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Solving Multi-Period Financial Planning Models: Combining Monte Carlo Tree Search and Neural Networks

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  • Afc{s}ar Onat Ayd{i}nhan
  • Xiaoyue Li
  • John M. Mulvey

Abstract

This paper introduces the MCTS algorithm to the financial world and focuses on solving significant multi-period financial planning models by combining a Monte Carlo Tree Search algorithm with a deep neural network. The MCTS provides an advanced start for the neural network so that the combined method outperforms either approach alone, yielding competitive results. Several innovations improve the computations, including a variant of the upper confidence bound applied to trees (UTC) and a special lookup search. We compare the two-step algorithm with employing dynamic programs/neural networks. Both approaches solve regime switching models with 50-time steps and transaction costs with twelve asset categories. Heretofore, these problems have been outside the range of solvable optimization models via traditional algorithms.

Suggested Citation

  • Afc{s}ar Onat Ayd{i}nhan & Xiaoyue Li & John M. Mulvey, 2022. "Solving Multi-Period Financial Planning Models: Combining Monte Carlo Tree Search and Neural Networks," Papers 2202.07734, arXiv.org, revised May 2022.
    • Handle: RePEc:arx:papers:2202.07734
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    References listed on IDEAS

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    1. Guillaume M. J-B. Chaslot & Mark H. M. Winands & H. Jaap Van Den Herik & Jos W. H. M. Uiterwijk & Bruno Bouzy, 2008. "Progressive Strategies For Monte-Carlo Tree Search," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 4(03), pages 343-357.
    2. Merton, Robert C, 1969. "Lifetime Portfolio Selection under Uncertainty: The Continuous-Time Case," The Review of Economics and Statistics, MIT Press, vol. 51(3), pages 247-257, August.
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    4. Peter Nystrup & Henrik Madsen & Erik Lindström, 2018. "Dynamic portfolio optimization across hidden market regimes," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 83-95, January.
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