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1992issue C011-7

Stop-order slippage as an execution cost filter

A triggered futures stop-order is filled as an unpriced market order, so the signed fill distance can add to or subtract from the trigger. In 297 cleaned stop-order trades, mean slippage was $38.16 per contract and was substantially larger than commissions paid by large firms. Editorial view: convert expected fill distance into dollars per contract and stack that slippage against commission before treating a technical trigger as executable.

  • A futures stop-order becomes an unpriced market order at the trigger, so the signed fill distance can be positive or negative.
  • Across 297 stop-order trades in 11 commodity futures, mean slippage was $38.16 per contract, with commodity means from $13.63 to $77.92.
  • A median regression associated larger slippage with a wider daily range, a New York venue, and a higher order-size-to-volume ratio; volume alone was not significant at the 5% level.
  • Editorial view: convert expected fill distance into dollars per contract and stack that slippage against commission before treating a technical trigger as executable.
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How a futures stop-order is filled

A stop-order is an instruction that becomes an unpriced market order once the contract trades at or through a specified trigger.

Once a futures stop is triggered it is filled as a market order with no price limit, so the signed fill distance can be positive or negative.

How slippage was measured

Slippage is the dollar gap between the stop trigger and the actual fill after scaling by contract size.

In this sample, slippage in dollars per contract was the contract-size-scaled gap between the target and the volume-weighted average fill, with the sign reversed for sells versus buys.

The cleaned sample

After market orders, limit orders, and open-triggered stops were removed, the sample was 297 stop-order trades in 11 commodity futures from July 1984 through December 1986.

Fill distance in dollars per contract

Mean slippage across all 297 trades was $38.16 per contract, commodity means ran from $13.63 to $77.92, and the largest single observation was $700.

Positive and negative slippage

Positive slippage occurred on 189 of 297 trades; zero or negative slippage occurred on 108.

Gold showed positive slippage on 90% of its trades, while Treasury bills showed positive slippage on only 29%.

Single-price and multi-price completion

The entire order filled at one price in 74% of trades, while 78 of 297 required more than one price to complete.

Conditions associated with larger slippage

Daily range is the session high-low span expressed in dollars per contract. Order-size-to-volume is contracts in the order divided by that day's contract volume.

A median regression associated larger slippage with a wider daily range, a New York venue dummy, and a higher order-size-to-volume ratio; volume alone was not significant at the 5% level.

Holding other regressors fixed, the New York dummy added $10.571 of median slippage per contract relative to Chicago, and the order-size-to-volume coefficient was 839.25.

Slippage next to commission

Measured stop slippage for this large technical account was about double a contemporaneous $17-per-contract average-trader figure and was substantially larger than commissions paid by large firms.

Reading the sample as a filter

Editorial view: market-impact is price movement associated with executing size, especially when similar stops trigger together. The sample's order-size-to-volume term is the size pressure that belongs in that filter, alongside daily range and venue. Commission analysis then sits on top of the dollar slippage figure so that implementation cost, not the technical trigger alone, decides whether the order is treated as executable.

Mean stop-order slippage by commodity

A large technical futures book paid far more than a typical retail fill. Mean signed slippage on 297 cleaned stop orders ran from $13.63 per contract in world sugar to $77.92 in platinum, and $38.16 across the whole sample — more than double the $17 average Angrist reported for the same 11 markets. Use those tabled means as a dollars-per-contract charge stacked on commission before treating a technical stop as executable. Figures are the source table of mean stop-order loss, not a reading of the histograms.
A large technical futures book paid far more than a typical retail fill. Mean signed slippage on 297 cleaned stop orders ran from $13.63 per contract in world sugar to $77.92 in platinum, and $38.16 across the whole sample — more than double the $17 average Angrist reported for the same 11 markets. Use those tabled means as a dollars-per-contract charge stacked on commission before treating a technical stop as executable. Figures are the source table of mean stop-order loss, not a reading of the histograms.Eleven commodity futures traded by one technical fund · July 1984–December 1986 · 1984-07-01T00:00:00.000Z to 1986-12-31T00:00:00.000Z

297 stop-order trades from one managed futures fund, July 1984–December 1986. Market and limit orders were dropped, as were stops triggered on the open. Slippage is signed dollars per contract from the weighted-average fill versus the stop trigger. The $17 reference is the single Angrist average for these same markets, not a per-commodity series.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
3 of 32 in the Commission analysis track
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  3. 1992Stop-order slippage as an execution cost filter
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  5. 2001Audit high-turnover operating conditions as one procedure
  6. 2002Front-load futures commission and slippage
  7. 2005Inactive account fees as hidden implementation cost
  8. 2010A pre-trade liquidity screen for futures contracts
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  10. 2012Filter futures contracts by liquidity and implementation cost
  11. 2012Futures commission versus one tick of cost
  12. 2012Ranking futures liquidity for executable orders
  13. 2013Filter option day trades by spread, volume, and fees
  14. 2013Filter futures by liquidity, open interest, and effective margin
  15. 2014Book futures data fees as implementation cost
  16. 2015Use a futures liquidity rank as a pre-trade checklist
  17. 2015Filter unexecutable futures by liquidity, open interest, and margin
  18. 2015Futures liquidity ranking as an execution filter
  19. 2015Filtering option trades by bid-ask width
  20. 2016Exchange quote fees as execution costs and liquidity filters
  21. 2016Filter futures by liquidity, open interest, and margin cost
  22. 2016Comparing dollar-index futures execution costs and liquidity
  23. 2016A futures liquidity ranking as a screen for executable orders
  24. 2017Filter a futures board by liquidity, open interest, and implementation cost
  25. 2017Filter futures contracts by liquidity and margin cost
  26. 2017How residency rules raise futures implementation costs
  27. 2018Screen listed futures by liquidity, open interest, and margin
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  29. 2018Filter futures execution by liquidity and margin
  30. 2018Commission analysis for brokerage execution costs
  31. 2019Ranking futures liquidity before you size the order
  32. 2020Brokerage selection as an implementation-cost problem
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