Using recent advances in network theory, we estimate the intra-industry connectedness for US publicly traded companies going back to the 1920s. We develop a stock-level composite centrality measure that captures multiple dimensions of a stock's interdependence with its industry peers. Using our...

This paper explores the link between systemic risk and firm value gain in the context of bank acquisitions. We find that bank acquisitions on average lead to an increase in acquires' systemic risk (a public cost), which is in turn associated with an increase in firm value (a private benefit) for...

In this paper, I interpret a time series spatial model (T-SAR) as a constrained Structural Vector Autoregressive (SVAR) model. Based on these restrictions, I propose a Minimum Distance approach to estimate the (row-standardized) network matrix and the overall network influence parameter of the...

In this paper, I interpret a time series spatial model (T-SAR) as a constrained Structural Vector Autoregressive (SVAR) model. Based on these restrictions, I propose a Minimum Distance approach to estimate the (row-standardized) network matrix and the overall network influence parameter of the...

We apply facial recognition analysis to FOMC press conference videos and quantify one of the most important aspects of nonverbal communication - facial expressions. Using minute-level data, we align our nonverbal communication measure with a set of financial assets to estimate the impact of the...

This paper investigates the ability of Generative Pre-training Transformer (GPT) models to decipher Fedspeak, a term used to describe the technical language used by the Federal Reserve to communicate on monetary policy decisions. We evaluate the ability of GPT models to classify the policy...