Event-study approach: The case of Airbnb and hotel stocks

Tavor, Tchai; Teitler-Regev, Sharon Article Event-study approach: The case of Airbnb and hotel stocks Journal of Applied Economics Provided in Cooperation with: University of CEMA, Buenos Aires Suggested Citation: Tavor, Tchai; Teitler-Regev, Sharon (2024) : Event-study approach: The case of Airbnb and hotel stocks, Journal of Applied Economics, ISSN 1667-6726, Taylor & Francis, Abingdon, Vol. 27, Iss. 1, pp. 1-25, https://doi.org/10.1080/15140326.2024.2316970 This Version is available at: https://hdl.handle.net/10419/314259 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/4.0/ Journal of Applied Economics ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/recs20 Event-study approach: the case of Airbnb and hotel stocks Tchai Tavor & Sharon Teitler-Regev To cite this article: Tchai Tavor & Sharon Teitler-Regev (2024) Event-study approach: the case of Airbnb and hotel stocks, Journal of Applied Economics, 27:1, 2316970, DOI: 10.1080/15140326.2024.2316970 To link to this article: https://doi.org/10.1080/15140326.2024.2316970 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 16 Feb 2024. Submit your article to this journal Article views: 1191 View related articles View Crossmark data Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=recs20 RESEARCH ARTICLE Event-study approach: the case of Airbnb and hotel stocks Tchai Tavor and Sharon Teitler-Regev Department of Economics and Management, The Max Stern Yezreel Valley College, Yezreel Valley, Israel ABSTRACT This study investigates the impact of Airbnb announcements on hotel stock prices across ten countries, distinguishing between exactand general-location announcements. We found that while general announcements have minimal impact, those with exact locations consistently reduce hotel stock prices, as evidenced by negative cumulative abnormal returns (CAR) trends. The primary impact occurs within the [−3, +1]-day window surrounding the announcement. These robust findings persist across various tests, underscoring their reliability. Implications include the importance of investor awareness regarding location-specific announcements and the need for regulatory examination of information disclosure practices on platforms like Airbnb., n.d. The study offers valuable insights for investors and policymakers navigating the dynamic landscape of the hospitality industry in the age of online platforms. ARTICLE HISTORY Received 25 July 2023 Accepted 5 February 2024 KEYWORDS Event studies approach; Airbnb; hotel companies; market efficiency 1. Introduction Recent decades have witnessed the development of the peer-to-peer (P2P) economy in response to a variety of technological and sociological changes. The term “peer-to-peer” refers to a transaction in which, for a specified fee, a party rents an unor under-used product to a party that temporarily needs it (Gupta et al., 2019). While some researchers use the term “sharing economy,” this term is not accurate, as the product is not really “shared” (Dolnicar, 2021). Having spread from the accommodation market to the car, fashion (Choi & He, 2019), and even electricity markets (Schneiders et al., 2022), the P2P economy now has the potential to profoundly alter the entire economy. The most conspicuous example of the P2P economy remains the P2P accommodation market, specifically the role of Airbnb (Gansky, 2011; Sundararajan, 2013). Airbnb links parties that have vacant housing available with parties (such as tourists) seeking temporary accommodations via a digital market (Botsman & Rogers, 2011; Zervas et al., 2017). Founded in 2008 by Brian Chesky and Joe Gebbia, by 2021 Airbnb had 12.7 million listings in 100,000 cities (Airbnb, n.d.). With a market value of $113 billion (Airbnb, n. d.), Airbnb now leases more rooms than the world’s three largest hotel companies combined. CONTACT Tchai Tavor [email protected] Department of Economics and Management, The Max Stern Yezreel Valley College, Yezreel Valley 1930600, Israel JOURNAL OF APPLIED ECONOMICS 2024, VOL. 27, NO. 1, 2316970 https://doi.org/10.1080/15140326.2024.2316970 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http:// creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. Airbnb poses a potentially serious threat to the traditional hospitality industry because it offers significantly less costly accommodations and more diversity than do hotels (Dolnicar, 2019). Some researchers claim that Airbnb is a substitute for hotels. For example, Guttentag and Smith (2017) found that over 60 percent of Americans use Airbnb instead of hotels. Similarly, Yang et al. (2021) found the two to be interchangeable options. Analyzing 466 estimates from 33 studies on the effect of Airbnb on hotel performance, they found