Machine learning can combine many features into return forecasts, but it does not remove the hard parts of quantitative research: point-in-time data, leakage prevention, validation design, interpretability, and cost-aware evaluation.
Begin with strong linear baselines, then add more flexible models only when the research question justifies them. See Machine Learning Models.