Non-Farm Payroll (NFP) Model Forecast – July 2015

This article presents the Trader Edge aggregate neural network model forecast for the July 2015 non-farm payroll data, which is scheduled to be released tomorrow morning at 8:30 AM EDT.

Non-Farm Payroll (NFP) Model Forecast - July 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: June and July 2015.  The June data was released last month and the non-farm payroll data for July 2015 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 77,000 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 June 2015 is in the second data row of the table (in purple).  The consensus estimate (reported by Briefing.com) for July 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 June 2015 to July 2015.

Figure 1: Non-Farm Payroll Table July 2015

Figure 1: Non-Farm Payroll Table July 2015

Model Commentary

The aggregate neural network model forecast for July is 274,000, which is up 21,000 jobs from last month's revised forecast of 253,000.  The modest increase in the forecast from June to July reflects a strengthening in the employment environment during the month of July. The Briefing.com consensus estimate for July is 229,000, which 6,000 higher than the June NFP data (215,000), indicating a very slight improvement in the employment environment.  The actual June data was below the revised June forecast (-0.39 S.E.) and the consensus estimate for July is notably below the model forecast (-0.58 S.E.).

If we ignore the large NFP outliers, there had been a gradual and sustained positive trend in the employment data from mid-2012 through January of 2015. 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 reversed for several months. Job growth appears to have resumed in the past few months.

The difference between the July NFP consensus and July NFP forecast increases the possibility of an upside surprise tomorrow.

Figure 2: Non-Farm Payroll Graph July 2015

Figure 2: Non-Farm Payroll Graph July 2015

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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