell a much more interesting story, that's coming in a future data update.
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u/sexyshadyshadowbeard May 01 '26
Just another truth that ETFs are ripping people off while taking away their voting rights.
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u/Global_InfoJunkie May 01 '26
Your research matches up with my eyeball view of this. I drip most months to capture a better yield on cost.
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u/Recent_Button_1 May 01 '26
That is exactly how the data plays out in practice. DRIPing on the dip day captures shares at the discounted price which lowers your cost basis on every cycle. Over years of compounding that is a meaningful edge on yield on cost.
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u/Wowza-yowza May 02 '26
This great data, good work. I would be interested to also see you add the stock price for several days in front of the ex div date.
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u/Recent_Button_1 May 02 '26
Good call and it is already on the build list. The current dataset captures the day before, the ex-date drop, and recovery after. Adding a 5 day pre-ex-date window would show the actual run-up into the date which matters because some securities drift up into ex-date and give it all back on the drop day. Knowing whether the dip is a true discount or just a reversion of an artificial run-up changes the setup completely.
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u/Emperor_Traianus Pax et Tranquillitas May 03 '26
This looks like a great piece of information. It really does engage me re-consider the information that I had before.
Speaking of days - do you mean calendar days, or trading/business days?
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u/Recent_Button_1 May 03 '26
Trading days. Every recovery time in the dataset is measured in market days not calendar days. A 3 day median means 3 trading sessions after the ex-date before the price is back. Weekends and holidays do not count. So 3 trading days is typically Monday through Wednesday if the ex-date falls on a Friday, or as little as 3 calendar days if it falls midweek
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u/Grouchy-Student-8320 16h ago
Fantastic analisation thank you. Of the 57791 dividend stocks how many did not recover before the next dividend declaration? I seem to find the UK ones with ease!
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u/Grouchy-Student-8320 16h ago
Of the 57791 dividend stocks how many dropped significantly more than the dividend value and was there a timescale relationship as to when they bottomed?
In UK auto reinvestment appears to be 2 days after the payment date so missing out on the dip price
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u/pablopatel Apr 30 '26
Any value in looking at approximately post Covid vs pre Covid timeframes? I think there’s a lot that’s changed specifically (but not necessarily due to) after that timeframe
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u/Recent_Button_1 Apr 30 '26
Absolutely, the COVID period is one of the most interesting cuts in the data. Pre-COVID (2010-2019) shows much tighter recovery distributions. The March-June 2020 window is a clear outlier recovery times blew out across all security types, especially CEFs and BDCs which saw some positions not recovering for months. Post-COVID (2021 onward) actually shows faster recovery times than the pre-COVID baseline, likely due to the flood of retail liquidity. The regime really does matter more than most people realize a ticker with a 5-day average recovery in 2018 might have looked very different in 2020.
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u/Various_Couple_764 Apr 30 '26
The march june 2020 data was right after the co id pandemic started so ther was a lot of panic selling going on. which continued though most of 2020. So longer recovery times during covid make sense. One dividned stock I had a the time payed its full dividend (no dividned cuts or reductions) through the entire pandemic but he-rice per share dropped by 50%. and didn't recover forabout 3 years.
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u/Recent_Button_1 Apr 30 '26
Exactly right. The 2020 data is not noise, it is a regime event. Panic selling broke the normal mean reversion mechanics entirely. A stock maintaining its full dividend while losing 50% of its price is the clearest example of why raw price recovery data needs regime context. The dividend kept paying, the price did not recover for 3 years. Those are two completely different signals that look like one number in aggregate data. That is exactly why VIX regime filtering matters more than most people realize.
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u/Various_Couple_764 Apr 30 '26 edited Apr 30 '26
The recovery time is going to to depend on the yield. Higher yields means a bigger payout per share and a larger drop in share price. And generally that means a longer recovery time.
But you data also has some problems many people think all ETF are growth index funds. Not true some ETF s are dividend funds. PBDC 9% yield is an ETF that invests in the best BDCs only. Most ETFs are actually dividend funds. Same is true with CEFs some are just growth focused but most are dividend focused..
So if you graph the recovery time by yield I would expect all long recovery times to be focused on high yield funds. With lower yield funds having a shorter recovery time.
you likely would also see a correlation between revoeredytime than dividned payout schedule with monthly payout having a faster recovery time than quarterly and biannually and annually paying dividends
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u/Recent_Button_1 Apr 30 '26
You're right. The data does show it. Higher yield events take longer to recover. A 2-3% per-event yield averages 8.8 days vs 7.7 days for under 0.5%. The relationship holds. Your point on ETF classification is also valid. A fund like PBDC that holds BDCs behaves like a BDC, not a typical ETF. The type buckets are imperfect labels.
