Rayhan Momin


I am a Finance PhD candidate at the University of Chicago Booth School of Business, where I also received an MBA en route to the PhD. Previously, I worked as a research assistant at the Federal Reserve Board of Governors and as an analyst at JPMorgan. I graduated from Columbia College, Columbia University with a BA in Economics-Mathematics.

I will be on the non-academic job market for 2024-2025.

Research Interests

Working Papers

Heterogeneous Treatment Effects and Counterfactual Policy Targeting Using Deep Neural Networks: An Application to Central Bank Corporate Credit Facilities (2025)
Abstract. I present a novel two-step semi-parametric difference-in-differences (DiD) estimator for computing dynamic (heterogeneous) treatment effects and policy counterfactuals. In the first step, deep neural networks are used to compute non-parametric terms in a setting with high-dimensional controls. These are inputs into the estimator evaluated in the second step. The estimator is applied to study the effects of the Federal Reserve’s Corporate Credit Facilities (CCFs) on the dynamics of firm cash holdings, leverage, payout, and investment. I show that the proposed estimator delivers comparable results to static (homogeneous) treatment effects obtained from DiD panel regressions and dynamic (homogeneous) treatment effects from event study regressions with two-way fixed effects, though with important differences attributable to selection bias and heterogeneity. Firms generally increased cash holdings and leverage, while payout and investment initially fell. Firms eligible for the CCFs accumulated less cash and began deleveraging in 2021, relative to ineligible firms. Eligible firms exhibit relatively larger payouts, while they do not invest more, suggesting that the CCFs failed to meet their objective of boosting real effects. Counterfactual eligibility criteria can possibly deliver an improvement in investment outcomes.

The Causal Effect of the Fed’s Corporate Credit Facilities on Eligible Issuer Bonds (2025)
Abstract. We study the effects of the Federal Reserve’s Corporate Credit Facilities (CCFs) that were launched in early 2020 amid significant volatility in the U.S. corporate bond market. We find that the initial announcement of the CCFs on March 23, 2020 benefited issuers eligible for direct primary and secondary support from the CCFs more than ineligible issuers. In contrast, we find that ineligible issuer bond spreads tightened more in the subsequent announcement of the CCF expansion on April 9, 2020. Inconsistent with the CCF eligibility criteria, most research has used issue ratings, rather than issuer ratings, to identify eligible bonds; we document that this results in a sizeable bias when estimating the April 9 effect and trace the source of this bias. We also provide an estimate of the potential (counterfactual) improvement in bond spreads ineligible issuers would have experienced, had they been eligible for the CCFs. Analysis of the channels through which the CCFs operated suggests that the liquidity channel was more important than the default risk channel. We also find that the start of the CCF’s purchases of ETFs on May 12, 2020 and bonds on June 16, 2020 had a smaller effect on bond spreads, though the latter was more impactful. Additionally, a causal machine learning approach that estimates these effects using high-dimensional controls, while allowing for rich, nonlinear interactions, produces similar results and recovers the distribution of conditional average treatment effects. We show that this distribution can be used to identify counterfactual policy targeting schemes that would have resulted in an even more significant reduction in the average treatment effect on the treated. We also discuss how this distribution can be used to decompose the channels through which the Fed CCFs may have operated.

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