This paper uses regression analysis on a national data set from the United States from 2001-2016 to analyze racial or ethnic group disparities in home ownership between whites and blacks, Asian and Pacific Islanders, Puerto Ricans, Cubans, Mexicans, other Hispanics and American Indians. I employ Integrated Public Use Microdata combined with Bureau of Labor Statistics data and Federal Reserve Economic Data from the Federal Reserve Bank of St. Louis. Controlling for demographic, educational, income and wealth, employment and housing characteristics, I find no significant differences between whites and Asian and Pacific Islanders, Mexicans and American Indians. However, blacks, Puerto Ricans, Cubans and other Hispanics face racial disadvantages in regard to home ownership. All minority racial or ethnic groups, except American Indians, lost home ownership parity to whites from 2007-2011, the years primarily affected by the economic crisis.

In the past two decades the Latin American region has experienced a number of credit crises stemming from large sovereign debt levels and sharp currency devaluations. This study aims to discover whether or not the sovereign credit default swaps (CDS) in the Latin American region lead equity markets prior to these sovereign credit events. Through a sample of the seven largest Latin American economies and daily return data from 2001 to 2018, I try to empirically test this question through a Generalized Least Squared model. The paper finds little significant evidence of CDS leading equity markets in price discovery prior to sovereign credit events. Additionally, the paper observes a potential momentum effect present amongst Latin American equity market returns. However, this effect is more likely serial correlation amongst equity market returns due to the illiquidity of these equity markets.

Many past literatures have examined the predictive power of implied volatility versus that of historical volatility, but they have showed divergent conclusions. One of the major differences among these studies is the methods that they used to obtain implied volatility. The VIX index, introduced in 1993, provides a model-free and directly observable source of implied volatility data. The VIX futures is an actively traded VIX derivative product, and its prices are believed to contain market’s expectation about future volatility. By analyzing the relationship between the VIX futures prices and the realized volatilities of the 30-day period that these VIX futures contracts cover, this paper finds that the VIX futures contracts with shorter maturities have predictive power on future realized volatility, but they are upwardly biased estimates. The predictive power, however, decreases as the time to maturity increases. The outstanding VIX futures contracts with the nearest expiration dates outperform GARCH estimates based on historical return data at predicting future realized volatility.

This paper investigates the reliability of the stock market’s initial reaction to M&A announcements as a predictor of actual post-acquisition performance. The two prevailing methods for evaluating M&A performance are event studies (stock market-based measures) and accounting-based measures. The present study combines these two performance evaluation approaches in a single empirical examination. Both the post-merger buy-and-hold abnormal returns and changes in ROA are used as actual post-acquisition performance variables. The acquirer’s cumulative abnormal return (CAR) around the announcement is used as the market predictor variable. An econometric model is employed to test the predictive power of the announcement-period CAR on the actual performance variables using a sample of 3,208 acquisitions by U.S. public companies from 2010 to 2014. This paper’s main contribution lies both in its methodology and its findings: on the one hand, long-term market and accounting variables are used as dependent variables measuring post-acquisition performance. On the other hand, this paper finds that short-term CAR is not a good predictor of subsequent M&A performance. The results suggest that the acquirer’s prior M&A experience is a positive predictor of post-acquisition performance.

Over the past decade, numerous engineered credit events and cases of market participants manipulating bond prices to influence Credit Default Swap (CDS) auction payouts have occurred. These cases have become increasingly common, and the CFTC has stated they may constitute market manipulation and undermine not only the CDS market but also the credit derivative and default markets. Although there is a plethora of news and media coverage on publicized cases, there is no previous empirical research on evidence of these practices. This paper is motivated by the desire to determine if there is indirect evidence of bond price manipulation around default and of market participants’ attempts to favorably move CDS’s underlying bond prices to achieve more profitable positions around default and emerging from CDS auctions. The analysis is performed by analyzing the effect of a bonds’ inclusion in CDS auctions on bond return volatility around the time of default while controlling for credit risk, illiquidity, firm fundamentals, and other bond-level controls. I find that bond return volatility around default is much higher as a result of a bond’s inclusion in a CDS auction, which serves as indirect evidence of bond price manipulation around default as market participants strive for more profitable CDS auction outcomes and possibly of manufactured credit events. Consistent with previous literature, I also find that bond illiquidity significantly impacts bond return volatility. My results are robust to propensity score matching, implementing double-robust estimators, and controlling for any time-varying cross-sectionally-invariant fluctuations in bond return volatility.

