Non-Farm Payroll (NFP) Model Forecast – June 2014

This article presents the Trader Edge aggregate neural network model forecast for the June 2014 non-farm payroll data, which is scheduled to be released tomorrow morning at 8:30 AM EDT -- one day early due to the fourth of July on Friday.

Non-Farm Payroll (NFP) Model Forecast - June 2014

The Trader Edge aggregate NFP model represents the average of three neural network forecasting models, each of which employs a different neural network architecture.  Unlike expert systems, neural networks use algorithms to identify and quantify complex relationships between variables based on historical data.  All three models derive their forecasts from seven explanatory variables and the changes in those variables over time.

The table in Figure 1 below includes the monthly non-farm payroll data for two months: May and June 2014.  The May data was released last month and the non-farm payroll data for June 2014 will be released tomorrow morning at 8:30 AM EDT.

The model forecasts are in the third data row of the table (in blue).  Note that past and current forecasts reflect the latest values of the independent variables, which means that forecasts will change when revisions are made to the historical economic data.

The monthly standard error of the model is approximately 80,300 jobs.  The first and last data rows of the table report the forecast plus 0.5 standard errors (in green) and the forecast minus 0.5 standard errors (in red), respectively.  All values are rounded to the nearest thousand.  If the model errors were normally distributed, roughly 31% of the observations would fall below -0.5 standard errors and another 31% of the observations would exceed +0.5 standard errors.

The actual non-farm payroll release for May 2014 is in the second data row of the table (in purple).  The consensus estimate (reported by for June 2014 is also in the second data row of the table (in purple).  The reported and consensus NFP values also include the deviation from the forecast NFP (as a multiple of the standard error of the estimate).  Finally, the last column of the table includes the estimated changes from May to June 2014.

Figure 1: Non-Farm Payroll (NFP) Table June 2014

Figure 1: Non-Farm Payroll (NFP) Table June 2014

Model Commentary

The aggregate neural network model forecast for June is 276,000, which is up 39,000 jobs from last month's revised forecast of 237,000.  The increase in the forecast from May to June reflects increasing strength in the employment environment. The consensus estimate for June is 210,000, which is down 7,000 jobs from the May report.  The actual May data was slightly lower than the revised May forecast (-0.25 S.E.) and the consensus estimate for June is materially below the the model forecast (-0.82S.E.).

If we ignore the extraordinary outlier in January (-1.92S.E), there has been a gradual and sustained positive trend in the employment data since late 2012.  Not surprisingly, the variation in the actual NFP reports is much wider than the variation in the NFP forecasts.  Unfortunately, the Government reports are notoriously noisy, so the trend is more apparent in the forecast data, which are based on several different economic variables that collectively give a much more accurate and reliable employment reading than the Government data.

The strength of the June forecast relative to the consensus estimate sets the stage for a possible upside surprise tomorrow.

Figure 2: Non-Farm Payroll Graph June 2014

Figure 2: Non-Farm Payroll Graph June 2014


Basic forecasting tools can help you identify unusual consensus economic estimates, which often lead to substantial surprises and market movements.  Identifying such environments in advance may help you protect your portfolio from these corrections and help you determine the optimal entry and exit points for your strategies.

In the case of the NFP data, the monthly report is notoriously unreliable and prone to substantial revisions.  As a result, having an independent and unbiased indicator of the health of the U.S. job market is especially important.

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About Brian Johnson

I have been an investment professional for over 30 years. I worked as a fixed income portfolio manager, personally managing over $13 billion in assets for institutional clients. I was also the President of a financial consulting and software development firm, developing artificial intelligence based forecasting and risk management systems for institutional investment managers. I am now a full-time proprietary trader in options, futures, stocks, and ETFs using both algorithmic and discretionary trading strategies. In addition to my professional investment experience, I designed and taught courses in financial derivatives for both MBA and undergraduate business programs on a part-time basis for a number of years. I have also written four books on options and derivative strategies.
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