Non-Farm Payroll (NFP) Forecast – November 2013

This article presents the Trader Edge aggregate neural network model forecast for the November 2013 non-farm payroll data, which is scheduled to be released tomorrow morning at 8:30 AM EST.

Non-Farm Payroll (NFP) Model Forecast - November 2013

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 2013.  The October data was released last month and the non-farm payroll data for November 2013 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 78,500 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 2013 is in the second data row of the table (in purple).  The consensus estimate (reported by Briefing.com) for November 2013 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 to November 2013.

Figure 1: Non-Farm Payroll Table November 2013

Figure 1: Non-Farm Payroll Table November 2013

Model Commentary

The aggregate model forecast for November is 200,000, which is down 43,000 jobs from last month's revised forecast of 243,000.  The Briefing.com consensus estimate for November is 188,000, which is 16,000 lower than the October NFP release.  The actual October data was below the revised October forecast (-0.50 S.E.) and the consensus estimate for November is slightly below the model forecast (-0.15 S.E.). The model does see evidence of weakening in the labor market from October to November, demonstrated by a forecast decrease of 43,000 jobs.

The February 2013 NFP data (the large, one-month spike above 300,000 in Figure 2 below) was clearly an anomaly. The NFP data had been trending lower since late last year, but stabilized over the past few months and is now beginning to inch higher. Unfortunately, the effects of the Government shutdown does make it more difficult to evaluate the short-term trend in the NFP data.

Figure 2: Non-Farm Payroll Graph November 2013

Figure 2: Non-Farm Payroll Graph November 2013

Summary

Basic forecasting tools can help you identify unusual consensus economic estimates, which often lead to substantial surprises and market movements.  Identifying such environments 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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Brian Johnson

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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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