Job Market Paper

The Importance of Investor Heterogeneity: An Examination of the Corporate Bond Market (2020) with Haiyue Yu

(Updated regularly)

Abstract: Corporate bond market participants are increasingly worried about liquidity. However, bid-ask spreads and other standard measures indicate that liquidity has not deteriorated significantly. This paper proposes a potential reconciliation. We show that credit yields have become four times more sensitive to bid-ask spreads. We then provide a model that connects this change to the rapid growth of mutual funds in the corporate bond market. The model features heterogeneous investors with different trading needs who choose between a risk free asset and illiquid bonds. As the risk free rate declines, more short term investors reach for yield and enter the bond market. These short term investors provide liquidity---reducing the selling pressure in each sub-market and so the bid-ask spreads. However, their greater trading needs amplify the sensitivity of credit yields to the bid-ask spreads, leading to a larger liquidity component. We next test the model's predictions using detailed data on investor holdings in the U.S. As predicted, we find that investor turnover amplifies the effect of illiquidity on credit yields. Bonds with more short term investors are traded at lower bid-ask spreads and their credit yields are more sensitive to the bid-ask spreads. Finally, we look across countries and show that, consistent with the model, larger declines in risk free rates are associated with higher growth in mutual fund shares. These results highlight the key role that investor heterogeneity plays in determining corporate bond yields and ultimately firms' financing conditions.

Working Papers

The Benchmark Inclusion Subsidy (2019) with Anil Kashyap, Natalia Kovrijnykh, and Anna Pavlova

Accepted at the 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.

Is There Too Much Benchmarking in Asset Management? (2019) with Anil Kashyap, Natalia Kovrijnykh, and Anna Pavlova

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 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.

Work in Progress

Financial Intermediation Chains and Stepwise Maturity Transformation (2020)

Abstract: Different from the simple banking system in the past, the modern financial system often has complicated chains of financial intermediation that perform maturity/liquidity and credit transformation step by step. I study whether dividing the process into layers makes the system more or less stable under different circumstances. In a dynamic bank-run model, the benefit of stepwise transformation is to reduce the liquidation loss at a particular level, this reduces the strategic complementarity among creditors at each level; however, it generates more frictions between layers. Moreover, I show that the decentralised equilibrium features too long financing chains: as each agent is optimally shortening his/her own maturity mismatch, this causes long chain in equilibrium and amplifies aggregate funding risk. While the current proposed liquidity index only considers the aggregate liquidity mismatch, the model shows that the distribution of liquidity mismatch within the financial sector is also important.

Platform Competition and Multi-tier Pricing Schedule (2020)

Abstract: Most security exchanges adopt some form of multi-tier pricing schedule regarding their transaction fees, i.e. higher transaction volume leads to lower per-unit transaction fee. I study the optimal design of volume-dependent fees when platforms face competition for order flows and evaluate its welfare implication on heterogeneous traders. When traders are strategic and are concerned about their price impact, as in Vayanos (1999), a quadratic subsidy (volume dependent rebate) can restore the first best. However, when two exchanges are competing for traders, I show that (1) when traders are homogeneous ex-ante, competition does result in the social optimal rebates. (2) But when traders are heterogeneous, i.e. some have high expected trading volume and some have low expected trading volume, the equilibrium features too high volume dependent rebates relative to what a social planner would set. High-volume traders benefit from this too-high rebates whereas low-volume traders suffer losses. Rebates are higher when the platforms are less differentiated along other dimensions or when it is easier for traders to participate in multiple platforms.