Non-Farm Payroll (NFP) Forecast – November 2012

I introduced a simple non-farm payroll forecasting model a few months ago and revised the model last month.  The revisions improved the fit and reduced the standard error.  Going forward, I may also attempt to build a neural network model to forecast the monthly non-farm payroll data.  This article explains the current model's forecast for the November non-farm payroll data, which will be released tomorrow morning.

Non-Farm Payroll (NFP) Model Forecast - November 2012

The table in Figure 1 below includes the monthly non-farm payroll data for two months: October 2012 and November 2012.  The October data was released last month and the non-farm payroll data for November 2012 will be released tomorrow at 8:30 AM EDT.

The model forecasts are in the third data row of the table (in black).  Note that past and current forecasts reflect the latest values of the independent variables, but both forecasts will change when revisions are made to the historical economic data.

The monthly standard error of the model is approximately 113,700, which is still sizable, but a lot of the variation is due to large anomalies in the reported survey data (e.g. the 2010 period in Figure 2 below).  The first and last data rows of the table report the forecast + 0.5 standard errors (in green) and the forecast - 0.5 standard error (in red), respectively.  All values are rounded to the nearest thousand.  Assuming the model errors are 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 October 2012 is in the second data row of the table (in purple).  The consensus estimate (reported by Briefing.com) for November 2012 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 October 2012 to November 2012.

Figure 1: NFP Table November 2012

Model Commentary

The model forecast for November is only 43,000, which is down 108,000 from last month's revised forecast.  The Briefing.com consensus estimate for November is 90,000, which is 0.41 standard errors above the model forecast.  The model forecast would appear to suggest a negative NFP surprise tomorrow.  If the actual NFP data for November were to come in 0.50 standard errors below the model forecast, NFP would be negative for November.  While this is unlikely, if it were to occur, it would inflict a serious psychological blow to the bulls.

There is another important caveat this month. Hurricane Sandy affected the employment market in November, but the exact magnitude and duration of the effect is difficult to quantify.  As a result, that increases the uncertainty surrounding the November NFP report.

As I mentioned last month, when the new model consistently underestimates the actual NFP data by a significant amount, it can lead to strong equity market performance in the future.  The opposite is also true.

As you can see from the graph in Figure 2 below, the trend in job gains is currently negative.

Figure 2: NFP Graph November 2012

Conclusion

Using basic forecasting tools can help you identify unusual consensus economic estimates, which often lead to substantial surprises and market movements.  Identifying such environments may help you protect your portfolio from these corrections and help you determine the optimal entry and exit points for your strategies.  In addition, significant deviations from the new model's forecasts may foretell subsequent moves in the equity markets and the economy.

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Brian Johnson

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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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