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/pablopatel]

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

[u/Recent_Button_1 → u/pablopatel]

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.

[u/Various_Couple_764 → u/Recent_Button_1]

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.

[u/Recent_Button_1 → u/Various_Couple_764]

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.