Stock market reaction to the military conflict between Russia and Ukraine: an event study for the European tourism and hospitality industry

ABSTRACT

This paper examines the short-term market reaction of European tourism and hospitality industry to the beginning of the military conflict between Russia and Ukraine (24 February 2022). Using an event study, for a sample of 165 listed firms, we observe a negative and statistically significant stock price reaction at and around the beginning of the military conflict between Russia and Ukraine. These results are consistent with investor sentiment hypothesis and asset-pricing perspective. These reactions are reinforced or mitigated by firm-specific characteristics such as liquidity, size, profitability, and institutional ownership. Finally, we find that listed firms located in: (i) countries in which Russia and Ukraine are the largest source of inbound foreign tourists, and (ii) countries formerly occupied by the Soviet Union and/or that share a common border with Russia, tend to show more negative abnormal returns.

KEYWORDS:

• War
• tourism
• stock returns
• event study

1. Introduction

The financial literature shows that stock markets tend to react drastically to the emergence of geopolitical-type news/events, such terrorist attacks, or wars (Berkman & Jacobsen, 2006; Karolyi & Martell, 2010). However, the impact of these negative events is not identical across industries (e.g., Cam, 2008). According to the author, the tourism and hospitality industry is among the industries most vulnerable to terrorist attacks and wars. Previous studies mention that this type of event tends to change tourists’ risk-perception, leading them to choose safer destinations (Araña & León, 2008; Ghaderi et al., 2017). In addition to this substitution effect, a side effect (contagion effect) can occur, given that the occurrence of military attacks deteriorates the destination image of neighbouring countries belonging to the same region (Enders et al., 1992; Sönmez & Graefe, 1998b), with tourists often avoiding the countries that comprise this region.

In this study, we investigate the stock market reaction to the beginning of the military conflict between Russia and Ukraine (24 February 2022) on the European tourism and hospitality industry. We expect a negative and significant stock market reaction, consistent with the asset-pricing perspective (e.g., Fama, 1991; McQueen & Roley, 1993) and “negative sentiment effect” (e.g., Akhtar et al., 2011). According to the asset pricing perspective, the stock market returns tend to decrease when there is an expectation of a decline in the firm’s prospective cash flows and/or an expectation of an increase in discount rates. Furthermore, we test whether abnormal returns vary across tourism and hospitality listed firms and are driven by firm-specific and country-specific characteristics.

This empirical study is motivated by the fact that, despite the magnitude of the negative effects caused by these events and the expanding literature on event studies and investor sentiment, the effect of the war on stock markets has been relatively little examined in the financial theory (e.g., Hudson & Urquhart, 2015). Circumstances that have changed recently with the ongoing military conflict between Russia and Ukraine (e.g., Balli et al., 2022; Boubaker et al., 2022; Boungou & Yatié, 2022; Glambosky & Peterburgsky, 2022; Tosun & Eshraghi, 2022; Umar et al., 2022; Yousaf et al., 2022; Kumari et al., 2023; Martins et al., 2023b; Pandey & Kumar, 2023; Pandey et al., 2023) and cross-border tensions (e.g., Hassan et al., 2022; Kumari et al., 2022; Pandey et al., 2023 which are at the origin of several studies that have employed the methodology of the study of events. Furthermore, this military conflict between Russia and Ukraine has unique characteristics (e.g., Kostadinova, 2000; Lanoszka, 2016; Martins et al., 2023a): (i) it involves a military superpower (Russia); (ii) threatens basic values; (iii) it threatens to expand to other countries (especially those that were under the Soviet Union’s domination and share a common border with Russia); (iv) threatens to escalate into nuclear war. According to Berkman and Jacobsen (2006) and Karolyi and Martell (2010) military conflicts with these characteristics have all the ingredients to have a significant impact on the financial market. Two recent studies, using an event study methodology examined the impact of the Russia-Ukraine war on the hospitality equity sector market in 26 countries (Balli et al., 2022) and on 134 travel and leisure firms from 31 countries (Pandey & Kumar, 2023). The present study differs from these two studies by the fact that it analyses the tourism and hospitality industry in Europe, for a sample of 165 listed firms, a region which, due to being close to the conflict, tends to register a greater penalty with the outbreak of war (Federle et al., 2022). In addition, unlike the two previous studies, we conducted an analysis of the stock market impact of the ongoing war on various sub-sectors of the tourism and hospitality industry. A gap in the literature that this study intends to fill.

