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Optimal trading under non-negativity constraints using approximate dynamic programming

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  • Shahin Abbaszadeh
  • Tri-Dung Nguyen
  • Yue Wu

Abstract

In this paper, we develop an extended dynamic programming (DP) approach to solve the problem of minimising execution cost in block trading of securities. To make the problem more practical, we add non-negativity constraints to the model and propose a novel approach to solve the resulting DP problem to near-optimal results. We also include time lags in the problem state to account for the autoregressive behaviour of most financial securities as a way of increasing problem sensitivity to variability of prices and information. The computation times achieved for the proposed algorithm are fast and allow for the possibility of live implementation. We demonstrate the benefits offered by the new approach through numerical analysis and simulation runs in comparison to the classic model without the non-negativity constraints.

Suggested Citation

  • Shahin Abbaszadeh & Tri-Dung Nguyen & Yue Wu, 2018. "Optimal trading under non-negativity constraints using approximate dynamic programming," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(9), pages 1406-1422, September.
    • Handle: RePEc:taf:tjorxx:v:69:y:2018:i:9:p:1406-1422
    DOI: 10.1080/01605682.2017.1398201
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    Cited by:

    1. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.

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