Machine learning has the potential to advance the state-of-the-art for modeling, prediction, and decision-making in finance. Realizing this potential, however, requires overcoming many complex challenges.
In a wide-ranging presentation, Two Sigma’s Justin Sirignano—who is also an Associate Professor in Mathematics at the University of Oxford—discusses opportunities and open challenges for machine learning in finance. Justin introduces examples of machine learning models for high-frequency data, and covers topics including training deep learning models to try to predict price moves, and using reinforcement learning to try to determine optimal order strategies.
Want to learn more? Watch below!