risk-assessment

name	risk-assessment
description	Bridgewater-style portfolio risk assessment - correlation and factor concentration, VaR / recession stress test, tail-risk scenarios, and hedges. Use when the user asks about risk, drawdown, concentration, correlation, stress test, factor exposure, or hedging.

Risk Assessment (Bridgewater style)

Decision Memory Protocol (load first, log after)

Before forming any view, load prior decisions so verdicts stay consistent across sessions:

• mcp__aifolimizer__get_cross_ticker_lessons (max_lessons=3) - portfolio-level win/loss patterns
• For any name you issue a per-ticker BUY/SELL/TRIM/HOLD/ADD on, also load mcp__aifolimizer__get_ticker_decision_history (ticker=…, max_decisions=5) and mcp__aifolimizer__get_ticker_reflection (symbol=…, n=3). If a prior decision exists and this run flips it, state explicitly WHY (new data / catalyst / price); never silently contradict a logged decision.

After output, log every actionable verdict: for each BUY/SELL/TRIM/ADD/HOLD issued, call mcp__aifolimizer__log_recommendation (skill="risk-assessment", ticker, action, conviction, rationale, target_pct, stop_pct). Skipping breaks the cross-session feedback loop and causes drift.

How to run

1. Call mcp__aifolimizer__get_profile - account context
2. Call mcp__aifolimizer__get_personal_context - province, marginal tax rate, risk tolerance, time horizon, account waterfall. Use the live risk tolerance/horizon instead of the generic buckets below, and ground §11 tax-aware rebalancing. If present=false, note the risk framing is generic and suggest profile-setup
3. Call mcp__aifolimizer__get_portfolio - current holdings
4. Call mcp__aifolimizer__get_risk_metrics - vol, Sharpe, Sortino, VaR, expected shortfall
5. Call mcp__aifolimizer__get_correlation_matrix - which positions move together
6. Call mcp__aifolimizer__get_concentration_warnings - over-allocation flags
7. Call mcp__aifolimizer__get_factor_exposure for the top 3-5 holdings by weight - multi-factor betas (market/size/value/profitability/investment/momentum) + annualized alpha. Surfaces hidden FACTOR concentration that name/sector diversification hides (e.g. 4 different names all loaded on momentum = one factor bet)
8. Call mcp__aifolimizer__get_factor_snapshot - current factor regime. A book heavy on a factor that is rolling over is a live risk even if the names look uncorrelated
9. Use Ray Dalio's all-weather/radical-transparency framework

Investor profile

• Canadian retail investor
• Multi-risk-profile investor (conservative, moderate, aggressive buckets)
• Exposure goals: growth stocks, index ETFs, dividends, crypto
• Account types and capital: always read from get_profile - never hardcode

Output structure

1. Correlation analysis - holdings moving together (cluster risk)
2. Sector concentration - % breakdown, max recommended vs actual
3. Geographic + currency risk - CAD vs USD vs international exposure
4. Interest rate sensitivity - most rate-sensitive positions
5. Recession stress test - estimated drawdown in -30% equity bear market
6. Liquidity risk rating per holding (high/medium/low)
7. Single-asset concentration warnings - any position >10% of portfolio
8. Factor concentration - shared factor loadings across holdings (from get_factor_exposure); flag if 3+ names share a dominant factor beta
9. Top 3 tail-risk scenarios with probability estimates
10. Hedging strategies to reduce top 2 risks
11. Rebalancing suggestions with specific target allocations. Before recommending any ADD / increase, call mcp__aifolimizer__get_positioning_signals on those names and gate on crowding: crowding_score >= 70 = consensus-crowded → defer the add; <= 30 = contrarian, favor when fundamentals support

Rules

• Format as risk management report with heat-map table at top
• Under 600 words
• Use actual ticker data from MCP - never invent positions

Gotchas

• get_risk_metrics cached 1h - state as-of timestamp; never claim "real-time" risk.
• get_correlation_matrix covers top-N holdings only; small positions excluded - do NOT claim full-portfolio correlation.
• VaR/ES are historical-distribution estimates - fail in regime changes. State this when stress-testing.
• Sharpe/Sortino use yfinance daily closes - illiquid tickers (e.g. some .TO microcaps) produce unreliable annualized vol. Flag tickers with <60 days of returns as low-confidence.
• Crypto positions NOT in get_risk_metrics - pull separately via get_crypto_data and discuss qualitatively.
• get_factor_exposure uses US Fama-French factors - loadings for .TO/non-US names are directional only; low R² (<0.2) means the factor model doesn't explain that name, so don't over-read its betas.