We develop a tractable framework to explore how beliefs about long-term economic growth shape macroeconomic and financial stability. By modeling belief distortions among productive capital users, we provide an analytical characterization of a novel phenomenon termed the “net worth trap”, where overly optimistic or pessimistic beliefs of productive agents prevent them from rebuilding wealth, causing permanent inefficiencies. A procyclical swing in beliefs reduces or exacerbates the instability, indicating that the type of belief when the economy is vulnerable has important con- sequences on financial stability and macroeconomic dynamics.

We study aggregate stock market elasticity in a general equilibrium model with heterogeneous investors, passive demand, and financial constraints. In a frictionless economy, interest rate movements fully offset risk premium responses and the macro elasticity of the endowment claim is infinite. Frictions break this offset, generating finite price impact. The equity claim has larger price impact since dividends are a levered share of output. A state global perturbation method characterizes the drivers of aggregate elasticity in closed form. We solve the full dynamic model with deep learning. The calibrated economy matches the equity premium, return volatility, and countercyclical price impact.

How do household frictions impact the portfolios of the financial sector and which households gain and lose as a result? To answer this question, we build a heterogeneous agent macro-finance model with households facing asset market participation constraints, banks providing deposits, funds providing insurance/pension products, and endogenous asset price volatility. We solve the model globally by developing a novel deep learning methodology for macro-finance models and calibrate the model to asset pricing dynamics and household portfolio choices. Counterfactual experiments reveal policy trade-offs. Tighter financial sector restrictions increase stability but at the expense of lower growth and/or higher inequality because richer households are better able to take advantage of the higher spreads created by the regulations.

This paper builds ALIENs (Active Learning Inspired Equilibrium Nets), that extend deep-learning methods for solving continuous-time equilibrium models with high-dimensional states, aggregate shocks, and nonlinear dynamics. The approach combines time-stepping with active learning to turn nonlinear problems into sequences of contraction mappings, improving stability and convergence. By focusing computation on economically important regions of the state space, ALIENs increase accuracy and efficiency. The method is validated across applications ranging from heterogeneous-agent models with free boundaries to high-dimensional asset-pricing models. Additionally, the paper introduces Deep-Macrofin+, a numerical library that facilitates the implementation of these techniques for researchers.

Financial crises amplify modest shocks into severe downturns and leave the economy slow to recover. This paper develops an intermediary-based macro-finance model in which the intermediaries’ continuation values depend on the sector’s intermediation capacity. Exit removes agents who would otherwise earn high risk premia and erodes a slow-moving stock of intermediation capacity. Lower capacity reduces the continuation values and induces further exit, so shocks to capacity set off a self-reinforcing loop. An analytical characterization shows that intermediary net worth can become trapped at low levels when capacity falls below a critical threshold. The calibrated dynamic model matches unconditional asset-pricing, macroeconomic moments, and crises moments. A benchmark model without exit and capacity dynamics exhibits an amplification-persistence trade-off because high risk premia recapitalize intermediaries too quickly.

We analyze a proprietary dataset of factoring transactions, where a financial intermediary, a factor, provides capital to support customers-supplier trade-credit transactions. At the empirical level, we characterize distinctive features that correlate customer and supplier capital structure determinants with the intensity of observed factoring transactions. Our key finding is that the characteristics of the production-related supply-chain network and downstream competition simultaneously shape the inter-firm trade- and bank-related debt chains and the firm-specific corporate financial policies. To match this evidence, we develop a structural model where the intensity of usage of factoring services is endogenously determined, jointly with the capital structures of the bank, the supplier, and the customer.