Application of Deep Learning for Factor Timing in Asset Management

arXiv:2404.18017v1 Announce Type: cross
Abstract: The paper examines the performance of regression models (OLS linear regression, Ridge regression, Random Forest, and Fully-connected Neural Network) on the prediction of CMA (Conservative Minus Aggressive) factor premium and the performance of factor timing investment with them. Out-of-sample R-squared shows that more flexible models have better performance in explaining the variance in factor premium of the unseen period, and the back testing affirms that the factor timing based on more flexible models tends to …

abstract application arxiv cma cs.lg deep learning investment linear linear regression management network neural network ols paper performance prediction q-fin.cp q-fin.pm random regression ridge r-squared sample shows them type