Correlation Structures and Regime Shifts in Nordic Stock Markets

Battle Count: 111

Highly relevant to quantitative trading and portfolio management. The paper provides a concrete, implementable regime-aware allocation framework that directly addresses portfolio construction during stress periods. The eigenvalue-ratio crisis indicator is a novel, data-driven signal for regime detection. The backtesting results demonstrate improved risk-adjusted performance (Sharpe, Sortino, Treynor ratios) over standard benchmarks. The methodology is practical for institutional portfolio managers seeking defensive positioning. However, it is limited to equity portfolio construction rather than high-frequency trading or derivatives strategies.

Moderate complexity. Requires: (1) rolling-window correlation matrix estimation, (2) eigendecomposition and Marchenko-Pastur bound computation, (3) eigenvalue clipping and Ledoit-Wolf shrinkage, (4) eigenportfolio construction and OLS regression, (5) regime classification via eigenvalue ratio, (6) constrained quadratic programming for portfolio optimization. All steps use standard numerical linear algebra and convex optimization tools (e.g., CVX, MATLAB quadprog). The main challenge is proper implementation of RMT-based denoising and the constrained optimization formulation. No deep learning or complex model training required.

The methodology is described in detail with explicit formulas for correlation estimation, eigenvalue decomposition, RMT bounds, denoising, shrinkage, and portfolio optimization. Data is sourced from Google Finance (publicly accessible). However, the data availability statement says 'available with the author' rather than a public repository. No code repository is provided. Window parameters (T=252, Δ=5, ℓ=10, γ1=0.3, γ2=0.2, δ=0.1) are specified. The analysis is reproducible in principle but requires implementation effort.