Surprises, expectations and volatility

Here is the idea that ties the whole track together and explains why markets react the way they do: markets trade on surprises, not on levels.

Why the number alone never tells you the reaction

Before every major release, the market has already formed a consensus expectation — economists' forecasts that are baked into prices in advance. The price reaction is driven by the gap between the actual figure and that expectation:

• Data in line with expectations → little reaction, because it was already priced in.
• Data above expectations (a "beat" / hawkish surprise) → markets reprice upward or downward depending on what the data implies.
• Data below expectations (a "miss") → the opposite repricing.

This is why a stock can fall on great earnings (they were merely as good as expected) and a currency can rally on weak inflation (weaker than feared). Always ask: what was the market expecting?

"Good news is bad news" — finally explained

Earlier you met the puzzle of strong jobs data sometimes sinking stocks. Now it makes sense: strong data raises the odds of higher interest rates, and via the rates-to-asset-prices chain, that can outweigh the benefit of a healthy economy. The market is not reacting to the economy directly — it is reacting to what the data means for policy.

Volatility clusters around the calendar

Because surprises move markets, the biggest, fastest moves cluster around scheduled releases: the Copom and Fed decisions, IPCA and US CPI, payrolls, GDP. Spreads widen and prices can gap in seconds. Practical takeaways:

• Keep an economic calendar and know when the big releases land.
• Expect elevated volatility around them, and size positions accordingly.
• Judge every number against the consensus, not against zero or against last month.

Master this single lens — surprise versus expectation, filtered through the policy chain — and the daily noise of the market resolves into something you can actually read.