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Machine Learning for Continuous-Time Finance

Author

Listed:
  • Victor Duarte
  • Diogo Duarte
  • Dejanir H Silva
Abstract
We develop an algorithm for solving a large class of nonlinear high-dimensional continuous-time models in finance. We approximate value and policy functions using deep learning and show that a combination of automatic differentiation and Ito’s lemma allows for the computation of exact expectations, resulting in a negligible computational cost that is independent of the number of state variables. We illustrate the applicability of our method to problems in asset pricing, corporate finance, and portfolio choice and show that the ability to solve high-dimensional problems allows us to derive new economic insights.

Suggested Citation

  • Victor Duarte & Diogo Duarte & Dejanir H Silva, 2024. "Machine Learning for Continuous-Time Finance," The Review of Financial Studies, Society for Financial Studies, vol. 37(11), pages 3217-3271.
    • Handle: RePEc:oup:rfinst:v:37:y:2024:i:11:p:3217-3271.
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    File URL: http://hdl.handle.net/10.1093/rfs/hhae043
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    More about this item

    Keywords

    G11; G12; G32;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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