Overview
Finance, Portfolio & Risk focuses on allocate capital under risk, constraints, and market uncertainty. In the map of OR, it connects Mean-variance, CVaR, Transaction costs to decisions that must be modeled, solved, explained, and revised as evidence changes.
Financial OR models portfolios, asset-liability management, transaction costs, risk measures, trading, credit, and stress scenarios. The practical use case is clearest in Asset management, Banking, Insurance, Treasury, where the method helps turn constraints and tradeoffs into a decision artifact someone can inspect.
Core ideas
Mean-variance
Mean-variance is a core checkpoint for Finance, Portfolio & Risk: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.
CVaR
CVaR is a core checkpoint for Finance, Portfolio & Risk: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.
Transaction costs
Transaction costs is a core checkpoint for Finance, Portfolio & Risk: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.
Asset-liability
Asset-liability is a core checkpoint for Finance, Portfolio & Risk: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.
Stress testing
Stress testing is a core checkpoint for Finance, Portfolio & Risk: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.
How to use it
- 1Start with Asset management: write the decision, time horizon, actors, and objective in operational language.
- 2Translate the problem into Mean-variance, CVaR, and Transaction costs; define units and data sources for each one.
- 3Build a small instance of Finance, Portfolio & Risk that can be solved or simulated by hand inspection before using full production data.
- 4Compare the recommendation against a baseline policy, not just against mathematical optimality.
- 5Document assumptions, sensitivity results, and the conditions under which the recommendation should be revisited.
Applications
- Asset management: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.
- Banking: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.
- Insurance: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.
- Treasury: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.
Common pitfalls
- Applying Finance, Portfolio & Risk because the label sounds appropriate while leaving the actual decision boundary vague.
- Treating Mean-variance as a technical detail instead of a modeling choice that affects the recommendation.
- Reporting one answer without showing sensitivity to demand, capacity, costs, or behavioral assumptions.
- Ignoring implementation details such as data quality, explainability, ownership, and how users will override bad recommendations.
Resources
- Portfolio Optimization — CVXPY Short Course
Topic-specific source curated for Finance, Portfolio & Risk.
- CVXPY Short Course
Hands-on examples for disciplined convex modeling in Python.
- Stanford EE364A — Convex Optimization
Convex analysis, duality, least squares, quadratic programs, and conic optimization.
- Convex Optimization — Boyd & Vandenberghe
Open textbook for convex optimization theory and modeling examples.