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<p>This paper reexamines bank-firm interactions through an agent-based model, focusing on how banks influence long-term capital reallocation in stochastic, cyclical economies. Using an agent-based model with firm demand modeled as Geometric Brownian Motion, the paper studies growth/decline epochs under varying volatility. Unlike prior work focused on liquidity, this paper emphasizes long-term capital stocks and introduces a radial matching mechanism to minimize spatial bias. It extends prior work by introducing preference alignment between banks and firms, modeling credit as a two-stage process: (1) firms signal capital needs, and (2) banks approve/deny requests based on top-down buffer policies or bottom-up firm performance selection. Simulations across growth/decline epochs with varying volatility reveal a policy trade-off: (1) top-down buffers dominate during long-term growth, stabilizing credit allocation and (2) bottom-up selection excels in declines, efficiently restricting capital to high-performing firms. Here, a hybrid approach, combining both strategies, might be most resilient across cycles, suggesting that optimal credit regulation is phase-dependent. These findings challenge one-size-fits-all regulatory approaches and advocate for cycle-sensitive credit policies.</p>

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Agent-based modeling of long-term bank credit: buffer policies vs. selective lending in stochastic growth and decline

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月10日

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  • Mitja Steinbacher

摘要

This paper reexamines bank-firm interactions through an agent-based model, focusing on how banks influence long-term capital reallocation in stochastic, cyclical economies. Using an agent-based model with firm demand modeled as Geometric Brownian Motion, the paper studies growth/decline epochs under varying volatility. Unlike prior work focused on liquidity, this paper emphasizes long-term capital stocks and introduces a radial matching mechanism to minimize spatial bias. It extends prior work by introducing preference alignment between banks and firms, modeling credit as a two-stage process: (1) firms signal capital needs, and (2) banks approve/deny requests based on top-down buffer policies or bottom-up firm performance selection. Simulations across growth/decline epochs with varying volatility reveal a policy trade-off: (1) top-down buffers dominate during long-term growth, stabilizing credit allocation and (2) bottom-up selection excels in declines, efficiently restricting capital to high-performing firms. Here, a hybrid approach, combining both strategies, might be most resilient across cycles, suggesting that optimal credit regulation is phase-dependent. These findings challenge one-size-fits-all regulatory approaches and advocate for cycle-sensitive credit policies.