Sparse portfolio optimization, which significantly boosts the out-of-sample performance of
traditional mean–variance methods, is widely studied in the fields of optimization and …

Effective online portfolio selection necessitates seamless integration of three key properties:
diversity, sparsity, and risk control. However, existing algorithms often prioritize one property …

This paper presents a general framework for addressing sparse portfolio optimization
problems using the mean-CVaR (Conditional Value-at-Risk) model and regularization …

In this paper, we introduce an innovative two-stage approach that utilizes deep learning
techniques to tackle non-convex investment strategy optimization challenges. Our …

In the realm of online portfolio selection, aggregating multiple experts is crucial for improving
investment decisions in complex market conditions. However, existing methods often …

To optimize the total cumulative wealth, it needs to rebalance the portfolio on a period-by-
period basis, using previously published portfolio values. The median and mean reversion …

This paper introduces an innovative approach leveraging deep learning techniques to
empower the performance of investment models in the complex landscape of investment …