that Airbnb’s effect was small but negative. Dogru, Mody, et al. (2020) researched ten major hotel markets in the United States between 2002 and 2018 and reported that the increase in Airbnb supply between 2008 and 2017 had a negative effect on hotel revenues, average prices, and occupancy rates. In addition, Dogru, Hanks, et al. (2020) found that in London, Paris, Sydney, and Tokyo, an increase of 1% in Airbnb listings reduced hotel revenue between 0.016% and 0.031%. According to Blal et al. (2018), in San Francisco overall hotel revenue per room was unrelated to the availability of Airbnb alternatives. However, in certain segments it was affected by the average price of Airbnb accommodations. Conversely, other researchers have argued that Airbnb is a complementary product. For example, Varma et al. (2016) surveyed hotel employees and found that Airbnb and hotels target different types of guests. Likewise, Sainaghi and Baggio (2020) determined that on weeknights, hotels usually serve business guests, while Airbnb houses leisure guests. Alongside the immediate impact Airbnb has had on the hospitality industry, including a rise in tourist numbers (Gutiérrez et al., 2017), it has also had a wider effect on the wider economy (Levendis & Dicle, 2016; Negi & Tripathi, 2022). One example is its impact on rental and housing prices (Barron et al., 2021; Benitez-Aurioles & Tussyadiah, 2020). Airbnb has been found to lead to increased crime rates (Ke et al., 2021) as well as neighborhood gentrification and overcrowding (Gyodi, 2019; van Holm, 2020). Findings from the Balearic Islands indicate that Airbnb engenders environmental degradation Martın et al. (2018. On the other hand, Airbnb has had a positive effect on the hotel and restaurant employment market (Dogru, Mody, et al., 2020; Mao et al., 2018) and on the revenues of local communities and authorities (Belarmino et al., 2021; Farmaki & Kaniadakis, 2020; Mao et al., 2018). The emergence of Airbnb has brought significant disruption to the hospitality sector, generating both positive and negative effects on local economies. As a result, local municipalities and governments have been prompted to reconsider their regulatory approaches towards Airbnb., n.d.In order to make well-informed policy decisions, it is crucial to gain a comprehensive understanding of Airbnb’s impact on markets. This study adopts a unique approach by examining stock market responses to Airbnb announcements in relation to hotel stock values, aiming to determine whether Airbnb operates as a substitute for or complementary service to traditional hotels. Unlike conventional research, which often focuses on specific geographic locations (Dann et al., 2019), this investigation offers a broader perspective on the dynamic relationship between Airbnb and the hotel industry. The disruptive potential of Airbnb in the stock markets of various countries has become a significant issue in academic discussions. Therefore, this study aims to investigate the financial implications of exactversus general-location Airbnb listings on hotel stock prices in these regions. Employing a novel methodological approach, which includes comparative assessments, parametric and nonparametric tests, and 2T. TAVOR AND S. TEITLER-REGEV robustness tests, this research seeks to provide insights of value to both capital market participants and policymakers. This study makes a substantial contribution to the existing literature on several points. First, it pioneers an examination of the differentiated impact of advertisements with exact versus those with general locations on stock markets within the context created by Airbnb, thus filling a gap in the current research. Second, by employing an extensive set of statistical tests-six parametric and nonparametric tests, along with four robustness tests – the study enhances the reliability and robustness of its findings beyond the conventional statistical tests typically applied in prior event research within the Airbnb domain. Last, the study utilizes primary data sourced directly from the Airbnb website, allowing for the segmentation of posts based on the respective countries’ locations. The results of the study suggest that announcements with general locations have a limited effect on hotel company stock prices, while those with exact locations lead to a clear decline in hotel stock prices. 