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Machine Learning for Algorithmic Trading and Trade Schedule Optimization

Robert Kissell and Jungsun “Sunny” Bae
The Journal of Trading Fall 2018, 13 (4) 138-147; DOI: https://doi.org/10.3905/jot.2018.13.4.138
Robert Kissell
is a professor of business at Molloy College in Rockville Center, NY, and president of the Kissell Research Group in Great Neck, NY
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Jungsun “Sunny” Bae
is a graduate student of information systems at Baruch College in New York, NY
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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.

TOPICS: Big data/machine learning, portfolio construction, performance measurement

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The Journal of Trading: 13 (4)
The Journal of Trading
Vol. 13, Issue 4
Fall 2018
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Machine Learning for Algorithmic Trading and Trade Schedule Optimization
Robert Kissell, Jungsun “Sunny” Bae
The Journal of Trading Oct 2018, 13 (4) 138-147; DOI: 10.3905/jot.2018.13.4.138

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Machine Learning for Algorithmic Trading and Trade Schedule Optimization
Robert Kissell, Jungsun “Sunny” Bae
The Journal of Trading Oct 2018, 13 (4) 138-147; DOI: 10.3905/jot.2018.13.4.138
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  • Article
    • Abstract
    • WHAT IS THE CURRENT TRADING ENVIRONMENT?
    • TRADE SCHEDULE OPTIMIZATION
    • MACHINE LEARNING
    • MACHINE LEARNING EXPERIMENT
    • PERFORMANCE RESULTS
    • CONCLUSION
    • ENDNOTES
    • REFERENCES
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