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Machine Learning for Algorithmic Trading and Trade Schedule Optimization
The Journal of Trading (2018)
  • Robert Kissell, Molloy College
  • Jungsun Bae
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.
Publication Date
Fall 2018
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
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