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Stochastically Structured Reservoir Computers for Financial and Economic System Identification

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  • Lendy Banegas
  • Fredy Vides

Abstract

This paper introduces a methodology for identifying and simulating financial and economic systems using stochastically structured reservoir computers (SSRCs). The framework combines structure-preserving embeddings with graph-informed coupling matrices to model inter-agent dynamics while enhancing interpretability. A constrained optimization scheme guarantees compliance with both stochastic and structural constraints. Two empirical case studies, a nonlinear stochastic dynamic model and regional inflation network dynamics, demonstrate the effectiveness of the approach in capturing complex nonlinear patterns and enabling interpretable predictive analysis under uncertainty.

Suggested Citation

  • Lendy Banegas & Fredy Vides, 2025. "Stochastically Structured Reservoir Computers for Financial and Economic System Identification," Papers 2507.17115, arXiv.org, revised Sep 2025.
    • Handle: RePEc:arx:papers:2507.17115
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    1. Ming-Hua Liu & Dimitris Margaritis & Yang Zhang, 2015. "Inflation Transmission in Greater China," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 23(6), pages 79-99, November.
    2. Eric T. Swanson, 2024. "The Macroeconomic Effects of the Federal Reserve’s Conventional and Unconventional Monetary Policies," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 72(3), pages 1152-1184, September.
    3. Martin Feldkircher & Florian Huber & Michael Pfarrhofer, 2021. "Measuring the effectiveness of US monetary policy during the COVID‐19 recession," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(3), pages 287-297, July.
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