We directly optimize portfolio weights as a function of firm characteristics via deep neural
networks by generalizing the parametric portfolio policy framework. Our results show that …

The supervised portfolios approach is an effective asset allocation strategy that engineers
optimal weights before feeding them to a supervised learning algorithm. Yet, supervised …

Empirical evidence shows that the relationship between firm characteristics and stock
returns is non-linear, with a stronger correlation at the extreme deciles of the characteristic …

The supervised portfolios approach is an effective asset allocation strategy that engineers
optimal weights before feeding them to a supervised learning algorithm. Yet, supervised …

This paper takes advantage of machine learning (ML) techniques to provide an innovative
method for portfolio optimization. We propose an end-to-end optimal tangency portfolio (TP) …

The fundamental objective of portfolio optimization is the determination of next period's
optimal asset allocation under conditions of uncertainty. In most settings the portfolio …

Mean-variance optimization (MVO) is known to be sensitive to estimation error in its inputs.
Norm penalization of MVO programs is a regularization technique that can mitigate the …

환율은 국가의 금리, 경제, 국가관계가 반영되어 화폐의 가치를 상대적으로 표현한 것이다. 작년
세계 각 국가는 인플레이션과 거시경제의 불안을 우려하여 적극적인 통화, 금융정책을 …

We directly optimize portfolio weights as a function of firm characteristics via deep neural
networks by generalizing the parametric portfolio policy framework. Our results show that …