The tourism and hospitality industries were chosen as the object of study because they belong to a sector that tends to exhibit high exposure to terrorist attacks and wars and substantial drops are expected in firms’ market values resulting from these events (e.g., Cam, 2008). There are still other reasons why this industry is sensitive to these events and shows itself to be adequate for this investigation. First, these events are associated with negative sentiment or feelings of increased fear and risk, so travel and leisure seem to be a natural field in which this would manifest itself because, for many, traveling is not a necessity. Second, given the size and breadth of the tourism and hospitality industry, any impact of wars or terrorist attacks evidenced in the industry would indicate a widespread and economically significant phenomenon. Third, and perhaps most important, given that tourism and hospitality firms lack the ability to store capacity and inventory, effects tend to be immediate and difficult to smooth out over time.

Using an event study methodology for 165 listed tourism and hospitality stocks, we show that stock returns react negatively to the beginning of the military conflict between Russia and Ukraine. The decrease of prospective cash-flows and/or the expectation of an increase in discount rate leads investors to rebalance their investment portfolio (Liu et al., 2003). According to the optimal investment portfolio strategy, it is expected that in times of uncertainty investors shift their holdings from risky investments (tourism and hospitality firms are among those most at risk) to relatively safe investments (namely bonds). In the context of a military conflict, this theory implies a shift from “war-sensitive” industries, such as the tourism and hospitality industry, to less risky industries. The negative price movement of tourism and hospitality industry should reflect the reorganization of equity portfolios of investors under war threat. Finally, we show that tourism and hospitality stock reactions are reinforced or mitigated by firm-specific characteristics such as liquidity, size, institutional ownership, and profitability. We also find that firms located in: (i) countries in which Russia and Ukraine are the largest source of inbound foreign tourists, and (ii) countries formerly occupied by the Soviet Union and/or that share a common border with Russia, tend to show more negative abnormal returns.

The remainder of this paper is organized as follows. Section 2 reviews the relevant literature. Section 3 presents the testable research hypotheses. Section 4 provides the data and the event study methodology. In Section 5 we present and discuss our main findings. Section 6 concludes.

2. Literature revision

2.1. Impact of war on the tourism and hospitality industry

Tourism and security are inevitably interconnected phenomena, as security often emerges as the determining factor in the choice of a given destination (Boakye, 2012; Pizam & Mansfeld, 1996; Sönmez & Graefe, 1998a). Security issues related to terrorism, war and political instability tend to constitute barriers to the decision travel and affect decision-making of tourist when choosing a destination (e.g., Hall et al., 2004; Pizam & Mansfeld, 1996). Chen et al. (2005) argue that security is the most important factor when international tourists make travel decisions, therefore a negative impact of military conflict on tourism and hospitality returns is expected.

Tourists tend to compare different destinations based not only on typical travel expenses (i.e., accommodation, travel, meals, entertainment) but also on other associated costs, including war and terrorism (Sönmez & Graefe, 1998b). In this regard, Mitchell (1992) highlights the relevance of risk perception in the consumer-buying process. The perceived level of security of the destination country is one of the main decisive factors in the choice of a destination country to travel (Kapuściński & Richards, 2016; Sönmez & Graefe, 1998b; Wåhlberg & Sjöberg, 2000). There are some examples in the literature that show that the perceived risk tends to cause more damage in the destination than the real risk (e.g., Rittichainuwat, 2011). Media coverage that is given to an event, such as a war conflict, greatly contributes to risk perception. Media exposure often exacerbate the real risk of traveling to a given destination and contribute to shaping risk perceptions toward destinations affected by a war (e.g., Rittichainuwat & Chakraborty, 2009; Sönmez, 1998).

