Corporate Finance

This paper analyzes the AI infrastructure boom as a physical capital buildout centered on data centers, power infrastructure, cooling systems, and specialized chips. It studies how this buildout is financed through hyperscalers, third-party developers, REITs, private credit, and structured finance, and discusses the implications for leverage, risk allocation, and financial stability.

There has been growing interest in applying reinforcement learning (RL) to inventory management, either by optimizing over temporal transitions or by learning directly from full historical demand trajectories. This contrasts sharply with classical data-driven approaches, which first estimate demand distributions from past data and then compute well-structured optimal policies via dynamic programming.

We use a dynamic spatial general equilibrium model of international investment and production to investigate the real implications of the last five decades of financial globalization. We introduce a wedge accounting framework to estimate country- and time-varying measures of outward and inward Revealed Financial Openness (RFO). These wedges are meant to capture all impediments to cross-border investment, rather than explicit policy measures alone.

Public financial information plays a critical role in the takeover market by helping acquirers search for and value potential targets. Using a difference-in-differences research design around a regulatory disclosure mandate that changed the granularity of financial disclosure for certain privately held banks, we find that banks with reduced disclosure are less likely to be targeted in M&A transactions. Acquirers adapt to information frictions arising from reduced disclosures by bidding for geographically proximate target banks and increasing the proportion of stock in their bids.

We introduce a novel measure of corporate hierarchies for over 3,100 U.S. public firms. This measure is obtained from online resumes of 7 million employees and a network estimation technique that allows us to identify hierarchical layers. Equipped with this measure, we document several facts about corporate hierarchies. Firms have on average ten hierarchical layers and a pyramidal organizational structure. More hierarchical firms have a more educated workforce, higher internal promotion rates, and longer employee tenure.

Individuals behave differently when they know the objective probability of events and when they do not. The smooth ambiguity model accommodates both ambiguity (uncertainty) and risk. For an incomplete, competitive asset market, we develop a revealed preference test for asset demand to be consistent with the maximization of smooth ambiguity preferences; and we show that ambiguity preferences constructed fromfinite observations converge to underlying ambiguity preferences as observations become dense.

We document gender and race dynamics in performance evaluations in a multi-national company, examining the impacts of a feature of the performance appraisal process: managers’ knowledge of employees’ self-evaluations. Generally, (White) women were rated higher than men and people of color were rated lower than White employees. Women of color gave themselves the lowest self-ratings. When self-evaluations were unavailable due to a quasi-exogenous shock, manager and self-ratings were less correlated.

Geoeconomic pressure—the use of existing economic relationships by governments to achieve geopolitical or economic ends—has become a prominent feature of global power dynamics. This paper introduces a methodology using large language models (LLMs) to systematically extract signals of geoeconomic pressure from large textual corpora. We quantify not just the direct effects of implemented policies but also the off-path threats that induce compliance without formal action. We systematically identify governments, firms, tools, and activities that are involved in this pressure.

Projects with high societal impact--such as biodiversity conservation and climate change mitigation--often offer financial returns that are too low, or too risky, to attract private capital. Under such circumstances, it can be difficult to raise adequate financing for these projects. A potential solution is blended finance, that is, the blending of concessional funding (e.g., from governments, multilateral development banks, or philanthropies) with private capital.