Abnormal stock returns and volume activity surrounding lodging firms CEO transition announcements

Transcription

1 Tourism Economics, 2016, 22 (1), doi: /te Abnormal stock returns and volume activity surrounding lodging firms CEO transition announcements BARRY A.N. BLOOM Executive Vice President & Chief Operating Officer, Xenia Hotels & Resorts, Inc, 200 S. Orange Avenue, Suite 1200, Orlando, FL 32801, USA. LEONARD A. JACKSON Cecil B. Day School of Hospitality Administration, J. Mack Robinson College of Business, Georgia State University, 35 Broad Street, Suite 214, Atlanta, GA 30303, USA. (Corresponding author.) This research empirically investigates the impact of lodging firms chief executive officer (CEO) transition announcements on stock performance and trading activity. Specifically, the study utilizes both parametric and non-parametric event study measures to investigate abnormal stock returns and volume activity during the periods surrounding the announcement of CEO transitions at lodging firms. The study finds significant negative abnormal returns in the periods before and after the announcement of a CEO transition. This suggests that the market did not perceive CEO transitions at lodging firms as value-enhancing events. Regarding trading volume, the study finds a decline in trading activity thirty days prior to announcement, but high trading activity during the immediate period surrounding announcements (5 days before the announcement date and 5 days after). This suggests investor uncertainty regarding the direction and value of the stocks of sampled firms. To minimize the negative impact on stock price and trading volume, it is important that lodging firms plan CEO transitions carefully and provide sufficient and timely information to stakeholders about new CEOs, their background and the value they will add to the firm. Such actions could assuage the negative impact of CEO transition announcement on firm value. Keywords: CEO transition announcement; lodging stocks; event study The chief executive officer (CEO) is generally the most senior management position in a firm. The individual serving in this role is responsible for achieving the firm s goals and objectives, and in most cases is the only employee who reports directly to the board of directors. In principle, CEOs are charged

2 142 TOURISM ECONOMICS with achieving optimal performance results and ensuring organizational profitability and viability in the long and short terms (Goleman, 2000). Firm performance, as indicated by accounting and market performance measures, is among the most studied areas of finance, with financial performance linked to a variety of factors including customer satisfaction, corporate diversification, market orientation and human resource management effectiveness. Several researchers have also attempted to establish a definitive link between CEO transition and financial performance (Osborn et al, 1981; Furtado and Karan, 1990; Khurana and Nohria, 2000;). Missing from the body of existing research are studies examining how such transitions impact stock performance of lodging firms, which operate in one of the most important segments of the hospitality and tourism industry. CEO transitions or executive transitions are defined as the process and period between a firm s announcement of the departure of its CEO and the three to six months after the appointment of a new executive is announced. For firms and their stakeholders, the impact of these transitions can be positive or negative, as indicated by corporate dysfunctions, suboptimal performance and crises. Hence, the transition from one CEO to another represents a critical event in a firm s history. As such, a smooth transition is essential for maintaining the confidence of investors, business partners, customer and employees. In addition, a smooth transition provides the incoming CEO with a solid platform from which to execute and implement corporate strategies. CEO transitions are typical occurrences in the normal operating life of a business. With the exception of mandatory retirement or death, executive transitions are usually the result of attempts by the board to correct or mitigate suboptimal firm and/or CEO performance and in the process, achieve expected or superior returns for stakeholders. For publicly traded firms, such attempts are typically executed by the firm s board of directors, which is charged with the responsibility of safeguarding and enhancing shareholder wealth through concerted efforts aimed at maintaining optimal match between the firm s needs, goals and objectives and the CEO s actions and behaviours (Fama and Jensen, 1980). CEO tenure is also affected by the stock market, which acts as an external mechanism monitoring managerial performance and thus, directly or indirectly, affecting CEO tenure (Jensen and Ruback, 1983). CEO transitions can be planned or unplanned. In either case, a transition announcement is a significant event for a firm and can affect its subsequent performance and direction. The significance is often reflected in the performance of the firm s stock price as well as its operating income during the transition period. Further, the transition itself can affect the structure of the industry in which the firm operates since strategic actions by one firm can affect all market participants, including the firm and its rivals. Accordingly, the compounded effect will be directly reflected in the firm s stock prices in an efficient market, since such events convey new and relevant information about the direction of the firm (Fama, 1991; Scott, 2012; Jackson, 2014; 2015). The significance of CEO transition for the firm and its stakeholders is due to the fact that as the most senior employee, CEOs are charged with shaping the firm s direction through the implementation of strategies, and mechanisms that directly and indirectly enhance or inhibit performance as well as its ability to grow and access capital. Therefore, the

