This article presents the Trader Edge aggregate neural network model forecast for the July 2014 non-farm payroll data, which is scheduled to be released tomorrow morning at 8:30 AM EDT.
Non-Farm Payroll (NFP) Model Forecast - July 2014
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 2014. The June data was released last month and the non-farm payroll data for July 2014 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 80,000 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 2014 is in the second data row of the table (in purple). The consensus estimate (reported by Briefing.com) for July 2014 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 2014.
The aggregate neural network model forecast for July is 255,000, which is down 30,000 jobs from last month's revised forecast of 285,000. The decrease in the forecast from June to July reflects a slight weakening in the employment environment over the past month. The Briefing.com consensus estimate for July is 220,000, which is down 68,000 jobs from the June report, indicating a more significant weakening in the employment environment. The actual June data was almost exactly equal to the revised June forecast (+0.04 S.E.) and the consensus estimate for July is notably below the the model forecast (-0.44S.E.).
If we ignore the extraordinary outlier in the December data (-1.92S.E), there has been a gradual and sustained positive trend in the employment data since late 2012. Not surprisingly, the variation in the actual NFP reports is much wider than the variation in the NFP forecasts. Unfortunately, the Government reports are notoriously noisy, so the trend is more apparent in the forecast data, which are based on several different economic variables that collectively give a much more accurate and reliable employment reading than the Government data.
The strength of the July forecast relative to the consensus estimate sets the stage for a relatively minor upside surprise tomorrow.
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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