Browsing by Author "Viswanathan, S"

This thesis examines how various agency frictions affect corporate financing, capital budgeting, managerial compensation and investment in dynamic settings. In the internal capital budgeting process, the agency issues considered are (i) the division manager privately observes project arrival and quality, and (ii) he can divert allocated capital. The optimal capital budgeting and compensation policies are jointly designed to mitigate agency costs that are endogenously determined. When the division's financial slack is low, positive NPV investments are possibly forgone and manager's pay-performance sensitivity is kept small. When the division's financial slack is high, projects are funded more efficiently and steeper incentives are provided. In the process of external financing, the key friction considered is that the agent has persistent private information about firm performance. In the optimal contract, the firm is financed by outside equity and a credit line contingent on compliance with a cash flow covenant. The agent is compensated via a combination of equity and stock options. As the level of persistence increases, the agent holds less equity and more stock options; the investors hold more equity. Investment is possibly efficient in the constrained firm and is varying with cash flow in the unconstrained firm.

Data collection and analytics are the core of firms' development in digital economies and have an enormous impact on consumer welfare. We build a monopolistic competition model with heterogeneous firms to incorporate both data collection and analytics investment. The model studies how the complementary effect between data collection and analytics affects firms' pricing, profit and consumer welfare. Data is divided into two categories: raw data and effective data. Raw data is a byproduct of production and does not benefit firms on its own. Effective data is a signal on consumers' taste and must be produced with both analytics and raw data. We then find analytics can not only reduce firms' uncertainty but also lower user cost of capital and markup. Lower cost of data analytics can increase consumers' welfare by increasing competition. We allow firms to differ in the size of complementary effect. The model shows that cheaper analytics has asymmetric effects on heterogeneous firms' product quality and profit. Firms with strong complementary effects produce higher quality goods, charge lower price-per-utile and benefit from the cheaper analytics. The opposite is true for firms with weak complementary effects.

In the second paper, We build a model to incorporate the buy-now-pay-later (BNPL) platform and study its welfare implication. BNPL platforms lend money to consumers, provide private data to partner firms and charge fee from in-platform merchants. Data can lower production cost. Two types of data are available: public data and private data. Data size of both types increases in the number of firms. Private data is only available for in-platform merchants. We find BNPL platforms can hurt non-platform users. The reason is that the platform fee can decrease the number of firms in the market and reduce public data, which increases out-of-platform firms' product prices. We then study a duopoly model with two platforms competing with each other. The model predicts that competition between platforms benefits non-platform users but can hurt platform users. The intuition is that competition splits the in-platform merchants and reduces private data for both platforms.

My dissertation consists of two parts. The first part examines standard microstructure topics from new perspectives. Chapter 2 investigates the market implications of high-frequency trading focusing on an ignored channel that existing market makers might reduce the capital exploited in market making facing the competition from high-frequency traders. I find that more high-frequency trading might not improve market quality exactly because of this channel. My model also generates several policy suggestions in improving the market quality with the presence of high-frequency trading. Chapter 3 studies the large traders’ execution problem to understand whether electronic trading brings execution advantage. I find that although an increase in trading frequency improves their executions, losing the ability to adopt reputation-based trading strategies due to the prevalence of anonymous trading in this electronic trading era can be so costly that large traders become worse-off in execution. Chapter 4 considers an insider trading problem with uncertainty over the insider’s existence. The competitive market making assumption is not consistent with this new element because the insider can achieve an unbounded payoff by taking advantage of the competition. Instead, I construct and analyze the equilibrium under the assumption of monopolistic market making. The model can explain the heterogeneous changes in liquidity of different stocks after a regulation.

The second part of my dissertation study two elements outside the scope of current microstructure literature but (in my opinion) are important and should be incorporated to achieve a comprehensive understanding of financial markets. Chapter 5 studies an attention allocation problem under the context of contract and information design. The principal needs to jointly design compensation for the agent and his attention allocation in acquiring signals to motivate the agent to exert the unobservable effort. The flexibility to allocate attention can lead to perpetual cooperation between the principal and the agent. Chapter 6 studies belief updating when the state space contains elements that we are not aware of with an axiomatic approach. The agent can learn from what he is currently aware of and updates his belief when awareness is related to the state realization. In this case, besides leading to Bayesian updating, the arrival of a new signal can also expand the agent’s awareness and trigger further updating. Its implication is discussed under the context of persuasion.

I study firms' debt maturity decisions. I provide two models for optimal debt maturity choices when facing stochastic productivity and rollover risk. The first model is based on firms' need to smooth their capital when facing uncertainties in external financing. When the capital market freezes, new external financing is difficult. Firms with large debt repayments due have to forego good investment opportunities and in severe cases cut back on dividends. Long-term debt reduces immediate repayments and allows firms to keep the borrowed capital for future production. Therefore, when freezes are likely, firms respond by using more long-term financing and are better prepared. However, when the probability of freezes is low, firms turn to short-term financing. When a freeze suddenly occurs, the impact is significant and costly. The model predicts that constrained firms use more short-term debt. Based on the model, I propose investment-debt sensitivity as a new measure for financial constraints.

The second model depicts an economy in which entrepreneurs reallocate capital resources through borrowing and lending in either short-term or long-term debt. In expansions, productivity is more persistent and uncertainty in productivity is low, so entrepreneurs can better predict their future prospects. Hence, they choose to use more long-term debt to finance their productions. In recessions, future prospects are less clear to the entrepreneurs; therefore, they choose to use more short-term debt. The model explains the documented facts on pro-cyclical debt maturity in the economy. It also highlights that the shortening debt maturity structure causes capital resources to be less efficiently allocated in recessions further exacerbates the bad times. I argue that the change in the predictability of TFP drives pro-cyclical debt maturity, and that the maturity structure further amplifies the fluctuations in aggregate production.

