Non-Farm Payroll (NFP) Model Forecast – October 2012

I introduced a simple non-farm payroll forecasting model last month.  Since then, I have revised the model, which improved the fit and reduced the standard error.  This article explains the current forecast for the October non-farm payroll data, which will be released tomorrow morning.

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

The table in Figure 1 below includes the monthly non-farm payroll data for two months: September 2012 and October 2012.  The September data was released last month and the non-farm payroll data for October 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 standard error of the model is approximately 113,600, 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 September 2012 is in the second data row of the table (in purple).  The consensus estimate (reported by for October 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 September 2012 to October 2012.

Figure 1: NFP Table Sep-Oct 2012

Model Commentary

The model forecast for September is 140,000, which is up 16,000 from last month's forecast.  The consensus estimate for September is 125,000, which is 0.13 standard errors below the model forecast.  The model forecast would appear to suggest the possibility of a positive NFP surprise tomorrow.

However, there is one important caveat this month. One of the model's independent or explanatory variables is weekly unemployment claims.  A few weeks ago, the weekly claims number was unusually low.  Several analysts have suggested that California might have significantly under-reported its claims that week.  If we adjust the claims number for that week to be consistent with the claims reported during the adjacent weeks, the model forecast for the month of October would drop from 140,000 to 121,000, which would be slightly below the consensus estimate.

The initial model that I created last month understated the NFP numbers for an extended period, but the new model is much more consistent with the historical data (see Figure 2 below).  However, positive and negative deviations from the new model NFP forecasts can still continue for several months, which has led to periods of positive and negative equity returns, respectively (at least anecdotally). In other words, 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.

There was a five-month period in mid-2008 when the actual data deviated from the model forecasts by an average of -0.95 standard errors, which would have provided a timely warning of the impending recession.  Conversely, there was a ten-month period in 2009 when the actual data deviated from the model forecasts by an average of +1.09 standard errors, which would have confirmed the beginning of the expansion. Neither of these periods is depicted in the chart below.

Figure 2: NFP Graph - Oct 2012


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. I have also written four books on options and derivative strategies.
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