What “slippage around news” means
Slippage around news refers to the gap between an intended trade price and the actual execution price when a market event (for example, an economic release) causes rapid price movement. In plain terms, it is the cost created by timing and execution quality, not only by the move in price.
To measure it, you need a definition that turns the idea into numbers. A common measurable definition is:
- Slippage (price basis) = Executed price − Intended (or reference) price.
Because “intended price” can be ambiguous, you must specify what your reference is. Examples of references include:
- the last quoted mid-price just before your decision time,
- the best available ask/bid at the decision time,
- or a synthetic “no-impact” price derived from a baseline window.
The measurement fields and timestamps you must lock down
A measurement becomes reliable only if the same fields are captured for every news-related instance.
Core fields (per event, per trade attempt):
- Event time marker (T_event): the moment you align to the news release (for measurement, use a consistent definition such as the published timestamp or your own standardized log time).
- Decision time (T_decision): when the order is submitted or when you define the “intended” reference price.
- Execution time (T_exec): when the fill occurs.
- Reference price (P_ref): your chosen intended/benchmark price at or near T_decision.
- Executed price (P_exec): the actual fill price.
- Transaction costs (optional but important): spread at T_decision, commission, fees, or any execution-related adjustments you can document.
Time-window rule: choose a fixed window for comparisons. For example, you might compare prices in:
- a pre-event baseline window (used only to define P_ref or an expected level), and
- an event/execution window (used to capture P_exec).
Without consistent windows, you end up mixing different market regimes and cannot attribute differences to “news impact” versus ordinary intraday movement.
Two practical ways to compute the number
Option A: Per-trade slippage (difference method)
For each trade attempt:
- Absolute slippage: ΔP_abs = P_exec − P_ref.
- Relative slippage: ΔP_rel = (P_exec − P_ref) / P_ref (for comparability across price levels).
Then summarize across many events with a statistic such as an average or median. The key assumption is that P_ref is defined the same way each time.
Option B: Baseline-adjusted “incremental impact”
Sometimes the market would have moved even without the news. To reduce that confound, define a baseline trend from a pre-event window and compare the executed price to what that baseline suggests.
A simple baseline approach (example assumptions must be stated):
- Choose a short pre-event window and fit a basic change (for instance, the difference between the first and last mid-price in that window).
- Use that change to project an expected level at T_exec.
- Compute slippage as the deviation from the projected level.
This “baseline-adjusted” method tries to isolate the portion of the move associated with the event timing, but it still depends on your baseline choice.
Material limitations and failure modes to expect
Even with good definitions, measurement can fail in predictable ways.
- Latency and timing misalignment: If your T_decision or T_exec is logged at different resolutions (or with delays), “slippage around news” becomes a clock problem rather than an execution problem.
- Widening spreads and liquidity gaps: Under fast news moves, the market may quote wider spreads or have thinner order books. A reference that assumes tight spreads may overstate slippage.
- Changing reference definitions: If P_ref uses different quote types (mid vs bid/ask) or shifts across events, comparisons become invalid.
- Survivorship bias in fills: If only certain orders get filled during volatile moments, the sample may exclude the worst cases, making slippage look smaller.
- Costs not fully captured: Commissions, financing, or execution-related fees may differ with time and liquidity. If you omit them, your computed “slippage” may not match total trading cost.
A key risk: historical relationships between news timing and slippage do not guarantee future results, because liquidity structure and execution quality can change.
How to verify your measurement independently
To verify that your method measures execution effects rather than artifacts:
- Run the same computation on non-news periods with identical window sizes.