You have probably watched it happen in real time. Futures lean lower an hour before a data release. The dollar edges higher. Bond yields twitch, liquidity thins, and then the number hits the tape and the “reaction” looks half‑spent. Commentators chalk it up to nerves or clairvoyance. It is neither. Markets often move before the news because people with different information, incentives and tools continuously update beliefs about what the news will be — and they express those beliefs through orders long before a headline appears.
🧩 What we mean by “markets move before the news”
The phrase is not a conspiracy theory. It describes a pattern: measurable price drift and liquidity shifts that occur in advance of scheduled macro releases. This is distinct from ordinary volatility which has no tie to an event calendar, and from the immediate price jump that follows a surprise print. Pre‑release moves reflect evolving expectations rather than the revelation itself.
Think of it as a timing problem. The market is an ongoing negotiation about tomorrow’s number. The negotiation produces prices, implied probabilities, and volumes that converge — or fail to converge — toward the eventual outcome. When enough participants change their minds ahead of time, prices move ahead of time.
Who does the moving? A familiar cast. Hedge funds that digest alternative datasets and nowcast models. Banks that see client flows and run macro books alongside them. High‑frequency firms that watch order‑book microstructure for pressure points. Proprietary traders who marry options surfaces to event risk. Even corporates that hedge interest rate or currency exposure based on near‑term operating signals. None of this is mystical. It is the market as an information‑processing machine, operating continuously rather than only at 8:30 a.m. on release day.
🟦 The transmission mechanisms: how macro indicators become price signals
A macro statistic travels through markets via several channels. The cleanest one is the expectations channel. Survey medians, futures curves, and option prices encode a distribution for the upcoming print. As new hints land — from high‑frequency labor data to energy prices that feed inflation models — the implied distribution shifts. Prices track those shifts because the payoff to being correctly positioned at the moment of release is convex. If the market thinks the odds of a “hot” CPI tick up, front‑end yields rise, equity duration sells off, and the dollar nudges higher before the number is published.
The microstructure channel is messier but no less powerful. Anticipatory orders get staged, liquidity providers widen spreads, and passive liquidity pulls back to avoid adverse selection. Execution algorithms slow down or switch to opportunistic modes. The result is a visible pre‑news drift if one side is more motivated, and a sharper post‑news gap if order books are already thin. This is why you can see prices move with seemingly little volume — the visible volume is small because the invisible liquidity has retreated.
Scheduled events catalyze both channels. Traders mark their calendars for payrolls, CPI, PMIs, central‑bank speeches, and auctions. Options dealers re‑hedge as event vol decays. Macro funds rebalance risk to comply with internal limits tied to event risk. Even if no one “knows” the number, behavior around the event changes the price path.
- Expectations: survey updates, model revisions, and option‑implied probabilities adjust ahead of time.
- Microstructure: order‑book thinning, wider spreads, and staged orders create drift.
- Options: event vol repricing and skew shifts signal perceived tails.
- Calendar effects: risk limits and execution protocols alter flows around scheduled releases.
💡 Why this matters now: speed, data, and incentive alignment
Pre‑news dynamics are not new. What is new is the speed at which raw observations become tradable views. Cheap compute, cloud marketplaces, and off‑the‑shelf nowcasting toolkits compress the time from “new signal” to “executed order.” Satellite imagery of parking lots used to be the punchline at conferences. Today it is one of hundreds of streams — web‑scraped prices, credit‑card panels, transactional tax data, bill of lading feeds — that can nudge a near‑term macro forecast and trigger an execution robot in minutes.
Incentives now favor that compression. The economics of automated trading reward being slightly earlier more than being slightly smarter. When you can scale infrastructure across dozens of events each month, small forecasting edges compound. Firms with strong data pipelines harvest a basis point here, a volatility point there, through disciplined, repeatable routines. The losers are those who still treat macro days as special occasions rather than daily flow problems.
Policy has played a role. Central banks embraced forward guidance which reduces the cadence of true surprises, but it increases the premium on accurately reading nuance and anticipating inflection points. Fiscal shocks arrive with less ceremony and more noise. A change in energy subsidies, a court decision on student loans, a strike settlement — each can shift the near‑term path of inflation or growth, often first visible in options and rate markets rather than in a press release.
