{
  "id": 12634560,
  "title": "US Dollar: Assessing NFP release reliability – Standard Chartered",
  "url": "https://urgent.news/2026/10/07/us-dollar-assessing-nfp-release-reliability-standard-chartered",
  "topic": "finance",
  "section": "Finance & Markets",
  "published": "2026-10-07T12:30:02.000Z",
  "source": {
    "name": "FXStreet",
    "slug": "fxstreet",
    "url": "https://www.fxstreet.com/news/us-dollar-assessing-nfp-release-reliability-standard-chartered-202610071230"
  },
  "original_language": "en",
  "account": "Standard Chartered economist Dan Pan has analyzed which US Nonfarm Payrolls (NFP) months provide the most reliable initial signals of labor-market conditions. He found that April and November are the most accurate months, while January, September, March, and May tend to have larger revisions. High volatility suggests that preliminary NFP releases should not be taken at face value. Instead, market attention has shifted towards moving averages of at least three months to smooth out the estimation errors caused by seasonal noise. However, even these trends are subject to monthly and annual benchmark revisions. Comparing preliminary releases with final benchmark revisions, Pan found that April's 1-month mean absolute error (MAE) and November's 1-month MAE are the lowest on a single-month basis, as well as the second-lowest on a three-month moving average (3mma) basis. In contrast, January, September, March, and May have the largest revisions, with MAEs almost twice as large as those in the most reliable months. On a 3mma basis, April, July, and October appear to be the most reliable, while January, May, and September are the least reliable. The gap between the most and least reliable months is significant, so it is important to trade with caution when assessing initial NFP releases.",
  "summary": "Standard Chartered economist Dan Pan analyses which US Nonfarm Payrolls (NFP) months provide the most reliable initial signals of labour-market conditions.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}