Does max pain pin?

Everyone repeats the claim; almost nobody grades it. Every night this site records a max pain level for the front expiration of 43+ tickers. Once those contracts expire, the forecast can be marked against the actual close — no hindsight, no cherry-picking, every observation listed below. This page regrades itself nightly as snapshots expire.

What exactly is being measured

Each nightly snapshot stores three things for the front expiration: the max pain level computed from that evening's open interest, the last price, and the price's own date stamp. Once the expiration date has passed, we fetch the actual closing price on expiration day and compute two distances, both in percent of the snapshot price:

d0 — how far the price stood from max pain on the snapshot day.
d1 — how far the expiration close finished from that same max pain level.

If the pinning story is right, d1 should usually come in smaller than d0: the price should drift toward the level as expiration approaches. Under a no-pull null — price moves but does not know where max pain is — finishing closer or farther is roughly a coin flip, so the "moved closer" rate should sit near or below 50%. That gives two falsifiable readouts: the share of observations where d1 < d0, and the median of each distance. Both are above.

One honesty constraint worth stating: snapshots of the same ticker and expiration taken on different nights are separate rows but correlated observations — they share the same expiration close. The observation count is therefore an upper bound on the effective sample.

By lead time

Lead time is measured from the price stamp to expiration. The pinning literature, where it finds anything, finds it only in the final days.

ETFs vs individual stocks

Where the effect should live, if it lives anywhere

A reader on r/options made the sharpest criticism this study has received: pooling 43 tickers assumes the mechanism exists everywhere. It probably does not. If pinning is real it is a by-product of dealer hedging, so it can only show up where a large share of open interest actually sits near the money and market makers are short gamma into expiration. A ticker whose open interest is scattered across dozens of strikes has no machinery to hold price anywhere, and pooling it with the rest just adds noise. So the study now splits the sample instead of averaging over it.

Two splitting variables, and they are not equally good. The honest one is the share of front-expiration open interest sitting within 1% of the max pain strike. Snapshots only started recording it on 2026-07-31 and it cannot be back-filled, because the nightly history file has always stored a summary rather than the full chain — so that column fills up from tonight forward. The available one is the gap between the call wall and the put wall, which every past observation carries. It is a crude stand-in: it says how far apart the two heaviest strikes sit, not how densely the open interest packs in between them.

The wall-gap split is cut at the sample median (10.7%), which makes it a description of this sample rather than a test. The open-interest split uses a threshold fixed in advance (15.0% of front-expiration open interest within 1% of max pain), so it can be read as a real test once it has rows.

So far the crude split finds nothing: 44.5% against 35.5%, on 218 and 217 observations, is two coin flips wearing different hats. That is a fair result for a weak variable, not evidence against the idea. The split worth watching is the one above it, and the honest status today is that it has 0 rows. The one split that does separate is ETFs against single stocks, in the table above, and that is a rough proxy for the same thing: index products carry denser, more concentrated open interest than a random single name.

The 284 observations we refused to count

Our first pass at this study had 719 candidate observations and produced numbers we nearly published. Then a sanity check caught something: the upstream open-interest file lags the price file by one to three days, so in most candidate rows the recorded "snapshot price" actually carried a date stamp on or after the expiration itself. Grading those rows would mean measuring the distance from a price that already knew the answer — d0 and d1 collapse into the same number and the study becomes circular. So the inclusion rule is strict: both the open-interest date and the price date must fall strictly before the expiration. That single rule cut the sample from 719 to 435. We would rather publish 435 honest rows than 719 flattering ones; the discarded count stays in the dataset and in this paragraph as a permanent methodological confession.

A commenter on r/options then supplied the reason the lag exists, which is more useful than the workaround. The OCC tallies open interest after the close and publishes it the following morning, so every open-interest figure in existence describes the previous session. Prices do not work that way. This is not a defect in one data vendor and it is not something a better pipeline fixes: every max pain number anywhere, ours and everyone else's, free or paid, pairs yesterday's positioning with today's price. The practical consequence is that the figure is least current exactly when people care about it most, since open interest churns fastest in the last days before expiration. We treat the offset as a permanent property to design around rather than a bug to chase, which is why the inclusion rule above is written against both date stamps instead of assuming they agree.

What the academic record says

The strongest published evidence is about strike clustering, not max pain. Ni, Pearson and Poteshman (Journal of Financial Economics 78(1), 2005, 49–87) found that optionable US stocks close unusually often at strike prices on expiration dates — an average return distortion of 16.5 basis points or more per expiration — driven by market-maker hedge rebalancing and stock price manipulation by firm proprietary traders. Clustering at the nearest heavy strike is a much weaker claim than convergence to the whole-chain max pain level, and the literature does not establish the latter. That gap is exactly why this page exists: the max pain version of the claim is repeated constantly and grades cheaply, so we grade it.

Every observation

The full table, newest expiration first. Distances in percent of the snapshot price. Regenerate it yourself: maxpain/pin_study.py in the repo reads the same history files this site publishes and writes this exact JSON.

Common questions

Quant Data. "Does max pain pin? Nightly max pain forecasts graded against expiration closes." quantdata.uk, figures as of 2026-08-31. https://quantdata.uk/research/does-max-pain-pin (accessed <date>).

@misc{quantdata-pin-study,
  author       = {Quant Data},
  title        = {Does max pain pin? Nightly max pain forecasts graded against expiration closes},
  year         = {2026},
  howpublished = {\url{https://quantdata.uk/research/does-max-pain-pin}},
  note         = {Figures recomputed nightly; this snapshot 2026-08-31}
}

https://quantdata.uk/research/does-max-pain-pin