Investors face reduced incentives to finance technological change that devalues their legacy investments. We formalize this “asset overhang” and apply our framework to the climate-banking nexus. Leveraging (1) firm-level data on green innovation and diffusion and (2) the sets of product and technology market peers, we implement a shift-share design that identifies banks’ credit facilities impacted by green firm activities. We find that green firms imposing an asset overhang across all lenders are 3 to 7 percentage points more likely to report tight credit supply conditions. The presence of legacy-free investors mitigates the asset overhang problem, thereby facilitating technological change.

In this paper, we propose a method to analyze the structure of the credit market. Using historical data from Japan, we explore banks’ lending patterns to the real economy. We find that generalist banks (with diversified lending) and specialist banks (with focused lending) coexist, and tend to stick to their strategies over time. Similarly, we also document the coexistence of generalist and specialist industries (based on their borrowing patterns). The observed interaction patterns in the credit market indicate a strong overlap in banks’ loan portfolios, mainly due to specialist banks focusing their investments on the very same generalist industries. A stylized model matches these patterns and allows us to identify economically meaningful sets of generalist banks/industries. Lastly, we find that generalist banks are not necessarily less vulnerable to shocks compared to specialists. In fact, high leverage levels can undo the benefits of diversification.

Financial networks have shown to be important in understanding systemic events in credit markets. In this paper, we investigate how the structure of those networks can affect the capacity of regulators to assess the level of systemic risk. We introduce a model to compute the individual and systemic probability of default in a system of banks connected in a generic network of credit contracts and exposed to external shocks with a generic correlation structure. Even in the presence of complete knowledge, we identify conditions on the network for the emergence of multiple equilibria. Multiple equilibria give rise to uncertainty in the determination of the default probability. We show how this uncertainty can affect the estimation of systemic risk in terms of expected losses. We further quantify the effects of cyclicality, leverage, volatility and correlations. Our results are relevant to the current policy discussions on new regulatory framework to deal with systemic events of distress as well as on the desirable level of regulatory data disclosure.