Finance
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Item Essays on Mutual Fund Performance Evaluation and Investors' Capital Allocations(2022) Cao, Bingkuan; Wermers, Russ; Business and Management: Finance; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The dissertation contains two chapters that studies the performance of mutual funds and investors' capital allocations. In the first chapter, I study mutual funds' portfolio management and investors' capital allocations in a unified framework under mandatory portfolio disclosure. By modeling fund managers and investors simultaneously, I show that more skill managers produce better performance by trading more actively, which causes investors to care about both fund performance and activeness when evaluating fund managers. This investor's behavior explains the convex flow-performance relation observed in the market. In addition, my model demonstrates that portfolio holdings information is more useful to investors than fund returns because portfolio holdings reveal manager activeness that is not fully captured by fund returns. My model offers three novel empirical predictions for which I find consistent evidence in the data. First, investor flows respond to both fund performance and activeness. Second, investor flows are more sensitive to the performance of illiquid holdings in the portfolio. Finally, in a diff-in-diff analysis, I show that investor flows become more sensitive to fund activeness when portfolios are disclosed more frequently. In the second chapter, I study the performance attribution of bond mutual funds. I build a comprehensive sample of U.S. actively managed bond mutual funds with a large cross section and long time series, and examine the characteristics of funds that are most associated with superior active bond fund performance. I construct several sets of covariates to measure different aspects of managerial ability, including risk management, credit analysis, activeness, beta timing, liquidity provision, and family synergy. Given the large set of covariates, I employ machine learning methods such as Boosted Regression Trees to select the best predictors of bond fund performance. Unlike equity funds, I find that risk management plays an important role in generating superior performance. In addition, funds that are better at credit analysis and charge lower fees outperform their peers.Item Triptych in Empirical Finance(2022) Delalay, Sylvain; Maksimovic, Vojislav; Santosh, Shrihari; Business and Management: Finance; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation contains three chapters that explore topics in empirical finance and political economy. In Chapter 1, I study how the fundraising revenues of political campaigns affect the outcome of U.S. elections. First, I assemble a novel and granular dataset that provides a comprehensive picture of cash flows and voting intentions during U.S. congressional races. Then, I extract weekly shocks to the fundraising revenues of campaigns by using machine learning on the dataset. I find that the effect of revenues on the vote share decreases over the course of general elections. In races involving an incumbent, an additional $100,000 in challenger revenues increases her vote share by 1.48pp in the first half of the general election, but has no effect in the second half. Early cash infusions are more valuable than late cash infusions because they provide flexibility to respond to the opponent’s actions and mitigate current and future financing constraints. In Chapter 2, I examine how strategic and financial considerations shape the spending behavior of political campaign committees. To discipline the empirical analysis, I derive a dynamic model of strategic investment under financing constraints. I test the predictions of the model using the revenue shocks constructed in Chapter 1. I find that a committee’s elasticity of advertising expenditures to the revenue shocks of its opponent is 8%, which is a third of a committee’s elasticity to its own shocks. Moreover, a committee that is relatively richer than its opponent reacts more aggressively to its opponent’s shocks, both in levels and as a fraction of cash reserves. This result suggests that the availability of internal financing can amplify the competitive aspect of political spending in electoral races. In Chapter 3, I identify investor overreaction in a setting where information flows are not observable and learning pertains to multiple dimensions of an asset. Specifically, I measure how investors react to the information released during merger attempts and whether they form rational beliefs about the probability of deal completion. Using a model of distorted learning that generates testable implications, I find evidence of relative mispricing in the cross-section of merger targets. Empirically, a low price-implied probability of success underestimates the actual probability of success, and vice versa, suggesting that investors overreact to deal-specific information. The overreaction is unrelated to the unconditional merger premium and not driven by exposure to traditional risk factors.Item ESSAYS ON EMPIRICAL ASSET PRICING(2020) xue, jinming; Wermers, Russ; Kyle, Albert; Business and Management: Finance; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In essay 1: This paper measures the time-varying provision of liquidity by buy-side customers (e.g., mutual funds and pension funds), relative to bond dealers, in corporate bond markets using a structural vector autoregression (SVAR) model. As indicated by my simple theory model, shocks to the relative willingness of customers and bond dealers to provide liquidity affect, in opposite directions, the choice of bond dealers between market-making (principal) and matchmaking (riskless principal) transactions. Motivated by this model, my SVAR empirically disentangles these shocks to customers versus bond dealers. My SVAR-derived patterns of these structural shocks provide fundamental insights into the mechanics in corporate bond markets following recent events, such as exposing the increased role of buy-side customers for liquidity provision after the many regulatory changes following the 2008 financial crisis. Furthermore, my empirical approach generates “factors” that provide an improved time-series asset-pricing model for yield spreads of corporate bonds of different credit ratings. In essay 2: We consider an approach to derive the conditional expectation of return quantities under the real-world probability measure, exploiting the form of the projected stochastic discount factor. Our treatment is formulaic in that the real-world expectation can be synthesized from the prices of the risk-free bond, the asset, and options on the asset. The method is free of distributional assumptions, and we use it to study empirical questions related to (i) conditional probability of a disaster and return upside and (ii) spanning hypothesis in the Treasury market. We examine empirical consistency and show that our theoretical treatment is relevant. In essay 3: Based on data until the mid 2000s, oil price changes were shown to predict international equity index returns with a negative predictive slope. Extending the sample to 2015, we document that this relationship has been reversed over the last ten years and therefore has not been stable over time. We then posit that oil price changes are still useful for forecasting equity returns once complemented with relevant information about oil supply and global economic activity. Using a structural VAR approach, we decompose oil price changes into oil supply shocks, global demand shocks, and oil-specific demand shocks. The hypothesis that oil supply shocks and oil-specific demand shocks (global demand shocks) predict equity returns with a negative (positive) slope is supported by the empirical evidence over the 1986--2015 period. The results are statistically and economically significant and do not appear to be consistent with time-varying risk premia.