AI Research Portfolio
Research focused on applying artificial intelligence and machine learning to financial markets, with emphasis on practical applications for investment decision-making.
Research Areas
Graph Neural Networks
Macroeconomic Forecasting
Sector Rotation
Portfolio Optimization
Genetic Algorithms
Risk Management
Key Projects
- GraphEconCast - GNN-based macroeconomic forecasting model for 26 global economies with R² 99.49%
- WWAI Sector Rotation - Multi-market sector rotation system using Fiedler eigenvalue for cohesion analysis
- WWAI-SAGE - Self-Adapting Genetic Algorithm for portfolio optimization
- Global VIX Dashboard - Multi-market volatility regime analysis
Methodologies
- Graph Neural Networks for economic relationship modeling
- Spectral graph theory (Fiedler eigenvalue) for market cohesion
- Evolutionary algorithms for portfolio optimization
- Regime detection using market volatility indicators
- Cross-market spillover analysis
Publications & Presentations
Academic contributions and industry presentations in quantitative finance and AI.
Recent Work
- GNN-based Macroeconomic Forecasting with Cross-Country Spillover Effects
- Sector Rotation Strategies Using Graph Spectral Analysis
- Self-Adapting Genetic Algorithm for Dynamic Portfolio Optimization
Technical Stack
Python
PyTorch
PyTorch Geometric
FastAPI
Next.js
PostgreSQL
Railway
Vercel
Contact & Links
- WWAI Portal: landing-three-ecru.vercel.app
- GitHub: github.com/cschung7