3 Lodging firms CEO transition announcements loss or gain of senior human capital, through executive transition will affect the success of such activities. Previous research has attempted to delineate the relationship between CEO transition and shareholders reaction to the announcement of such transitions (Coughlan and Schmidt, 1985; Beatty and Zajac, 1987; Lubatkin et al, 1989; Clayton et al, 2005). The focus of such investigations is generally on the impact of such announcements on firms stock performance, with specific emphasis on abnormal returns or the differences between firms actual stock performance and their expected performance following transition announcements. Since abnormal returns is a summary of how actual returns differ from anticipated or predicted returns, they can be either negative or positive. Further, information originating from firms acts as signals about their future direction in regards to performance and profitability. As such, the direction and magnitude of the impact of transition announcement on firms stock performance is generally a function of how the market perceives the transition will impact the firm s future direction and profitability. Studies suggest that equity securities issued by lodging firms, such as stocks, perform differently from similar securities associated with other industry sectors (Quan et al, 2002), and may be undervalued relative to stocks from other industry sectors (Lee and Upneja, 2007). This also includes stocks issued by real estate investment trusts (REITs), which generally underperform relative to REITs from other sectors (Jackson, 2008, 2009). While the academic financial literature is replete with studies examining market reaction to CEO transition, there is paucity of research examining market reaction, as indicated by abnormal returns and its direction, volume or the quantity of shares traded following CEO transition announcement within the lodging sector. It is important to investigate the impact of CEO transition in this sector to shed more light on how the market perceives lodging CEO transitions, especially given the insular nature of the industry. As such, the purpose of this study is to investigate abnormal returns and stock volume activity surrounding lodging firms CEO transition announcements. 143 Literature review Leadership styles, functions and role of CEOs The academic and business literature is replete with articles addressing the issue of the functions and role of firms CEOs. Arguably, some of the more practical and applicable discussions regarding the function and leadership role of CEOs have appeared in the popular business press rather than academic journals. Goleman (2000) identified six unique leadership styles, based on his earlier work on emotional intelligence (Goleman, 1995), that are essential for executive and CEO success. The six leadership styles are coercive, authoritative, affiliative, democratic, pacesetting and coaching. According to Goleman (2000), the most effective leaders use a combination of these six leadership styles depending on the situation and the desired outcome. Lafley (2009), defined the role of the CEO as an inherent focus on the firm at the enterprise level, while simultaneously examining the internal and external environments for opportunities that will add value to the firm. To accomplish this task effectively, CEOs are

4 144 TOURISM ECONOMICS required to: (a) analyse and interpret the firm s external environment for internal stakeholders; (b) identify which business segments the company should participate in; (c) balance pursue of income in the short term with necessary investments for the future; and (d) shape the values and culture of the organization (Lafley, 2009). CEO transitions and impact on returns CEO transitions represent a major event for most firms and occur when the firm s chief executive retires, resigns or is dismissed (Furtado and Karan, 1990). These transitions are also seen as value increasing events that are either voluntary or involuntary (Neumann and Voetmann, 2005). Although the likelihood of annual CEO transitions is relatively low (Warner et al, 1988; Weisbach, 1988; Parrino, 1997; Allgood and Farrell, 2000), when they occur they can be traumatic events for firms and their stakeholders (Kesner and Sebora, 1994). Evidence from stock price reactions to CEO transitions suggests abnormal returns following CEO transition announcements (Borokhovich et al, 1996). Further, the direction and magnitude of a firm s stock performance following CEO transition are influenced by whether or not the transition was voluntary or involuntary. Involuntary or forced transitions are: those that occur when the CEO departs a firm prior to natural or planned retirement; in response to pressure from the firm s board of directors; and those following scandals, reorganizations, demotion, policy or personality disagreements or poor performance (Parrino, 1997; Farrell and Whidbee, 2000). Another form of involuntary CEO transition is the outcome of corporate power struggles between the CEO and inside stakeholders and outside directors (Boeker, 1992). Thus, involuntary transition occurs when the CEO s departure is initiated by the firm and such dismissal in not linked to any formal policy, such as mandatory or statutory retirement, and is generally against the will of the CEO (Hatfield et al, 1999). Involuntarily CEO transitions arising from poor performance support the agency theory and the rational adaption of organizational change theory. Agency theory suggests that dismissal of underperforming CEOs are important internal mechanisms of corporate control (Fama, 1980; Walsh and Seward, 1990). Under the rational or adaptive theory, CEO dismissal represents critical adaptation mechanisms that firms use to realign resources to the dynamic business environment (Salancik and Pfeffer, 1980). Voluntary CEO transitions are those related to natural retirement or unforced, timely, transitions. Empirical evidence suggests that voluntarily CEO transition and its effects on stakeholders differ from involuntarily separations (Hatfield et al, 1999). In fact, most CEO transitions are voluntarily and involve the incumbent relinquishing the reigns of corporate control at a mutually agreed time (Freidman and Singh, 1989; Carnella and Shen, 2001). While such transitions convey signals to the market, stakeholders are often unaware of the exact reasons for executive change since firms seldom disclose the true reasons for CEO departure (Fredrickson et al, 1988; Denis and Denis, 1995). Uncertainty about the reasons behind announcements of transitions and their potential impacts on firms performance has led to mixed reactions from the market. For example, Weisbach (1988) found that such announcements conveyed relevant information to the market while Warner et al (1988) found

