Learning to trade with insider information

Authors:
Sanmay Das
Affiliations:
University of California: San Diego, La Jolla, CA
Venue:
Proceedings of the ninth international conference on Electronic commerce
Year:
2007

Citing 5
Cited 0

Temporal difference learning and TD-Gammon

Communications of the ACM
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Neuro-Dynamic Programming

Neuro-Dynamic Programming
Reinforcement learning: a survey

Journal of Artificial Intelligence Research
Learning to trade via direct reinforcement

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces algorithms for learning how to trade using insider (superior) information in Kyle's model of financial markets. Prior results in finance theory relied on the insider having perfect knowledge of the structure and parameters of the market. I show here that it is possible to learn the equilibrium trading strategy when its form is known even without knowledge of the parameters governing trading in the model. However, the rate of convergence to equilibrium is slow, and an approximate algorithm that does not converge to the equilibrium strategy achieves better utility when the horizon is limited. I analyze this approximate algorithm from the perspective of reinforcement learning and discuss the importance of domain knowledge in designing a successful learning algorithm.