Risk-aware Trading Portfolio Optimization

ArXiv ID: 2503.04662 “View on arXiv”

Authors: Unknown

Abstract

We investigate portfolio optimization in financial markets from a trading and risk management perspective. We term this task Risk-Aware Trading Portfolio Optimization (RATPO), formulate the corresponding optimization problem, and propose an efficient Risk-Aware Trading Swarm (RATS) algorithm to solve it. The key elements of RATPO are a generic initial portfolio P, a specific set of Unique Eligible Instruments (UEIs), their combination into an Eligible Optimization Strategy (EOS), an objective function, and a set of constraints. RATS searches for an optimal EOS that, added to P, improves the objective function repecting the constraints. RATS is a specialized Particle Swarm Optimization method that leverages the parameterization of P in terms of UEIs, enables parallel computation with a large number of particles, and is fully general with respect to specific choices of the key elements, which can be customized to encode financial knowledge and needs of traders and risk managers. We showcase two RATPO applications involving a real trading portfolio made of hundreds of different financial instruments, an objective function combining both market risk (VaR) and profit&loss measures, constrains on market sensitivities and UEIs trading costs. In the case of small-sized EOS, RATS successfully identifies the optimal solution and demonstrates robustness with respect to hyper-parameters tuning. In the case of large-sized EOS, RATS markedly improves the portfolio objective value, optimizing risk and capital charge while respecting risk limits and preserving expected profits. Our work bridges the gap between the implementation of effective trading strategies and compliance with stringent regulatory and economic capital requirements, allowing a better alignment of business and risk management objectives.

Keywords: portfolio optimization, particle swarm optimization, risk management (VaR), trading strategies, capital allocation

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail
  • Why: The paper employs advanced meta-heuristics (Particle Swarm Optimization) and complex optimization formulations with constraints and non-linear risk measures, indicating high mathematical density. It also demonstrates backtest-ready empirical results on real trading portfolios with hundreds of instruments, using actual market data and performance metrics, indicating high empirical rigor.
  flowchart TD
    A["Research Goal:<br>Risk-Aware Trading<br>Portfolio Optimization RATPO"] --> B["Key Methodology:<br>RATS Algorithm<br>Particle Swarm Optimization"]
    B --> C["Data & Inputs:<br>Initial Portfolio P,<br>Unique Eligible Instruments UEIs,<br>Constraints & Objectives"]
    C --> D["Computational Process:<br>Parallel Swarm Search<br>for Optimal Eligible Optimization Strategy EOS"]
    D --> E["Key Findings:<br>Optimal Portfolio with<br>Balanced Risk (VaR) & Profit,<br>Respects Capital & Regulatory Limits"]