Modeling Inverse Demand Function with Explainable Dual Neural Networks

Zhiyu Cao, Zihan Chen, Prerna Mishra, Hamed Amini, Zachary Feinstein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Financial contagion has been widely recognized as a fundamental risk to the financial system. Particularly potent is price-mediated contagion, wherein forced liquidations by firms depress asset prices and propagate financial stress, enabling crises to proliferate across a broad spectrum of seemingly unrelated entities. Price impacts are currently modeled via exogenous inverse demand functions. However, in real-world scenarios, only the initial shocks and the final equilibrium asset prices are typically observable, leaving actual asset liquidations largely obscured. This missing data presents significant limitations to calibrating the existing models. To address these challenges, we introduce a novel dual neural network structure that operates in two sequential stages: the first neural network maps initial shocks to predicted asset liquidations, and the second network utilizes these liquidations to derive resultant equilibrium prices. This data-driven approach can capture both linear and non-linear forms without pre-specifying an analytical structure; furthermore, it functions effectively even in the absence of observable liquidation data. Experiments with simulated datasets demonstrate that our model can accurately predict equilibrium asset prices based solely on initial shocks, while revealing a strong alignment between predicted and true liquidations. Our explainable framework contributes to the understanding and modeling of price-mediated contagion and provides valuable insights for financial authorities to construct effective stress tests and regulatory policies.

Original languageEnglish
Title of host publicationICAIF 2023 - 4th ACM International Conference on AI in Finance
Pages108-115
Number of pages8
ISBN (Electronic)9798400702402
DOIs
StatePublished - 27 Nov 2023
Event4th ACM International Conference on AI in Finance, ICAIF 2023 - New York City, United States
Duration: 27 Nov 202329 Nov 2023

Publication series

NameICAIF 2023 - 4th ACM International Conference on AI in Finance

Conference

Conference4th ACM International Conference on AI in Finance, ICAIF 2023
Country/TerritoryUnited States
CityNew York City
Period27/11/2329/11/23

Keywords

  • FinTech
  • asset liquidation
  • deep learning
  • explainable machine learning
  • financial contagion
  • inverse demand function

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