Advanced computational methods for optimizing financial portfolio management in volatile market conditions
Posted: Sep 27, 2022
Abstract
Financial portfolio optimization represents one of the most challenging problems in quantitative finance, particularly under conditions of market volatility and economic uncertainty. Traditional approaches to portfolio management, rooted in modern portfolio theory established by Markowitz, have demonstrated significant limitations when applied to turbulent market environments characterized by non-normal return distributions, time-varying correlations, and extreme risk events. This research addresses these limitations by developing a novel computational framework that integrates quantum-inspired optimization techniques with advanced machine learning methodologies. The proposed approach moves beyond traditional optimization paradigms by incorporating adaptive learning mechanisms that can capture complex market dynamics and respond to changing volatility regimes. Our methodology represents a significant departure from conventional portfolio optimization by treating the problem as a dynamic, multi-objective optimization challenge rather than a static allocation problem.
Downloads: 49
Abstract Views: 1104
Rank: 100259