2. Literature review The efficient market theory (EMH) posits that share prices reflect all information known to the market. Investors seeking profit avenues look for information that is predictive of stock prices. Extensive research has therefore been performed on how different information published in various media affects stock prices. One common way to study the impact of news on markets is to apply event-study methodology. This method has been widely used in many areas, including marketing (Sorescu et al., 2017), economics (Lee & Mas, 2012), accounting (Jiang et al., 2015), health (Maneenop & Kotcharin, 2020), air travel (Kumari et al., 2022, 2023), the events industry (Seraphin, 2021), and tourism (Pandey 2021; Papakyriakou et al., 2019). In the hospitality industry, the event-study approach has been used by many researchers. For example, Che Ahmat et al. (2023) applied it to test the impact of a minimum hospitality industry wage on the stock prices of hotel companies in Malaysia, finding that introducing or increasing a minimum wage led to a decline in stock values. Bloom and Jackson (2016) found a negative effect associated with changes in hotel company CEOs. Likewise, Dogru (2017) found that acquisition has a positive effect, its size depending on the financial constraints and organizational structure involved. Kim (2023) observed divergent impacts on the offering bidder and on the target of announcements related to hotel mergers. In their investigation of the effects of the Russia – Ukraine war on global tourism stocks, Pandey and Kumar (2022) revealed distinct effects across various markets. Focusing specifically on Airbnb, Garcia-López et al. (2020) used several models, including the event-study model, to test the effect of Airbnb on Barcelona’s housing and rental prices. They found that since 2014, when Airbnb became an important factor in Barcelona, housing and rental prices increased in neighborhoods where Airbnb was present, unlike neighborhoods in which it was not. This result was also obtained by Bibler et al. (2022) in Chicago and San Francisco; however, they also found that growth in Airbnb listings helps the individual’s listers economic situation. Similarly, Gonçalves (2020) focused on the ban on Airbnb in Lisbon, Portugal, finding that there was a sharp increase in providers’ JOURNAL OF APPLIED ECONOMICS 3 registration on Airbnb between the time the ban was announced and its implementation, and that housing buyers liked the option of being able to participate in the Airbnb market. Studying the effect on funding on Airbnb and hotels at different business stages, Bianco, Zach, and Liu (2022) found that the startup phase for traditional hotels had a negative effect on stock markets, while Airbnb at a similar stage experienced a positive effect. Examining the connection between Airbnb and hotel companies, Bianco, Zach, and Singal (2022) used a sample of publicly traded hotel management companies and hotel real estate investment trusts in the United States before and after Airbnb was recognized as a competitor (2013–2014). Using the event-study methodology, they found that after 2014, new products or services offered by Airbnb had a negative effect on markets. Focusing on this connection but extending it to Airbnb worldwide, Teitler-Regev and Tavor (2023) tested how announcements on the Airbnb website affected stock values of hotel companies, finding a negative connection. In addition, they found that positive Airbnb announcements led to a decline in stock values, while announcements regarding families had a longer-term effect. Numerous studies (e.g., Bianco, Zach, & Singal, 2022; Kim, 2023; Teitler-Regev & Tavor, 2023) have supported the conclusion that negative events, such as cyber-attacks (Arcuri et al., 2020), terrorist attacks (Markoulis & Neofytou, 2019), political uncertainties (Das et al., 2020), and COVID-19 (Clark et al., 2021; Sharma & Nicolau, 2020; Shin et al., 2021) have a negative effect on markets. Moreover, the inclusion of location information in announcements may result in varied effects on hotel stock prices. For example, Viljoen (2016) investigated the impact of news announcements on stocks with dual listings, revealing that these announcements not only influenced stocks within the specific market but also had a spillover effect on the broader market. Another study by Kumari et al. (2023), examining the effects of the Russia – Ukraine war, demonstrated that companies situated in distant regions such as Asia and America remained unaffected, whereas those in proximity to the event, such as Europe, the Middle East, and Africa, experienced significant impacts. Building upon these empirical findings, the study formulates the following hypothesis: Hypothesis: Airbnb announcements specifying an exact location will exert a distinct influence on local hotel stock prices when compared to announcements without location specificity. This study aims to fill a gap in the existing literature by undertaking a comparative analysis of the effects of Airbnb announcements with specific location references versus those without such specificity. Drawing on the foundational research by Teitler-Regev and Tavor (2023), this investigation employs sophisticated statistical models to scrutinize the impact of these distinct types of announcements on hotel stock prices. 