This study analyzes the impact of merger and acquisition financing method on buyer cumulative abnormal returns. The model builds on findings in previous literature by including deal structure variables, company variables, industry variables, time variables, and post-acquisition announcement return data from 2000 to 2018. The analysis does not find a statistically significant relationship between cash plus debt/stock financing and cumulative abnormal returns. However, significant coefficients for buyer and target industry suggest that deal structure varies and ultimately effects cumulative abnormal returns within specific industries. Additionally, significant results for buyer profitability and time variables provide insight on how the financial market interprets synergy realization and economic crises in relation to security valuation and the mergers and acquisitions market.

Historically, merger and acquisition (or M&A) activity has occurred in cyclical patterns, forming what are known as “merger waves.” To date, there have been a total of seven waves. Though it is widely acknowledged that merger waves exist, there is no consensus on what drives these waves. Through both qualitative and quantitative analysis, this paper aims to determine the causes of merger waves and looks at those causes through two different lenses: the neoclassical view, which states that economic shocks cause merger waves, and the behavioral view, which states that increases in merger activity are due to managerial behavior and decisions. By analyzing the economic, political, and technological landscapes as well as valuation and interest rate data during periods of intense merger activity, I conclude that neoclassical theories are stronger in explaining the first three waves, whereas behavioral theories are stronger in explaining the last three waves.

In the first essay, I create a hedge fund informed trading measure (ITM) that separates information related trades from liquidity driven trades. The results indicate that ITM predicts future stock returns at the trade level, thus is associated with information. By aggregating the most informed trades at the stock level, I find that stocks heavily purchased by informed hedge funds earn a significant alpha. The results indicate that the ITM performs better than some previously documented measures and is robust to two different versions of the measure. The second essay exploits the expiring nature of hedge fund lockups to create a new, within-fund proxy of funding liquidity risk. When funds have lower funding liquidity risk, risk-adjusted performance improves and exposure to tail risk increases. We use fund fixed-effect, a placebo approach, and a regression discontinuity design to establish a link between funding liquidity risk and the ability of funds to capitalize on risky mispricing. The third essay explores hedge fund managers ability to identify and trade on stock mispricing opportunity. We refer to the amount of capital that are is locked up and refrained from redemption as the stable capital, and study how it affects stock mispricing. We find that when funds have more lockup capital, they are more likely to take mispricing risks. Taking all funds together, more stable capital in the industry is driving the reduction or even correction of market-wide stock mispricing. Underpriced stocks benefit more than overpriced stock from hedge funds stable capital.

In the first chapter, I find counties more likely to be affected by climate change pay more in underwriting fees and initial yields to issue long-term municipal bonds compared to counties unlikely to be affected by climate change. This difference disappears when comparing short-term municipal bonds, implying the market prices climate change risks for long-term securities only. Higher issuance costs for climate risk counties are driven by bonds with lower credit ratings. Investor attention is a driving factor, as the difference in issuance costs on bonds issued by climate and non-climate affected counties increases after the release of the 2006 Stern Review on climate change. In the second chapter, I document the investment value of alternative data and examine how market participants react to the data's dissemination. Using satellite images of parking lots of US retailers, I find a long-short trading strategy based on growth in car count earns an alpha of 1.6% per month. I then show that, after the release of satellite data, hedge fund trades are more sensitive to growth in car count and are more profitable in affected stocks. Conversely, individual investor demand becomes less sensitive to growth in car count and less profitable in affected stocks. Further, the increase in information asymmetry between investors due to the availability of alternative data leads to a decrease in the liquidity of affected firms.

This dissertation contains two essays. The first essay investigates the measure of FX liquidity and determinants of the change in FX liquidity. Using 20 cross currency exchange rates over spanning period of 1999 to 2016, funding constraints and global risks are responsible for the main drivers of changing in FX liquidity. The magnitudes of both G7 and emerging volatility index are offsetting each other in all the regression models indicating that FX investors take diversification trading strategies to diversify their portfolios. The financial crisis provides an evidence that the more financial constraint issues contribute to the change in FX market illiquidity more than non-financial crisis period. Extending to liquidity predictability, I find, however, that the lag of market FX liquidity is responsible for the change in FX liquidity than any other explanatory variables My second essay investigates the momentum returns of U.S. equities by presenting comprehensive approaches. Traditionally, momentum portfolios are constructed by ranking based on excess returns. Using this sorting technique, I confirm that there is a presence of momentum returns in U.S. equities for all of the 48 industries. The results also indicate that the portfolios that are sorted by idiosyncratic volatility as well as by diversification strategy cannot achieve the highest returns as for sorting based on excess returns. Further, I examine the momentum portfolio predictability using the inverse conditional volatility proposed by Moreira and Muir (2017), and show that the momentum returns are affected by the size of liquidity and the risk factors rather than by the economic variables.