Due on Friday at 0830 US Eastern time, the Core PCE data will be the focus. The Personal Consumption Expenditure (PCE) data is a key measure of inflation that tracks changes in the prices of goods and services purchased by consumers. It is reported monthly by the Bureau of Economic Analysis (BEA) and is an essential tool used by the Federal Reserve to assess inflation and guide monetary policy.
There are two main types of PCE data:
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Destination PCE: This measures the total change in prices for all goods and services. It includes volatile components such as food and energy, which can change significantly due to supply shocks, seasonal changes, or geopolitical events.
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Main PCE: This excludes the more volatile food and energy prices to provide a clearer picture of core inflation trends. Core PCE is the preferred inflation measure for the Federal Reserve because it provides a more stable picture of long-term inflationary pressures.
PCE is similar to the Consumer Price Index (CPI), but PCE has a broader range and reflects changes in consumer behavior, such as replacing products when prices rise .
You can see the median estimates for the various PCE data points below in the table.
The areas for 'core' steps are (why these are important is explained below):
Core PCE Price Index m/m
and for y/y
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Why is knowledge of such areas important?
Data results that are outside of the market's low and high expectations tend to move markets more for several reasons:
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Surprise factor: Markets often price in anticipation based on previous forecasts and trends. When data is significantly different from these expectations, it creates a surprising effect. This can lead to a rapid revaluation of assets as investors and traders reassess their positions based on the new information.
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Psychological Influence: Investors and traders are influenced by psychological factors. Extreme data points can trigger strong emotional reactions, leading to overreactions in the market. This can increase market movements, especially in the short term.
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Risk Reassessment: Unexpected data can lead to risk reassessment. If data significantly underperforms expectations, it can change the perceived risk of certain investments. For example, better-than-expected economic data could reduce the risk of investing in equities, leading to a market rally.
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Automated Trading Promotion: In today's markets, a large part of trading is done by algorithms. These automated systems often have pre-set conditions or thresholds that, when unexpected data can trigger large-scale buying or selling.
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Impact on Monetary and Fiscal Policies: Data that are very far from expectations can have an impact on the policies of central banks and governments. In fact, this is what Powell said, revealing the Fed's focus on this data point: “Estimates based on the consumer price index and other data indicate that PCE prices totaled 2.5 percent over the 12 months ended November and that, without. in the volatile food and energy sectors, PCE core prices rose 2.8 percent.” That baseline estimate from Powell is in the middle of the range.
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Liquidity and Market Depth: In some cases, extreme data points can affect market liquidity. If the unexpected data is insufficient, it could lead to a temporary imbalance in buyers and sellers, causing larger market movements until a new balance is found.
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Chain reactions and correlations: Financial markets are interconnected. A large movement in one market or asset class due to unexpected data can lead to related movements in other markets, increasing the overall market effect.
