Portfolio Construction: Using the Optimizers

This guide covers the practical steps for using QuanterLab's three portfolio optimization modules and its two risk analysis tools.

Mean-Variance Optimization (FM098MVOX)

Access from the Dashboard → Fundamental Analysis → Portfolio Optimization cluster, or from the Fundamental ribbon.

Configuration

1. Enter tickers — Type or paste the symbols you want to optimize. These are typically your top candidates from the screener
2. Select return estimation method — Historical Mean (simple), CAPM (beta-based), or Black-Litterman (equilibrium-based). Black-Litterman generally produces the most sensible weights
3. Select covariance method — Sample (direct), Ledoit-Wolf Shrinkage (recommended), or Exponential Weighted (recent-data emphasis)
4. Set constraints — Minimum and maximum weight per stock, maximum number of holdings. Constraints prevent extreme concentration
5. Set lookback period — 1-5 years of historical data for estimation. Default is 2 years
6. Choose optimization goal — Maximum Sharpe Ratio or Minimum Variance

Output

• Efficient frontier chart — 30 portfolios plotted on a return vs. volatility curve. Your optimized portfolio is highlighted
• Weight allocation table — Each ticker with its optimal weight percentage, sorted by weight
• Portfolio metrics — Expected annual return, annualized volatility, Sharpe ratio
• Sector allocation — Percentage breakdown by sector
• Covariance matrix — Visual correlation structure of the top 20 holdings

Hierarchical Risk Parity (FM097HRPX)

Configuration

1. Enter tickers — Same as MVO
2. Select distance metric — Standard Correlation (positive correlation = similar) or Absolute Correlation (treats negative correlation as similar too)
3. Select linkage method — Single, Complete, Average, or Ward. Ward linkage is recommended as it minimizes within-group variance and produces the most balanced clusters
4. Set constraints — Min/max weight per stock, maximum holdings
5. Set correlation filter threshold — Stocks with correlation above this value (default 0.85) are filtered to avoid redundancy. Known share class pairs (GOOG/GOOGL, BRK.A/BRK.B) are detected automatically

Output

• Dendrogram — Tree visualization showing how assets cluster together. Nearby branches are highly correlated
• Ordered correlation matrix — Quasi-diagonal form showing the block structure after clustering
• Weight allocation table — Optimal weights per ticker
• Cluster assignments — Which assets belong to which cluster
• Correlation filtering report — Which stocks were removed due to high correlation and what their correlation was

Inverse Volatility (FM096IVOX)

Configuration

1. Enter tickers
2. Select volatility calculation — Standard (equal-weighted historical) or Exponential Weighted (emphasizes recent data, halflife parameter adjustable)
3. Set constraints — Min/max weight, maximum holdings

Output

• Weight allocation table — With individual volatility and inverse volatility scores for each holding
• Volatility distribution — Histogram of all screened stocks' volatilities
• Sector allocation
• Portfolio metrics — Realized return, volatility, Sharpe ratio

Risk Analysis Tools

Monte Carlo Simulation (FM095MCSX)

Enter a ticker or portfolio. The module runs 1,000 simulated price paths using Geometric Brownian Motion over your chosen time horizon (1 month, 3 months, 6 months, or 1 year).

The output includes:

• 40 sample paths — Visual representation of possible price trajectories
• Percentile bands — 5th, 25th, 50th, 75th, 95th percentile paths showing the distribution of outcomes
• Terminal value histogram — Distribution of ending portfolio values across all simulations
• Probability analysis — Probability of gain, probability of 50%+ gain, probability of doubling, probability of loss, probability of 25%+ loss

Value at Risk (FM094VARX)

Calculates three VaR measures across multiple confidence levels (90%, 95%, 99%) and time horizons (1 day to 1 year):

• Historical VaR — Uses actual return distribution (no distribution assumption)
• Parametric VaR — Assumes normal distribution (faster, but underestimates tail risk)
• Conditional VaR (CVaR) — Expected loss beyond the VaR threshold (more conservative tail risk measure)

Additional outputs include per-stock risk contribution, rolling VaR chart (60-day window), historical VaR breach count, and return distribution histogram with VaR thresholds overlaid.