4T. TAVOR AND S. TEITLER-REGEV 3. Method and methodology 3.1. Data This research investigates the impact of Airbnb’s country-specific announcements on the stock performance of hotel companies across ten prominent countries globally: the United States, France, Australia, India, Japan, the United Kingdom, China, Germany, Thailand, and Spain. The selection of these countries is based on Airbnb’s substantial activity within these regions and the prevalence of publicly traded hotel entities within their borders. The categorization of announcements is detailed in Table 1, distinguishing between those specifying exact locations and those offering a more general location. Specifically, announcements are classified as either having a specific country location or providing a broader representation of a region or continent, as detailed in Appendix A. For Airbnb listings with exact location details, yield data were gathered for hotel companies situated in the corresponding country. Conversely, for announcements lacking specific location details, return data were systematically collected for hotel companies situated within the specified announcement area (e.g., the continent) and sampled countries. The dataset encompasses 48 announcements related to 145 stocks with exact locations and 132 announcements related to 969 stocks with general locations. The data collection period, starting in 2017, aligns with Airbnb’s notable expansion and acquisition efforts in the hospitality sector during that year. Additionally, it coincides with a substantial increase in the rate of announcements published on the website during this period. The outbreak of the COVID-19 pandemic, which profoundly impacted the hospitality and tourism markets, made it necessary to terminate data collection in 2019. To evaluate the influence of Airbnb announcements on the stock prices of hotel companies, we gathered return data for the specified hotel firms (outlined in Appendix B). Additionally, we considered market returns using the ten leading stock indices of the respective countries as benchmarks. These indices, obtained from Yahoofinance.com and Investing.com, include Standard & Poor’s 500 (S&P 500), Cotation Assistée en Continu (CAC 40), Standard & Poor’s Australian Securities Exchange 200 (S&P/ASX 200), BSE SENSEX 30 (BSE Sensex 30), Nikkei Stock Average 225 (Nikkei 225), Financial Times Stock Exchange 100 Index (FTSE 100), Shanghai Stock Exchange 50 Index (Shanghai SE 50), Deutscher Aktienindex (DAX), Stock Exchange of Thailand 100 Index (SET 100), and Índice Bursátil Español (IBEX 35). Table 1. Announcements with exact and general location. Announcements Stocks 2017 2018 2019 All Sample 180 1114 100% 100% 100% Exact location 48 145 11% 28% 29% General location 132 969 89% 72% 71% The table presents data pertaining to the distribution of announcements categorized by specificity of location, delineating between those with exact location and those characterized by a more general indication of location. JOURNAL OF APPLIED ECONOMICS 5 3.2. Empirical strategy The event-study approach was developed as a statistical approach to measuring how an economic event affects the market by utilizing abnormal returns (AR) (Luoma, 2011), specifically testing the efficient market theory (EMT) developed by Fama (1970). The first research published using the event-study approach was carried out by Dolley (1933) in the early 1930s. In the late 1960s, research by R. Ball and Brown (1968) and Fama et al. (1969) introduced the methodology that is still in use today in much economics and finance research. However, several modifications have been made over time, specifically using daily instead of monthly data and employing more sophisticated methods to estimate the abnormal returns (Brown & Warner, 1980, 1985; J. Y. Campbell et al., 1997). While the conventional event-study approach to measuring abnormal returns around a specific day is widely used, it is problematic in several respects. First, stock prices are not necessarily normally distributed (Kolari & Pynnönen, 2010). Additionally, when there is non-synchronous trading, bias could appear in the ordinary least squares (OLS) estimations (Dutta, 2014), and an increase in the variance of the returns might lead to misspecification of the model (Brown & Warner, 1980, 1985). Several researchers have suggested ways to address some of these problems, and other tests have been developed to increase accuracy and robustness. For example, Boehmer et al. (1991), assuming that the event-induced variance is identical for all stocks, argued that in obtaining the test result the