No. of Recommendations: 4
Tetherdog: but precisely what generated those results ?
First the formula:
The SSRN paper used the formula (REVT−COGS−(XSGA−XRD)−XINT) / (BE+MIB)
The terms are from compustat, I wanted to test this on Portfolio123 which now uses factset data but in the
Past used compustat and I have a table cross referencing P123 and compustat. I tried to show the relationship
Of compustat and P123 above but I’ll try again:
Compustat REVT revenue total
Compustat COGS cost of goods sold
Compustat SALE-COGS is equivalent to P123 GrossProfit ??don't understand compustat REVT vs SALE
Compustat XSGA Administrative expenses
Compustat XRD Research and Development Expense
And Compustat XSGA-XRD is equivalent to P123 SGandA
Compustat XINT Interest and Related Expense Total is equivalent to P123 IntExp
Compustat BE Book Equity is equivalent to BookVal
Compustat MIB is minority interest which is almost always negligible and I could not find a P123 equv so I left it out
Result a P123 version of the formula base on the last quarterly values is:
(SalesQ -CostGQ -SGandAQ -IntExpQ)/BookValQ
Tetherdog: So you created a screen/backtest based on this value measure, and your table is the result of this?
Could you elaborate on the screen, e.g. is there some threshold for which you drop stocks, etc? Or perhaps I misunderstood?
Not exactly, I created a sort of histogram of the performance equations actual return over history:
1. For the 1st historical date rank all 1500 of the SP1500 stocks using the performance equation
2. Sort the resulting 1500 by the descending value of the performance equation
3. Group the equities into 20 equal “predicted” performance bins from highest to lowest
4. Average the gain for each bin over the future sample period in this case 4 weeks. If the prediction has any value the first bins will have a higher average gain than the last bins.
5. Repeat the above for the next period and accumulate the gains from each bins.
The resulting 1st bin gives the actual performance of the highest predicted by the performance equation and the 20 bin the lowest predicted. There was no calculation of any trading friction nor any measurement of how consistent the ranking was between sample date.
Last Yes I have posted about Machine Learning here but the feedback wasn’t exactly positive, the topic is different enough that ML posts are better posted on a ML blog than here. But thanks for the bringing up the performance equation, I’ll obviously drop it into my ML factors and see if it helps.
