Algorithms in Future Capital Markets

23 Pages Posted: 11 Mar 2020 Last revised: 13 May 2020

See all articles by Adriano Koshiyama

Adriano Koshiyama

Department of Computer Science, University College London

Nick Firoozye

UCL - Computer Science

Philip Treleaven

University College London

Date Written: January 29, 2020

Abstract

This paper reviews Artificial Intelligence (AI), Machine Learning (ML) and associated algorithms in future Capital Markets. New AI algorithms are constantly emerging, with each ‘strain’ mimicking a new form of human learning, reasoning, knowledge, and decision-making. The current main disrupting forms of learning include Deep Learning, Adversarial Learning, Transfer and Meta Learning. Albeit these modes of learning have been in the AI/ML field more than a decade, they now are more applicable due to the availability of data, computing power and infrastructure. These forms of learning have produced new models (e.g., Long Short-Term Memory, Generative Adversarial Networks) and leverage important applications (e.g., Natural Language Processing, Adversarial Examples, Deep Fakes, etc.). These new models and applications will drive changes in future Capital Markets, so it is important to understand their computational strengths and weaknesses.

Since ML algorithms effectively self-program and evolve dynamically, financial institutions and regulators are becoming increasingly concerned with ensuring there remains a modicum of human control, focusing on Algorithmic Interpretability/Explainability, Robustness and Legality. For example, the concern is that, in the future, an ecology of trading algorithms across different institutions may ‘conspire’ and become unintentionally fraudulent (cf. LIBOR) or subject to subversion through compromised datasets (e.g. Microsoft Tay). New and unique forms of systemic risks can emerge, potentially coming from excessive algorithmic complexity.

The contribution of this paper is to review AI, ML and associated algorithms, their computational strengths and weaknesses, and discuss their future impact on the Capital Markets.

Keywords: Finance, Artificial Intelligence, Machine Learning, Algorithms, Review

Suggested Citation

Koshiyama, Adriano and Firoozye, Nick and Treleaven, Philip, Algorithms in Future Capital Markets (January 29, 2020). Available at SSRN: https://ssrn.com/abstract=3527511 or http://dx.doi.org/10.2139/ssrn.3527511

Adriano Koshiyama (Contact Author)

Department of Computer Science, University College London ( email )

Gower Street
London, London WC1E 6BT
United Kingdom

Nick Firoozye

UCL - Computer Science ( email )

Gower Street
London, WC1E 6BT
United Kingdom

Philip Treleaven

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
477
Abstract Views
1,911
rank
63,336
PlumX Metrics