Online Learning Algorithms for Statistical Arbitrage

11/01/2018
by   Christopher Mohri, et al.
0

Statistical arbitrage is a class of financial trading strategies using mean reversion models. The corresponding techniques rely on a number of assumptions which may not hold for general non-stationary stochastic processes. This paper presents an alternative technique for statistical arbitrage based on online learning which does not require such assumptions and which benefits from strong learning guarantees.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/15/2020

On Learnability under General Stochastic Processes

Statistical learning theory under independent and identically distribute...
research
11/02/2010

A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

Sequential prediction problems such as imitation learning, where future ...
research
03/11/2022

Universally Consistent Online Learning with Arbitrarily Dependent Responses

This work provides an online learning rule that is universally consisten...
research
09/16/2023

Efficient Methods for Non-stationary Online Learning

Non-stationary online learning has drawn much attention in recent years....
research
02/24/2019

Combining Online Learning Guarantees

We show how to take any two parameter-free online learning algorithms wi...
research
01/16/2022

Universal Online Learning: an Optimistically Universal Learning Rule

We study the subject of universal online learning with non-i.i.d. proces...
research
09/05/2022

Online Decision Making for Trading Wind Energy

This paper proposes and develops a new algorithm for trading wind energy...

Please sign up or login with your details

Forgot password? Click here to reset