Machine Learning and Deep Learning Methods for Quantitative Stock Selection

Summary

This educational-session overview introduces machine-learning and deep-learning approaches to quantitative stock selection. It lists core workflow topics: defining prediction labels, engineering features, separating training, validation, and test sets, and choosing between classification and ranking models. Traditional machine-learning examples include random forests and gradient-boosted tree methods such as XGBoost and LightGBM. For deep learning, it contrasts the general approach with traditional methods and names LSTM and Transformer models, alongside constructing input sequences, training models, and ranking predicted stocks.

The document is an outline for a presentation and points readers to a recording and example strategies, but it contains no model specifications, evaluation results, or reported returns. It therefore introduces a research workflow rather than establishing that any model is effective. It does not explain how labels avoid look-ahead bias, how validation is performed through time, or how trading costs and portfolio constraints affect results. Those choices would need to be addressed when turning the overview into a reproducible and investable strategy.

Key ideas

• Stock-selection modeling begins with defining labels and constructing predictive features.
• Training, validation, and test data have distinct roles in developing and assessing a model.
• Classification and ranking are presented as alternative modeling setups.
• The overview names random forests, boosted trees, LSTMs, and Transformers as possible methods.
• It outlines a workflow but provides no reported performance or strategy evaluation.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.