Research Paper Published in Journal of Computational Finance
📚 Academic milestone! Proud to announce the publication of my research paper in the Journal of Computational Finance!
Paper Details
Title: “Deep Learning for Cross-Asset Risk Prediction: A Multi-Modal Approach”
Abstract: We present a novel deep learning framework that combines order book data, market sentiment, and macroeconomic indicators to predict portfolio risk across multiple asset classes with unprecedented accuracy.
Key Contributions
Novel Architecture
- Multi-Modal Fusion: Combines structured market data with unstructured text
- Attention Mechanisms: Custom attention layers for temporal financial sequences
- Transfer Learning: Pre-trained models adapted for financial time series
Theoretical Insights
- Risk Decomposition: Mathematical framework for multi-asset risk attribution
- Uncertainty Quantification: Bayesian neural networks for prediction confidence
- Regime Detection: Automatic identification of market regime changes
Research Impact
Performance Results
- 40% improvement in risk prediction accuracy vs. traditional VaR models
- Real-time processing of 10M+ market data points per second
- Cross-asset validation across equities, bonds, commodities, and FX
Industry Applications
- Portfolio Management: Enhanced risk-adjusted return optimization
- Regulatory Reporting: More accurate stress testing and capital allocation
- Market Making: Improved inventory risk management
Collaboration
This work was conducted in collaboration with:
- Interactive Brokers Research Team: Real-world data and validation
- Georgia Tech RIPL Lab: Theoretical foundations and methodology
- Citadel Securities: Industry feedback and performance benchmarking
Future Directions
Building on this research:
- Real-time Implementation: Deploying models in production trading systems
- Regulatory Applications: Adapting framework for Basel III compliance
- Open Source: Planning to release anonymized datasets and code
Personal Reflection
This publication represents the culmination of my interdisciplinary journey - combining MS in Computer Science and Quantitative Finance with practical industry experience. It demonstrates how academic rigor can drive real-world financial innovation.
From classroom theory to production trading systems to peer-reviewed research - the circle is complete! 🔄📊