5 Lodging firms CEO transition announcements no indication of significant market response to such events. However, frequency of CEO transitions and the probability of CEO change were found to be inversely related to stock price performance (Coughlan and Schmidt, 1985). This suggests that firms stock performance could be a predictor of CEO transition. Previous research suggests that if CEO transitions are anticipated the impact of the impending event on stock prices, trading patterns and volume is negligible in the immediate pre-event period. However, changes in volume, trading patterns and stock prices were seen in the days immediately following the announcement approximately two to four days (Beatty and Zajac, 1987). Similar findings were reported by Jackson (2014), who found that the markets reacted in a delayed manner following hospitality CEO transition announcements. Further, Warner et al (1988), in their seminal study, found evidence of post-announcement price drop in the period immediately following CEO transition announcement, except during the one to four day period following the announcement. Another seminal study, by Lubatkin et al (1989) examined the impact of CEO transition announcement on stock prices performance over a variety of periods ranging from 50 days prior to an announcement to 300 days after an announcement. The authors found that in the 51 days prior to the announcement there was a positive performance effect, while in the day prior to and including the announcement date there was no performance effect, and in the 50 days after the announcement there was a negative performance effect. On the contrary, Denis and Denis (1995) found significant negative cumulative abnormal returns over the 250 days preceding a CEO transition announcement, far greater than any other researchers had previously found. However, the researchers did not find statistically significant abnormal returns on either the day of or the day prior to the announcement. Regarding abnormal returns, forced transitions were found to exhibit significantly higher negative abnormal returns than did voluntary or normal transitions. In conjunction, volatility in firms stock prices following CEO transitions were also found to be higher for forced transitions than voluntarily or planned transitions (Denis and Denis, 1995). With regard to time period impact of executive transitions, another seminal study by Clayton et al (2005), employed a volatility event study methodology, which uses the log ratio of post-event to pre-event standard deviations. The authors found that volatility increases significantly in the first year following executive transitions of any type, with the highest volatility found in the first year following forced transitions (Clayton et al, 2005). Given the importance of CEOs and CEO transitions for organizations, Lubatkin et al (1989) analysed the relevant horizon lengths that could be utilized to measure the impact of CEO transitions on organizations, and noted that although the typical two-day announcement period prevalent in the literature might be relevant, the impact of CEO transition on stock price could and should be measured across a variety of periods. The literature suggests that CEO transitions, whether voluntarily or involuntary, anticipated or unanticipated, impact the performance of stock prices, which typically experience changes in volume, trading patterns. The magnitude of the impact of the transition is typically evident in the trading volume and prices of the firms stock prices in the period leading up to, following and on the day of the transition announcement. The literature also suggests that the magnitude, trading volume time and direction of the impact appears to vary 145

6 146 TOURISM ECONOMICS based on whether or not the vent was anticipated, as well as the period leading up to, the event date and the period after the event or announcement. The effect of the transition is often indicated by abnormal returns, or the difference between the expected return and the actual return on an investment, or simply put, the difference between the return on a stock (or entire portfolio) and the performance of an index, such as the S&P 500, or the CRSP indices. Hence, the abnormal return can be positive (performed better than the index) or negative (underperformed the index). Although the issue of CEO transition and its impact on firm stock prices has been extensively explored, to date, as indicated by the lack of literature, there is paucity of research addressing the issue of lodging firms CEO transition and its impact on the firms, trading patterns, volume and returns. This is despite the fact that previous research has identified that lodging stocks perform differently from other investments and other types of stocks (Quan et al, 2002; Lee and Upneja, 2007; Jackson, 2009). Hence, focusing on the main issues discussed above, this study attempts to fill this gap in existing research and is designed specifically to address the following six research hypotheses which are related to lodging CEO transitions announcement and their impact on abnormal stock returns and volume activity. Hypothesis 1: Cumulative abnormal price return for companies that announced chief executive officer transitions will be greater than zero for the 30 trading days prior to transition announcements. Hypothesis 2: Daily abnormal price return for companies that announced chief executive officer transitions will be greater than zero for the 5 days prior to, the day of, and the 5 days after the announcement date. Hypothesis 3: Cumulative abnormal price return for companies that announced chief executive officer transitions will be greater than zero for the 10 days after the announcement date. Hypothesis 4: Daily abnormal log-transformed trading volume for companies the announced transitions will be greater than zero for the 30 trading days prior to the announcement date. Hypothesis 5: Daily abnormal log-transformed trading volume for companies that announced CEO transitions will be greater than zero for the 5 days prior to, the day of, and the 5 days after the announcement date. Hypothesis 6: Daily abnormal log-transformed trading volume for companies that announced transitions will be greater than zero for the 10 days after the announcement date. Methodology Data source A typical event study analysis was used to determine whether the announcement of CEO transitions resulted in abnormal volume and returns for the periods prior to, surrounding, and after transition announcements. Since this study follows an event study methodology, identifying the correct event date was of utmost importance. For the purpose of this study, the event date is defined as

