This paper evaluates multiple machine learning algorithms for forecasting the 30 second forward spread of ETH/USD. The experiments include using boosted decision trees (LightGBM, XGBoost), higher forms of linear regression and Ensembling. The Ethereum orderbook was constructed through the...

We propose an automatic machine-learning system to forecast realized volatility for S&P 100 stocks using 118 features and five machine learning algorithms. A simple average ensemble model combining all learning algorithms delivers extraordinary performance across forecast horizons, and the...

This paper applies machine learning algorithms to the modeling of realized betas for the purposes of forecasting stock systematic risk. Forecast horizons range from 1 week up to 1 month. The machine learning algorithms employed are ridge regression, decision tree learning, adaptive boosting,...