Market Power in the Securities Lending Market
November 2023, with S. Chen and R. Kaniel [SSRN]
Annual Conference in Financial Economics at IDC-Herzliya Best Paper Award
NFA Best Paper Award in Corporate Finance and Financial Intermediation
We document the presence of market power in the equity securities lending market and evaluate its impact on different investor groups and valuations. Our analysis reveals high market concentration, non-competitive fees, and low inventory utilization in the cross-section of stocks. Motivated by this evidence, we develop and estimate a dynamic asymmetric-information model that sheds light on the benefits of this current market structure for both security lenders and short sellers. We find that lending fee income raises shares lenders’ equity valuations by 1.5% for large-cap, low-fee stocks, by up to 25% for small-cap stocks, and by even more than 100% for nano-cap stocks. Our model yields estimates of the distribution of alphas from shorting different segments of the cross-section of stocks, indicating that fees reduce short sellers’ profits by about 60%.
Countries committed to reducing global warming must contend with the reality that emissions are largely caused by activities in foreign countries that fall beyond direct domestic regulatory control. While governments may attempt to use their regulatory authority pertaining to domestic firms to tax the emissions associated with the production of international inputs, such approaches often have to rely on voluntary disclosures by foreign trade partners. Recognizing these challenges, we study a government's optimal regulatory policy when facing domestic firms that source inputs from international supply chain partners who cannot be forced to provide accurate emissions disclosures, but rather choose them voluntarily. We show that contrary to the prescriptions of the Pigouvian tax principle underlying carbon credit markets, taxes should be a convex function of disclosed emissions within a supply chain relation. Our analysis characterizes how domestic households' exposure to the damages associated with global emissions shape both a government's optimal regulatory interventions and the granularity of foreign firms' voluntary disclosures.
We propose a tractable framework to examine the role of intermediary capital in the allocation and pricing of credit. In our model, regulated financial intermediaries compete with unregulated investors, targeting distributions of heterogeneous borrowers. We derive a sufficient statistic that characterizes intermediaries’ cross-sectional lending decisions and provide a novel intermediary asset pricing equation that accounts for the endogenous segmentation of marginal investors across securities. These formulae reveal the central role of intermediaries’ shadow cost of capital in both credit allocation and pricing. Our results can concurrently rationalize a broad array of empirical facts documented in the context of credit markets.
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.