The paper trains an XGBoost model to predict investment outcomes for startup investments from the time period 2014-2016. The model is trained on investments prior to 2014, and uses features from Pitchbook. It says the most important features are text descriptions of the company and CEO, which are featurized as unigram and ngram TF-IDF counts.

The paper avoids the very first methodological problem one would check for, which is using a held-out set that is mixed in time. That is: at least it uses a heldout _period_ (2014-2016), instead of individually held-out samples. Using individually held-out samples would let the model learn about future trends. If you know some companies that will be successful in 2026, that will help a lot in deciding what to invest in today, even if you can't pick those companies. So at least the paper doesn't do that.

However, the paper doesn't seem to have limited itself to information written and documented about the companies at the time of the investment decisions. Descriptions about the CEO will change over time. Once a company is looking to go public, maybe they emphasise that their CEO has an MBA. If your company died at seed, maybe this detail isn't included in the text. Even the people listed as the founders of the company is something that might be revised over time (Tesla is a famous example).

The paper really doesn't give us much insight into what the model learns. This means that we need to consider competing explanations for the model's accuracy --- we're not forced to accept the author's conclusion that the investments were indeed "predictably bad" in a way that could be profitably implemented as a trading strategy.

By itself, the fact that a black-box machine learning model with a large number of features can distinguish between two classes on a held-out set really isn't very strong evidence that the model has learned something of practical importance, that will generalise beyond the train and held-out sets you've collected. You need to show us what it's actually learned. If it's supposed to identify wolves, is it looking at the canines or the snow? For a study like this, I'd want to see feature pruning down to where the model is still accurate but with only a few textual features, and then show us what those features are. If you can't do that in a way that actually leads to human insight, it's extremely unlikely that the model is learning anything real.