⚙️ Common misconceptions and simplistic explanations
“Markets always know everything” is a comforting story that flatters the system. They do not. Prices summarize a probability‑weighted crowd view, with all the errors that implies. Expectations can be systematically off when models share the same blind spots. Herding, incentive constraints, and narrative momentum cause overreactions. Pre‑news moves sometimes point the wrong way and sometimes overprice a tail that never shows up.
“It must be illegal insider trading” is the darker story and it occasionally happens. Enforcement actions remind us that embargoes are sometimes breached and that privileged access is abused. The more common case is lawful edge. Firms infer, rather than steal, information. They aggregate public streams faster, or they observe the intentions of others through flow and positioning. They build better translation layers between observable data and macro variables. A set of purchase receipts can tell you about retail sales without any embargo violation. A shift in swap spreads can tell you the market is re‑weighting growth risks without any leaked minutes.
Confusing correlation with causation is another trap. A bond selloff ahead of a hawkish speech may be explained by participants buying options the day before, which forced dealers to hedge in the same direction. Headlines attach a causal label only after the fact. The underlying mechanism often involves positioning and hedging rather than foreknowledge.
🟦 Evidence and case studies
Event studies across major releases show recurring patterns. Ahead of US nonfarm payrolls, you often see reduced depth and wider spreads in Treasury futures, modest drift in front‑end yields, and reshaping of options skew in rates and FX. Into CPI, options markets tend to inflate event vol, then bleed it as consensus hardens — with directional skew shifting when gasoline or shelter components move independently. Purchasing Managers’ Index updates leave footprints in cyclicals and credit spreads the day before if supply‑chain or shipping indicators flash.
Central‑bank communication offers vivid examples. The path of two‑year yields relative to fed funds futures frequently hints at how the market reads the next statement. During the taper‑tantrum era, there were days when options markets priced the probability of a faster balance sheet runoff well before the press conference crystallized it. In FX, yen crosses have repeatedly priced policy tolerance bands ahead of Bank of Japan tweaks by watching basis markets, cross‑currency basis swaps, and exporter hedging flows.
Options and futures are revealing. A build‑up in put skew in equity indices into an inflation print often signals fear of a “hot” right‑tail for yields, which mechanically hurts long‑duration equities. In rates, the price of event straddles embeds the implied surprise distribution — and the skew tells you which tail the market fears. Futures positioning data, while noisier and slower, still marks periods when speculative nets lean heavily one way before a data shock. The path of re‑pricing can begin days before a release as funds reduce exposure to avoid a forced unwind.
To make this more concrete, here is a compact map of where pre‑news signals tend to show up:
| Indicator/Event | Typical pre-release behavior | Most sensitive markets |
|---|---|---|
| US CPI | Event vol builds, skew shifts with energy/shelter proxies | Front-end rates, equity index options, USD |
| Nonfarm Payrolls | Order-book thinning, drift in 2y yields, FX carry pares | Treasury futures, fed funds futures, G10 FX |
| PMIs | Cyclical/defensive rotation, credit spread wobble | Sector ETFs, IG/HY CDX, industrial metals |
| FOMC decision | OIS curve reshapes, terminal rate repriced, vol-of-vol rises | OIS swaps, Eurodollar/SOFR futures, USDJPY |
| Auctions | Term premium wobble, RV flows, basis moves | Treasury cash/futures basis, swap spreads |
None of this guarantees predictive power. It documents that markets talk to themselves before the rest of us get the memo.
🟦 Alternative explanations and healthy skepticism
Some apparent predictability is an artefact of how we look. If you cherry‑pick windows, tweak parameters until a backtest sings, or stop once the line looks pretty, you will “discover” pre‑news edges that vanish in live trading. Markets adapt. Once a pattern becomes popular, liquidity shifts and the edge decays. Structural changes — like the transition from LIBOR to SOFR or a new inflation basket — can break models with no warning.
Randomness is also underrated. With enough events, some will show pre‑release drift by chance. If you reshuffle timestamps and still find similar patterns, you have a problem. Statistics help, but so does economic storytelling. A mechanism grounds the data. If you cannot explain who would act, why they would act, and how their action feeds through the microstructure, treat the result as an invitation to dig deeper rather than as a green light to deploy capital.
