This article presents the Trader Edge aggregate neural network model forecast for the May 2016 non-farm payroll data, which is scheduled to be released tomorrow morning at 8:30 AM EDT.
Non-Farm Payroll (NFP) Model Forecast - May 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: April and May 2016. The April data was released last month and the non-farm payroll data for May 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 81,600 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 April is in the second data row of the table (in purple). The consensus estimate (reported by Briefing.com) for May 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 April to May 2016.
The aggregate neural network model forecast for May is 220,000, which is down only 8,000 jobs from last month's revised forecast of 228,000, reflecting a very slight weakening in the employment environment during the month of May. The Briefing.com consensus estimate for May is 155,000, which is 5,000 lower than the April NFP data (160,000), also suggesting a very slight weakening in the employment environment.
The actual April data was well below revised April forecast (-0.83 S.E.) and the consensus estimate for May is also significantly below the May model forecast (-0.80 S.E.). The fact that the consensus estimates for both April and May were materially below their respective forecasts increases the probability of a material upside surprise tomorrow. In other words, there is an increased probability that the actual May NFP data will exceed the consensus estimate by a significant amount.
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 has now leveled out. In addition, the last two model forecasts were slightly above the moving average. This is a promising development, but 14 of the previous 16 forecast observations were below the moving average line. The employment environment had clearly been weakening for some time, but may have begun to stabilize.
The model forecasts for April and May suggest there is an increased probability that the actual May NFP data will exceed the consensus estimate by a material amount tomorrow. This could force the Fed's hands and would significantly increase the probability of a rate hike at the June meeting.
According to the latest CME Group FedWatch probability calculation, the implied probability of a rate hike at the June meeting is currently only 21%. In other words, a June rate hike is not expected and is not priced into the market.
As a result, a material upside NFP surprise tomorrow would have significant implications for equity, bond, commodity, and currency markets.
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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