I come from a different field (Machine learning/AI/data science), but aim to ask a philosophical question with the utmost respect: Why do quantitative financial analysts (analysts/traders/etc.) prefer (or at least seem) traditional statistical methods (traditional = frequentist/regression/normal correlation methods/ts analysis) over newer AI/machine learning methods? I've read a million models, but it seems biased? Background: I recently joined a 1B AUM (I know it's not a ton) asset management firm. I was asked to build a new model for a sector rotation strategy (basically predicting which SP 500 sector would do the best over 6 months-- chose to use forward rolling 6 month returns) they employ and my first inclination was to combine ARIMA (traditional) with random forest (feature selection) and a categorical (based on normal distribution standard deviation) gradient boosted classifier for ETFs in each sector. Not to be rude, but I beat the ValuLine timeliness for each sector. I used the above mentioned returns as my indicator and pretty much threw everthing at the wall for predictors initially (basically just combing FRED), then used randomForest to select features. I ended up combining EMA and percent change to create a pretty solid model that, like I said, beat ValuLine. I've read a lot of literature, and I haven't seen anyone do anything like this. Any help in terms of pointing me in the right direction for literature? Or any answers to the overarching idea of why isn't there more machine learning in equity markets (forgetting social/news analysis)? EDIT: For clarification, I'm really interested in long-term predictions (I think Shiller was right) based on macro predictors. Thanks PS- I've been lurking for a while. Thanks for all the awesome questions, answers, and discussions.