It's a huge day for data from the US on Thursday 11 January, with interest centring on the inflation data at 8.30 am US Eastern time:
Taking a look at the range of expectations compared to the median consensus (the 'expected' in the screenshot above) for the key data points:
December CPI Headline y/y
- 3.0 - 3.4%
December CPI Headline m/m
- 0.1 to 0.4%
December CPI excluding food and energy (the core rate of inflation) y/y
- 3.8 - 4.0%
December CPI excluding food and energy (the core rate of inflation) m/m
- 0.1 to 0.3%
And, while we are at it, for the Initial Jobless Claims
- 202K to 220K
Why its important is below the links, but the TL;DR is that a result falling outside the range can prompt larger market moves. Obviously with 4 inflation data points outlined above, there are multiple moving parts. My main focus will be on the m/m for both headline and core. And also on why am I awake at 12.30am?
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Earlier previews:
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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:
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, in the case of the inflation data due today, weaker than expected will fuel speculation of nearer and larger Federal Open Market Committee (FOMC) rate cuts. A stronger (i.e. higher) CPI report 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.
