Fails to Deliver Explained: What FTD Data Means and What It Does Not Prove
A careful explanation of SEC fails-to-deliver data, cumulative balances, settlement context, legitimate causes, and the limits of using FTDs as proof of manipulation.
- Published
- Jun 23, 2026
- Reading time
- 4 min
- Format
- Research workflow

Fails to deliver, often shortened to FTDs, attract a lot of attention because they sound dramatic. The SEC publishes fails-to-deliver data and explains that a fail on a given day is a cumulative number of all fails outstanding until that day, plus new fails that occur, less fails that settle. It is not simply a daily count of new failures.
That distinction matters. FTD data can be useful for monitoring settlement stress, but it is often overinterpreted. A fail can have multiple causes, and the presence of FTDs does not automatically prove manipulation, naked short selling, or a hidden short position.
Understand the cumulative balance
The SEC notes that fails-to-deliver data reflects an aggregate net balance as of a settlement date. If the balance is zero, no record appears for that date. Because the value is cumulative, a series of days cannot be added together as if each day were a separate new fail amount.
This is one of the most important interpretation rules. Adding daily FTD balances can greatly exaggerate the scale of the issue. The proper reading starts with the settlement date, security, quantity, and whether the balance persists or resolves.
- Treat FTD data as a settlement-date balance.
- Do not add daily balances as if they were new daily fails.
- Check whether records persist across multiple settlement dates.
- Compare FTD quantity with float, volume, and market value.
Know what FTDs can and cannot show
FTDs can show that delivery obligations remained unsettled as of the reporting date. They can highlight securities where settlement issues deserve attention, especially if balances are persistent or unusually large relative to liquidity.
They cannot, by themselves, tell you intent, strategy, holder identity, or whether a fail resulted from abusive activity. SEC materials on Regulation SHO note that fails may arise from short or long sales and can have legitimate operational causes, while also recognizing naked shorting can produce fails.
- Use FTDs to identify settlement questions.
- Do not infer intent from the data alone.
- Check persistence, scale, and timing around events.
- Read Regulation SHO context before drawing conclusions.
Compare FTDs with other market structure data
FTDs are more useful when compared with short interest, short-sale volume, borrow context where available, float, liquidity, corporate actions, and price movement. A single FTD print without context may not say much.
If FTDs rise around a complex corporate action, symbol change, split, or volatile trading period, operational context may matter. If they persist at large scale, the monitoring question becomes more serious.
- Compare FTDs with short interest and volume.
- Review corporate actions and settlement events.
- Check whether the issue persists or resolves quickly.
- Use market structure data as context, not proof by itself.
Write cautious conclusions
A good FTD note avoids dramatic conclusions that the data cannot support. It says what the SEC data shows, what it does not show, and what follow-up evidence would matter. That is especially important because FTD discussions online often blur settlement mechanics, short interest, short volume, and manipulation claims.
The strongest note is boring and precise: date, quantity, price, issuer, persistence, related events, and open questions. If the data is incomplete or ambiguous, say so.
- State the settlement date and reported quantity.
- Avoid adding balances across dates.
- Separate observation from accusation.
- Define the next data point that would change the view.
FTD data can raise a settlement question, but it does not answer every market-structure question.
Fails-to-deliver data is useful when read precisely. Treat it as a cumulative settlement-date balance, compare it with liquidity and other market data, and avoid conclusions the dataset cannot prove.
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