Python and Machine Learning for Asset Management
Apply advanced optimization and machine learning to improve investment performance. Solving multi-period financial optimization models via combining traditional financial methods with deep neural networks. Address transaction and market impact costs for applications including trading models and long-term investors such as pension plans, university endowments, and individual investors. Include economic regimes in the modelling framework.
John M. Mulvey is a Professor at Princeton University, Operations Research and Financial Engineering Department. He is a founding member of Princeton’s Bendheim Center for Finance and the Center for Statistics and Machine Learning. His specialty is financial optimization and dynamic investment strategies. For over 40 years, he has designed and implemented asset-liability management systems for numerous organizations, including PIMCO, Towers Perrin/Tillinghast, AXA, Siemens, Munich Re-Insurance, Renaissance Re-Insurance, Ant Group/Alibaba, and hedge funds. His current research addresses regime identification and factor approaches for long-term investors, including family offices and pension plans, with an emphasis on optimizing performance by means of goal-based investing. He has published over 160 articles and edited 5 books, including the first implementation of a fully-integrated advisor system for individual investors in 1998.