Article
Machine Learning for Algorithmic Trading and Trade Schedule Optimization
The Journal of Trading
(2018)
Abstract
In this paper we present a machine learning technique that can be used in conjunction with multi-period trade schedule optimization used in program trading. The technique is based on an artificial neural network (ANN) model that determines a better starting solution for the non-linear optimization routine. This technique provides calculation time improvements that are 30% faster for small baskets (n = 10 stocks), 50% faster for baskets of (n = 100 stocks) and up to 70% faster for large baskets (n ≥ 300 stocks). Unlike many of the industry approaches that use heuristics and numerical approximation, our machine learning approach solves for the exact problem and provides a dramatic improvement in calculation time.
Disciplines
Publication Date
Fall 2018
DOI
10.3905/jot.2018.13.4.138
Citation Information
Robert Kissell and Jungsun Bae. "Machine Learning for Algorithmic Trading and Trade Schedule Optimization" The Journal of Trading Vol. 13 Iss. 4 (2018) p. 138 - 147 ISSN: 1559-3967 Available at: http://works.bepress.com/robert-kissell/1/