7 Lodging firms CEO transition announcements the date when the market first obtained information about each CEO s transition. Since announcements of firm specific events can be subject to discrepancies and announcement delays, several information sources are examined to establish the accurate transition announcement date for each of the sampled firms. These sources included general Internet searches, Businesswire.com, Wall Street Journal index, Factavia reports, Wall Street Journal, Dow Jones News Service, Reuters, Standard & Poor s Register of Corporations and the PR Newswire. Announcements found through these sources were crossed checked against each firm s Securities and Exchange Commission (SEC) 8K and 10K filings and other types of relevant documents found on each firm s investor relations website. Using multiple sources to identify the exact announcement date is deemed important since it increased event identification accuracy while simultaneously enhancing the accuracy and predictability of the impact of each transition on the firms stock prices. The final sample comprised 27 CEO transition announcements or events during the period 3 March 2003 to 14 September Stock market data used in this research were obtained from the Wharton Research Data Service (WRDS), which provides access to the Center for Research in Security Prices (CRSP) data published by the University of Chicago. 1 This includes stock return data for each firm analysed as well as the market proxy data. CRSP is the primary database used for academic research on stock price and trading volume. Because of the importance of the market model in conducting event studies, the selection of the market proxy or benchmark is of significant importance. The market model suggests that while returns on securities depends on conditions germane to a firm, the return on a security also depends on the return on the market portfolio and the extent of the security s responsiveness as measured by beta. Hence, for studies in which the majority of the events analysed are found in a specific stock market index, it is appropriate to use that index, such as the Standard & Poor s 500 Composite Index (S&P 500). However, when the events are related to stocks that are traded on a variety of stock exchanges, it is often appropriate to use a broader index. CRSP calculates two broad indices consisting of all stocks traded on the New York Stock Exchange (NYSE) composite index, American Stock Exchange (AMEX) and National Association of Securities Dealers Automated Quotations (NASDAQ) markets. One of the CRSP indices is equally weighted while the other is value weighted. Valueweighted indices are generally preferable for use in event study analysis since they represent a portfolio more likely to be held by investors, and have generally been identified as having less bias than equal-weighted indices (Canina et al, 1998). Hence, the present study used the CRSP value-weighted index for the market proxy or benchmark. Traditional event study statistical methods Event studies using a market model residual method with daily stock data are well documented (Brown and Warner, 1985). The event study procedure typically calculates abnormal returns for an event-time portfolio. Each security in the sample is regressed for a time series of daily returns against the yields from a market index using the equation: 147