This dissertation comprises two chapters studying how information transmitted in the economy and the financial markets becomes compressed in communications and its consequences. In chapter one, the compression is due to limited communication bandwidth, and in chapter two, it is due to limited attention on the receiver’s end.

Chapter one discusses how information intermediaries selectively present evidence to serve financial decision-makers. Faced with a space limit for their communication reports, the information intermediaries present information selectively. I solve the model for the optimal messages in the intermediaries' communication with the decision-makers and investigate the relationship between the apparent messages and the inferred economic fundamentals. The two main findings are: (i) the model matches many stylized facts about content biases such as prior or extreme biases; (ii) I derive an analytical relationship between the messages and the inferred fundamentals in the asymptotics. This relationship can be conveniently used to interpret observed content biases and quantitatively analyze the effects of the context on the interpretation of contents. The theory also shows that content biases may improve rather than decrease welfare. The model relates to empirical content analysis using frequency-based proxies and can be used to analyze contextual effects on contents.

In chapter two, I develop and analyze a theoretical model that shows how investors allocate their limited attention resources to monitor a wide selection of target firms. An investor with limited attention demands information about the types of her portfolio firms before investing. The firms strategically supply good news and withhold bad news. The investor may press companies to reveal more information by allocating more costly attention to them. Because the benefit to attention is convex, the investor will optimally focus on a subset of firms and acquire complete information while giving up learning anything about the other firms. Firms in the scrutinized subset have low investigation costs and a high Expected Value of Perfect Information (EVPI), and they always receive an efficient amount of capital. The other firms are provided with an inefficient level of capital and suffer from extreme asymmetry in information transparency. The result rationalizes convertible debt as a socially optimal financing instrument for private firms. It can be applied to a venture capital context to analyze entrepreneurial investment relationships.

This paper analyzes a manager's optimal ex-ante reporting system using a Bayesian persuasion approach (Kamenica and Gentzkow (2011)) in a setting where investors affect cash flows through their decision to finance the firm's investment opportunities, possibly assisted by the costly acquisition of additional information (inspection). I examine how the informativeness and the bias of the optimal system are determined by investors' inspection cost, the degree of incentive alignment between the manager and the investor, and the prior belief that the project is profitable. I find that a mis-aligned manager's system is informative

only when the market prior is pessimistic and is always positively biased; this bias decreases as investors' inspection cost decreases. In contrast, a well-aligned manager's system is fully revealing when investors' inspection cost is high, and is counter-cyclical to the market belief when the inspection cost is low: It is positively (negatively) biased when the market belief is pessimistic (optimistic). Furthermore, I explore the extent to which the results generalize to a case with managerial manipulation and discuss the implications for investment efficiency. Overall, the analysis describes the complex interactions among determinants of firm disclosures and governance, and offers explanations for the mixed empirical results in this area.

Bank stress test has become a centerpiece of post crisis bank supervision. Current studies have thus far examined the optimal policies on stand-alone single banks, but financial systems are interconnected in practice, and disclosure about banks influences the counterparty risks of other banks. This dissertation studies the optimal stress test design in a financial network, where banks' endogenous default outcomes are determined by a fixed point payment problem that accounts for both project qualities and interbank contagion.

The first part examines the joint stress test design on all banks in the financial network. In addition to the cross-state risk sharing in models of single banks, this model highlights the novel cross-bank risk sharing that arises from the spillover effects of disclosures via interbank payments. When expected bank profitability is high or counterparty exposures are large, disclosure is non-discriminatory, and either all banks pass or all banks fail the stress tests; otherwise only less impaired banks may pass. For network structures, I find: (i) in a ring network, banks at least a specific distance away from the nearest bank with asset impairment may pass; (ii) a more connected network is not necessarily more stable under the optimal disclosure; (iii) typically more connected banks receive preferred treatment.

The second part studies a selective stress test in a financial network, where the regulator selects an optimal subset of banks for stress tests and accordingly design the optimal disclosures only on these banks. Compared with the first part, systemic risk becomes more important as the regulator is less able to fine tune beliefs about contagion and needs to contain the risks from unselected banks. For network structures, I find: (i) in a ring network, stress test is either "balanced'' on banks positioned evenly and disclosure is non-discriminatory, or on "connected'' banks and disclosure is truth-telling on potential shocks; (ii) in a star network, stress test is conducted on the center bank when counterparty exposure is either sufficiently small or sufficiently large.

In my first chapter, I present a model in which sellers can signal the quality of an asset both by retaining a fraction of the asset and by choosing the liquidity of the market in which they search for buyers. Although these signals may seem interchangeable, I present two settings which show they are not. In the first setting, sellers have private information regarding only asset quality, and I show that liquidity dominates retention as a signal in equilibrium. In the second setting, both asset quality and seller impatience are privately known, and I show that both retention and liquidity operate simultaneously to fully separate the two dimensions of private information. Contrary to received theory, the fully separating equilibrium of the second setting may contain regions where market liquidity is increasing in asset quality. Finally, I show that if sellers design an asset-backed security before receiving private information regarding its quality, then the optimality of standard debt is robust to the paper's various settings.

In my second chapter, I explore the question of how informative bank stress tests should be. I use Bayesian persuasion to formalize stress tests and show that regulators can reduce the likelihood of a bank run by performing tests which are only partially informative. Optimal stress tests give just enough failing grades to keep passing grades credible enough to avoid runs. The worse the state of the banking system, the more stringent stress tests must be to prevent runs. I find that optimal stress tests, by reducing the probability of runs, reduce the optimal level of banks' capital cushions. I also examine the impact of anticipated stress tests on banks' ex ante incentive to invest in risky versus safe assets.