risk-balancing

Business

Balance portfolio risk and return using Markowitz mean-variance, CVaR, Risk parity, and Kelly criterion methods.

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Risk Balancing

Purpose

Construct a portfolio that achieves acceptable returns while managing downside risk, correlation between failures, and tail events. Applies Markowitz-style thinking beyond finance to any domain with uncertain outcomes.

When to use

Candidates have uncertain outcomes with estimable distributions
Correlation between candidate failures matters
Downside protection is as important as upside
Stakeholders have explicit risk tolerance levels

Budget

Dimension	Target
Candidates evaluated	8-20
Risk factors modeled	2-5
Correlation pairs assessed	key pairs
Efficient frontier points	>=5

State Ledger

Field	Type	Description
candidates	list	Candidates with expected return and risk estimates
correlation_matrix	matrix	Pairwise correlation of candidate outcomes
risk_tolerance	number	Stakeholder risk appetite parameter
efficient_frontier	list	Risk-return trade-off curve
selected_portfolio	list	Final allocation balancing risk and return

Available Tactics

Tactic	When
pareto-frontier-construction	Building the efficient frontier (risk vs return)
scenario-stress-testing	Testing portfolio under adverse scenarios

Available SOPs

SOP	Purpose
objective-definition	Define risk and return metrics
optimization-run	Compute efficient frontier
pareto-visualization	Visualize risk-return trade-off
selection-from-frontier	Select portfolio matching risk tolerance
scenario-construction	Define stress scenarios
portfolio-evaluation-per-scenario	Test portfolio under stress

Execution Guidance

Define risk and return metrics via objective-definition
Estimate expected returns, variances, and correlations for candidates
Construct efficient frontier via optimization-run
Visualize risk-return trade-off via pareto-visualization
Select portfolio matching risk tolerance via selection-from-frontier
Optionally stress-test via scenario-stress-testing tactic

Output Format

strategy: risk-balancing
selected_portfolio:
  - candidate: <name>
    allocation_weight: <0-1>
    expected_return: <value>
    risk_contribution: <value>
portfolio_expected_return: <aggregate>
portfolio_risk: <variance or CVaR>
sharpe_ratio: <risk-adjusted return>
method_used: <mean-variance|CVaR|risk-parity|Kelly>

Available Tactics

Optional, no fixed order; the final leaf is always a sop.

Tactic	When to use
pareto-frontier-construction	Build the Pareto frontier from multi-objective optimization, visualize trade-offs, and select a portfolio from non-dominated solutions.
scenario-stress-testing	Construct distinct future scenarios, evaluate portfolio performance under each, and identify vulnerabilities and robustness characteristics.

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
objective-definition	Define optimization objectives, constraints, and trade-off preferences from context and candidate information.
optimization-run	Execute multi-objective optimization on candidates to produce a Pareto front of non-dominated solutions.
pareto-visualization	Create visual representation of the Pareto frontier showing trade-offs between objectives with narrative explanation.
portfolio-evaluation-per-scenario	Evaluate a specific portfolio's performance metrics and vulnerabilities under a given scenario.
scenario-construction	Construct distinct future scenarios spanning key uncertainties for portfolio stress testing.
selection-from-frontier	Select the final portfolio from the Pareto front by applying stakeholder preferences and decision criteria.