Where does the Stimulus go? Deep Generative Model for Commercial Banking Deposits
This work provides insights for economic decision-making and bank management by forecasting deposit splits, but it is incremental as it applies existing methods to a new domain-specific dataset.
The paper tackles the problem of predicting how retail and wholesale deposits in U.S. banking are affected by macroeconomic factors like quantitative easing, using a generative model to estimate the deposit split from 2000 to 2020. It finds that increases in reserves (representing QE) boost wholesale but not retail deposits, while loans increase both evenly, indicating QE benefited large companies more than individuals.
This paper examines deposits of individuals ("retail") and large companies ("wholesale") in the U.S. banking industry, and how these deposit types are impacted by macroeconomic factors, such as quantitative easing (QE). Actual data for deposits by holder are unavailable. We use a dataset on banks' financial information and probabilistic generative model to predict industry retail-wholesale deposit split from 2000 to 2020. Our model assumes account balances arise from separate retail and wholesale lognormal distributions and fit parameters of distributions by minimizing error between actual bank metrics and simulated metrics using the model's generative process. We use time-series regression to forward predict retail-wholesale deposits as function of loans, retail loans, and reserve balances at Fed banks. We find increase in reserves (representing QE) increases wholesale but not retail deposits, and increase in loans increase both wholesale and retail deposits evenly. The result shows that QE following the 2008 financial crisis benefited large companies more than average individuals, a relevant finding for economic decision making. In addition, this work benefits bank management strategy by providing forecasting capability for retail-wholesale deposits.