Reinforcement learning policy recommendation for interbank network stability

Alessio Brini, Gabriele Tedeschi and Daniele Tantari

Journal of Financial Stability, 2023, vol. 67, issue C

Abstract: In this paper, we analyze the effect of a policy recommendation on the performance of an artificial interbank market. Financial institutions stipulate lending agreements following a public recommendation and their individual information. The former is modeled by a reinforcement learning optimal policy that maximizes the system’s fitness and gathers information on the economic environment. The policy recommendation directs economic actors to create credit relationships through the optimal choice between a low interest rate or a high liquidity supply. The latter, based on the agents’ balance sheet, allows determining the liquidity supply and interest rate that the banks optimally offer their clients within the market. Thanks to the combination between the public and the private signal, financial institutions create or cut their credit connections over time via a preferential attachment evolving procedure able to generate a dynamic network. Our results show that the emergence of a core–periphery interbank network, combined with a certain level of homogeneity in the size of lenders and borrowers, is essential to ensure the system’s resilience. Moreover, the optimal policy recommendation obtained through reinforcement learning is crucial in mitigating systemic risk.

Keywords: Financial networks; Policy recommendation; Machine Learning; Reinforcement learning; Network stability (search for similar items in EconPapers)
Date: 2023
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Working Paper: Reinforcement Learning Policy Recommendation for Interbank Network Stability (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finsta:v:67:y:2023:i:c:s1572308923000396

DOI: 10.1016/j.jfs.2023.101139

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