Portfolio Management with ML
Categories:
This project was built for the McGill–FIAM Asset Management Hackathon 2025, focusing on AI-driven bottom-up portfolio construction.
We combined quantitative and textual data to design a global long-short strategy with measurable alpha.
Project Overview
We designed a machine-learning-based investment strategy that predicts next-month stock returns using 147 firm characteristics and FinBERT-encoded text signals from MD&A and Risk Factor sections of 10-K/10-Q filings.
The model generates monthly forecasts from 2015 – 2025 and translates them into a 100-to-250-stock long-short portfolio, rebalanced monthly and benchmarked against the S&P 500.
Methodology
- Models used: LightGBM, CatBoost, and a simple MLP neural network.
- Text features: FinBERT embeddings capturing sentiment and uncertainty from management discussions.
- Training: Expanding-window approach (train 2005–2014 → test 2015–2025), strictly out-of-sample.
- Blending: Equal-weighted ensemble of model predictions, country/industry-neutralized.
- Portfolio construction: Long top-ranked 100–125 stocks, short bottom 100–125, dollar-neutral, equal-weighted, monthly rebalancing.
Results
| Metric (OOS 2015-05 → 2025-05) | Portfolio | S&P 500 |
|---|---|---|
| CAGR (geom) | 1.56 × | 1.12 × |
| Annual Return (mean × 12) | 49.6 % | 12.8 % |
| Annual Volatility | 22.2 % | 17.9 % |
| Sharpe Ratio | 2.24 | 0.71 |
| Information Ratio | 1.31 | — |
| Max Drawdown | −23.5 % | −34 % |
| Positive Months | 80 % | 62 % |
Outcome:
The ensemble consistently outperformed the S&P 500 in the out-of-sample period, showing positive OOS R², significant monthly alpha ≈ 4.1 bps, and strong risk-adjusted returns after estimated transaction costs.
Adding the FinBERT text factor improved both stability and predictive power, especially in volatile regimes.
“Better data + smarter models = better portfolios.”
This project demonstrates how combining structured and textual financial data with modern ML architectures can yield persistent alpha in global equity markets.