AI and ML-Driven Bond Portfolio Optimisation
Leading UAE bank
The bank wanted a bond portfolio optimisation workflow that was transparent, risk-aware and execution-ready, and that worked inside its regulatory and investment constraints rather than around them. It also had to integrate with the bank's existing market data infrastructure without model mismatch or operational disruption.
Scope of work
Portfolio analytics layer giving full visibility of portfolio health before any optimisation runs. The current portfolio and tradable universe are uploaded, and the layer surfaces:
- DV01 and key rate duration heatmaps across the book
- Liquidity and sentiment overlays on each holding
- Concentration risk, low-liquidity holdings and negative sentiment exposure
Quantitative feature engine computing modified duration and convexity per bond using continuous discounting, and key rate durations at standard maturity buckets, giving first and second order rate sensitivity for hedging through non-parallel curve moves.
Machine learning layer producing four signals that feed the optimiser as penalties and constraints:
- Anomaly score, trained on yield movements, spread changes, option-adjusted spread against peer curves and volatility indicators
- Spread volatility and direction forecast, built from historical series and unstructured news signals
- Regime probability, classifying macro and credit conditions as risk-on, risk-off or stressed
- Peer deviation score, identifying issuers trading cheap or rich against their sector curve
Multi-objective optimisation engine solving for an optimal allocation vector, with each run optimising a single objective under mandate-specific weighting and multiple objectives evaluated in sequence. Objectives include:
- Return on capital, asset swap spread and net interest margin
- Basel capital minimisation, LCR, NSFR and HQLA optimisation
- Liquidity score across the resulting portfolio
Two-category constraint architecture separating hard constraints on long-only, rating floor and FX volatility from soft constraints on duration, sector limits and model score thresholds. A relaxation loop expands the soft bounds step by step until a feasible solution is found, generating an efficient contour of valid portfolios.
Constraints taken from the bank's own guidelines, covering eligibility, issuer and sector concentration including ESG, maturity bands, interest rate risk limits and currency and hedging requirements.
Outcome and value
- Execution-ready output: automated buy and sell trade lists with the reasoning attached to each recommendation.
- Risk budgets that tighten automatically during stressed regimes rather than on manual intervention.
- Recommendations that survive review, because the regulatory position is built into the problem.
- No parallel model to maintain, through integration with the existing market data infrastructure.