UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Algorithms in future capital markets: A survey on AI, ML and associated algorithms in capital markets

Koshiyama, A; Firoozye, N; Treleaven, P; (2020) Algorithms in future capital markets: A survey on AI, ML and associated algorithms in capital markets. In: ICAIF '20: Proceedings of the First ACM International Conference on AI in Finance. (pp. pp. 1-8). ACM Green open access

[thumbnail of Treleaven_AIgorithms in future Capital Markets review - Final.pdf]
Preview
Text
Treleaven_AIgorithms in future Capital Markets review - Final.pdf - Accepted Version

Download (1MB) | Preview

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 decisionmaking. 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.

Type: Proceedings paper
Title: Algorithms in future capital markets: A survey on AI, ML and associated algorithms in capital markets
Event: First ACM International Conference on AI in Finance
ISBN-13: 9781450375849
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3383455.3422539
Publisher version: https://doi.org/10.1145/3383455.3422539
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10139834
Downloads since deposit
Loading...
555Downloads
Download activity - last month
Loading...
Download activity - last 12 months
Loading...
Downloads by country - last 12 months
Loading...

Archive Staff Only

View Item View Item