8 148 TOURISM ECONOMICS R t = α + βrm t + e t, (1) where R t denotes the return on the security for time period t, RM t denotes the return on a market index for period t and e t represents a firm-specific return (Sharpe, 1963, 1964; Lintner, 1965). Inherent in the market model is an assumption that e t is unrelated to the overall market and has an expected value of zero. The estimates of the constant and coefficient obtained from the regression are then used to generate a time series of return predictions and, ultimately, a time series of excess returns, which are then divided by the prediction to compute the standardized excess return. The data were analysed using Eventus software (Cowan, 2010), in which parameters are estimated using a pre-event period sample with ordinary least squares (OLS) regression and the parameter estimates and the event period stock and market index returns are then used to estimate the abnormal returns. This study used an estimation period of 255 days ending 46 days prior to the event date for each stock. This estimation period was deemed appropriate for this study, given that there are no universal principle establishing the appropriate estimation period for research (Seiler, 2004). The resulting individual excess returns are then compared to the daily and cumulative abnormal returns using a Patell Z-score (Patell, 1976), which reports the statistical significance of the abnormal return relative to the period of interest. The Patell Z-score represents an aggregation across security-event dates by summarizing the individual t- statistics derived for each firm and dividing the sum by the square root of the sample size. This equation is expressed as: A j,0 T p = 1 m j=1 Σm Var (Aj,0). (2) One of the challenges in using OLS regression for daily stock data is that there is an underlying assumption that the excess return data are normally distributed and cross-sectionally independent. The most commonly used statistical test in event studies, the Patell Z-test, a parametric, standardized abnormal return test, utilizes such an assumption (Patell, 1976). Since data were analysed using the Eventus software, below is a detail explanation of the analysis, tests, technical and statistical issues addressed and techniques employed using the software. Addressing the issue of non-normality in the data It has long been recognized that daily stock data are not normally distributed (Mandelbrot, 1963; Fama, 1965; Officer, 1972), and as a result, care must be taken in analysing event study results that assume the data are normally distributed. Although Brown and Warner (1985) in their benchmark research did not find that non-normality had any obvious impact on event study methodologies and that standard parametric tests for significance are well specified in samples with as few as five securities, many later researchers have challenged their assumptions. The most popular approach for addressing nonnormality of the data can be provided by nonparametric tests, specifically the sign test and the rank test (Campbell et al, 1997). Corrado (1989), discussed at length the rank test, finding that it is more powerful in detecting abnormal

9 Lodging firms CEO transition announcements stock price changes than are typical parametric tests. In a rank test, each firm s abnormal return is ranked over the combined period, including both the estimation and event windows, and then compared with the expected average rank under the null hypothesis of no abnormal return. Cowan (1992) expanded on this work, finding that although the rank test performs better under conditions in which stocks are well traded, there is little variance in the eventdate return, and the event window is short, the generalized sign test is the preferred test over event study windows of several days when a single stock is a significant outlier and when stocks in the analysis are thinly traded. The generalized sign test looks at the number of stocks with positive cumulative abnormal returns in the event window as compared to the expected number in the absence of abnormal performance based on the fraction of positive abnormal returns in the estimation period. There are few, if any, potential shortcomings to using nonparametric tests, particularly given that nonparametric tests are typically not used in isolation, but rather in conjunction with parametric tests so that each can provide a check on the robustness of conclusions as compared to the other (Campbell et al, 1997). Addressing the issue of cross-sectional dependence in the data Another challenge with utilizing OLS regression for daily stock data is that there is an underlying assumption that the data are cross-sectionally independent. Again, the most commonly used test statistic in event studies, the Patell Z-test, a parametric, standardized abnormal return test, utilizes this assumption as well (Patell, 1976). Cross-sectional dependence is particularly likely when at least some of the returns used in an event study are correlated due to common macroeconomic or industry-specific activity or due to a single or clustered event date (Prabhala, 1997). Cross-sectional dependence inflates test statistics because the number of sample firms overstates the number of independent observations (Lyon et al, 1999). The most common cases for this issue occur when the event being analysed occurs on the same date for all firms (such as a regulatory event or market shock), but it can be an issue anytime that at least some of the returns are sampled from common time periods (Bernard, 1987). The challenge of crosssectional dependence is exacerbated when a common event is tested in a single industry, as is the case for this study (Strong, 1992). There is a significant body of literature that has developed around potential solutions to address cross-sectional dependence in the data with few conclusions regarding the best method or even whether cross-sectional dependence needs to be addressed at all. Beaver (1968) found that an increase in the cross-sectional dispersion of abnormal returns at the time of an event announcement implies that the announcement conveyed information and that researchers need to control for factors leading to varying announcement effects across firms. Brown and Warner (1980) suggested that cross-sectional dependence should be addressed through a crude adjustment technique, in which the standard deviation of the average residuals is estimated from the time series of the average abnormal returns over the estimation period. However, in their later work, Brown and Warner (1985) found that non-normality of daily and abnormal returns had no obvious impact on event study methodologies and that the mean abnormal return in a cross-section of securities comes closer to normality as the 149