event-period returns need to be standardized according to the estimation-period standard deviation. In addition, the cross-sectional mean of the standardized returns should be divided by the cross-sectional standard deviation. Brown and Warner (1980, 1985) demonstrated that when the event day is the same for several industries, the use of the market model reduces abnormal return intercorrelation to close to zero. However, this is not the case when the stocks are from the same industry, which can lead to over-rejection of the null hypothesis. To address this problem, Kolari and Pynnönen (2010) offered a variation of Patell’s standardized t-test (Patell, 1976) that assumes cross-sectional independence and controls the impact of large standard aberrations and even conscious changes in the variance of the returns (Hussain et al., 2021). Boehmer et al. (1991) used the cross-sectional variance while ignoring the estimationperiod residual variance. Using maximum likelihood estimation (MLE) on stock return data, C. Ball and Torous (1988) simultaneously estimated event-period returns, the variance of these returns, and the probability of the event’s occurrence for any given day in the event window. Their results suggest that while the null hypothesis is rejected more often when using the MLE method than with the traditional Brown and Warner (1985) method, the null hypothesis is not rejected too often when it is true. The standardized residual test assumes that the residuals are not correlated and that the event-induced variance is insignificant. Applying this test, as did Brown and Warner (1985) and Boehmer et al. (1991), the event-period residuals are divided by their standard deviation, thereby enabling them to adjust and reflect the forecast error. Nonparametric tests are well-specified and effective in detecting a false null hypothesis of no abnormal return. Using nonparametric sign and rank tests, researchers including Corrado (1989), Corrado and Zivney (1992), Cowan (1992), C. Campbell and Wasley (1993), and Corrado and Truong (2008) have shown that these tests produce better specification and statistical power than parametric tests. 6T. TAVOR AND S. TEITLER-REGEV Zivney and Thompson (1989) performed risk adjustment, adjusting the sign test to deal with skewness. To overcome the problem of event-induced variance, Corrado (1989) offered a nonparametric rank test which relaxes the assumption of normality and thus provides more robust results. This test applies for a one-day abnormal return, but Corrado’s claim is that it can be used for multiple-day events if the estimation period is divided by intervals according to the number of days in the cumulative annual returns (CAR) windows. For longer time periods, however, the number of observations becomes very small, thereby weakening the model estimation. As a result of this problem, Cowan (1992) and C. Campbell and Wasley (1993) used the CARs on Corrado’s rank test (Corrado, 1989). The shortcoming of this method is a loss of power to detect abnormal returns, specifically when the event windows are long. To avoid this problem, Kolari and Pynnonen (2011) developed a generalized rank test that uses the generalized standardized abnormal returns to test both single and cumulative abnormal returns. The test they offer includes robust-to-abnormalreturn serial correlation, event-induced volatility, and cross-sectional correlation of abnormal returns. One of the shortcomings of the sign test is the loss of information due to the use of positive or negative signs. The Wilcoxon signed-ranks test (WSRT; Wilcoxon, 1945) reflects this limitation, as it not only tests observed values relative to the median but also considers their relative sizes (Zoungrana et al., 2021). In our research, the day of the event refers to the day the announcements about Airbnb were posted, and is defined as t = 0. If the event occurs on a non-trading day, the event day will be the first business day following the event. The time points t = T 0 +1, T 0 +2, . . . , T 1 are the days of the estimates as related to the event day. During this period, we calculate the statistical values that are the basis for testing the event. Finally, t = T 1 +1, T 1 +2, . . . , 0, . . . , T 2 are the days of the event window related to the event day. The event-study methodology lacks a standardized rule governing the specific duration of event and estimation windows. Over the years, researchers have adjusted the length of these windows to align with the unique requirements of their investigations (Alkhatib & Harasheh, 2018; R. Ball & Brown, 1968; Brown & Warner, 1985; Fama et al., 1969; Palatnik et al., 2019; Teitler-Regev & Tavor, 2023). In the current investigation, the event window, represented by t ∈ [−30, +30], is defined in