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Machine Learning for Algorithmic Trading and Trade Schedule Optimization

Robert Kissell & Jungsun “Sunny” Bae

The Journal of Trading2018https://doi.org/10.3905/jot.2018.13.4.138article
ABDC C
Weight
0.29

What the paper says

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 (<i>n</i> = 10 <i>stocks</i>), 50% faster for baskets of (<i>n</i> = 100 <i>stocks</i>) and up to 70% faster for large baskets (<i>n</i> ≥ 300 <i>stocks</i>). 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. <b>TOPICS:</b>Big data/machine learning, portfolio construction, performance measurement

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https://doi.org/https://doi.org/10.3905/jot.2018.13.4.138

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@article{robert2018,
  title        = {{Machine Learning for Algorithmic Trading and Trade Schedule Optimization}},
  author       = {Robert Kissell & Jungsun “Sunny” Bae},
  journal      = {The Journal of Trading},
  year         = {2018},
  doi          = {https://doi.org/https://doi.org/10.3905/jot.2018.13.4.138},
}

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Machine Learning for Algorithmic Trading and Trade Schedule Optimization

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Evidence weight

0.29

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.08 × 0.4 = 0.03
M · momentum0.20 × 0.15 = 0.03
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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