Can Machine Learning Be Applied To The Problem Of Trading?

A new academic paper, Machine Learning for Trading, is the first conclusive study that shows success from having a machine learning-based trading strategy. The author, Gordon Ritter, Adjunct Professor in the Mathematics in Finance Program, New York University, constructed an artificial system which he knew would admit a profitable strategy, to see if a machine would find it. Newsweek is hosting an AI and Data Science in Capital Markets conference in NYC, Dec. 6-7. In order to train a machine learning algorithm to behave as a rational risk-averse investor required appropriate reinforcement learning, specifically a mathematical technique called Q-learning (playing some sort of game where you are trying to maximise the reward function that may occur at several periods in the future). The machine learning agent found and exploited arbitrage opportunities in the presence of transaction costs in a simulated market proof of concept.