We document evidence of market power in the equity securities lending market, including non-competitive fee levels, market concentration, and low inventory utilization. Motivated by this evidence, we develop a theory explaining why this market structure may be preferred by both informed traders and shares lenders. Key elements of our asymmetric-information model are informed traders' concerns about information leakage and a recognition of the fact that shorting is a two-step transaction whereby securities are first borrowed and only thereafter can be sold. We estimate our dynamic model and evaluate the implications of non-competitive fees for stock valuations. We find that shares lenders obtain an incremental present value due to fee income ranging from 1.5% of extra value for large-cap, low-fee stocks to value inflations up to 25% for small-cap stocks, and even more than 100% for nano-cap stocks.
We propose a model of strategic debt renegotiation in which businesses are sequentially interconnected through their liabilities. This financing structure, which we refer to as a debt chain, gives rise to externalities, as a lender's willingness to provide concessions to its privately-informed borrower depends on how the lender's own liabilities are expected to be renegotiated. We highlight how government interventions that aim to prevent default waves should account for these private renegotiation incentives and their interlinkages. In particular, we contrast the consequences of targeted subsidies vs. debt reduction programs following economic shocks such as a pandemic or financial crisis.
We classify asset pricing anomalies into those that exacerbate mispricing (build-up anomalies) and those that resolve it (resolution anomalies). To this end, we estimate the dynamics of price wedges for a large number of well-known anomaly portfolios in the factor zoo and map them to firm-level mispricings. We find that several prominent anomalies like momentum and profitability further dislocate prices. While mispricing buildup is often quick, the subsequent resolution tends to be slow, suggesting the potential for material real economic consequences. Our results suggest that financial intermediaries chasing build-up anomalies in fact negatively affect price efficiency and associated real capital allocation.
We propose a novel conceptual approach to characterizing the credit market equilibrium in economies with multi-dimensional borrower heterogeneity. Our method is centered around a micro-founded representation of borrowers' aggregate demand correspondence for bank capital. The framework yields closed-form expressions for the composition and pricing of credit, including a sufficient statistic for the provision of bank loans. Our analysis sheds light on the roots of compositional shifts in credit toward risky borrowers prior to the most recent crises in the U.S. and Europe, as well as the macroprudential effects of bank regulations, policy interventions, and financial innovations providing alternatives to banks.
Blockholders play a prominent role in distressed firms' access to finance. I develop a dynamic model of the interaction between these investors and distressed firms to examine blockholders' impact on efficiency and the distribution of value. The model captures key empirical facts on distressed equity issuances, including the provision of substantial discounts to large investors. Blockholders' impact on debt overhang problems is generically non-monotone. Whereas inefficiencies are exacerbated for intermediate levels of distress, they are alleviated in deep distress, when blocks are acquired in last-minute interventions. The paper proposes a novel set of modeling tricks that yield global solutions in environments with optimal default and learning, while only requiring the inversion of sparse matrices.
We present a novel modeling approach for granular general equilibrium economies with persistent heterogeneity that yields exact global solutions. A key feature of our approach is the use of stochastic lumpy adjustment (SLA) technologies. The associated stochastic structure can capture any degree of granularity in adjustments of asset positions, and is thus more flexible than standard technologies. We show how SLA technologies can be employed in the context of both capital investment and the trading of financial assets. As our approach does not impose any restrictions on the shape of the state variable distribution, it can also be used to evaluate the conditions under which previous solution methods are likely to succeed. Obtaining exact solutions in these granular economies primarily involves inverting sparse matrices, a computational operation that can take full advantage of recent advances in high-performance parallel computing architectures.
We analyze the effectiveness of preventive investments aimed at increasing agents' life expectancy, with a focus on influenza and COVID-19 mitigation. Maximizing overall life expectancy requires allocating resources across hazards so as to equalize investments' marginal effectiveness. Based on estimates for the marginal effectiveness of influenza vaccines, we determine the level of COVID-19 mitigation investments that would imply such equalization. Given current projections for COVID-19 mitigation costs, our results suggest that wide-spread influenza vaccination would be an effective life-preserving investment.
This paper studies intertemporal information acquisition by agents that are rational Bayesian learners and that dynamically optimize over consumption, investment in capital, and investment in information. The model predicts that investors acquire more information in times when future capital productivity is expected to be high, the cost of capital is low, new technologies are expected to have a persistent impact on productivity, and the scalability of investments is expected to be high. My results shed light on the economic mechanisms behind various dynamic aspects of information production by the financial sector, such as the sources of variation in returns on information acquisition for investment banks or private equity funds.
Episodes of boom-bust cycles tend to occur in sectors with recent arrivals of new technologies and are often related to excessive funding by the financial sector. In this paper, I develop a dynamic general equilibrium model consistent with a role for the financial sector in propagation during such episodes. I extend a standard Schumpeterian growth model by incorporating (a) a monopolistically competitive financial sector and (b) time-varying technological conditions in real sectors. I identify two propagation channels. The first operates through financial firms' acquisition of sector-specific knowledge (skill channel); financial firms chase "hot sectors" and thereby amplify fluctuations. The second channel originates in an interaction between competition in the financial sector and patent races in product markets (competition channel). Financial firms' temporary competitive advantages in access to new ventures imply market segmentation: financial firms maximize the surplus generated by the client firms they can currently attract, anticipating competing financial firms' future screening and funding decisions. Relative to the Pareto optimum, the competition channel generates overinvestment in sectors with temporarily improved technological conditions; excessively high growth in these sectors comes at the cost of lower growth in the economy as a whole. The model links financial propagation to time variation in the cross section of asset prices. Exposures to aggregate risk dampen amplification effects.