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arXiv:1811.09549q-fin.TR

Idiosyncrasies and challenges of data driven learning in electronic trading

Vangelis Bacoyannis, Vacslav Glukhov, Tom Jin, Jonathan Kochems, Doo Re Song

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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.

arXiv comments
Accepted for NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy
Journal reference
DOI
Subjects
q-fin.TRTrading and Market Microstructure q-fin.CPComputational Finance

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