StockWell: Measuring the Financial Wellness of S&P 500 Companies

KWK Machine Learning x Goldman Sachs — Final Project By: Hailey Muñiz

📌 Overview

StockWell is a machine learning project designed to evaluate the financial stability of S&P 500 companies using publicly available financial metrics.

The goal is to create a simple, transparent Financial Health Score (FHS) that anyone can use — students, new investors, or anyone curious about company stability — without relying on private or proprietary scoring systems.

🎯 Research Question

Can we use machine learning to predict a company’s near-future financial health using a few key indicators such as:

• EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization)
• Revenue Growth
• Current Price
• Market Capitalization

📊 Dataset

Source: S&P 500 Stocks (Larxel — Kaggle Dataset) This dataset includes financial and company information for all S&P 500 companies.

🔧 Methods & Workflow

1. Data Cleaning

• Filled missing values using the median
• Removed non-essential columns
• Converted categorical variables (sector, industry) into numeric form
• Standardized numeric features using Z-score scaling

2. Feature Engineering — Financial Health Score (FHS)

Created a custom score inspired by the Altman Z-Score:

FHS = Z(Ebitda) + Z(RevenueGrowth) + Z(CurrentPrice) + Z(MarketCap)

Higher FHS → better financial health.

3. Modeling — Random Forest Regression

Trained 3 models with different depths + tree counts to predict next-period FHS.

4. Evaluation Metrics

• MAE: ~0.88–0.92
• RMSE: ~2.15
• R²: ~0.55

Model 1 (100 trees, depth 4) performed best.

⭐ Key Findings

1️⃣ Market Capitalization dominates

It was the strongest predictor (90%+ feature importance). → Larger companies tend to be more financially stable.

2️⃣ Short-term indicators matter less

Revenue growth, EBITDA, and stock price had much smaller influence.

3️⃣ Simple model performed best

More complex models (deeper trees, more estimators) did not improve performance.

Limitations

• Only uses four financial metrics
• Only includes large S&P 500 companies
• Small dataset → limits model complexity
• Market cap may overly dominate predictions
• No historical time series data

🚀 Future Improvements

• Add financial ratios (debt, liquidity, profitability)
• Use historical data instead of single snapshots
• Try advanced models: Gradient Boosting, XGBoost, Neural Networks
• Build scenario analysis tools (e.g., recession, interest rate changes)

📁 Files in This Repository

• StockWell_Measuring_the_Financial_Wellness_of_S&P_500_Companies_(_KWK_Machine_Learning_x_Finance_Challenge)_Final_Project.ipynb – Main notebook with code + results
• StockWell Measuring the Financial Wellness of the S&P 500 (KWK Machine Learning x Finance Challenge).pdf – Slide deck for the project

🎥 Video Presentation

▶️ Watch the Presentation: https://www.tella.tv/video/stockwell-sandp-500-financial-health-scoring-3-bnoo

Acknowledgements

This project was completed for the KWK Machine Learning x Goldman Sachs. Special thanks to mentors, instructors, and open-source resources used throughout the process.