TY - JOUR T1 - Machine Learning for Algorithmic Trading and Trade Schedule Optimization JF - The Journal of Trading SP - 138 LP - 147 DO - 10.3905/jot.2018.13.4.138 VL - 13 IS - 4 AU - Robert Kissell AU - Jungsun “Sunny” Bae Y1 - 2018/10/31 UR - https://pm-research.com/content/13/4/138.abstract N2 - 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 ER -