Posts filed under “Data Analysis”
Here’s a fun thing to do between now and Friday’s NFP data: You can play Payroll Employment: The Home Game:
"Every month billions of dollars change hands based upon the monthly
report on the employment situation. This report is released the first
Friday of each month.
With such an exciting opportunity, traders are tempted to participate in stocks, bonds, or a direct "bet" on the number.
Here is where you can practice online for the employment report."
The game (found here), allows you to bet on ‘True’ job growth as well as ‘Actual’ BLS change (hedonically adjusted and massaged though it may be).
To me, the most interesting aspect of the Payroll Employment Game are the Technical Notes. They lay out a very specific mind set that governs how real traders and investors position themselves around the monthly NFP report:
"The key points the Employment Game is designed to make are the
nature of the
information contained in the job report and the risks inherent in
acting on a
mistaken belief that the BLS job growth estimate for the current month
represents an absolutely exact and true measure of the economy.
We do not intend to be critical of BLS in general nor of the
Survey in particular. We believe the Establishment Survey is an
excellent survey conducted, analyzed, and reported by dedicated and
Moreover, we believe the Establishment Survey provides an estimate of
the change in seasonally adjusted jobs from one month to the next which
is as accurate as it is possible to be in measuring anything at all
involving actual humans in that time frame. Nor does the BLS
misrepresent their work. They go to great lengths to explain what they
do, why, and how and to explain how to interpret the results. As the
old guy said: ‘The fault, dear Brutus, is not in our stars, but in
I agree. The only factor I found wanting was the lack of the BLS Birth-Death adjustment.
However, given the inherent difficulty in counting 140 million employed persons in the US, and then estimating the monthly change in that data, rather than making a specific numerical estimate, I find myself very comfortable sticking with the cowardly binary choice of "Over" or "Under."
But I do like the idea of making this into a playable and educational game.