Understanding data represents the key competency for gaining and maintaining the competitive advantage.
Our main point of interest is the data analysis in the field of capital markets, however, we work with data from various areas. We apply functions and methods specifically developed for a particular field in a multidisciplinary way, it is possible to repeatedly compare experimental results between each other. Owing to this, you can reach results which will move your work to the next level.
Data Harvesting – Extract, Transform, Loadevent_note 11.10.2021 person Tereza Zemanová
A good machine learning model needs rich and wide datasets.
Complexity of each calculation in the StockPicking Labevent_note 24.08.2021 person Maša Vodalov
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.
New SFA featuresevent_note 28.07.2021 person Maša Vodalov
We have deployed a new version of our SFA (Summary of Financial Articles) software.
Model Diagnostics with Learning Curvesevent_note 22.07.2021 person Maša Vodalov
Learning curves can bring important insight during the design process of a machine learning model.
Feature Selectionevent_note 28.06.2021 person Tereza Zemanová
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.
Feature Binning and Quantile Transformationevent_note 11.06.2021 person admin
We have recently implemented the method Feature Binning and Quantile Transformation. Due to upgraded data preparation, our ML models now achieve better results.