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

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

Non-Farm Payroll (NFP) Model Forecast - July 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: June and July 2016.  The June data was released last month and the non-farm payroll data for July 2016 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 82,300 jobs.  The first and last data rows of the table report the forecast plus one standard error (in green) and the forecast minus one standard error (in red), respectively.  All values are rounded to the nearest thousand.  If the model errors were normally distributed, roughly 16% of the observations would fall below minus one standard error and another 16% of the observations would exceed plus one standard error.

The actual non-farm payroll release for June is in the second data row of the table (in purple).  The consensus estimate (reported by Briefing.com) for July 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 June to July 2016.

Figure 1: Non-Farm Payroll Table July 2016

Figure 1: Non-Farm Payroll Table July 2016

Model Commentary

The aggregate neural network model forecast for July is 219,000, which is almost unchanged (-3,000) from last months forecast of 222,000, reflecting a static employment environment during the month of July. The Briefing.com consensus estimate for July is 185,000, which is down sharply (-102,000) from the June NFP data (287,000). Normally, this would suggest that the market was forecasting a significant weakening in the employment environment. In this case, it simply indicates that the actual June data was an outlier.

The actual May data was an extreme outlier: over 2 standard errors (-2.45) below the forecast data. These anomalies are normally reversed in subsequent months; this explains the positive outlier in June (+0.79 S.E.).  The consensus estimate for July is materially below the July model forecast (-0.41 S.E.). Unfortunately, the sequence of two large outliers in May and June makes it even more difficult to forecast the July NFP data.

Figure 2: Non-Farm Payroll Graph July 2016

Figure 2: Non-Farm Payroll Graph July 2016

I added a new chart recently (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.

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 had been negative since early 2015, but may finally be stabilizing. The one-month slope change is positive in July, which has only happened three times in the past 18 months.  The last two monthly forecasts were also above the 12-month moving average line, which has only happened four times in the last 20 months. The employment environment had clearly been weakening for some time, but may be beginning to stabilize.

Figure 3: NFP Forecast MA Graph July 2016

Figure 3: NFP Forecast MA Graph July 2016

Summary

The sequence of two consecutive large outliers in May and June makes it more difficult than usual to forecast the NFP data in July. The model forecasts for July is slightly higher than the consensus estimate, but the actual NFP July data could also be affected by the May and/or June outliers.

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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