In this way, military conflicts, by causing a change in the risk-perception in these countries, starting to be seen as high-risk countries, tend to cause a substitution effect, and consequently a change in travel plans to safer destinations, with potential tourists to avoid regions where there have been military conflicts or simply postponing the decision to travel to these destinations (Araña & León, 2008; Balli et al., 2022; Ghaderi et al., 2017; Ivanov et al., 2017; Pandey & Kumar, 2023). In addition to the substitution effect, a side effect (contagion effect) can occur, given that the occurrence of military conflicts deteriorates the destination image of neighbouring countries belonging to the same region (Enders et al., 1992; Sönmez, 1998), with tourists often avoiding the countries that comprise this region. This phenomenon is also identified in the literature by the term generalization/contagion effect. Figure 1 summarizes the chain of impacts arising from a military conflict.

Figure 1. The chain of impacts arising from a military conflict.

Source: Figure adapted from Cró et al. (2020)

As a rule, empirical evidence reveals that war and terrorism have a negative and significant impact in tourist flows, and thus on the revenues and profitability of tourism and hospitality firms (e.g., Cam, 2008; Chen et al., 2005; Frey et al., 2007; Ghaderi et al., 2017; Perles-Ribes et al., 2018). In other words, security is a fundamental prerequisite for the success of the tourism and hospitality industry as tourists’ perception of risk has a significant impact on demand, causing insecure destinations to have difficulty attracting tourists (Seabra et al., 2013).

2.2. Stock market impact of war: the case of tourism and hospitality listed firms

Under the null hypothesis of efficient markets, market prices should reflect the significance of any unexpected news that influences cash flows (CF) (or dividends), or discount rates (R) (e.g., Fama, 1991). From a theoretical asset-pricing perspective, in its most simplistic form, the value of the stock prices (V) at time t are given by:

(1) $V_t=\underset{0}{\overset{\mathrm{\infty }}{\int }}{\mathbb{E}}_t\left[\mathit{C}{F}_t\left(\mathit{n}\right)\right]e^{-r_t\mathit{n}}\mathit{dn}$(1)

where ${\mathbb{E}}_t\left[\mathit{C}{F}_t\left(\mathit{n}\right)\right]$ are the expected cash-flows in the next n years and r_t is the expected discount rate in the next n years. According to the asset-pricing theory, the market value of shares tends to decrease when there is an expectation of a decline in the firm’s prospective cash flows and/or an expectation of an increase in discount rates. Changes in discount rates are the result, among other factors, of uncertainty about long-term growth rates or shifting risk attitudes (Campbell, 2000).

According to asset-pricing perspective is expected that military conflicts result in significant falls in stock prices of listed firms in the tourism and hospitality industry. This is because, on the one hand, there tends to be a reduction in firms’ cash flows, given the panic generated by the military conflict and resulting fear of travelling (e.g., Cam, 2008; Zopiatis et al., 2019). This negative demand shock had been identified in tourism and transportation industries post-11, September (e.g., Drakos, 2004). Cam (2008) also shows a drop in demand for tourism industry following 11 September. On the other hand, for most industries, including the tourism and hospitality industry, there is an increase in risk, which is reflected in an increase in discount rates, which leads investors to rebalance their investment portfolio (Liu et al., 2003). In the context of a military conflict, this theory implies a shift from “war-sensitive” industries (where we can include the tourism and hospitality industry) to less risky sectors.

Another important theory is behavioural finance, which emphasizes the role of psychological factors in decision-making, particularly in times of uncertainty and heightened risk. For example, Akhtar et al. (2011) highlight the importance of “negative sentiment announcements effect” (from the psychology literature) for understanding the market reaction to the emergence of negative events such as the case of the military conflict studied here. Hudson and Urquhart (2015) analysed the effect of World War II on the British stock market and found support for the “negativity effect”. The negative effect implies a special type of asymmetry, i.e., a negative reaction to bad news but a negligible reaction to good news. A practical example of this effect can be found in the study by Kaplanski and Levy (2010). The authors examine the effect of aviation disasters on stock prices. They provide evidence of a significant negative event effect with stock market losses of more than $60 billion per aviation disaster, whereas the estimated actual loss is not more than $1 billion. Military conflicts, such as aviation disasters, tend to have high media coverage that tends to provoke bad mood, anxiety, and fear – negative sentiment effects, which may induce investors to be more pessimistic, not to take risks, or both (Kaplanski & Levy, 2010).

