I think there are certain types of tabular data that lend themselves naturally to tree models. But when you're talking about tabular data for finance I guarantee you very few hedge funds are running tree models for trading strategies. When your scale of data is the past X quarters of all stock prices and trade volumes you have enough data that you can fit an NN and there are a number of techniques you can use to reduce overfitting (large amount of data, good regularization, dropout, etc.)
> But when you're talking about tabular data for finance I guarantee you very few hedge funds are running tree models for trading strategies
What do you base this on? Having only neural nets on tabular data is mostly done due to laziness of the creator since neural nets are much easier to use, not because neural nets perform better even with large amounts of data. In general you want both since they are good at finding different kinds of patterns.
