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This dissertation presented three studies applying event study methodology to lodging stock performance. Each of these studies utilized both parametric and nonparametric

techniques in an effort to identify abnormal returns and volume activity of lodging stocks under certain event conditions, namely (a) the announcement of mergers and acquisitions; (b) the announcement of chief executive officer transitions; (c) the announcement of weekly RevPAR data for the prior week by STR, a leading lodging industry consulting firm.

In comparison to other discrete industries, the performance of lodging stocks has not been fully explored. The event studies conducted as part of this dissertation are of benefit to the industry by further highlighting the differentiation between the performance of lodging stocks and the overall stock market, which further identifies the complexity of the lodging industry. These studies are also of benefit to a broad range of practitioners including investors, research analysts, and company executives who seek to better understand lodging stock performance and to profit from capitalizing on abnormal market activity. The specific questions explored were:

1. Is there abnormal stock performance for lodging stocks surrounding specified events that could indicate market inefficiencies that can be exploited by market actors?

2. Are there event study methodologies that are more or less robust for use in lodging stock event studies that should be considered in future research?

Each study stands alone on an individual basis and contributes to the extant literature on lodging stock events. Overall, these studies advance the body of knowledge in the lodging event study literature by discussing and then applying both parametric and nonparametric statistical methods to the events being studied. As stocks within any

individual industry, event studies conducted among lodging stocks may contain challenges related to heteroskedasticity and dependence due to the derived abnormal returns being (a) correlated in event time, (b) having different variances across firms, and (c) not being independent across time for individual firms. These issues of non-normality and cross-

sectional dependence in the data can be addressed utilizing nonparametric statistical methods, which can then confirm the more traditionally used parametric statistical methods.

The first paper, entitled “Parametric and Nonparametric Analysis of Abnormal Stock Return and Volume Activity for Lodging Stock Mergers from 2004 to 2007,” presented a study on the unprecedented number of hotel company mergers that took place between 2004 and 2007. The purpose of this study was to determine, using both parametric and

nonparametric event study methodologies, whether there were abnormal stock returns or volume activity in the periods surrounding the merger announcement in the trading of 19 public hotel companies that were merged during this period. The study identified statistically significant abnormal returns only on the merger announcement date and statistically

significant volume activity only on the announcement date and thereafter, indicating that there was little prior knowledge of these transactions.

In this study, as the abnormal returns were so strong and so strongly evident only on the merger announcement date, there was no material difference between the results of the parametric and nonparametric statistical tests. The results of this study, as compared to prior

studies conducted in this area, highlight changes over time in the concentration of abnormal returns on the event announcement date rather than spread out among surrounding dates.

The second paper, entitled, “Abnormal Stock Return and Volume Activity Surrounding CEO Transition Announcements for Lodging Companies,” presented an investigation into whether or not there were abnormal stock market returns and volume activity for lodging stocks in the periods surrounding the announcement of Chief Executive Officer (CEO) transitions for these companies from 2003 to 2009. The study found that there were statistically significant negative abnormal returns in the periods prior to and after the announcement of a CEO transition. In particular, the persistent and strong negative returns evident even after the announcement date could represent an opportunity for investors to capitalize on this potential market inefficiency.

This study identifies some of the challenges in relying only on parametric statistical techniques that assume normality and cross-sectional independence in the data. While statistical significance was similar for the mean cumulative average abnormal returns for the 30 days prior to the CEO transition announcement date for the parametric and nonparametric tests, the results were much stronger for the parametric tests than for the nonparametric tests for the 10 trading days following the CEO transition announcement date. Researchers relying only on parametric methods could be led to believe that trading opportunities exist when the statistical significance was generated from only a small subset of the sample firms. Statistically significant abnormal volume was identified in the period after the announcement of a CEO transition. This is the first study in the hospitality industry to investigate abnormal stock returns related to senior management transitions.

The third paper, entitled “The Impact of the Announcement of Weekly Lodging RevPAR on Lodging Stock Performance,” presents an investigation on whether or not there were abnormal stock market returns on the announcement date of weekly RevPAR data by the lodging industry research firm STR. The study found that there were not statistically significant abnormal returns on the weekly RevPAR announcement date (typically Wednesdays) for the period from 2004 to 2009. The study also developed a fixed effects regression model for predicting abnormal stock returns using weekly RevPAR, but the model was not found to be statistically significant.

Although the results of this study did not directly identify any potential trading opportunities for investors, it was noted that the day prior to the weekly RevPAR

announcement date exhibited statistical significance in the parametric tests. While these results were not confirmed by the more robust nonparametric tests, there may be potential trading arbitrage opportunities based on these findings.

The first two studies contained in this dissertation are the first studies to identify abnormal volume activity for certain lodging events. Abnormal volume activity can serve to confirm abnormal return activity or identify abnormal trading activity that may not have resulted in abnormal return activity.