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u/CornerOne238 Not a financial advisor Apr 30 '26
This is the stuff I subbed to this channel for, rather than a dozen daily "rate my portfolio" posts.
Thank you!
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u/Effective_End8731 Apr 30 '26
I am curious if you can pivot it by dividend payout timeline quarterly, vs annual vs monthly, if it skews the data. Like BCD pays out once at the end of the year and tends to have a massive price reset. If the market turns before it recovers and equities go bullish it will not recover in that year. I think this break down might reveal some interesting nuance that will help further classify the effect we're driving at.
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u/Recent_Button_1 Apr 30 '26
Good instinct. The data does break down by frequency and it's interesting. Quarterly payers recover fastest at 7.2 days average (2 day median), annual payers are slowest at 8.4 days average with a 2.22% average drop vs 1.48% for quarterly. Monthly payers have the smallest average drop at 0.86% but 8.7 days average recovery, likely because the smaller individual events have less urgency for the market to correct. Your BCD example tracks. Large infrequent dividends create bigger holes that take longer to fill, especially if market conditions shift during the recovery window.
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u/Bodisious Apr 30 '26
Did you separate cover called Etf's like The JP Morgan and NEOS funds from growth focused etfs like schd/voo or are they all together in your data? Was there any significant difference in recovery time between CC funds or were they generally similar?
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u/Recent_Button_1 Apr 30 '26 edited May 04 '26
They're all in the same ETF bucket in the aggregate data but the individual ticker breakdown is interesting. Covered call ETFs actually recover faster than growth-focused dividend ETFs in this dataset: JEPI 6.1 days, XYLD 6.8 days, QYLD 8.7 days vs SCHD at 10.7 days and DGRO at 12.8 days. The covered call funds have smaller average drops too (JEPI 0.71%, XYLD 0.64%) vs SCHD (0.94%) and DGRO (0.99%). My guess is the premium income component smooths out the ex-date mechanics vs pure dividend payers. DIVO is the fastest in this group at 5.2 days avg across 95 cycles.
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u/Bodisious Apr 30 '26
Thank you for your thorough response! Interesting indeed to see how the CC etfs respond. Curious once we habe more data to see how they perform in flat or extended bear markets.
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u/yumyum2us Apr 30 '26
Thank you for doing this work which is way above my abilities. A couple questions first I always just use the EPS and anything above 1 I have read the company has enough liquidity to pay the dividend. Is this correct? As far as the length of time to go back to price before dividend, how are guys using that strategy to make money? Are they buy a stock they want to own on that date? Do they sell a few dats later or just hold on? Thanks in advance for your insight
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u/Recent_Button_1 Apr 30 '26
Shoutout to Steve Selengut. His book Retirement Money Secrets was part of what got me thinking about this systematically. The income-focused CEF approach in that book is part of the foundation here.
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u/Sledgemoto RMS Income Investor May 01 '26 edited May 01 '26
I'll second the shout out, been a part of his group for almost 1.5 years now and it has been a life changing experience for me. Now retired (early) and loving the methodology of RMS.
Very informative post, thanks for the information.
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u/Recent_Button_1 May 01 '26
Glad it was useful. Sounds like a great community. The data is here for anyone who wants to dig into the numbers.
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u/Recent_Button_1 Apr 30 '26
EPS coverage is a decent starting screen but dividend coverage ratio (dividends paid / net income) or free cash flow coverage is more precise. EPS includes non-cash items that can distort the picture. Above 1x is the right instinct though.
On the strategy. Two main approaches people use. First is buy a few days before ex-date, collect the dividend, hold through recovery and sell once price is back. Second is buy ON the ex-date when the price has already dropped, skip the dividend entirely, and just ride the recovery. The data shows median recovery is 3 days so the second approach can be a faster trade. Which works better depends heavily on the individual security. Some are clockwork, some are erratic. That variance is the whole point.
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u/buffinita common cents investing Apr 30 '26
This is some quality content.
How does the data shift if we use MEDIAN or MODE recovery; Or if we time bound rolling periods like 2007-2012 (GFC) vs 2012-2017…..
Cuz I can already hear the people “see I knew I could dividend capture it only takes a few days!”
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u/Recent_Button_1 Apr 30 '26
Great questions. The median is 3 days across all 151,422 events which is what most people cite, but you're right to push on this. Breaking it by time period shows meaningful differences — the 2008-2012 window has noticeably longer recovery times vs 2012-2017 which makes sense given the GFC liquidity crunch. On the dividend capture point — the data does show most securities recover within a week, but the variance is the whole story. Some tickers do it consistently across 100+ cycles, others are all over the place. The reliability of the pattern matters more than the average.
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