Hello, I'm Umang.

I am a PhD candidate at the University of Iowa.

My research interests include financial intermediation, market microstructure, asset pricing, and household finance.

Before graduate school, I worked at the Fixed Income and Foreign Exchange Markets division of J.P. Morgan.

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Research Papers

Select presentations: SFS Cavalcade North America 2024, Office of Financial Research (OFR) Rising Scholars 2024, American Finance Association (AFA) PhD Poster 2024, Bank of Canada, Bank of England, University of Iowa.

We study interest rate risk sharing across the financial system using novel data on cross-sector interest rate swap positions. We show that pension funds and insurers (PF&I) are natural counterparties to banks and corporations: PF&I buy duration, whereas banks and corporations sell duration (figure on the right). However, demand is highly segmented across maturities, resulting in significant imbalances at various maturity points. We calibrate a preferred-habitat investors model with risk-averse arbitrageurs to study how demand imbalances interact with supply side constraints to impact swap spreads. Our framework helps quantify the spillover effects of demand shifts, which informs policy discussions on financial institutions’ hedging requirements.

This figure shows net outstanding positions in $ billion at the start of every month for five end-user sectors and the dealer sector. A positive value on the y-axis indicates a net receive fixed position while a negative value indicates a net pay fixed position.

[2] Corporate Trading in Over-the-counter FX Markets

Select presentations: Western Finance Association (WFA) Meetings 2022, 4th Future of Financial Information Conference 2022, Central Bank Conference in Microstructure of Financial Markets 2022, University of Missouri.

Currencies are one of the largest but least understood financial assets. In this paper, we tackle a longstanding question: what makes foreign exchange rates volatile? Using unique sector-level FX spot trading data across 40 currency pairs, we show that demand from liquidity-seeking investors such as corporations can drive short-term price volatility. We use changes in the timing of corporate trading due to month-end calendar effects (figure on the right) and identify a causal link between order imbalance and volumes, bid-ask spreads, and price volatility.

This figure shows that corporate order flow imbalance (absolute of buy minus sell volume) jumps two and three days before a month-end, in response to a "T+2" settlement cycle in the FX spot market.

[3] Unemployment Insurance Fraud in the Debit Card Market

Select presentations: American Economic Association (AEA) Annual Meetings 2024, Midwest Finance Association 2024, NBER Innovative Data in Household Finance Conference 2023, MIT Rising Scholars Conference.

We study fraud in the unemployment insurance (UI) system using a dataset of 35 million debit card transactions. We apply machine learning techniques to group cards into clusters corresponding to varying levels of suspicious or potentially fraudulent activity. We then conduct a difference-in-differences analysis based on the staggered adoption of state-level identity verification systems between 2020 and 2021 to assess the effectiveness of screening for reducing fraud. Our findings suggest that identity verification reduced payouts to suspicious cards by 34% (figure on the right), while non-suspicious cards were largely unaffected by these technologies. Our results indicate that identity screening may be an effective mechanism for mitigating fraud in the UI system and for ensuring the integrity of benefits programs more broadly.

This figure shows that unemployment insurance benefits disbursed to suspicious cards declined after the introduction of identity verification measures.

Draft available upon request.

[4] Process Innovation and the Corporate Control Market

Select presentations: Midwest Finance Association 2024, Eastern Finance Association 2024, Financial Management Association 2023, University of Iowa.

Between a third to a fifth of all patents filed in the US seek process or operational improvement (figure on the right). We show that specificity of process innovation affects merger decisions. We measure the composition of a firm’s innovation portfolio by machine-reading 90 million patent claims and show that firms with a higher share of process innovation generate more firm-specific knowledge. While process innovation is value-enhancing for the stand-alone firm, its specificity reduces synergistic gains from an acquisition. Consistent with the specificity explanation, the negative effect of process innovation on acquisition likelihood is dampened if the bidder manufactures similar products and, therefore, can apply the target’s innovative processes to its own product line. Our study provides the first large-sample evidence on the fungibility of innovation and its impact on mergers and acquisitions.

This figure plots the average share of process claims over the years 1980 through 2020. We identify process claims using a machine-read textual classification algorithm.