What slippage around news means
Slippage is the difference between the price you expect to get for an order and the price where the order actually executes. Around news releases, prices can change quickly and liquidity can temporarily thin out, so your order may be filled at a worse price than planned. This explanation covers costs that can influence that difference, without assuming any single market outcome.
Mechanism: how costs turn into worse fills
Slippage is usually driven by two broad categories of factors: direct costs and indirect execution frictions.
Direct costs (measurable charges)
Direct costs reduce the value of the fill and can therefore increase the effective “gap” between expected and realized execution. Common direct cost components are:
- Spread: the difference between the quoted buy and sell prices. If the spread widens during news, the next available execution may be at a less favorable side of the market.
- Commissions and platform fees: fixed or per-trade charges that make the realized price less attractive than the pre-trade expectation.
- Financing or funding-related costs (where applicable): holding-related charges can change the total economic result of an execution.
To make any calculation consistent, state assumptions such as the order type (market vs. limit), whether the comparison uses mid-price or last price, and which fees are included in “expected.”
Indirect costs (market structure and timing)
Indirect factors are often the main reason slippage looks “bigger” around news, even when the quoted charges are unchanged.
- Liquidity and depth changes: order books can lose depth, meaning fewer orders sit close to the current price. When your order consumes available liquidity, the next prices can be worse.
- Quoted prices vs. executable prices: quotes can update faster than your ability to trade at them, especially under high volatility.
- Execution delays: even small delays between order submission and execution can matter when prices are moving.
- Re-pricing speed during the event: news can trigger a rapid chain of repricing, so the market you “signed up for” may not be the market that fills you.
A simple cost decomposition example (with assumptions)
Assume you compare expected execution to actual execution using the mid-price at the moment you submit an order. Also assume you know your spread at submission and your fees per trade.
- If the spread widened before execution, part of the slippage can be attributed to the market moving from your original quotes to less favorable executable prices.
- If your recorded fill price is worse than the level implied by the quotes at submission, then indirect factors (liquidity thinning, delayed fill, faster repricing) likely explain the remainder.
Key assumption: your “expected price” definition must match how you measure mid/last and which timestamp you use.
Material limitations and failure modes
At least one major limitation is that slippage around news is highly variable. Even if you observe a pattern before, historical relationships between news timing and slippage do not guarantee future behavior.
Common failure modes in cost reasoning include:
- Using inconsistent baselines (mid vs. last, submission vs. quote update time).
- Mixing direct and indirect components without recording execution timestamps and fee details.
- Ignoring order type: limit orders can avoid worse prices but may lead to partial or no fills, shifting the “cost” into different outcomes.
- Over-attributing to one cause: fees may be constant, while liquidity and speed dominate the realized difference.
How to verify what drove slippage
Because news-driven execution is dynamic, verification should focus on what you can observe in your own trade records.
A practical verification approach:
- Record expected vs. actual execution: store the price you based your expectation on (mid or last) and the actual fill price.
- Break out direct costs: confirm the commission/fees and any other included charges from your account statements.
- Check timing: compare timestamps for order submission, quote/reference time, and execution.
- Assess liquidity proxies (if available): note changes in spread and visible market depth around the event window.
- Run sensitivity checks: repeat the decomposition across multiple similar events and compare how much varies.
Next question to clarify
To explain a specific slippage outcome, you need to specify: order type, price reference (mid/last), timestamp basis, and which cost components are included (fees only, or total economic cost).