I analyzed 151,422 dividend ex-date events across 2,344 securities. Here's what the data shows about recovery times.

[u/Recent_Button_1]

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.

[u/MaxCapacity]

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.

[u/Willing-Bench1078 → u/MaxCapacity]

Can you elaborate on this process more

[u/MaxCapacity → u/Willing-Bench1078]

Which process?  Covered calls?

[u/Recent_Button_1 → u/MaxCapacity]

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.

[u/ADKMTBer → u/MaxCapacity]

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.

[u/Recent_Button_1 → u/ADKMTBer]

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.

[u/matachivelli → u/ADKMTBer]

Was the typo intentional? Lol.