accordance with the methodology articulated in the studies of Chowdhury et al. (2022) and Teitler-Regev and Tavor (2023). We used abnormal returns and cumulative abnormal returns to analyze the responses of hotel company stock returns to Airbnb announcements. In addition, we built a market model to describe the correlation of hotel company stock returns for event i on day t, (R it ), to the market return on that day, (R mt ) under normal circumstances; meaning a situation when no significant unpredictable events occurred. The market return is represented by the return on the index of the stock that is tested: Rit ¼αiþβiRmt þ�it;t2  330;31½ �;i¼1;2;...:; N:(1) The return (R it ) is characterized with weak white-noise random variables, with E[R it ] = μ i and Var [R it ] = σ2 i for all t and Cov[R it , R ih ] = 0 for all t ≠ h. The normal return, E(R it |I t ), for information I t on day t is based on ordinary least squares regression with the estimators ^ αi and ^ βi: JOURNAL OF APPLIED ECONOMICS 7 et al. (2020), Kim (2023)), Markoulis and Neofytou (2019), Sharma and Nicolau (2020), Shin et al. (2021) and Teitler-Regev and Tavor (2023). To strengthen the significant results obtained for announcements with an exact location we additionally present Figures 2 & 3. Figure 2 shows the results of the parametric and nonparametric tests during the 21 days around the event day, beginning on the tenth day before the announcement was published and ending ten days after the announcement was published. The dashed horizontal lines denote statistical significance at the 5% level. The lines in black and gray indicate the results of the parametric and nonparametric tests respectively. Analyzing the figure, it can be seen that the trend in the parametric tests is usually opposite to that in the nonparametric tests. The most volatile parametric and nonparametric tests are Patell and WSRT, respectively. Also, there are five days when the majority of the tests are statistically significant at the 5% level: t = −10, −8, −6, −2, and 0. However, calculating the strongest effect of the event according to CARt1;t2 and significance statistics highlights the effect in the [−3,+1] window. Figure 3 describes the cumulative percentage of announcements with negative CAR3;t2 during the seven days around the event day, beginning three days before the announcement and ending three days after it, for announcements with general locations (marked in gray) and exact locations (marked in black). It can be seen from the figure that, on average, 51.5% of the announcements with general locations and 62.1% of those with exact locations have a negative CAR3;t2 during the test period. The result supports the statistically significant benefit gained from CARt1;t2 in announcements with exact locations in all the event windows, compared to the lack of change in announcements with general locations in most of the event windows. 5. Robustness checks This section describes two robustness checks to provide corroborating evidence for the empirical results presented in the previous section. Results of the first robustness test are presented in Table 3 and 4; they compare investors’ attention in the short term to influence over the medium term. Panels A and B of the table present the results for the [−10, +10] and the [−3, +1] windows respectively. The results in Table 4 show that for the most part, announcements with a general location published on Airbnb do not affect hotel stock prices, but announcements with specific locations do influence stock price results in all window types. The robustness test also shows that the window with the highest significance is [−3, +1] with CAR −3,+1 = −1.682% (ORDIN = −5.363) and CAR −3,+1 = −1.674% (ORDIN = −5.283) in the [−10, +10] and [−3,+1] windows respectively. The second robustness test, presented in Table 5, tests the effect of announcements published on Airbnb on hotel stock prices using two accepted models for calculating normal return: the index model (IM), presented in panel A, and mean adjusted returns (MAR), presented in panel B. This table also leads to the conclusion that generally, announcements with general locations posted on the Airbnb site do not affect hotel stock prices. For announcements with an exact location, an effect is seen 14 T. TAVOR AND S. TEITLER-REGEV Table 4. Cumulative abnormal return (CAR) behavior for general and exact locations in the short term. Panel A: Event window [−10,+10] Panel B: Event window [−3,+1] General location Exact location General location Exact location Daily time CAR(%) ORDIN CAR(%) ORDIN CAR(%) ORDIN CAR(%) ORDIN Event window surrounding the event day CAR[−1,+1] 0.012 0.112 −1.143*** −4.705 0.012 0.110 −1.135*** −4.626 CAR[−3,+1] 0.015 0.110 −1.682*** −5.363 0.015 0.107 −1.674*** −5.283 CAR[−5,+5] 0.132 0.641 −1.288*** −2.768 CAR[−10,+10] 0.058 0.203 −1.579** −2.457 Preand post-event windows CAR[−3,0] 0.018 0.142 −1.450*** −5.167 0.016 0.129 −1.443*** −5.092 CAR[−2,0] 0.035 0.323 −1.218*** −5.012 0.034 0.315 −1.208*** −4.924 CAR[−1,0] 0.014 0.164 −0.911*** −4.592 0.013 0.149 −0.905*** −4.514 CAR[0,0] 0.026 0.418 −0.673*** −4.795 0.025 0.402 −0.670*** −4.727 CAR[0,+1] 0.024 0.269 −0.905*** −4.561 0.024 0.270 −0.901*** −4.495 CAR[0,+2] −0.037 −0.346 −0.812*** −3.342 CAR[0,+3] 0.079 0.638 −0.897*** −3.197 Panels A and B represent the CAR in the [−10, +10] and [−3, +1] windows, respectively. In each panel, the first two columns refer to CAR and t-statistics (displayed as ORDIN) of announcements with general location, and the last two columns refer to CAR and t-statistics (displayed as ORDIN) of announcements with exact locations. For p-values, *, **, and *** denote statistical significance at the 10%, 5,% and 1% levels, respectively. JOURNAL OF APPLIED ECONOMICS 15 Table 5. Cumulative abnormal return (CAR) behavior according to the IM and MAR models for general and exact locations. Panel A: Index Model (IM) Panel B: Mean Adjusted Returns (MAR) General location Exact location General location Exact location Daily time CAR(%) ORDIN CAR(%) ORDIN CAR(%) ORDIN CAR(%) ORDIN Event window surrounding the event day CAR[−1,+1] 0.033 0.302 −1.233*** −5.047 −0.049 −0.329 −0.770** −2.290 CAR[−3,+1] 0.041 0.293 −1.746*** −5.537 −0.074 −0.386 −1.238*** −2.853 CAR[−5,+5] 0.167 0.805 −1.407*** −3.007 −0.075 −0.263 −0.577 −0.896 CAR[−10,+10] 0.076 0.265 −1.774*** −2.745 −0.068 −0.174 −0.543 −0.611 Preand post-event windows CAR[−3,0] 0.043 0.348 −1.430*** −5.069 −0.112 −0.650 −1.138*** −2.931 CAR[−2,0] 0.057 0.530 −1.201*** −4.915 −0.056 −0.377 −0.968*** −2.880 CAR[−1,0] 0.035 0.398 −0.917*** −4.596 −0.086 −0.710 −0.670** −2.440 CAR[0,0] 0.047 0.753 −0.681*** −4.827 −0.090 −1.043 −0.472** −2.432 CAR[0,+1] 0.045 0.504 −0.997*** −4.998 −0.052 −0.430 −0.572** −2.085 CAR[0,+2] −0.003 −0.032 −0.965*** −3.952 −0.164 −1.101 −0.417 −1.239 CAR[0,+3] 0.109 0.873 −1.069*** −3.790 −0.090 −0.522 −0.350 −0.900 Panels A and B represent the CAR according to the index model (IM) and mean adjusted returns (MAR), respectively. In each panel, the first two columns refer to CAR and t-statistics (displayed as ORDIN) of announcements with general location, and the last two columns refer to CAR and t-statistics (displayed as ORDIN) of announcements with exact location. For p-values, *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. 16 T. TAVOR AND S. TEITLER-REGEV on stock prices for all types of windows according to the IM model, which considers the market return as a basis for calculating the normal return as in the market model. In the MAR model, most windows show an effect, but not all of them. A possible explanation is that this model does not include the market return in calculating the normal return, but only the average of historical returns on each stock. The second robustness test again shows that the window with the highest significance for announcements with an exact location is [−3, +1], with CAR −3, +1 = −1.746% (ORDIN = −5.537) and CAR −3,+1 = −1.238% (ORDIN = −2.853), according to IM and MAR respectively. In summary, both robustness tests show similar results to the main test, namely, that announcements with general locations published on the Airbnb website do not affect hotel stock prices, while announcements with an exact location have a negative effect on them. This leads to two major conclusions. The first is that investors with advance information who are exposed to announcements with an exact location on the Airbnb site can short sell hotel stocks in the area identified in an announcement three days before the announcement is posted and close the position one day after the announcement to make an excess profit. The second conclusion is that Airbnb provides a substitute to hotel services. 6. Conclusions and policy implications 6.1. Conclusions This study aimed to assess the impact of announcements disseminated on the Airbnb platform on the stock prices of hotel companies, with a specific focus on distinguishing between announcements specifying exact locations and those of a general nature. The investigation encompassed ten prominent countries globally, namely the United States, France, Australia, India, Japan, the United Kingdom, China, Germany, Thailand, and Spain, selected based on the extensive operational presence of Airbnb and the prevalence of publicly traded hotel entities within these countries. The results of the event study reveal a nuanced pattern in the relationship between Airbnb announcements and hotel stock prices. For the most part, there appears to be no discernible impact on hotel stock