Later today, Tuesday, February 13, 2024, we will receive US consumer inflation data:
Let’s look at the range of expectations versus the median consensus (the “expected” in the screenshot above) for key data points:
January headline CPI year-on-year, expected at 2.9% with a range indicating:
January headline CPI m/m is expected at 0.2% with a range indicating:
January CPI excluding food and energy (the underlying inflation rate) year-on-year is expected at 3.7% with a range indicating:
January CPI excluding food and energy (the underlying inflation rate) m/m is expected at 0.3% with a range showing:
While you’re here, JP Morgan says:
- We expect the headline CPI to have risen 0.1% in January while the core index rose 0.22%, both below consensus estimates and market prices.
Why is knowledge of these beaches important?
Data results that do not match market low and high expectations tend to move markets more significantly for several reasons:
Surprise factor: Markets often incorporate expectations based on previous forecasts and trends. When the data deviates significantly from these expectations, it creates a surprise effect. This can lead to a rapid revaluation of assets as investors and traders reevaluate their positions based on the new information.
Psychological impact: Investors and traders are influenced by psychological factors. Extreme data points can elicit strong emotional reactions, leading to overreactions in the market. This can amplify market movements, especially in the short term.
Reassessment of risks: Unexpected data can lead to a reassessment of risks. If data significantly underperforms or exceeds expectations, it can change the perception of risk of certain investments. For example, better-than-expected economic data can reduce the perceived risk of investing in stocks, leading to a market rally.
Triggering Automated Trading: In today’s markets, a significant portion of trading is done by algorithms. These automated systems often have predefined conditions or thresholds that, when triggered by unexpected data, can lead to large-scale purchases or sales.
Impact on monetary and fiscal policies: data that is far from expectations can influence the policies of central banks and governments. For example, in the case of inflation data due today, weaker-than-expected data will fuel speculation about earlier and deeper rate cuts from the Federal Open Market Committee (FOMC). A stronger (i.e. higher) CPI report would lower these expectations.
Market Liquidity and Depth: In some cases, extreme data can affect market liquidity. If the data is unexpected enough, it could cause a temporary imbalance between buyers and sellers, causing larger market movements until a new equilibrium is found.
Chain reactions and correlations: financial markets are interconnected. A significant movement in one market or asset class due to unexpected data can lead to correlated movements in other markets, thereby amplifying the overall market impact.