This article presents the Trader Edge aggregate neural network model forecast for the May 2015 non-farm payroll data, which is scheduled to be released tomorrow morning at 8:30 AM EDT.
Non-Farm Payroll (NFP) Model Forecast - May 2015
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 2015. The April data was released last month and the non-farm payroll data for May 2015 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 77,000 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 April 2015 is in the second data row of the table (in purple). The consensus estimate (reported by Briefing.com) for May 2015 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 2015 to May 2015.
Figure 1: Non-Farm Payroll Table May 2015
Model Commentary
The aggregate neural network model forecast for May is 197,000, which is up 17,000 jobs from last month's revised forecast of 180,000. The small increase in the forecast from April to May reflects a slight strengthening in the employment environment during the month of May. The Briefing.com consensus estimate for May is 225,000, which almost identical to the April NFP data (+223,000), indicating no change in the employment environment. The actual April data was notably above the revised April forecast (+0.56 S.E.). The consensus estimate for May is also above the model forecast (+0.36 S.E.).
If we ignore the large NFP outliers, there had been a gradual and sustained positive trend in the employment data from mid-2012 through January of 2015. The trend is easier to see in the forecast data due to fewer outliers. To test the strength of the trend that began in mid-2012, a few months ago I ran a simple linear regression on the monthly NFP forecast data (dependent variable) against time (independent variable).
The R-squared of the linear regression was 56%, indicating that 56% of the variation in the forecast data was explained by the simple linear trend model. The coefficient for the monthly slope variable was 3.12, which represented an incremental increase of 3,100 jobs per month. The slope coefficient was highly significant with a t-statistic of 6.41 and a P-value of 3.31E-07. The standard error of the model was 27,850 jobs.
The simple linear regression model cannot be used long-term due to the cyclical nature of employment, but it can be useful for quantifying short-term trends in the data. The linear regression model forecast for May 2015 is 277,000 which is much higher than the aggregate model forecast of only 197,000.
The positive trend in the model forecasts definitely leveled off and there is increasing evidence that the trend started to reverse direction in early 2015. However, over the past few months, the forecasts have begun to rebound slightly.
The difference between the May NFP consensus and May NFP forecast suggests an slight probability of an downside surprise tomorrow.
Figure 2: Non-Farm Payroll Graph May 2015
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.
Print and Kindle Versions of Brian Johnson's 2nd Book are Available on Amazon
Exploiting Earnings Volatility: An Innovative New Approach to Evaluating, Optimizing, and Trading Option Strategies to Profit from Earnings Announcements.
Print and Kindle Versions of Brian Johnson's 1st Book are Available on Amazon (35 5-Star Reviews)
Option Strategy Risk / Return Ratios: A Revolutionary New Approach to Optimizing, Adjusting, and Trading Any Option Income Strategy
Trader Edge Strategy E-Subscription Now Available: 20% ROR
The Trader Edge Asset Allocation Rotational (AAR) Strategy is a conservative, long-only, asset allocation strategy that rotates monthly among five large asset classes. The AAR strategy has generated annual returns of approximately 20% over the combined back and forward test period. Please use the above link to learn more about the AAR strategy.
Feedback
Your comments, feedback, and questions are always welcome and appreciated. Please use the comment section at the bottom of this page or send me an email.
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Brian Johnson
Copyright 2015 - Trading Insights, LLC - All Rights Reserved.
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. I have also written four books on options and derivative strategies.
Non-Farm Payroll (NFP) Model Forecast – May 2015
This article presents the Trader Edge aggregate neural network model forecast for the May 2015 non-farm payroll data, which is scheduled to be released tomorrow morning at 8:30 AM EDT.
Non-Farm Payroll (NFP) Model Forecast - May 2015
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 2015. The April data was released last month and the non-farm payroll data for May 2015 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 77,000 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 April 2015 is in the second data row of the table (in purple). The consensus estimate (reported by Briefing.com) for May 2015 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 2015 to May 2015.
Figure 1: Non-Farm Payroll Table May 2015
Model Commentary
The aggregate neural network model forecast for May is 197,000, which is up 17,000 jobs from last month's revised forecast of 180,000. The small increase in the forecast from April to May reflects a slight strengthening in the employment environment during the month of May. The Briefing.com consensus estimate for May is 225,000, which almost identical to the April NFP data (+223,000), indicating no change in the employment environment. The actual April data was notably above the revised April forecast (+0.56 S.E.). The consensus estimate for May is also above the model forecast (+0.36 S.E.).
If we ignore the large NFP outliers, there had been a gradual and sustained positive trend in the employment data from mid-2012 through January of 2015. The trend is easier to see in the forecast data due to fewer outliers. To test the strength of the trend that began in mid-2012, a few months ago I ran a simple linear regression on the monthly NFP forecast data (dependent variable) against time (independent variable).
The R-squared of the linear regression was 56%, indicating that 56% of the variation in the forecast data was explained by the simple linear trend model. The coefficient for the monthly slope variable was 3.12, which represented an incremental increase of 3,100 jobs per month. The slope coefficient was highly significant with a t-statistic of 6.41 and a P-value of 3.31E-07. The standard error of the model was 27,850 jobs.
The simple linear regression model cannot be used long-term due to the cyclical nature of employment, but it can be useful for quantifying short-term trends in the data. The linear regression model forecast for May 2015 is 277,000 which is much higher than the aggregate model forecast of only 197,000.
The positive trend in the model forecasts definitely leveled off and there is increasing evidence that the trend started to reverse direction in early 2015. However, over the past few months, the forecasts have begun to rebound slightly.
The difference between the May NFP consensus and May NFP forecast suggests an slight probability of an downside surprise tomorrow.
Figure 2: Non-Farm Payroll Graph May 2015
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.
Print and Kindle Versions of Brian Johnson's 2nd Book are Available on Amazon
Exploiting Earnings Volatility: An Innovative New Approach to Evaluating, Optimizing, and Trading Option Strategies to Profit from Earnings Announcements.
Print and Kindle Versions of Brian Johnson's 1st Book are Available on Amazon (35 5-Star Reviews)
Option Strategy Risk / Return Ratios: A Revolutionary New Approach to Optimizing, Adjusting, and Trading Any Option Income Strategy
Trader Edge Strategy E-Subscription Now Available: 20% ROR
The Trader Edge Asset Allocation Rotational (AAR) Strategy is a conservative, long-only, asset allocation strategy that rotates monthly among five large asset classes. The AAR strategy has generated annual returns of approximately 20% over the combined back and forward test period. Please use the above link to learn more about the AAR strategy.
Feedback
Your comments, feedback, and questions are always welcome and appreciated. Please use the comment section at the bottom of this page or send me an email.
Referrals
If you found the information on www.TraderEdge.Net helpful, please pass along the link to your friends and colleagues or share the link with your social or professional networks.
The "Share / Save" button below contains links to all major social and professional networks. If you do not see your network listed, use the down-arrow to access the entire list of networking sites.
Thank you for your support.
Brian Johnson
Copyright 2015 - Trading Insights, LLC - All Rights Reserved.
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. I have also written four books on options and derivative strategies.