Idiosyncrasies and challenges of data driven learning in electronic trading
Trading and Market Microstructure
2018-12-03 v2 Computational Finance
Abstract
We outline the idiosyncrasies of neural information processing and machine learning in quantitative finance. We also present some of the approaches we take towards solving the fundamental challenges we face.
Cite
@article{arxiv.1811.09549,
title = {Idiosyncrasies and challenges of data driven learning in electronic trading},
author = {Vangelis Bacoyannis and Vacslav Glukhov and Tom Jin and Jonathan Kochems and Doo Re Song},
journal= {arXiv preprint arXiv:1811.09549},
year = {2018}
}
Comments
Accepted for NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy