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arXiv Machine Learning By Yousef Nejatbakhsh, Malihe Aliasgari, Parsa Safaee, Sina Shokoohyar

An Interaction-Aware Decision Framework for FDIC-Assisted Distressed Bank Acquisitions: Empirical Evidence from the FDIC Failed-Bank Panel and the SVB-First Citizens Case

Read the original on arXiv Machine Learning →

The paper introduces the Spider Network Filtering Framework, an interaction‑aware decision model for FDIC‑assisted distressed bank acquisitions. It encodes dependencies among five dimensions—acquisition value, regulatory protection, risk, buyer strength, and execution difficulty—into four interpretable rules, producing graph‑inspired features for a logistic model and a Pursue/Caution/Avoid decision rule. Evaluated on 55 FDIC‑assisted acquisitions from 2008‑2024, the Spider‑enhanced model achieves a mean AUC of 0.594, slightly lower than logistic (0.615) and ridge (0.632) regressions, and permutation tests show only weak evidence of performance above chance.

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