Is There Too Much Benchmarking in Asset Management? (2023) with Anil Kashyap, Natalia Kovrijnykh, and Anna Pavlova
American Economic Review
Abstract: The use of benchmarks for performance evaluation is commonplace in asset management, and yet, surprisingly, such contracts have not received much attention in the literature. This paper builds a model of delegated asset management in which benchmarking arises endogenously and analyzes the unintended consequences of benchmarking. The fund managers' portfolios are unobservable and so is the asset management cost. We show that conditioning managers' compensation on performance of a benchmark portfolio partially protects them from market risk and encourages them to generate more alpha. In general equilibrium, however, the use of such incentive contracts creates a pecuniary externality. Benchmarking inflates asset prices and gives rise to crowded trades, thereby reducing the effectiveness of incentive contracts for others. We show that privately-optimal contracts chosen by fund investors diverge from socially-optimal ones. A social planner, recognizing the crowding, opts for less benchmarking and less incentive provision. Privately-optimal contracts end up forcing managers to excessively pursue alpha, at too high a cost, and the planner corrects this. The planner's choice of benchmark portfolio weights also differs from the privately-optimal one.
The Benchmark Inclusion Subsidy (2021) with Anil Kashyap, Natalia Kovrijnykh, and Anna Pavlova
Journal of Financial Economics
Abstract: We study the effects of evaluating asset managers against a benchmark on corporate decisions, e.g., investments, M&A, and IPOs. We introduce asset managers into an otherwise standard model and show that firms inside the benchmark are effectively subsidized by the asset managers. This "benchmark inclusion subsidy" arises because asset managers have incentives to hold some of the equity of firms in the benchmark regardless of their risk characteristics. Due to the benchmark inclusion subsidy, a firm inside the benchmark values an investment project more than the one outside. The same wedge arises for valuing M&A, spinoffs, and IPOs. These findings are in contrast to the standard result in corporate finance that the value of an investment is independent of the entity considering it. We show that the higher the cash-flow risk of an investment and the more correlated the existing and new cash flows are, the larger the subsidy; the subsidy is zero for safe projects. We review a host of empirical evidence that is consistent with the model's implications.
Investor Composition and the Liquidity Component in the U.S. Corporate Bond Market (2023) with Haiyue Yu
Abstract: The link between corporate bond credit spreads and secondary market illiquidity in the cross section has grown stronger since 2005, resulting in higher liquidity component in credit spreads. Using U.S. investor holdings data, we show that short-term investors (e.g., mutual funds/ETFs) increase trading activities in the secondary market, amplifying the effect of secondary market frictions on prices. We provide a model featuring heterogeneous investors with different trading needs and heterogeneous bonds to investigate the impact of the rapid growing mutual fund/ETF sector on the corporate bond market. We find the change in investor composition can quantitatively explain the aggregate trend.
Intermediation via Credit Chains (2023) with Zhiguo He
Abstract: The modern financial system features complicated financial intermediation chains, with each layer performing a certain degree of credit/maturity transformation. We develop a dynamic model in which an entrepreneur borrows from overlapping-generation households via layers of funds, forming a credit chain. Each intermediary fund in the chain faces rollover risks from its lenders, and the optimal debt contracts among layers are time invariant and layer independent. The model delivers new insights regarding the benefits of intermediation via layers: the chain structure insulates interim negative fundamental shocks and protects the underlying cash flows from being discounted heavily during bad times, resulting in a greater borrowing capacity. We show that the equilibrium chain length minimizes the run risk for any given contract and find that restricting credit chain length can improve total welfare once the available funding from households has been endogenized.
Corporate Bond Multipliers: Substitutes Matter (2023) with Manav Chaudhary and Zhiyu Fu
Abstract: Textbook theory tells us that the price impact of demand shocks depends on the ability of investors to identify close substitutes and trade against the mispricing. Corporate bonds' salient characteristics, such as credit rating and maturity, make identifying such substitutes particularly easy. Yet existing estimates of corporate bond multipliers (the price increase in response to demand shocks) typically assume all bonds, regardless of their characteristics, are equally good substitutes. In this paper, we introduce rich heterogeneous substitution patterns among bonds and demonstrate that security-level multipliers are an order of magnitude smaller than previously estimated and are essentially zero. Nonetheless, aggregated portfolios exhibit substantially larger multipliers, reflecting the reduced availability of near substitutes for more aggregated portfolios. The price impact of demand shocks reverts after a quarter. Finally, we find that the multiplier is larger for high-yield bonds, longer-maturity bonds, and bonds with greater arbitrage risks.
The Value of Data to Fixed Income Investors (2023) with Jennie Bai and Asaf Manela
Abstract: Using a structural model, we estimate the value of data to fixed income investors and study its main drivers. In the model, data is more valuable for bonds that are volatile and for which price-insensitive liquidity trades are more likely. Empirically, we find that the value of data on corporate bonds increases with yield, time-to-maturity, size, callability, liquidity, and uncertainty during normal times. However, these cross-sectional differences vanish as the value of data falls during financial crises. Using a regression discontinuity based on maturity, we provide causal evidence that investor composition affects the value of data.
Borrowing from a Bigtech Platform (2022) with Stefano Pegoraro
Abstract: We model competition in the credit market between banks and a bigtech platform which offers a marketplace for merchants. We show that, unlike banks, the platform lends to merchants based on their revenues and network externalities. To enforce partial loan repayment, the platform increases borrowers' transaction fees. Credit markets become partially segmented, with the platform targeting borrowers of low and medium credit quality. The platform benefits from advantageous selection at the expense of banks, reducing equilibrium welfare for intermediate-credit-quality merchants. When revenues, network externalities, or advantageous-selection rents are large, the platform does not value superior information about credit quality.
The Pricing and Welfare Implications of Non-Anonymous Trading (2020) with Ehsan Azarmsa
Abstract: A key distinction between over-the-counter markets and centralized exchanges is the non-anonymity of the transactions. In this paper, we develop a model of non-anonymous trading and compare its prices, liquidity, and efficiency of asset allocations against a baseline with anonymous transactions. The non-anonymity improves the market liquidity by reducing the concerns for adverse selection. More specifically, it allows the market participants to learn valuable information about their counter-parties through repeated interactions and consequently enables them to form trading relationships. However, it could harm the market liquidity by increasing the dealers' bargaining power, as the dealers learn more about their clients' liquidity needs. Our theory predicts that the bid-ask spread is smaller in non-anonymous markets, and more so for bonds with low credit-ratings, and at times of high uncertainty. The non-anonymity improves the allocative efficiency for assets with high volatility, with higher degree of asymmetric information, and with less interest among liquidity traders. Using a novel dataset of U.S. corporate bond trades, we find confirming evidence that for high-yield bonds, the bid-ask spread for non-anonymous orders is 20% smaller than that for anonymous orders, while no such price improvement is observed for investment-grade bonds. By examining the waiting times and execution probabilities in our dataset, we present evidence that differentiates our channel from search-based theories.