Trading with Expert Dealers R&R in The Journal of Finance
with Vincent Glode. 2025. SSRN Link.
We jointly model investors' allocation of order flow among over-the-counter dealers and dealers' acquisition of expertise that increases their ability to take advantage of investors across transactions. Investors choose dealers based on their level of expertise and the liquidity they are expected to provide whereas dealers choose their level of expertise based on the number of transactions they expect to intermediate and the cost of acquiring expertise. Our model rationalizes why the most sought-after dealers often are those with the best data, technology, and skills, despite the significant adverse selection concerns triggered by their expertise.
The Value of Accounting Noise: Credit Line Revocations and Aggregate Liquidity Shocks
with Christian Laux and Angel Tengulov; 2019. SSRN
We discuss a novel role for covenants and accounting performance measures in credit lines. During aggregate liquidity shortages, banks need to ration liquidity. Absent complete contracts, the bank has discretion over which firms get liquidity. Accounting-based covenants reduce bank discretion and the cost of liquidity to firms. The optimal implicit contract implies that banks revoke credit lines of covenant violators only after an aggregate shock, not in normal times. Noise in accounting performance measures introduces randomness in covenant violations, which substitutes for bank discretion when banks have to ration liquidity among homogeneous firms after an aggregate shock. Consistent with the prediction of our model, we find a positive association between covenant violations and credit line revocations in the crisis of 2007 and 2008, controlling for firm fundamentals, but not outside the crisis.
Previous versions:
Discretion and Systemic Risk in Credit Line Contracts (2018 draft)
Why Do Mutual Funds Hold Cash?
with Christoph Scheuch; 2018. SSRN
We examine liquidity risks of mutual funds and the role of liquidity management in a parsimonious model of active portfolio management with trading costs. We argue that redemptions which following bad performance pose no dilution risk to remaining investors, and what appears to be liquidity management by mutual funds might be managers collecting rent. Liquidations of illiquid assets to satisfy such redemptions are efficient and do not justify regulatory interventions. Accommodating redemptions with cash only, as managers with performance-sensitive compensation do, exaggerates outflows and destabilizes the fund.
The Dark Side of Liquid Bonds in Fire Sales.
with Alexander Muermann and Christoph Scheuch; accepted for publication in The Management Science on November 11, 2025. SSRN
We investigate which bonds investors should sell when they need to raise cash quickly. Our model shows that the intuitive strategy of selling the most liquid bonds can backfire. In over-the-counter markets, liquid bonds trade fastest, but when many investors simultaneously sell the same liquid, widely held bonds while buying capital is scarce, their prices fall the most. Individual investors do not fully internalize how their sales amplify the liquidation losses of others, making the outcome privately optimal but collectively inefficient. We test these predictions using Property & Casualty insurers around major natural catastrophes and find that insurers sell liquid bonds first, only partially avoid crowded bonds, and that liquid bonds experience the largest price declines during fire sales. The overlap in liquid holdings thus emerges as a key source of systemic risk that should receive greater weight in regulation and risk measurement.
Dynamic Financing: How Firms Adjust Debt Maturity, Dispersion, Leverage, and Cash to Accommodate Shocks
accepted for publication in The Review of Corporate Finance Studies on August 17, 2025. version 2023 SSRN.
I study how firms adjust leverage, debt maturity and cash to manage profitability shocks, and show that time-variation in concentration of maturity dates arises endogenously. To avoid rollover risk, firms prefer long-term debt with dispersed maturity dates. However, severe negative shocks force firms to borrow above an optimal level. They issue short-term debt as a commitment to delever in the next period. This concentrates maturity dates in the next period. The calibrated version of the model matches empirical facts and makes novel predictions regarding dynamics of debt maturity dispersion.
Previous Versions and Related Papers:
(Idiosyncratic) Credit-spread Risk and the Dynamics of Liquidity, Leverage and Maturity of Debt (2016 draft).
Hedging News with Cash and Debt (2015 draft).
The Pre-Borrowing Motive: a Model of Coexistent Debt and Cash Holdings (2013 draft).
Maturity Premium
with Patrick Weiss and Josef Zechner; 2022 in Journal of Financial Economics. SSRN Link, JFE Link.
We analyze asset-pricing implications of debt maturity. Firms financed with long-term debt have weaker incentives to deliver after negative shocks and thus exhibit high leverage during extended downturns. The resulting increase in beta is a risk for which shareholders require compensation. As a result long-term financed firms have higher expected returns than short-term financed firms, controlling for the average systematic risk exposure. We demonstrate this in a model and document empirically a 0.21% monthly premium for buying long-maturity financed firms and selling short-maturity financed firms.
Predators and Prey on Wall Street
with Richard C. Green; Review of Asset Pricing Studies 2014; 4 (1): 1-38.
Much financial activity is zero-sum. While providing transactional and diversification services to others, participants also prey upon each other. High-ability predators trade opportunistically with less-able prey. In our dynamic model these features amplify real shocks. The presence of more low-ability traders reduces expected losses to high-ability traders, leading to equilibria with high levels of financial activity and employment. Shocks to profits can motivate exit by low-ability traders, rendering those of intermediate skill more vulnerable. Thus, our relatively simple model generates boom-bust dynamics suggestive of Wall Street.