Goldman Sachs Preview of Nonfarm Payrolls: Data Needs a "Significant Surprise" to Shake the Fed's April Rate Cut Expectations

BlockBeats News, January 9 — Goldman Sachs stated that the upcoming US non-farm payroll report for December 2025, to be released on Friday evening, is unlikely to materially change market expectations for Federal Reserve policy unless there is a significant surprise in the data, as current market pricing is firmly anchored on the path of easing policy beginning mid-year.

In a research report to clients, Goldman Sachs expects the increase in non-farm payrolls to be around 70,000, which is basically in line with general expectations. Although informal market “whisper numbers” suggest a slight upside risk, the bank believes that a result close to expectations will reinforce rather than disrupt the existing macroeconomic narrative.

The market is currently pricing in two 25 basis point rate cuts by the Federal Reserve this year, with the first 25 basis point cut expected around late April.

Goldman Sachs stated that a “rather dramatic” upside or downside surprise in labor data would be needed to significantly bring forward or delay this timing.

From a market perspective, Goldman Sachs described non-farm payroll data falling in the 70,000 to 100,000 range as the most favorable outcome for the stock market, which aligns with continued economic expansion that neither reignites inflation concerns nor threatens the rate-cutting cycle. Such a result would support the view that the US economy is gradually slowing rather than coming to an abrupt halt.

In contrast, if non-farm payroll data comes in below 50,000, it would be interpreted as below the estimated level needed to maintain stable employment growth, potentially unsettling investors due to concerns about a sharp slowdown in growth.

At the other extreme, Goldman Sachs stated that if the data exceeds 125,000, it could prompt the market to reassess the timing of the Federal Reserve’s first rate cut, pushing expectations for a rate cut back to June. (Golden Ten Data)