Portfolio Optimization

AI and quantum methods optimize portfolios for better returns, risk, and efficiency in finance

Business impact

• Portfolio return — Optimized asset allocation increases overall investment returns
• Risk-adjusted return — Better balancing of risk and reward improves Sharpe ratios and stability
• Operational efficiency — Automation and faster computations reduce time and costs in portfolio management
• Client satisfaction — More personalized and responsive portfolio management enhances client experience
• Computational speed — Quantum-inspired and AI methods accelerate complex optimization calculations

Data requirements

• Market data (Numeric) — Used for asset price, volatility, and correlation inputs in optimization models
• Financial statements (Structured) — Provide fundamental data for asset valuation and risk assessment
• Transaction records (Structured) — Inform portfolio constraints and historical performance analysis
• Alternative data (Text) — Enhance predictive models with sentiment, news, and macroeconomic indicators
• Synthetic data (Numeric) — Augments training datasets to improve model robustness and scenario testing

AI methods and techniques

• Predictive AI — Forecast asset returns and risks to inform portfolio construction
• Agentic AI — Automate decision-making and dynamic portfolio rebalancing in real time
• Generative AI — Create synthetic data for scenario analysis and model training augmentation
• Symbolic AI — Incorporate regulatory and business rules as constraints in optimization

AI models and model families

GPT-4o, Claude, Llama, Variational Quantum Eigensolver, Quantum Approximate Optimization Algorithm

Industries

Real-world evidence

12 documented case studies on record.

Companies using this: Barclays, BlackRock, Citigroup, Goldman Sachs, Gurobi Optimization LLC, Instituto Ci Tecnologia Ita, Japan Post Bank, Morgan Stanley, Nat West, Ping An Wealth Management, Royal Bank Canada, Standard Chartered.