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Identifying Instability in U.S. Capi...

Purnell, Daren Lee, Jr.,

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Identifying Instability in U.S. Capital Markets: An Explainable Machine-Learning Based Approach for Assessing Risks in Complex Financial Systems /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Identifying Instability in U.S. Capital Markets: An Explainable Machine-Learning Based Approach for Assessing Risks in Complex Financial Systems // Daren Lee Purnell Jr.
Author:	Purnell, Daren Lee, Jr.,
Description:	1 electronic resource (156 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
Contained By:	Dissertations Abstracts International86-06B.
Subject:	Systems science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31638078
ISBN:	9798346805403

Identifying Instability in U.S. Capital Markets: An Explainable Machine-Learning Based Approach for Assessing Risks in Complex Financial Systems /
Purnell, Daren Lee, Jr.,

Identifying Instability in U.S. Capital Markets: An Explainable Machine-Learning Based Approach for Assessing Risks in Complex Financial Systems /Daren Lee Purnell Jr. - 1 electronic resource (156 pages)

Source: Dissertations Abstracts International, Volume: 86-06, Section: B.

We introduce a novel methodology to capture and represent the diverse relationships between financial market institutions to prevent and mitigate negative economic shocks. An ensemble of machine learning models identifies influential features representing these relationships, with SHapley Additive exPlanations (SHAP) and a modified Borda count method selecting the most significant predictors. These features train an explainable linear model, providing a transparent and interpretable representation of inter-network relationships. The methodology is validated through case studies utilizing data from the FY20 U.S. recession and the failure of Silicon Valley Bank in March of 2023, demonstrating the model's accuracy in predicting financial stress and identifying institutional outliers less affected by economic shocks. This approach is significant for researchers in regulated fields, where model explainability is crucial, as it provides a framework to analyze high-dimensional, infrequently reported data and reveal valuable insights. As financial markets evolve and generate complex datasets, this methodology offers a robust tool for understanding and predicting financial system behavior, enhancing risk management, and financial stability.

English

ISBN: 9798346805403Subjects--Topical Terms:

3168411
Systems science.
Subjects--Index Terms:

Complex systems

Identifying Instability in U.S. Capital Markets: An Explainable Machine-Learning Based Approach for Assessing Risks in Complex Financial Systems /
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520 $a We introduce a novel methodology to capture and represent the diverse relationships between financial market institutions to prevent and mitigate negative economic shocks. An ensemble of machine learning models identifies influential features representing these relationships, with SHapley Additive exPlanations (SHAP) and a modified Borda count method selecting the most significant predictors. These features train an explainable linear model, providing a transparent and interpretable representation of inter-network relationships. The methodology is validated through case studies utilizing data from the FY20 U.S. recession and the failure of Silicon Valley Bank in March of 2023, demonstrating the model's accuracy in predicting financial stress and identifying institutional outliers less affected by economic shocks. This approach is significant for researchers in regulated fields, where model explainability is crucial, as it provides a framework to analyze high-dimensional, infrequently reported data and reveal valuable insights. As financial markets evolve and generate complex datasets, this methodology offers a robust tool for understanding and predicting financial system behavior, enhancing risk management, and financial stability.
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650 4 $a Computer science. $3 523869
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653 $a Complex systems
653 $a Explainability
653 $a Feature selection
653 $a Financial stability
653 $a Machine learning
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