# Best way to treat negative P/E and extremely high P/S in data?

I'm working on a data that deals with start-up/similar companies data. A lot of the companies have negative P/E and/or little to no sales. Is there good ways to create meaningful data (like averages, medians) from this?

I obviously should not use negative values in calculations, since low P/E is meant be a positive factor. So far, I've tried two alternative ways, neither of which are great:

1. Ignore all negative P/E and huge/undividable P/S data. However, this significantly does skew the data as you are easily dropping over 50% of the companies and thus gives much rosier picture of the market than it should.

2. Assign a large dummy value for negative P/E and huge/undividable P/S. For example, any negative P/E becomes positive 50, while any missing P/S becomes 100. However, since these numbers are picked arbitrarily, it can unnecessary skew the data to higher-than-real averages.

Any ideas about better approaches? (And yes, I know this question is not necessarily a perfect fit for quant finance, but there really isn't any better place for it either...)

• I don't know what your end goal is. However, ignoring negative P/E for some start-ups gives a "skewed" observation of your dataset, as you also imply in 1. If you really don't want to work with negative P/E's then you could essentially shift all of your ratios such that the lowest (most negative) P/E ratio becomes 0. This will affect the mean, and still give an impractical picture of the companies in terms of evaluation. If I was in your situation, I would just work with the data "as it is" or find other metrics that are more suitable for start-ups (or your data).
– Pleb
Jan 18, 2021 at 11:20
• In a sense a negative P/E is a P/E that has gone to positive infinity and wrapped around. So the negative P/Es are in a sense the highest P/Es of all. I you divide your P/Es into quartiles you would have: small positives, medium positives, large positives and then the negative P/Es. Jan 18, 2021 at 12:13

The best solution according to most quants, is not to use P/E (Price to Earnings) at all but use E/P (Earnings to Price).

When you do this the negative E/P stocks are the lowest E/P, lower than the positive but small E/P stocks, which in turn are lower than the others. So the natural order is preserved.

When you use P/E the negative P/E need to "shifted" into the highest P/E category. Mathematically a negative is considered smaller than a positive, but this is contrary to the intuition about negative P/E: financially these are the most generously valued ("highest P/E") of all. So don't use P/E, the rank ordering of P/E's does not make sense.

• Seconded - look at E,S,orB/P and it's called "earnings yield", "sales yield" and "book yield" in institutional circles for exactly all the reasons cited above. Jan 19, 2021 at 1:00