10 150 TOURISM ECONOMICS number of securities in the sample increases. Boehmer et al (1991) proposed what is known as the standardized cross-sectional test or BMP test but as a hybrid of the Patell (1976), test and an ordinary cross-sectional test, in which the average event-period residual is divided by its contemporaneous crosssectional error. Although they found that event-date clustering did not affect their results, their test still relies on an assumption that security residuals are uncorrelated across firms. Lyon et al (1999), discussed extensively the use of potential methods for eliminating some of the challenges of cross-sectional dependence along with other misspecifications of test statistics including new listing bias, rebalancing bias, skewness bias and bad asset pricing models. Their recommended method utilizes the calculation of calendar-time portfolio abnormal returns, which may be either equally weighted or value weighted. In this method, calendar-time abnormal returns are calculated for sampled firms and then a t-statistic is derived from the time-series of the monthly calendar-time portfolio abnormal returns. The advantage of this approach is that it eliminates the issue of crosssectional dependence among sample firms. The disadvantage of this approach is that it provides an abnormal return measure that does not precisely measure the actual experience of investors over the specified time period. The preceding arguments and statistical methods discussed suggest that there are no definitive or uniform agreement regarding a single best solution to address cross-sectional dependence in event studies. Additional statistical methods applied In addition to the commonly used Patell test, the present study also performed two additional parametric tests. The first parametric test is a standardized crosssectional test developed by Boehmer et al (1991), which compensates for possible variance increases on the event date itself by incorporating a crosssectional variance adjustment. The second additional parametric test applied in this study is a time-series standard deviation test also known as the crude dependence adjustment (CDA) proposed by Brown and Warner (1980, 1985). This test computes the standard deviation from the time series of portfolio mean abnormal returns during the estimation period. Two nonparametric tests were also performed on the data. The first nonparametric test is the generalized sign test, which looks at the number of stocks with positive cumulative abnormal returns in the event window as compared to the expected number in the absence of abnormal performance based on the fraction of positive abnormal returns in the estimation period (Cowan, 1992). The second nonparametric test is the rank test, in which each firm s abnormal return is ranked over the combined period, including both the estimation and event windows, and then compared with the expected average rank under the null hypothesis of no abnormal return (Corrado, 1989). Although event studies are most commonly conducted using abnormal returns related to stock price, they can also be conducted using volume data. Abnormal trading volume is generally calculated using the log-transformed percentage of shares outstanding for each security as compared with an estimated market model abnormal trading volume (Ajinkya and Jain, 1989; Cready and Ramanan, 1991; Biktimirov et al, 2004). As with price event studies, both

11 Lodging firms CEO transition announcements parametric and nonparametric tests are indicated and the same tests utilized for abnormal price returns were used for the abnormal volume returns (Campbell and Wasley, 1996). Cumulative abnormal return was calculated for the 30 days prior to and following the CEO transition announcement date and daily abnormal return was analysed for the 10 trading days prior to, the day of, and the 10 trading days following the CEO transition announcement date. 151 Results and discussion The research examined abnormal stock market returns and trading volume activity in the periods surrounding CEO transition announcement by lodging firms. Six hypotheses were developed, the results of which are discussed below. Hypothesis 1 A summary of the daily abnormal returns is presented in Table 1. The study identified negative cumulative daily abnormal returns (relative to the daily CRSP value-weighted index return) of 7.60% for the 30 trading days preceding the announcement date (Table 2). These returns were statistically significant at the 0.01 level for all tests conducted, including the Patell, CDA, standardized cross-sectional, generalized sign and calendar-time tests and at the 0.05 level for the rank test, as noted in Table 2. Since the cumulative abnormal returns (CARs) measures the total impact of an event through a particular time period, the significant negative abnormal returns 30 trading days prior to the announcements is indicative of known potential challenges at sampled firms that ultimately may have led to a CEO transition. These could include general dissatisfaction with management, poor earnings announcements, or advance knowledge of a planned CEO transition by the market. In any case, findings suggest that for the sampled lodging firms, 30 days prior to lodging firm CEO transition announcements, the market is privy to information of potential CEO transition, however, it did not perceive the transitions as value enhancing events. Hypothesis 2 The study also identified negative daily abnormal returns (compared to the daily CRSP value-weighted index return) of 2.44% for the 11 days (5 ante the announcement date itself and 5 post) including and immediately surrounding the CEO transition announcement (Table 2). However, this relationship was only statistically significant at the 0.05 level for the Patell test. Using data that are both ante- and post-transition announcement in the same analysis is helpful as this accounts for both potential information leakage. This is important since the timing of the announcement could impact the day prior to, the day of, or the day after an announcement is made depending on the specific time at which an announcement was made. Based on the literature review, it is not particularly surprising that the overall announcement of a CEO transition is generally viewed as a negative event by the market on the event date and the five days before and after the event. Furthermore, given the nature of the industry which

12 152 TOURISM ECONOMICS Table 1. Daily mean abnormal returns and test statistics for CEO change announcements (N = 27). Day Mean Patell Z Portfolio StdCsect Z Sign Rank test Z Calendar abnormal time series positive: time t relative (CDA) t negative a volume % : : : : *** *** *** 6:21<< ** : : : : : * ** : * : : : : : * ** * 9: * * * : : : : : : ** *** ** 9: ** * ** ** * 9: * * : : : * :19< * * : : * * : * : : ** ** ** 8:19< ** * : : : ** ** * 9: * * : : Notes: a < denotes p < 0.05; << denotes p < 0.01, where the direction of the symbols designates the direction of the test. * p < 0.05; ** p < 0.01; *** p <

