CHOOSE
Portfolio Management
Optimize asset allocation to maximize risk-adjusted returns while satisfying regulatory constraints, ESG criteria, and transaction cost considerations in financial portfolios.
Understanding the Problem
Portfolio management involves selecting optimal allocations of assets that maximize expected return while minimizing risk, subject to real-world constraints including transaction costs, sector diversification requirements, regulatory limits, and ESG considerations. As portfolios grow to thousands of assets and constraints become more complex, traditional methods face computational scalability challenges.
THE CHALLENGE
What Makes it Hard
Portfolio optimization balances risk and return across thousands of assets while satisfying complex constraints. The problem scales with assets and time periods, creating large-scale quadratic programming challenges.
WHO FACES IT
Quadratic scaling with covariance matrix: N² covariance terms dominate portfolio risk as assets increase
Extreme sensitivity to inputs: small errors in expected returns lead to drastically different optimal portfolios
Dynamic market conditions: correlations and volatility change over time, violating stationarity assumptions
BUSINESS IMPACT
Portfolio optimization improves Sharpe ratios by 40%, reduces rebalancing costs by 60-70%, and enables quantum-scale solutions through 80% problem size reduction.
Sharpe Ratio
40%
Improvement[1]
Portfolio optimization using shrinkage methods improves out-of-sample Sharpe ratios by 40% on average.
Problem Size
80%
Reduction[2]
JPMorgan's decomposition pipeline reduces portfolio optimization problems by approximately 80%, enabling quantum-scale solutions.
Rebalancing Costs
60-70%
Reduction[3]
Automated portfolio rebalancing systems reduce rebalancing costs by 60-70% through optimized trade generation.
How We Solve It
Classical quadratic programming and mixed-integer quadratic programming form the foundation. For large-scale problems (10,000+ assets), hybrid quantum-classical methods show promise. Modern approaches include hierarchical risk parity, risk-based portfolios, and ESG-constrained multi-objective optimization.
Hybrid Compute
What We Bring
Quadratic and conic optimization for mean-variance portfolio construction
Mixed-integer programming for cardinality constraints and threshold positions
Stochastic programming for multi-period optimization under uncertainty
Multi-objective optimization balancing returns, risk, and ESG criteria
FUTURE POSSIBILITIES
The
Quantum Horizon
Quantum algorithms show promise for ultra-large portfolios (10,000+ assets) where classical n³·⁵ scaling becomes challenging. Current demonstrations include 60-109 qubit implementations on real quantum hardware.
Exploratory Work
Major financial institutions (JPMorgan, Goldman Sachs, Vanguard, Citi, Wells Fargo, Barclays) are investing heavily in quantum computing research. While no practical quantum advantage exists yet for most portfolio sizes, hybrid quantum-classical approaches and problem decomposition techniques are advancing rapidly. The UK FCA notes that portfolio optimization remains a highly promising use case for near-term quantum applications in financial services.
Current Research Directions
Quantum Interior Point Method (QIPM) by Goldman Sachs and AWS for large-scale portfolio optimization
D-Wave quantum annealing for portfolio optimization with 60-stock portfolios matching classical solver performance
IBM-Vanguard collaboration using 109 qubits on Heron r1 processor for bond ETF construction
JPMorgan decomposition pipeline reducing problem sizes by up to 80% for quantum-solvable instances
Interested in quantum research?
Explore proof-of-concept implementations with our team.
Ready to solve this problem?
Talk to our experts about how Strangeworks can help with portfolio management.