Machine Learning in Asset Management

JFDS: https://jfds.pm-research.com/content/2/1/10

65 Pages Posted: 18 Jul 2019 Last revised: 23 Jun 2020

See all articles by Derek Snow

Derek Snow

The Alan Turing Institute; New York University (NYU) - Finance and Risk Engineering Department; University of Auckland

Date Written: July 16, 2019

Abstract

This paper investigates various machine learning trading and portfolio optimisation models and techniques. The notebooks to this paper are Python based. By last count there are about 15 distinct trading varieties and around 100 trading strategies. Code and data are made available where appropriate. The hope is that this informal paper will organically grow with future developments in machine learning and data processing techniques. Changes can be tracked on the GitHub repository. This draft paper has been repackaged for the Journal of Financial Data Science.

Keywords: asset management, portfolio, machine learning, trading strategies

JEL Classification: G11, G10, C15, C11, C22, C61, C63, C53, C52

Suggested Citation

Snow, Derek, Machine Learning in Asset Management (July 16, 2019). JFDS: https://jfds.pm-research.com/content/2/1/10 , Available at SSRN: https://ssrn.com/abstract=3420952 or http://dx.doi.org/10.2139/ssrn.3420952

Derek Snow (Contact Author)

The Alan Turing Institute ( email )

British Library, 96 Euston Rd
London, NW1 2DB
United Kingdom

HOME PAGE: http://https://www.turing.ac.uk/

New York University (NYU) - Finance and Risk Engineering Department ( email )

6 Metrotech Center
New York, NY 11201
United States

University of Auckland ( email )

Private Bag 92019
Auckland Mail Centre
Auckland, 1142
New Zealand

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