Job Market Paper (solo): “Generating Exposures with Large Language Models: Insights into M&A Activity”
I propose a Large Language Model to generate time-varying exposure of firms to arbitrary user-defined risks using conference calls. This approach respects language complexity, does not require an expert to generate an extensive list of keywords, produces exposure measures with respect to any query and is directly applicable to any textual input. Leveraging this methodology, I generate firm-level exposures to geopolitical risk, population aging, climate change and demonstrate how exposure to such risks affects M&A activity. Geopolitical risk makes firms more likely to be an acquirer and less a target. It is propagated through supply chains and makes firms acquire targets in the U.S. with higher investment irreversibility driving vertical integration. Population aging increases aggregate M&A activity doubling the effect from labor shortage on M&A activity. Managers pay larger premia for low climate change risk targets, while investors react negatively to deal announcements of targets undergoing structural change. Finally, I describe other potential applications of the methodology in finance.
Research
“Perpetual Futures Contracts and Cryptocurrency Market Quality" with Qihong Ruan, Economics PhD Candidate, Cornell University
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We examine perpetual futures contracts’ impact on cryptocurrency spot market
quality. Using high-frequency order book data from 2017 to 2023, we document
that spot market quality follows a U-shaped pattern over perpetual contracts’
eight-hour funding cycles. Exploiting both the exogenous termination of per-
petual trading at Huobi Exchange and 95 staggered contract introductions, we
identify a seemingly puzzling liquidity pattern: perpetual contracts increase spot
trading volume while widening quoted spreads. To resolve this puzzle, we demon-
strate that this pattern reflects increased informed trading, particularly during
funding settlement hours and periods of larger funding fee magnitudes. Market
makers respond to heightened adverse selection risk by widening quoted spreads.
“Keeping the Faith (and the Returns): An AI approach to Values-based Investing” with Maureen O’Hara, Professor of Finance, Cornell University
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We demonstrate a new approach for SRI investing that uses techniques from Artificial Intelligence (AI) to enhance investor returns. Focusing on faith-based investing, our paper draws on Large Language Models (LLMs) and Deep Reinforcement Learning (DRL) to address the challenges posed by moral constraints on portfolio selection. Using the Global X S&P500 CATH ETF (the largest Catholic values fund) as an example, we use textual analysis to identify companies consistent with the values mandate, allowing us to create “synthetic CATH” portfolios of different sizes and with longer time horizons. We further optimize each portfolio using DRL to arrive at an optimal set of portfolio weights that maximize out-of-sample Sharpe ratios. Using the tools of AI, we demonstrate dramatic improvements in risk-adjusted returns closing the performance gap in values-based investing.