Research
My dissertation explores how data-intensive and algorithmic technologies, and their concentration in the hands of a few players, are transforming discretionary decision-making in financial institutions, and in so doing, changing the dynamics of the circulation and accumulation of wealth and information in society.
- How should we regulate artificial intelligence and the data industry? How must we adapt existing laws for the new information regimes that characterize a digital, hyperconnected world?
- To answer this question, we need a better understanding of how information, computation and communication technologies and practices interplay with social institutions and impact social decision-making and collective intelligence.
- My dissertation works towards this by exploring human-AI interaction in the context of investment decisions in financial markets.
Other work in progress:
- "Divestment without Decarbonization: Private Equity and the Organizational Ecology of Dirty Work" (co-authored with Adam Goldstein): CO2-equivalent emissions from the U.S. industrial sector have remained remarkably stable in recent years, despite growing pressures for firms to adopt more sustainable business practices. Drawing on theories of means-ends decoupling and organizational fissuring as a mechanism of liability avoidance, we hypothesize that intensified financial, reputational, and regulatory pressures are prompting publicly-traded firms to shed environmental liabilities by divesting carbon-emitting assets. However, rather than reducing net emissions, carbon-intensive plants are relocating to more opaque privately-owned and private equity firms, which face fewer pressures to decarbonize. The result, we suggest, is a dynamic of divestment without decarbonization. We test this by constructing a unique linked dataset covering the period from 2010-2021 for the universe of more than 8000 facilities that reported emissions data to the EPA’s Greenhouse Gas Reporting Program, with time-varying data on the ownership of each facility’s parent firm. We presented this project at SASE 2023 in Rio.
- "The Real Effect of Analyst Forecasts: Managing Earnings and Uncertainty in Non-Financial Firms": In this empirical project, I investigate earnings management practices and share repurchase operations by U.S. publicly traded firms over the past 20 years. I rely on new statistical analyses of corporate data covering the period of 1999 to 2019 combined with past findings from qualitative researchers to explore the dynamics of managers’ earnings guidance and sell-side analysts’ earnings forecasts, and the relational construction of earnings surprises. I replicate and extend previously published results in financial econometrics by showing that, contrary to what has been suggested, earnings surprises cannot be used as a causal instrument in most cases, given their embeddedness within managerial decision-making processes. More broadly, I discuss when and why endogeneity problems may result from using as an instrument or treatment a variable that encodes prediction practices within the field studied.
