Abstract
This paper proposes a discrete-time model for dynamic trading, interconnecting cash and asset stocks. The trading action or control is based on the evolution of the asset prices, and any suitable asset price predictor can be used. Based on the model introduced, a one step ahead optimal control strategy, based on linear programming, is proposed. This leads to a trading algorithm which specfies a rule to buy or sell assets in a given portfolio. The addition of trade trigger logic to the basic scheme is also proposed, in order to allow return and risk to be traded off in the dynamic one step ahead trading scheme. The proposed one step ahead optimal policy is independent of the predictor of prices and their variances, chosen in this paper as the moving average, but replaceable by any desired estimator. Numerical examples are given to show that the proposed strategy performs reasonably well, with and without risk reduction, over datasets relating to different portfolios (banks, computers, ETFs and stocks).
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Acknowledgements
This work was supported in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES) Finance Code 001. The research of the first and third authors was supported by the Grant Bolsa PVE E-26/201.972/2016 from the Rio de Janeiro state agency Fundação Carlos Chagas Filho de Amparo á Pesquisa do Estado do Rio de Janeiro (FAPERJ). The second author was partially supported by the research grants (BPP/PQ 310646/2016-2) & Universal (406106/2016-9) from the Brazilian Government agency Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). We thank the reviewers for their helpful comments and suggestions.
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The authors AB and EK conceived and wrote the manuscript and the LVF coded and performed the numerical experiments. All authors read and approved the final manuscript.
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Bhaya, A., Kaszkurewicz, E. & Ferreira, L.V. A Dynamic Trading Model for Use with a One Step Ahead Optimal Strategy. Comput Econ 63, 1575–1608 (2024). https://doi.org/10.1007/s10614-023-10375-6
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DOI: https://doi.org/10.1007/s10614-023-10375-6