Abstract
New research on Machine Learning is the subject of a report. According to news originating from Aarhus, Denmark, by NewsRx correspondents, research stated, “We inspect how accurate machine learning (ML) is at forecasting realized variance of the Dow Jones Industrial Average index constituents. We compare several ML algorithms, including regularization, regression trees, and neural networks, to multiple heterogeneous autoregressive (HAR) models.” Financial supporters for this research include Det Frie Forskningsrad (DFF), CREATES. Our news journalists obtained a quote from the research from Aarhus University, “ML is implemented with minimal hyperparameter tuning. In spite of this, ML is competitive and beats the HAR lineage, even when the only predictors are the daily, weekly, and monthly lags of realized variance. The forecast gains are more pronounced at longer horizons. We attribute this to higher persistence in the ML models, which helps to approximate the long memory of realized variance. ML also excels at locating incremental information about future volatility from additional predictors. Lastly, we propose an ML measure of variable importance based on accumulated local effects.”
NETL
NSTL
Oxford Univ Press