Project Overview
Quantitative equity strategies rely on factor models to systematically rank securities. This workbook implements a Z-Score screening framework: it computes Altman Z-Scores for a universe of equities to flag financial distress risk, then layers on additional factor z-scores (valuation, profitability, momentum) to generate a composite ranking. The output is a portfolio inclusion/exclusion list with supporting factor attribution — the foundation of any systematic long/short equity strategy.
📋Problem Statement
Build a systematic, data-driven framework to screen a large equity universe for financial health and rank stocks by their attractiveness — removing the subjectivity inherent in discretionary stock picking.
🎯Analytical Approach
Implemented the Altman Z-Score formula on each company's balance sheet and income statement data to identify financially stressed firms. Extended the analysis with cross-sectional z-score normalization of key financial ratios (ROE, EV/EBITDA, revenue growth) to create a composite multi-factor score.
💾Data Sources
Financial ratios for S&P constituents: working capital, retained earnings, EBIT, market cap, book value of debt, and revenue — sourced from public company filings.
🔧Quantitative Methods
Altman Z-Score: Z = 1.2X₁ + 1.4X₂ + 3.3X₃ + 0.6X₄ + 1.0X₅. Cross-sectional z-score normalization: (value − mean) / std dev for each factor. Composite score: equal-weighted average of factor z-scores. Classification: Z > 2.99 (safe zone), 1.81–2.99 (grey zone), < 1.81 (distress zone).
✨Key Results
Ranked portfolio of equities by composite score, with Altman Z-Score distress flags filtering out financially weak names. Top-decile companies show strong combined financial health and attractive valuation/momentum characteristics — providing a disciplined, repeatable selection process.
🧠Key Learnings
Factor z-score normalization is essential for combining metrics on different scales — without it, high-magnitude factors (like revenue in billions) dominate the composite score. Understanding factor construction is the first step toward building institutional-grade quant strategies.