%0 Journal Article %A Robert Kissell %A Jungsun “Sunny” Bae %T Machine Learning for Algorithmic Trading and Trade Schedule Optimization %D 2018 %R 10.3905/jot.2018.13.4.138 %J The Journal of Trading %P 138-147 %V 13 %N 4 %X 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.TOPICS: Big data/machine learning, portfolio construction, performance measurement %U https://jot.pm-research.com/content/iijtrade/13/4/138.full.pdf