Feature Engineering Methods on Multivariate Time-Series Data for Financial Data Science Competitions
This work addresses predictive modeling in financial data science competitions, but it is incremental as it builds on existing methods without new breakthroughs.
The authors applied various feature engineering methods to US market price data to improve predictive models for financial time-series, testing them against Numerai-Signals targets, but no concrete results or numbers are provided as it is a work in progress.
This paper is a work in progress. We are looking for collaborators to provide us financial datasets in Equity/Futures market to conduct more bench-marking studies. The authors have papers employing similar methods applied on the Numerai dataset, which is freely available but obfuscated. We apply different feature engineering methods for time-series to US market price data. The predictive power of models are tested against Numerai-Signals targets.