This article presents the Trader Edge aggregate neural network model forecast for the April 2014 non-farm payroll data, which is scheduled to be released tomorrow morning at 8:30 AM EDT.
Non-Farm Payroll (NFP) Model Forecast - April 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: March and April 2014. The March data was released last month and the non-farm payroll data for April 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 80,800 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 March 2014 is in the second data row of the table (in purple). The consensus estimate (reported by Briefing.com) for April 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 March to April 2014.
The aggregate neural network model forecast for April is 227,000, which is down 40,000 jobs from last month's revised forecast of 267,000. The Briefing.com consensus estimate for April is only 210,000. The actual March data was significantly below the revised March forecast (-0.93 S.E.) and the consensus estimate for April is marginally below the model forecast (-0.21S.E.).
Surprisingly, the model is forecasting a significant decrease in the NFP data from March to April (-40,000 jobs), while the April consensus estimate represents an increase of 18,000 jobs over the reported March NFP. Given that the consensus estimate of 210,000 is slightly below the model forecast of 227,000 for April, there is a small probability of a modest bullish 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 is notoriously unreliable 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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