UC Berkeley

The stability of national and, increasingly more often, the global economy relies on well-functioning financial markets. Households' consumption and saving decisions, firms' investment choices, and governments' financing strategies critically depend on the stability of financial markets. These markets, however, are composed of individuals and institutions that may have different objectives, information sets, and beliefs, making them a very complex object that we do not fully comprehend. Motivated by this, my dissertation focuses on understanding how informational asymmetries and belief heterogeneity impact financial markets, and therefore, the macro economy. More specifically, this dissertation explores the sources of informational asymmetries among market participants. How do different financial market structures provide incentives for private information acquisition? Is information acquisition desirable? What types of policies can be implemented to increase liquidity and "discipline" in financial markets? Could business cycles be related to information or belief cycles? I tackle these questions from three separate angles. First, I study how alternative market designs bring forth different levels of private information generation, "market discipline," and liquidity. Second, I investigate how information sets of key market participants are determined. Finally, I focus on how information and belief fluctuations may affect key macroeconomic variables and economic fluctuations.

In Chapter 1, ``Information Acquisition vs. Liquidity in Financial Markets," I propose a parsimonious framework to study markets for asset-backed securities (ABS). These markets play an important role in providing lending capacity to the banking industry by allowing banks to sell the cashflows of their loans and thus recycle capital and reduce the riskiness of their portfolios. In the financial crash of 2008, however, in which certain ABS played a substantial role, we witnessed a collapse in the issuance of all ABS classes. Given the importance of these markets for the real economy, policy makers in the US and Europe have geared their efforts towards reviving them. A good framework to think about these markets is imperative when thinking about financial regulation. The contribution of this chapter is to propose a model that captures the two main problems that have been shown to be present in the practice of securitization. First, the increase in securitization has led to a decline in lending standards, suggesting that liquid markets for ABS reduce incentives to issue good quality loans. Second, securitizers have used private information about loan quality when choosing which loans to securitize, indicating that a problem of asymmetric information is present in ABS markets. A natural question then arises: how should ABS be designed to provide incentives to issue good quality loans and, at the same time, to preserve liquidity and trade in these markets?

To address this question, I propose a framework to study ABS where both incentives and liquidity issues are considered and linked through a loan issuer's information acquisition decision. Loan issuers acquire private information about potential borrowers, use this information to screen loans, and later design and sell securities backed by these loans when in need of funds. While information is beneficial ex-ante when used to screen loans, it becomes detrimental ex-post because it introduces a problem of adverse selection that hinders trade in ABS markets. The model matches key features of these markets, such as the issuance of senior and junior tranches, and it predicts that when gains from trade in ABS markets are `sufficiently' large, information acquisition and loan screening are inefficiently low. There are two channels that drive this inefficiency. First, when gains from trade are large, a loan issuer is tempted ex-post to sell a large portion of its cashflows and thus does not internalize that lower retention implements less information acquisition. Second, the presence of adverse selection in secondary markets creates informational rents for issuers holding low quality loans, reducing the value of loan screening. This suggests that incentives for loan screening not only depend on the portion of loans retained by issuers, but also on how the market prices the issued tranches. Turning to financial regulation, I characterize the optimal mechanism and show that it can be implemented with a simple tax scheme. The obtained results, therefore, contribute to the recent debate on how to regulate markets for ABS.

In Chapter 2, I present joint work with Matthew Botsch, ``Learning by Lending, Do Banks Learn?" where we investigate how banks form their information sets about the quality of their borrowers. There is a vast empirical and theoretical literature that points to the importance of borrower-lender relationships for firms' access to credit. In this chapter, we investigate one particular mechanism through which long-term relationships might improve access to credit. We hypothesize that while lending to a firm, a bank receives signals that allow it to learn and better understand the firm's fundamentals; and that this learning is private; that is, it is information that is not fully reflected in publicly-observable variables. We test this hypothesis using a dataset for 7,618 syndicated loans made between 1987 and 2003. We construct a variable that proxies for firm quality and is unobservable by the bank, so it cannot be priced when the firm enters our sample. We show that the loading on this factor in the pricing equation increases with relationship time, hinting that banks are able to learn about firm quality when they are in an established relationship with the firm. Our finding is robust to controlling for market-wide learning about firm fundamentals. This suggests that a significant portion of bank learning is private and is not shared by all market participants.

The results obtained in this study underpin one of the main assumptions of the model presented in Chapter 1: that banks have a special ability to privately acquire valuable information about potential borrowers. While the model is static, the data suggests that the process of lending and of information acquisition is a dynamic one. Consistent with this, the last chapter of this dissertation studies the macroeconomic implications of dynamic learning by financial intermediaries.

Chapter 3 presents joint work with Vladimir Asriyan titled ``Informed Intermediation over the Cycle." In this paper, we construct a dynamic model of financial intermediation in which changes in the information held by financial intermediaries generate asymmetric credit cycles as the one observed in the data. We model financial intermediaries as ''expert'' agents who have a unique ability to acquire information about firm fundamentals. While the level of ''expertise'' in the economy grows in tandem with information that the ''experts'' possess, the gains from intermediation are hindered by informational asymmetries. We find the optimal financial contracts and show that the economy inherits not only the dynamic nature of information flow, but also the interaction of information with the contractual setting. We introduce a cyclical component to information by supposing that the fundamentals about which experts acquire information are stochastic. While persistence of fundamentals is essential for information to be valuable, their randomness acts as an opposing force and diminishes the value of expert learning. Our setting then features economic fluctuations due to waves of ``confidence'' in the intermediaries' ability to allocate funds profitably.