Finance Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2771
Browse
2 results
Search Results
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 Essays on Market Microstructure and Asset Pricing(2019) Hu, Bo; Kyle, Albert S; Loewenstein, Mark V; Business and Management: Finance; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation contains three essays that explore various topics in market microstructure and asset pricing. These topics include statistical arbitrage, algorithmic trading, market manipulation, and term-structure modeling. Chapter 1 studies a model of statistical arbitrage trading in an environment with fat-tailed information. I show that if risk-neutral arbitrageurs are uncertain about the variance of fat-tail shocks and if they implement max-min robust optimization, they will choose to ignore a wide range of pricing errors. Although model risk hinders their willingness to trade, arbitrageurs can capture the most profitable opportunities because they follow a linear momentum strategy beyond the inaction zone. This is exactly equivalent to a famous machine-learning algorithm called LASSO. Arbitrageurs can also amass market power due to their conservative trading under this strategy. Their uncoordinated exercise of robust control facilitates tacit collusion, protecting their profits from being competed away even if their number goes to infinity. This work sheds light on how algorithmic trading by arbitrageurs may adversely affect the competitiveness and efficiency of financial markets. Chapter 2 extends the basic model in Chapter 1 by considering an insider who strategically interacts with a group of algorithmic arbitrageurs who follow machine-learning-type trading strategies. When market liquidity is good enough, arbitrageurs may be induced to trade too aggressively, giving the insider a reversal trading opportunity. In this case, the insider may play a pump-and-dump strategy to trick those arbitrageurs. This strategy is very similar to those controversial trading practices (such as momentum ignition and stop-loss hunting) in reality. We show that such strategies can largely distort price informativeness and threaten market stability at the expense of common investors. This study reveals a list of economic conditions under which this type of trade-based manipulations are likely to occur. Policy implications are discussed as well. Chapter 3 provides a simple proof for the long-run pricing kernel decomposition developed by Hansen and Scheinkman (Econometrica, 2009). In a stationary Markovian economy, the long forward rate should be flat so that the pricing kernel can be easily factorized in a multiplicative form of the transitory and permanent components. The permanent (martingale) component plays a key role as it induces the change of probabilities to the long forward measure where the long-maturity discount bond serves as the numeraire. I derive an explicit expression for this martingale component. It reveals a strong restriction on the market prices of risk in a popular approach of interest rates modeling. This approach neglects the permanent martingale component and restricts risk premia in a way undesirable for model calibration. Further analysis demonstrates the advantages of equilibrium modeling of a production economy since it is featured with a path-dependent pricing kernel that has a non-degenerate permanent martingale.