Due on Friday at 0830 US Eastern time, the Core PCE data is the focus. The Personal Consumption Expenditures (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 a critical 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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Headline PCE: This measures the overall change in prices for all goods and services. It includes volatile components like food and energy, which can fluctuate sharply due to supply shocks, seasonal changes, or geopolitical events.
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Core PCE: This excludes the more volatile food and energy prices to provide a clearer view of underlying inflation trends. Core PCE is the preferred inflation gauge for the Federal Reserve because it gives a more stable picture of long-term inflation pressures.
PCE is similar to the Consumer Price Index (CPI), but PCE is broader in scope and reflects changes in consumer behavior, such as substituting products when prices rise.
You can see the median estimates for the various PCE data points below in the table.
The ranges for ‘core’ measures (why these are important is explained below) are:
Core PCE Price Index m/m
and for the y/y
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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:
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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.
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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.
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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.
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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.
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Impact on Monetary and Fiscal Policies: Data that is significantly off from expectations can influence the policies of central banks and governments. Indeed, this is what Powell said, indicating the focus the Fed has on this data point: “Estimates based on the consumer-price index and other data indicate that total PCE prices rose 2.5 percent over the 12 months ending in November and that, excluding the volatile food and energy categories, core PCE prices rose 2.8 percent.” That y/y core estimate from Powell is bang 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 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.
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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.
