NRDS
NerdWallet, Inc. Class A Common Stock
stock NASDAQ

Seems like I am late to the party. Hopefully I can still get your take on some of these thoughts I had reading this…
1) Problem I often see in these studies is the survivorship bias which in a way you touched upon as well. If you take a universe of multibaggers “after the fact” and then tweak your model to those tickers, you are going to destil some parameters that are common for multibaggers, but that doesn’t tell you how many failed companies had the same features but failed. In other words, it doesn’t tell the story of statistical relevance of the destilled parameters. And if you are going to use something as a “screener” which in practice is what you want (to get to the holy grail), this is more important than just finding common traits of multibaggers. You want the traits that are going to separate them from the crowd. And that back-test is really hard to do, because histories of many failed companies are not captured properly in the archives for example (and yet survivors are), and there are many other technical difficulties to properly do that ultimate test.
2) I checked some of the tickers people posted here as the tickers that come out if you use the above parameters as a “screener”. I found it interesting because I recognized some names from my screener (STNE, NRDS, TASK, UPWK, PGY) and my portfolio (DLO, PAYO, GCT). As expected, it looks like it favors financials and insurances which always have strong cash flows until one day they don’t. Also financials with customer deposits have elevated cash flows. Not to mention stocks with high SBC which also seem to pop-up. A lot of these are also cheap by traditional metrics (P/E, P/FCF, PEG, PSG, etc), yet there is always some underlying risk which market is not willing to take to price them higher. So the question that remains difficult to answer is how to meaningfully use this in practice.

Seems like I am late to the party. Hopefully I can still get your take on some of these thoughts I had reading this…
1) Problem I often see in these studies is the survivorship bias which in a way you touched upon as well. If you take a universe of multibaggers “after the fact” and then tweak your model to those tickers, you are going to destil some parameters that are common for multibaggers, but that doesn’t tell you how many failed companies had the same features but failed. In other words, it doesn’t tell the story of statistical relevance of the destilled parameters. And if you are going to use something as a “screener” which in practice is what you want (to get to the holy grail), this is more important than just finding common traits of multibaggers. You want the traits that are going to separate them from the crowd. And that back-test is really hard to do, because histories of many failed companies are not captured properly in the archives for example (and yet survivors are), and there are many other technical difficulties to properly do that ultimate test.
2) I checked some of the tickers people posted here as the tickers that come out if you use the above parameters as a “screener”. I found it interesting because I recognized some names from my screener (STNE, NRDS, TASK, UPWK, PGY) and my portfolio (DLO, PAYO, GCT). As expected, it looks like it favors financials and insurances which always have strong cash flows until one day they don’t. Also financials with customer deposits have elevated cash flows. Not to mention stocks with high SBC which also seem to pop-up. A lot of these are also cheap by traditional metrics (P/E, P/FCF, PEG, PSG, etc), yet there is always some underlying risk which market is not willing to take to price them higher. So the question that remains difficult to answer is how to meaningfully use this in practice.