Learning to Trade with Insider Information
This paper introduces algorithms for learning how to trade usinginsider (superior) information in Kyle's model of financial markets.Prior results in finance theory relied on the insider having perfectknowledge of the structure and parameters of the market. I show herethat it is possible to learn the equilibrium trading strategy whenits form is known even without knowledge of the parameters governingtrading in the model. However, the rate of convergence toequilibrium is slow, and an approximate algorithm that does notconverge to the equilibrium strategy achieves better utility whenthe horizon is limited. I analyze this approximate algorithm fromthe perspective of reinforcement learning and discuss the importanceof domain knowledge in designing a successful learning algorithm.