IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2202.07734.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2202.07734
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    3. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peter Nystrup & Stephen Boyd & Erik Lindström & Henrik Madsen, 2019. "Multi-period portfolio selection with drawdown control," Annals of Operations Research, Springer, vol. 282(1), pages 245-271, November.
    2. Monica Paiella, 2007. "The Forgone Gains of Incomplete Portfolios," The Review of Financial Studies, Society for Financial Studies, vol. 20(5), pages 1623-1646, 2007 13.
    3. Francisco Gomes & Alexander Michaelides, 2003. "Portfolio Choice With Internal Habit Formation: A Life-Cycle Model With Uninsurable Labor Income Risk," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 6(4), pages 729-766, October.
    4. Maite Cubas‐Díaz & Miguel Ángel Martínez Sedano, 2018. "Measures for Sustainable Investment Decisions and Business Strategy – A Triple Bottom Line Approach," Business Strategy and the Environment, Wiley Blackwell, vol. 27(1), pages 16-38, January.
    5. Florent Gallien & Serge Kassibrakis & Semyon Malamud, 2018. "Hedge or Rebalance: Optimal Risk Management with Transaction Costs," Risks, MDPI, vol. 6(4), pages 1-14, October.
    6. Anne Lavigne, 2006. "Gouvernance et investissement des fonds de pension privés aux Etats-Unis," Working Papers halshs-00081401, HAL.
    7. An Chen & Thai Nguyen & Thorsten Sehner, 2022. "Unit-Linked Tontine: Utility-Based Design, Pricing and Performance," Risks, MDPI, vol. 10(4), pages 1-27, April.
    8. Alan J. Auerbach, 1981. "Evaluating the Taxation of Risky Assets," NBER Working Papers 0806, National Bureau of Economic Research, Inc.
    9. Hong, Claire Yurong & Lu, Xiaomeng & Pan, Jun, 2021. "FinTech adoption and household risk-taking," BOFIT Discussion Papers 14/2021, Bank of Finland Institute for Emerging Economies (BOFIT).
    10. Curatola, Giuliano, 2022. "Price impact, strategic interaction and portfolio choice," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    11. Felix Chopra & Ingar Haaland, 2023. "Conducting qualitative interviews with AI," CEBI working paper series 23-06, University of Copenhagen. Department of Economics. The Center for Economic Behavior and Inequality (CEBI).
    12. Auffret, Philippe, 2001. "An alternative unifying measure of welfare gains from risk-sharing," Policy Research Working Paper Series 2676, The World Bank.
    13. John H. Cochrane, 2014. "A Mean-Variance Benchmark for Intertemporal Portfolio Theory," Journal of Finance, American Finance Association, vol. 69(1), pages 1-49, February.
    14. Luo, Yulei, 2015. "Robustly Strategic Consumption-Portfolio Rules with Informational Frictions," MPRA Paper 64312, University Library of Munich, Germany.
    15. Roel van Elk & Marc van der Steeg & Dinand Webbink, 2013. "The effects of a special program for multi-problem school dropouts on educational enrolment, employment and criminal behaviour; Evidence from a field experiment," CPB Discussion Paper 241.rdf, CPB Netherlands Bureau for Economic Policy Analysis.
    16. Marcos Escobar-Anel & Matt Davison & Yichen Zhu, 2022. "Derivatives-based portfolio decisions: an expected utility insight," Annals of Finance, Springer, vol. 18(2), pages 217-246, June.
    17. Chen, An & Hieber, Peter & Sureth, Caren, 2022. "Pay for tax certainty? Advance tax rulings for risky investment under multi-dimensional tax uncertainty," arqus Discussion Papers in Quantitative Tax Research 273, arqus - Arbeitskreis Quantitative Steuerlehre.
    18. Zhenya Liu & Yuhao Mu, 2022. "Optimal Stopping Methods for Investment Decisions: A Literature Review," IJFS, MDPI, vol. 10(4), pages 1-23, October.
    19. Bo Hu & Jiaxi Li & Shuang Li & Jie Yang, 2019. "A Hybrid End-to-End Control Strategy Combining Dueling Deep Q-network and PID for Transient Boost Control of a Diesel Engine with Variable Geometry Turbocharger and Cooled EGR," Energies, MDPI, vol. 12(19), pages 1-15, September.
    20. Mayank Goel & Suresh Kumar K., 2006. "A Risk-Sensitive Portfolio Optimisation Problem with Stochastic Interest Rate," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 5(3), pages 263-282, December.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2202.07734. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.