A disciplined skepticism keeps you honest. Ask whether the signal is robust across markets, whether it survives transaction costs and slippage on event days, and whether it degrades when you simulate the crowd discovering it. Keep your priors updated. When the regime changes — say, from supply‑driven inflation to demand‑driven disinflation — the same flows can mean different things.
- Guard against data‑mining: pre‑register hypotheses and fix windows before looking.
- Stress test: add noise, change venues, and include realistic slippage.
- Demand a mechanism: specify who trades, through which instruments, and why.
- Track decay: assume crowding reduces edge and plan for turnover.
🟦 Implications for investors, policymakers and journalists
For investors, the first implication is operational. Execution risk around events can dominate signal quality. If you trade pre‑news moves, size positions to liquidity, and plan your exits. Use derivatives to shape exposure to the part of the distribution you actually want — event straddles to own uncertainty, calendars to express views on decay, and spreads to limit capital at risk. Cross‑market checks are your friend. If rates, FX and credit are not telling the same story, ask why.
Portfolio construction also matters. Strategies that rely on being the last to know will suffer. Replace “react to the number” with “react to the reaction path.” Some investors are better served by systematic hedges that monetize pre‑news skew rather than by directional bets. Others can accept that certain days are noise and reduce activity. There is no virtue in being involved when the game favors someone else’s edge.
For policymakers, anticipatory pricing is a feature to manage, not a bug to eliminate. Clear calendars, consistent release protocols, and plain‑English guidance reduce harmful uncertainty while preserving the market’s ability to incorporate new information. Recognize that small wording changes can move implied distributions well before a podium moment. If that is intentional, fine. If not, simplify.
Journalists have a role as translators. Avoid declaring that “markets were shocked” when derivatives and futures priced the shift the day before. Note how positioning and hedging influenced the path. Embrace the distributional view — “the market priced a higher probability of X” beats “the market expected X.” It is more accurate and less brittle.
Check how disciplined your event‑day playbook really is.
🟦 A practical toolkit and final takeaways
You do not need a data center on a fjord to read pre‑news signals. Build a small kit that monitors a few robust proxies and links them to clear actions. Options give you implied distributions. Watch event straddles and skew for CPI, payrolls, PMIs, and central‑bank days. Futures and swaps give you order‑flow‑adjacent signals. Track OIS curves, fed funds and SOFR futures, and basic basis metrics. Liquidity dashboards — depth, spreads, and order‑book imbalance — tell you whether the path will be smooth or jagged. Alternative data can help if it is clean and timely, but do not let novelty outweigh reliability.
Combine statistics with narrative discipline. If you see event vol bid, front‑end yields creeping up, and the dollar firming as energy prices jump, you have a coherent story. If only one of those lights is blinking, slow down. Always include execution in your plan. Event windows compress time. Be explicit about time horizons and stop rules so a harmless pre‑news wobble does not become a post‑news problem.
Here is a short checklist you can adapt before each major release:
- Cross‑market consistency: do rates, FX, and options tell the same story?
- Distribution, not point: what do straddles and skew say about tails?
- Liquidity map: how thin are books and how wide are spreads?
- Positioning: are speculative nets extended or neutral?
- Regime context: has the policy or macro narrative shifted recently?
- Execution plan: entry, exit, size, and what will make you stand down.
A personal note to end. I have learned to treat pre‑news moves as conversations. Sometimes people mumble, sometimes they tease, and sometimes they blurt. The skill is less about predicting the future and more about reading who is saying what and why. If you listen with a clear framework, you will occasionally hear the answer before the question is asked.
Run a pre‑release checklist before your next CPI day.
📚 Related Reading
– “Reading the Yield Curve Without Getting Lost” — https://axplusb.media/articles/reading-the-yield-curve
– “How To Trade Event Risk Without Guessing the Number” — https://axplusb.media/articles/trading-event-risk
– “Liquidity, Microstructure, and the Myth of the ‘No‑Volume’ Move” — https://axplusb.media/articles/liquidity-microstructure-myth