Applied Portfolio Management - Investment Strategy Analysis

Problem

Develop and test a 130/30 strategy that improves risk-adjusted performance using financial health factors.

Outcome

Isolated factor configurations that improved risk-adjusted performance in low-rate regimes and underperformed in higher-rate regimes, informing a regime-aware allocation rule.

Data

Historical equity data, factor inputs, and macro regimes for stress testing under different rate environments.

Approach

Backtested factor signals with parameter tuning, then evaluated predictive power using a decision tree with cross-validation.

What I built

A Python-based strategy engine with allocation rules, rebalancing logic, and performance diagnostics.

Output / Insights

Benchmarked performance across rate regimes and identified factor configurations with stronger predictive signals.