AI-driven optimisation of sustainable investment fund construction
Optimising sustainable investment funds with AI: Sharpe ratio, ESG & diversification
Constructing a sustainable investment fund is a mathematical portfolio decision. The aim is not to select individual ‘good’ securities, but to calculate an optimal weighting that simultaneously meets requirements for return, risk, sustainability and diversification.
Objective
Maximising the Sharpe ratio whilst adhering to ESG requirements, sector limits and turnover limits relative to a benchmark.
Assessment Inputs
- Expected return per asset: μᵢ
- Covariance matrix of returns: Σ
- ESG score per asset (0–100)
- Sector classification
- Benchmark weights (for turnover restrictions)
Mathematical model
Portfolio weights are modelled as continuous variables:
- wᵢ ≥ 0 (long-only)
- Σ wᵢ = 1 (fully invested)
The risk-adjusted return is optimised:
max Sharpe Ratio = (wᵀ μ − rf) / √(wᵀ Σ w)
In doing so, the model simultaneously takes into account return expectations, risk correlations as well as regulatory and sustainability-related constraints.
Constraints
- Portfolio ESG average ≥ 80
- No sector > 25% of the total portfolio
- Turnover limit relative to the benchmark
Result
- Optimal asset weights under ESG and diversification rules
- Transparent presentation of target achievement and compliance with restrictions
- Traceable trade-offs between return, risk and sustainability
- Documentation suitable for committee and compliance purposes
Technology
StratePlan implements continuous portfolio optimisation under linear and non-linear constraints. ESG criteria are integrated in a structured manner via MCDA (Multi-Criteria Decision Analysis), ensuring that sustainability requirements are incorporated into the optimisation in a quantitatively consistent manner.
Common Patterns Across Cases
Assessment
Qualitative and quantitative factors are converted into comparable scores – using scales, evaluation models or structured expert assessment. The aim is to establish a consistent, decision-ready evaluation basis.
Ranking
Elements are prioritised. However, ranking is rarely the final decision. In complex environments, prioritisation is frequently embedded directly into a combinatorial optimisation process in order to systematically account for interactions and constraints.
Group Selection
The final selection goes beyond a simple ‘Top-k’ approach. StratePlan solves structured selection problems such as knapsack, portfolio or scheduling models and calculates the optimal combination under real-world constraints.
Constraints
Constraints reflect real-world scarcity: capital, time, resources, risk appetite, regulatory requirements, strategic mandates or sustainability requirements. They are an integral part of the decision-making logic.
Technologies
Hybrid use of MCDA methods (e.g. AHP, TOPSIS) for structured evaluation combined with StratePlan for constraint-aware group or portfolio selection.
These cases demonstrate how StratePlan evolves decision-making processes from pure ranking to intelligent, constraint-aware portfolio construction. Evaluation data is translated into actionable, optimised group decisions – aligned with financial, strategic and sustainability-related objectives.
The underlying core logic – structured evaluation → quantitative prioritisation → constrained group selection – scales across different sectors and is adapted in each case to domain-specific success metrics and constraints.
