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Evaluating and Mitigating Transaction Costs with Recurrent Neural Networks

Ran, Li (2024) Evaluating and Mitigating Transaction Costs with Recurrent Neural Networks. PhD thesis, Dublin City University.

Abstract
This thesis develops a method for evaluating and mitigating the effect of transaction costs on trading strategies with many assets. An iteration procedure yields the cost-adjusted portfolio return, enabling the formulation of portfolio-choice problems as optimization of Recurrent Neural Networks (RNN). This method reproduces the theoretical results available for one risky asset and the numerical approximations available for two risky assets through finite-elements. Crucially, the RNN model scales to several assets and is fully interpretable, as its parameters identify their no-trade region. Importance-sampling significantly enhances the model’s performance, especially with several assets. An application to equally-weighted funds demonstrates the method’s ability to reduce both tracking error and tracking difference from an empirical target.
Metadata
Item Type:Thesis (PhD)
Date of Award:August 2024
Refereed:No
Supervisor(s):Guasoni, Paolo and Wong, Kwok Chuen
Subjects:Mathematics
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Science and Health > School of Mathematical Sciences
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 License. View License
ID Code:30314
Deposited On:26 Nov 2024 10:47 by Ran Li . Last Modified 26 Nov 2024 10:47
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