13 Lodging firms CEO transition announcements 153 Table 2. Daily mean cumulative abnormal returns and test statistics for CEO change announcements (N = 27). Day Mean Precision Mean CT Patell Z Portfolio StdCsect Z Sign Rank test Z Calendar CAAR % weighted portfolio time series positive: time t CAAR % cumulative (CDA) t negative a CAAR % ( 30, 1) ** ** ** 6:21<< * ** ( 5,+5) * : (+1,+10) *** *** * 12: * * Notes: a << denotes p < 0.01, where the direction of the symbols designates the direction of the test. * p < 0.05; ** p < 0.01; *** p <

14 154 TOURISM ECONOMICS relies heavily on relationship building, the loss or impending loss of a CEO also signals the loss of relationships which could be leveraged for the benefit of the firm. Hypothesis 3 Negative daily abnormal returns relative to the daily CRSP value-weighted index return of 4.75% were also identified for the 10 days following the CEO transition announcement and this relationship is statistically significant at the level for the Patell and CDA tests (Table 2). The significance of this result is confirmed at the 0.05 level of significance for the standardized cross-sectional, rank and calendar time tests but not confirmed by the generalized sign test. The level of negative returns is unusually strong and it is noted that the negative returns are persistent on most trading days following the announcement and that one of the least negative abnormal returns is on the announcement day itself. These data clearly indicate that, overall, the announcement of a CEO transition is generally viewed as a negative event by the market. Hypothesis 4 Table 3 presents a summary of the daily abnormal log transformed trading volume for each daily return. Although the average daily returns were positive 30 days prior to the announcements (26.51%), it found cumulative negative abnormal volume for days 30 to 1 of 21.4% (Table 4). This volume decrease is very close to zero on an average daily basis and is not statistically significant at the 0.05 level in any of the tests conducted. This price movement suggests that investors expect bad news from the sampled firms regarding the CEO transitions, hence the decrease in volume. It could also indicate uncertainty regarding the direction of the firm, and as such, investors are skittish about investing in the firms stock. Hypothesis 5 It is interesting to note that the study identified cumulative abnormal volume (compared to the CRSP value-weighted index) of 166% for the 11 days (5 ante, the announcement date, and 5 post) including and immediately surrounding the CEO transition announcement. This return is statistically significant at the level for the Patell test, statistically significant at the 0.01 level for the CDA and rank tests, and statistically significant at the 0.05 level for the standardized cross-sectional test but not statistically significant for the generalized sign test, as noted in Table 4. It is interesting to note that abnormal volume was statistically significant at the 0.01 level on the announcement date for all tests except the generalized sign test and statistically significant at the level on day +1 for all tests with the exception of the generalized sign test, for which it was statistically significant at the 0.01 level. This volume likely represents trading by market participants who viewed uncertainty in the announcement of a CEO transition and wanted to mitigate risks and as such, were not interested in remaining invested in the stock at that point. It could also indicate that short sellers were active in the market and hoped to profit from the resignation transactions.

15 Lodging firms CEO transition announcements 155 Table 3. Daily mean abnormal relative volume and test statistics for CEO change announcements (N = 27). Day Mean Patell Z Portfolio StdCsect Z Sign Rank test Z abnormal time series positive: relative (CDA) t negative a volume % * : : : : : :9> : : : : : : : : : : : : : : : : : : : : : : : : *** ** *** 16: ** *** *** *** 19:8>> *** ** * * 16: * : * * * 18:9> : * * 17: * : : : : Notes: a > denotes p < 0.05; >> denotes p < 0.01, where the direction of the symbols designates the direction of the test. * p < 0.05; ** p < 0.01; *** p <

16 156 TOURISM ECONOMICS Table 4. Mean cumulative abnormal relative volume and test statistics for CEO change announcements (N = 27). Day Mean Precision Patell Z Portfolio StdCsect Z Sign Rank test Z cumulative weighted time series positive: abnormal CAARV % (CDA) t negative a relative volume % ( 30, 1) : ( 5,+5) *** *** * 16: ** (+1,+10) *** *** * 15: *** Note: * p < 0.05; ** p < 0.01; *** p <

