A good machine learning model needs rich and wide datasets.

When calculating the results and preparing a list of undervalued and overvalued stocks in our StockPicking Lab, for every asset, stock, and ticker we use 2237 features.

We have deployed a new version of our SFA (Summary of Financial Articles) software.

Learning curves can bring important insight during the design process of a machine learning model.

We have been researching the selection of variables enterings. When there are too many variables, the model has a worse ability to generalize and is more prone to errors.

We have recently implemented the method Feature Binning and Quantile Transformation. Due to upgraded data preparation, our ML models now achieve better results.