UBS are arguing the retail sales data could tip the balance between a 25bp and 50bp rate cut:
- retail sales and industrial production data … weakness in these could potentially influence the Fed decision to cut its Fed Funds rate by 50bp instead of 25bp
I am not so sure that one data point (or these two) can have that much weight attached (ps. I’m a 25er). But, if so, check this out.
For example, retail sales well above consensus will argue for 25bp, well below and the case will be for 50.
Consensus expectations are in the table below. This snapshot is from the ForexLive economic data calendar, access it here.
For Retail sales m/m:
- -0.6% to +0.6%
For Retail sales excl autos m/m:
- -0.8% to +0.9%
And, while I’m here, for Industrial production m/m
- -0.3% to +0.8%
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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.
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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.