Preprocessing Stock Features and Labels for Deep Learning
Summary
This brief response addresses preprocessing for deep-learning models applied to stock data, including Boolean values and infinite observations. Its direct recommendation for infinities is to remove them. For broader feature preparation, it lists outlier treatment, standardization, neutralization, and binning as candidate steps. These are common operations in quantitative equity research: they can limit the influence of extreme observations, put feature scales on comparable footing, adjust for systematic exposures, and convert continuous variables into groups.
The response does not explain how to encode true/false values, define the order of preprocessing, handle missing values, or distinguish feature transformations from label processing. It also gives no dataset, model, validation procedure, or empirical evidence that one sequence or treatment works best. Researchers would need to decide these details in context and avoid using information unavailable at the prediction date when fitting transformations. The material is therefore a compact checklist of possible preprocessing actions, not a complete deep-learning pipeline or tested prescription.
Key ideas
- The response recommends removing infinite observations from stock data before model use.
- It lists outlier treatment, standardization, neutralization, and binning as possible preprocessing steps.
- It raises Boolean data and labels as questions but does not specify how to encode or transform them.
- It does not provide an ordering, missing-data method, model setup, or empirical comparison of preprocessing choices.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.