Echo state networks applied to market making problems

Abstract: In this talk, we discuss how a special type of recurrent neural network called an Echo State Network (ESN) can be applied to supervised learning problems involving time series. We train the ESN using linear regression, and despite the training process being entirely linear, the ESN retains the universal approximation property.

We discuss briefly how an ESN can be used to solve supervised learning problems, before moving onto the more complex problem of reinforcement learning. We demonstrate the theory by applying the ESN to a simple market making problem that appears in mathematical finance.

Computer scienceMathematicsPhysics

Audience: researchers in the topic

Data Science and Computational Statistics Seminar