US inflation report coming up on Friday when markets will be closed – ranges to watch

PCE inflation data for February will be released! What is this?

The Federal Reserve prefers the Core Personal Consumption Expenditures (PCE) Price Index over the Consumer Price Index (CPI) as a measure of inflation for several key reasons:

  • The Core PCE covers a broader range of goods and services than the CPI. While CPI focuses on out-of-pocket expenses for urban consumers, PCE includes expenditures on behalf of households, such as employer-paid health insurance and Medicare. This wider scope makes PCE a more comprehensive measure of consumer spending.
  • PCE adjusts for the substitution effect, where consumers might switch from higher-priced goods to lower-priced alternatives as prices change. CPI, on the other hand, uses a fixed basket of goods and services, which can overstate inflation if consumers shift their consumption patterns in response to price changes.
  • PCE specifically measures spending by individuals and can more accurately reflect the consumption patterns that are central to the U.S. economy.
  • The ‘core’ version of both indices (Core PCE and Core CPI) excludes food and energy prices, which are volatile. However, the Fed often gives more weight to Core PCE because of its broader coverage and substitution bias adjustment.
  • PCE data are subject to regular and comprehensive revisions that reflect the latest and most accurate information available. This can make PCE a more reliable measure over the long term.
  • Core PCE is a more stable and accurate reflection of the long-term inflation trends that guide monetary policy.


The data is due at 0830 US Eastern time, which is 1230 GMT:

For the Core PCE Price Index m/m, expected is 0.3%

  • prior was 0.4%
  • range of estimates is 0.3% to 0.3% (yes, all the same)

Core PCE Price Index y/y, expected 2.8%

  • prior was also 2.8%
  • range of estimates is 2.7 % to 2.8 %

Why is knowledge of such ranges important?

Data results that fall outside of market low and high expectations tend to move markets more significantly for several reasons:

  • Surprise Factor: Markets often price in expectations based on forecasts and previous trends. When data significantly deviates from these expectations, it creates a surprise effect. This can lead to rapid revaluation of assets as investors and traders reassess their positions based on the new information.

  • Psychological Impact: Investors and traders are influenced by psychological factors. Extreme data points can evoke strong emotional reactions, leading to overreactions in the market. This can amplify market movements, especially in the short term.

  • Risk Reassessment: Unexpected data can lead to a reassessment of risk. If data significantly underperforms or outperforms expectations, it can change the perceived risk of certain investments. For instance, better-than-expected economic data may reduce the perceived risk of investing in equities, leading to a market rally.

  • Triggering of Automated Trading: In today’s markets, a significant portion of trading is done by algorithms. These automated systems often have pre-set conditions or thresholds that, when triggered by unexpected data, can lead to large-scale buying or selling.

  • Impact on Monetary and Fiscal Policies: Data that is significantly off from expectations can influence the policies of central banks and governments. For example, weaker data will fuel speculation of nearer and larger Federal Open Market Committee (FOMC) rate cuts. A stronger result will diminish such expectations.

  • Liquidity and Market Depth: In some cases, extreme data points can affect market liquidity. If the data is unexpected enough, it might lead to a temporary imbalance in buyers and sellers, causing larger market moves until a new equilibrium is found.

  • Chain Reactions and Correlations: Financial markets are interconnected. A significant move in one market or asset class due to unexpected data can lead to correlated moves in other markets, amplifying the overall market impact.

This article was written by Eamonn Sheridan at Source