Non-farm Payrolls Forecast: Is 140,000 to 175,000 the real key range?

Forexlive analysts have released the estimated range for today's US non-farm payroll report. These ranges are crucial for market reactions, as unexpected effects occur when actual data deviates from expectations. Another important factor influencing market reactions is the distribution of forecast values.

In fact, while we may have an estimated range, most forecasts may be concentrated at the upper limit of the range. Therefore, even if the data falls within the estimated range but at the lower end of the range, unexpected effects can still occur.

Forecast Distribution

• Non-farm Employment

The estimated range is 100,000 to 246,000

Most concentrated between 140,000 to 175,000

• Unemployment Rate

4.4% (3%)

4.3% (35%)

4.2% (58%)

4.1% (4%)

• Average Hourly Earnings Annual Rate

3.8% (3%)

3.7% (76%)

3.6% (21%)

• Average Hourly Earnings Monthly Rate

0.4% (2%)

0.3% (75%)

0.2% (21%)

0.1% (2%)

• Average Weekly Hours

34.4 (4%)

34.3 (78%)

34.2 (19%)

The focus will be on non-farm employment data and the unemployment rate, as the Federal Reserve is currently not concerned about wage growth.

Why is it important to understand these expected ranges?

Data results that exceed the market's expected lows and highs often have a more significant impact on the market for the following reasons:

Surprise factor: Markets typically price in based on forecasts and past trends. When data significantly deviates from these expectations, a surprise effect occurs. This leads investors and traders to quickly reassess assets based on new information, triggering significant price fluctuations.

Psychological impact: Investors and traders are influenced by psychological factors. Extreme data points can trigger strong emotional reactions, leading to market overreactions. This amplifies market volatility, especially in the short term.

Risk reassessment: Unexpected data can lead to a reassessment of risks. If data is significantly lower or higher than expected, it can change the perceived risk of certain investments. For example, better-than-expected economic data may reduce the perceived risk of investing in stocks, leading to a market rally.

Triggering automated trading: In today's market, most trades are executed by algorithms. These automated systems often have preset conditions or thresholds, and when unexpected data triggers these conditions, it can lead to large-scale buying or selling activities.

Impact on monetary and fiscal policy: Data significantly deviating from expectations can influence central banks and government policies. For example, in today's upcoming non-farm payroll report, a weaker employment report will increase speculation of more and potentially larger Federal Open Market Committee (FOMC) rate cuts. A stronger report, on the other hand, will reduce such expectations.

Liquidity and market depth: In certain cases, extreme data points can affect market liquidity. If the data is surprising enough, it may lead to a temporary imbalance between buyers and sellers until a new equilibrium is found, potentially causing significant market fluctuations Chain reactions and correlations: Financial markets are interrelated. Unexpected data leading to significant fluctuations in one market or asset class may trigger correlated fluctuations in other markets, amplifying the overall market impact