17 Lodging firms CEO transition announcements In addition, the volume during this period could suggest that the market received information that indicated that the transition was a certain event, however, there were uncertainty about the direction of the firm as it relates to value enhancement. Hypothesis 6 The study found cumulative abnormal volume (compared to the CRSP valueweighted index) of 171%, representing a daily average of 17%, for days +1 to +10 (Table 4). This volume increase is statistically significant at the level for the Patell, CDA and rank tests and statistically significant at the 0.05 level for the standardized cross-sectional test but not statistically significant for the generalized sign test. As indicated in Table 4, the average cumulative abnormal relative volume is positive but not statistically significant for most of the individual days observed. Abnormal volume in the period after a CEO transition announcement is typically provided by market participants who may wish to exit a stock as they believe that the uncertainty may increase the volatility of the stock price. A summary of each daily return is found in Table 3, which shows that average abnormal relative volume is positive but not statistically significant for most of the individual days observed. Abnormal volume in the period after a CEO transition announcement is typically a sign that market participants may wish to exit a stock as they believe the uncertainty may increase volatility of the stock price. 157 Summary and conclusion This study has added to our understanding of how lodging stocks are impacted by CEO transition announcements. Given the insular nature of the lodging industry, it is important that studies are conducted to empirically examine how lodging stocks perform when there are senior leadership change announcement within the industry. Specifically, the study focused on attainment of a better understanding of the abnormal returns and trading volume activity surrounding such announcements. For a fact, financial markets loathe uncertainty and the change of a firm s CEO can result in volatility, the effects of which are demonstrated in positive or negative abnormal returns and trading volume activity in the period surrounding such events. Hence, in keeping with the decision theory and efficient market securities theory, positive or negative returns and volume suggest good or bad news to investors. Regarding abnormal returns, the study found significant negative abnormal returns in the 30 trading days leading up to the announcements, suggesting that the market was privy to information about the possibility of potential challenges and impending CEO changes at the sampled firms. Negative abnormal returns were also reported during the immediate period surrounding the announcements, (5 days ante the announcement date and 5 days post announcement). Furthermore, assuming an efficient market, the negative abnormal returns constitutes evidence that investors on average did not react favourably to the news of lodging firms CEO resignation announcements. This finding further suggests that the market did not perceive the transitions as value

18 158 TOURISM ECONOMICS enhancing events, as indicated by the negative abnormal returns during the immediate ante, announcement date and post periods. This could also be an indication that investors wanted to take a cautious approach due to uncertainty following a CEO transition announcement. In essence, the study found that the significant negative abnormal return experienced by lodging stocks following CEO transition announcements is pronounced and did not support Neumann and Voetmann s (2005) notion that CEO transitions are value increasing events. This finding is not surprising since markets often perceive CEO transitions as negative events due to the uncertainty surrounding firms future performance. The results could also indicate the presence of short selling as prudent traders could also sell short the lodging stock that made the announcement and potentially achieve a significant level of return over a very short period of time. This is due to the fact that for CEO changes, speculation about the future direction of the firm could cause firms security prices to decline, thereby enabling them to be repurchased at a lower price by short sellers to make a profit. Regarding volume activity, the study found decline in trading volume activity 30 days prior to transition announcements. However, the findings suggest high trading volume during the immediate period surrounding announcements. The high volume activity during this period appears to be in line with the old adage in financial markets that investors will sell on factual information (such as certainty about a CEO transition). Hence, during the immediate period surrounding the transition announcement, there was a high and significant level of trading activity. This suggested that investors received credible information about the transitions and wanted to exit the stock. Overall, the findings of the study suggests that CEO transition announcements are value relevant events in that they affect the value of the firm in the short term. However, while they are value relevant events, they are not perceived as value enhancing events as indicated by negative abnormal returns. The implication of this research is that, if lodging firms want to minimize negative impact on stock price and trading volume and enhance shareholder value, it behoves them to plan CEO transitions and carefully and timely inform the market about the new CEOs, their background and the value they will add to the firm. Like most empirical studies, this study had limitations which future studies are encouraged to overcome. First, it did not differentiate between anticipated transitions and unanticipated transitions; instead, the sample included both. It is likely that a distinction between these types of transitions could lead to interesting results. This is due to the fact that, for planned or anticipated transitions in the lodging industry, the market is made aware of the new CEO, their leadership background and track record. This information reduces the risk of uncertainty and thus volatility, as it pertains to the potential of the CEO to enhance the value of the firm. Conversely, unplanned or unanticipated CEO transitions typically result in uncertainty and volatility since the market often times does not have sufficient information about the new CEO, their management and leadership philosophy and, importantly, their value enhancing potential. Furthermore, although uncertainty in financial markets is a given, for CEO transition it is plausible to anticipate that the level of uncertainty and its impact of stock performance could be a function of whether or not the CEO transition is anticipated and the new CEO s track record is known, or if the transition