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

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

Non-Farm Payroll (NFP) Model Forecast - February 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: January and February 2016.  The January data was released last month and the non-farm payroll data for February 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 82,200 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 January is in the second data row of the table (in purple).  The consensus estimate (reported by Briefing.com) for February 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 January to February 2016.

Figure 1: Non-Farm Payroll Table February 2016

Figure 1: Non-Farm Payroll Table February 2016

Model Commentary

The aggregate neural network model forecast for February is 192,000, which is down 5,000 jobs from last month's revised forecast of 197,000, reflecting a very slight weakening in the employment environment during the month of February. The Briefing.com consensus estimate for January is 190,000, which is 39,000 higher than the January NFP data (151,000), suggesting a modest strengthening in employment environment.

The actual January data was materially below the revised January forecast (-0.56 S.E.) and the consensus estimate for February is almost identical to the February model forecast (-0.02 S.E.). The small spread between the February forecast and consensus estimate would not normally suggest a material surprise tomorrow, but the low NFP data outlier last month could be followed by an artificially inflated NFP observation in February.

FIgure 2: Non-Farm Payroll Graph February 2016

Figure 2: Non-Farm Payroll Graph February 2016

I added a new chart (Figure 3) below to make it easier to observe trends in the employment environment. The blue line depicts the model forecasts (including the latest revisions) and is exactly the same as the Forecast NFP line in Figure 2 above. However, Figure 3 also contains a purple line, which shows the 12-month moving average of the NFP model forecasts.

Why plot the moving average of the model forecasts instead of the actual NFP data? Because the actual NFP data is notoriously noisy. The Forecast NFP data more accurately captures the strength of the employment environment and the stability of the data series makes it easier to observe the trend in employment. This is especially important given the Trader Edge recession model warning signal last month.

We can use the chart below in Figure 3 in two ways to identify the trend in employment. First, we can observe the forecast NFP data relative to the moving average. Observations below the moving average indicate a weakening in employment and vice versa. Second, we can observe the slope of the moving average line. When the moving average line is downward-sloping, employment is weakening and vice versa.

As you can see from the chart in Figure 3, the slope is clearly negative and has been since early 2015. The most recent NFP forecast is below the moving average, which is consistent with the recent data. In fact, 12 of the last 15 forecast observations have been below the moving average line. The employment environment has clearly been weakening for some time and that trend continues. The chart is not shown, but the same trend is evident in the actual NFP data as well.

Figure 3: Non-Farm Payroll MA Graph February 2016

Figure 3: Non-Farm Payroll MA Graph February 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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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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