In this paper we analyze the effect of a policy recommendation on the performances of an artificial interbank market. Financial institutions stipulate lending agreements following a public recommendation and their individual information. The former, modeled by a reinforcement learning optimal policy trying to maximize the long term fitness of the system, gathers information on the economic environment and directs economic actors to create credit relationships based on the optimal choice between a low interest rate or high liquidity supply.The latter, based on the agents' balance sheet, allows to determine the liquidity supply and interest rate that the banks optimally offer on the market. Based on 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 on the size of lenders and borrowers, are essential features to ensure the resilience of the system. Moreover, the reinforcement learning optimal policy recommendation plays a crucial role in mitigating systemic risk with respect to alternative policy instruments.
Classical portfolio optimization often requires forecasting asset returns and their corresponding variances in spite of the low signal-to-noise ratio provided in the financial markets. Deep reinforcement learning (DRL) offers a framework for optimizing sequential trader decisions through an objective which represents its reward function penalized by risk and transaction costs. We investigate the performance of model-free DRL traders in a market environment with frictions and different meanreverting factors driving the dynamics of the returns. Since this framework admits an exact dynamic programming solution, we can assess limits and capabilities of different value-based algorithms to retrieve meaningful trading signals in a datadriven manner and to reach the benchmark performance. Moreover, extensive simulations show that this approach guarantees flexibility, outperforming the benchmark when the price dynamics is misspecified and some original assumptions on the market environment are violated with the presence of extreme events and volatility clustering.
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