This article presents the Trader Edge aggregate neural network model forecast for the November 2015 non-farm payroll data, which is scheduled to be released tomorrow morning at 8:30 AM EST.
Non-Farm Payroll (NFP) Model Forecast - November 2015
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: October and November 2015. The October data was released last month and the non-farm payroll data for November 2015 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 October 2015 is in the second data row of the table (in purple). The consensus estimate (reported by Briefing.com) for November 2015 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 October 2015 to November 2015.
The aggregate neural network model forecast for November is 231,000, which is up 16,000 jobs from last month's revised forecast of 215,000, reflecting a slight increase in the strength of the employment environment during the month of November. The Briefing.com consensus estimate for November is 196,000, which is 75,000 lower than the October NFP data (271,000), suggesting a significant weakening in the employment environment.
The actual October data was materially above the revised October forecast (+0.73 S.E.), indicating a possible outlier, which tend to be reversed in subsequent months. However, the actual NFP data for September was also an outlier (on the low end). If the two outliers effectively cancel each other out, then the forecast of 231,000 jobs would be reasonable. If not, then the large positive outlier in October might be reversed in November, which would suggest an actual NFP number below the model forecast. The previous negative and positive monthly outliers create additional uncertainty for the November NFP report, which is unfortunate given the importance of this number for the Fed's December rate decision.
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 leveled off in early 2015 and has now reversed. The trend in employment growth has definitely weakened in 2015, but may have begun to pick up slightly over the past few months.
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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