Where does the Stimulus go? Deep Generative Model for Commercial Banking Deposits

TL;DR

This study develops a probabilistic generative model to predict retail and wholesale banking deposits in the US, revealing how macroeconomic factors like QE influence deposit types and aiding bank management strategies.

Contribution

It introduces a novel generative modeling approach to estimate retail-wholesale deposit splits using limited data and forecasts deposit responses to macroeconomic variables.

Findings

QE increases wholesale deposits but not retail deposits

Loans increase both retail and wholesale deposits

Model provides forecasting capabilities for deposit management

Abstract

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.