Regarding what frequency is best for AI…
Here’s an excerpt from a recent AI paper mentioned in this post Stock picking with machine learning
Almost all of the abovementioned studies use ML models for monthly predictions based on monthly data. In contrast, we analyze shorter term predictability and focus on weekly data and weekly predictions. Analyzing weekly predictions provides two major advantages: First, the larger number of predictions and trades in an associated trading strategy provides higher statistical evidence due to the larger sample size. Second, ML modes require large training sets. Therefore, studies analyzing monthly predictions require very long training sets of at least 10 years. Given the dynamics of financial markets and the changing correlations in financial data over time, it could be suboptimal to train ML models on very old data, which is not any more relevant for today’s financial world due to changing market conditions. Because our study builds on weekly data, we are able to reduce the length of the training set to only 3 years while still having enough observations for training complex ML models.