This presentation delves into successes, opportunities, challenges of ML applications for QWIM: • classification and pattern recognition • network analysis and clustering • time series forecasting • reinforcement learning • synthetic financial data generation • testing investment strategies and portfolios • factor-based investment strategies • nowcasting • incorporating market states and regimes into investment portfolios It also presents practical challenges for ML within context of QWIM: • lack of sufficient data • need to satisfy privacy, fairness and regulatory requirements • model overfitting • causality • explainability and interpretability • hyperparameter tuning
Cristian is part of the Portfolio Analytics team within Chief Investment Office, Global Wealth and Investment Management division Bank of America Merrill Lynch. He is developing and investigating quantitative solutions in areas such as investment strategies, goals-based wealth management, asset allocation, machine learning and big data analysis, factor-based investing and risk factor models, portfolio risk and attribution, stress testing and scenario construction. He is very interested in application of state-of-the-art algorithms and numerical methods in wealth and investment management, and in high-performance computing. Prior to joining Bank of America Merrill Lynch, Cristian was a front office quant for Wachovia and Wells Fargo. After supporting interest rate trading desk, he was the lead quant for FX and Commodities trading desks. He has a PhD from Florida State University in computational and applied mathematics, and MSc degrees from University of Paris XI and University of Craiova.