TSX 60 Portfolio Optimization (MPT)

How ClearLedger Analytics Performs TSX 60 Optimization

ClearLedger Analytics evaluates a TSX 60 portfolio using a full variance‑covariance matrix constructed from the selected time horizon. Each security’s variance, covariance, and correlation contributes directly to the portfolio’s aggregate risk profile.

Mathematical Framework & Constraints

The engine builds the efficient frontier by solving a constrained quadratic optimization problem. Weight limits, diversification rules, and risk‑adjusted targets ensure the resulting asset mix remains realistic and practical to execute. Read more about how ClearLedger optimizes portfolio weights.

Directional Signals (Buy / Hold / Sell)

Directional signals act as mathematical permissions inside the solver:

• Buy — The optimizer is permitted to increase position weight up to defined maximum boundaries when mathematically favorable.
• Sell — The optimizer is permitted to reduce exposure down to minimum thresholds to mitigate systemic or uncompensated risk.
• Hold — The position is restricted to a narrow target band around its existing weight.

Handling Concentration & Sector Clustering

Because TSX 60 constituents often exhibit heavy sector concentration and correlation clustering, the optimizer frequently trims overweight positions in highly correlated large-cap assets while reallocating capital into lower‑correlation components.

This strategic rebalancing shifts all metrics simultaneously — driving the structural improvements displayed in the technical summary below.

Tracing Metric Drivers & Variance Across the Model

To answer how performance and risk shifts are achieved and audited, the model breaks down performance via security-level attribution across all core metrics:

• Security-Level Attribution: Located in the engine sheet under the Variances and metrics blocks, individual asset columns isolate how specific holdings contribute to aggregate portfolio variance, expected return, and risk-adjusted efficiency based on their weight and covariance interactions.
• Key Portfolio Drivers: High-impact leaders—such as Celestica Inc. (CLS.TO), Imperial Oil (IMO.TO), and Fairfax Financial Holdings (FFH.TO)—anchor the optimization by driving the largest positive expected return shifts when allocation weights are rebalanced toward efficient targets.
• Matrix Interactivity ($w^T \Sigma w$): Rather than relying on guesswork, the spreadsheet computes portfolio variance and risk metrics dynamically, letting you audit how shifting an individual holding's weight scales its marginal contribution to the portfolio's aggregate profile.

Total Portfolio Technical Summary

Every metric improves because the optimizer targets total portfolio risk-adjusted efficiency rather than individual stock selection.