Non-Farm Payroll (NFP) Model Forecast – January 2016

This article presents the Trader Edge aggregate neural network model forecast for the January 2016 non-farm payroll data, which is scheduled to be released tomorrow morning at 8:30 AM EST.

Non-Farm Payroll (NFP) Model Forecast - January 2016

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: December 2015 and January 2016.  The December data was released last month and the non-farm payroll data for January 2016 will be released tomorrow morning at 8:30 AM EST.

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 76,600 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 December 2015 is in the second data row of the table (in purple).  The consensus estimate (reported by Briefing.com) for January 2016 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 December 2015 to January 2016.

Figure 1: Non-Farm Payroll Table January 2016

Figure 1: Non-Farm Payroll Table January 2016

Model Commentary

The aggregate neural network model forecast for January is 201,000, which is down 64,000 jobs from last month's revised forecast of 265,000, reflecting a significant weakening in the employment environment during the month of January. The Briefing.com consensus estimate for January is 188,000, which is 104,000 lower than the December NFP data (292,000), suggesting a very sharp decline in the strength of the employment environment.

The actual December data was above the revised December forecast (+0.35 S.E.) and the consensus estimate for January is slightly below the January model forecast (-0.17 S.E.). The small spread between the January forecast and consensus does not suggest a material surprise tomorrow.

If we ignore the large NFP outliers, there had been a gradual and sustained positive trend in the employment data from mid-2012 through late 2014. The trend is easier to see in the forecast data due to fewer outliers. The positive trend in the model forecasts definitely reversed in early 2015, but strengthened briefly. The sharp decline in the strength of the employment in the last month is particularly troubling, especially given the large spike in the Trader Edge recession model forecast last month.

Figure 2: Non-Farm Payroll Graph January 2016

Figure 2: Non-Farm Payroll Graph January 2016

Summary

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 data is highly variable 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.
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