Traders and investors place enormous weight on newly released economic figures—especially when the data contradicts expectations. But are these reactions always rational, and more importantly, do they hold? A closer look at U.S. Treasury market behavior reveals that while bond prices often move in predictable ways immediately following headline data releases, those reactions can quickly reverse. This blog dives into how reported changes in economic indicators influence bond returns and why markets tend to overreact.
Understanding Economic “Information States”
Before we get to the market implications, let’s define what we mean by an “information state.” This term refers to the latest public update of a macroeconomic indicator—think of GDP growth, inflation rates, or employment numbers. It’s important to note that these states reflect what the market knows at the time, not necessarily what happened during the observation period. For instance, a GDP figure released on March 6 might pertain to activity from February.
Markets digest these updates as new knowledge, meaning that daily end-of-day prices are a reflection of all currently available macroeconomic information. As such, information states are useful in examining how data releases shape market behavior.
From Information to Market Signals
While economic trends unfold over time, changes in reported figures—especially surprises—can trigger immediate market reactions. Bond traders, in particular, keep a close eye on metrics tied to inflation, growth, and employment. These indicators are prominently featured in economic calendars and often spark swift trading activity.
However, there’s a distinction between understanding a trend and reacting to a newsflash. Many traders focus solely on the latest data release, ignoring whether the change was expected or if it aligns with broader trends. This behavior often leads to what economists call “overreaction”—an exaggerated market move that later unwinds.
Calculating Changes in Economic Perception
So how do we quantify changes in economic perception?
Information state changes are essentially the first differences in macroeconomic time series. But calculating these correctly requires a nuanced approach. Revisions, statistical updates, and methodological tweaks can all affect the reported values. Some indicators update more frequently than others, which also matters—low-frequency updates (e.g., quarterly reports) tend to carry more weight than daily revisions.
Crucially, not every change is a surprise. Market participants often anticipate certain shifts using related indicators or statistical relationships. For example, if a data point is a three-month average and the most recent month diverges sharply from the prior trend, a revision is likely—and savvy traders may have already positioned for it.
Another important distinction: changes in information states aren’t the same as indicators of economic change. The former are updates in perception, while the latter measure actual shifts in the economy. It’s possible for information to change even when the underlying economy remains steady—and vice versa.
Making Changes Comparable Across Indicators
Because macro indicators vary so widely, aggregation requires standardization. That process involves:
- Normalization: Expressing changes in terms of standard deviations so they’re comparable across indicators.
- Frequency Adjustment: Giving more weight to updates that cover longer timeframes (e.g., quarterly versus monthly).
- Time Aggregation: Summarizing data over a lookback period—usually around a month.
- Indicator Aggregation: Combining multiple data sources that reflect a common theme, such as inflation or employment.
Together, these steps create composite signals that better reflect shifts in economic perception and help filter out noise.
U.S. Macro Signals and Treasury Returns
To illustrate these principles, we examined changes in U.S. information states for growth, inflation, and labor markets—using two core indicators for each category. For growth, we used nowcasts and traditional GDP trackers. Inflation was gauged through CPI and PPI data, and labor market dynamics were captured via employment growth and jobless claims.
The impact of these signals on 10-year U.S. Treasury excess returns was revealing. During the month of a data release, bond returns moved in expected directions: stronger growth or inflation led to weaker bond performance, while disappointing data tended to boost returns.
But these relationships were weak in magnitude, with only growth changes showing a statistically significant impact. Labor market data, particularly jobless claims, had the least influence—likely due to its instability.
The Surprising Power of Reversal
More striking was what happened in the months following these data releases. The correlations flipped. Positive surprises that led to immediate bond selloffs were followed by gains in subsequent months. Conversely, disappointing data that initially lifted bonds often preceded weaker performance.
This pattern suggests a market tendency to overreact to high-profile economic releases. Publicity drives positioning, which can become crowded and vulnerable to reversal. Given the volatility of many monthly indicators, the market may often trade on noise rather than substance.
Final Thoughts: Trading the Reaction, Not Just the News
The lesson for investors is clear: not all economic surprises deserve the attention they get. While it’s natural to react to breaking data, the more prudent strategy may lie in anticipating how others will react—and when they’re likely to overreach.
By systematically tracking changes in economic perception and filtering them through normalization and aggregation, traders can identify signals that matter and avoid those that mislead. And in markets like U.S. Treasuries, where even small shifts in sentiment ripple quickly, this edge can make all the difference.