prices, from announcements featuring a general location, except within specific isolated time windows. This suggests that investors tend to maintain their investment strategies when confronted with announcements lacking exact locations, resulting in minimal fluctuations in hotel stock prices. Conversely, announcements specifying an exact location are associated with an adverse influence on the stock prices of hotels within the announcement area. CAR consistently exhibit negative trends across all tested windows, indicating a sustained negative impact from ten days prior to the announcement to 10 days following it. Notably, the primary impact of announcements delineating precise locations is concentrated within the five-day window [−3, +1] surrounding the event. This suggests that investors with early access to information may engage in strategic short selling of hotel company shares three days prior to the announcements, closing the position the day after the announcement. Simultaneously, the wider investing public may capitalize on short selling at the moment of the announcement, subsequently closing the position the day after, leading to an excess profit. Furthermore, the robustness of these findings was substantiated through four JOURNAL OF APPLIED ECONOMICS 17 additional tests, encompassing different event windows and models for calculating normal returns and reinforcing the consistency and reliability of the obtained results. This study is limited by its relatively short time frame, resulting in a limited number of years under study and, consequently, a restricted dataset of Airbnb announcements. Moreover, the geographical coverage of the study is confined to a relatively small number of countries. To enhance the robustness and generalizability of future research, it is recommended to include a more diverse set of countries, extend the temporal scope, and increase the volume of analyzed announcements. Additionally, forthcoming studies could explore alternative sources of information related to Airbnb announcements, enabling a more comprehensive examination of the adverse effects of announcements on hotel stock prices. 7. Policy implications These findings carry significant policy implications for both investors and regulatory bodies. The observed differential impact of Airbnb announcements based on location specificity underscores the importance of informed decision making and proactive investment strategies. Investors should be aware of the potential consequences associated with announcements specifying exact locations, considering the observed negative trends in hotel stock prices within the affected regions. Regulatory authorities may find merit in evaluating the information disclosure practices of platforms like Airbnb, especially concerning the specificity of location details in their announcements. There may be a need for enhanced transparency or guidelines to mitigate potential market distortions arising from the selective disclosure of precise location information. In conclusion, the study’s findings not only contribute to the understanding of the dynamics between platform-generated announcements and stock prices but also offer valuable insights for investors and policymakers seeking to navigate and regulate the evolving landscape of the hospitality industry in the context of emerging online platforms. Disclosure statement No potential conflict of interest was reported by the author(s). Notes on contributors Tchai Tavor, an associate professor at the Department of Economics and Management at Yezreel Valley College (YVC), possesses a profound academic background and an extensive research portfolio encompassing various domains such as economics, marketing, finance, and behavioral finance. In the realm of economics, Tavor has extensively investigated fundamental issues pertaining to optimal pricing, with a particular focus on authoring multiple scholarly articles concerning optimal price discrimination policies. Within the realm of finance, Tavor’s research efforts have spanned both theoretical and empirical domains. Notably, empirical investigations have entailed employing event study methodology to explore hypotheses regarding market efficiency. These endeavors aim to illuminate the process through which information disseminates and reaches investors via diverse events, while concurrently evaluating whether investors can exploit such information to generate abnormal profits. 18 T. TAVOR AND S. TEITLER-REGEV Sharon Teitler Regev holds a Ph.D. in Economics from the University of Haifa. She has a Master of Science in economics from the Technion, Israel Institute of Technology, and a Master of Science in Hotel administration from the University of Las Vegas. 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