Future Directions for Deep Learning in Financial Portfolio Optimization
First, Deepmind’s AlphaGo algorithm was applied to quantitative trading of the largest and most liquid market which is foreign exchange (FX or currencies trading) with good results. In this talk, we focus on new research directions for the use of deep learning in financial portfolio optimization. This new research is inspired by recent results in human neuroscience, and proposes using algorithms such as OpenAI Five, Meta-Learning Shared Hierarchies (MLSH) but with neural nets instead of sub-routines, and more.
Charles is an investor in capital markets, an AI researcher, and an advisor to startup companies, and he helped some with pioneering multiple new industries such as AI- based programmatic marketing, the “intercloud” that is a network of clouds interconnected via software-defined networks (SDN), and more. He has mentored startups before and after the top startup accelerators y combinator and Techstars, 25 winners of MassChallenge, a top prize winner of the MIT 100K Competition, and 12 individuals of the Forbes 30 Under 30. Startups helped have had 7 exits, including one IPO.