Recent literature about the financial impact of the ongoing military conflict between Russia and Ukraine, employing an event study methodology, shows negative cumulative abnormal returns for the global/world (e.g., Boubaker et al., 2022; Boungou & Yatié, 2022), G20 (e.g., Yousaf et al., 2022) and European Union (e.g., Kumari et al., 2023) stock market indices. The military conflict between Russia and Ukraine was also responsible for the significant rise in the inflation (e.g., Maurya et al., 2023), a negative impact on the value of the global currencies (e.g., Chortane & Pandey, 2022) and a more severe market quality deterioration for foreign stocks compared to the stocks’ domestic counterparts (Clancey-Shang & Fu, 2023). This last effect is especially strong for firms from countries considered friendlier towards Russia. Regarding the tourism and hospitality sector, Balli et al. (2022) reveal that the impact of ongoing war on hospitality markets is somewhat limited for mature markets that have a more diversified tourism demand and energy imports. However, neighbouring countries, primarily dependent on Russia for tourism demand and energy imports, are highly vulnerable and severely affected. Therefore, the transmission of return shocks is severe for the neighbouring countries. A second study carried out by Pandey and Kumar (2023) suggests that the impacts are different for firms in different markets. While abnormal returns of firms in Europe, the Middle East, and Africa, as well as the Pacific, are negative and statistically significant, those in the Americas and Asia are insignificant. The authors also show that country-specific variables -travel and country competitiveness index, trade, and exchange rates; and firm-specific variables – stock price volatility, liquidity, size, leverage, and performance, significantly impact the cumulative abnormal returns.

2.3. Cross-sectional analysis of market impact

We evaluate the relation between the observed abnormal performance and a set of firm-specific and industry attributes found important by previous studies. They include size, leverage, liquidity, profitability, institutional ownership and two dummies controlling the listed firms located in countries that were previously part of the Warsaw Pact and/or share a common border with Russia and the listed firms located in countries where the Russian or Ukrainian hospitality and tourism market is among the Top 5 in terms of foreign inbound tourism flows. The set of firm-specific variables are based on empirical studies of Carter et al. (2022), Chen (2010), Leung and Lee (2006), Song et al. (2021) for the hospitality and tourism industry.

Firm size is one of the firm-specific standard control variables. Literature shows that firm size affects the firm’s market power advantage, economies of scale, and financial performance in the end. Titman and Wessels (1988) refer that large firms tend to diversify their businesses more efficiently and are less prone to bankruptcy. In general, firms with larger size have greater resources and the ability to raise funds when needed, which leads to positive market valuation. Carter et al. (2022) and Song et al. (2021) find a positive relation between abnormal returns and the variable firm size for hospitality and tourism industries.

Mayers (1977) argues that maintaining high liquidity might help in reducing firms’ financial distress. Given that the military conflict may depress firms’ sales, they are forced to seek liquidity to cover costs. According to Almeida et al. (2004) and Bates et al. (2009), liquidity acts as a precautionary buffer to adverse shocks. Under normal circumstances, adequate liquidity is important for a firm to ensure coverage of its recurring cash obligations. This need tends to be greater when negative events occur that can restrict firm revenues, as in the case of a military conflict. However, high liquidity might also indicate that available resources are not wisely invested, which may increase investors’ risk perception (Chen, 2010). Carter et al. (2022) and Song et al. (2021) reveal that firms with greater liquidity present less negative abnormal returns.

We include the effect of leverage in our cross-sectional analysis because firms with less debt, and therefore lower value of fixed debt payments, would be more likely to survive any financial stress caused by a military conflict (e.g., Carter et al., 2022; Song et al., 2021). Empirical results, however, are contradictory in this regard. While Carter et al. (2022) find an inverse relationship between leverage and abnormal returns, Song et al. (2021) state that restaurants with more leverage are more resilient to COVID-19 pandemic. Song et al. (2021) mention that investors may see some extra value in a restaurant firm with high leverage due to tax benefits from leverage.

Regarding the profitability variable, there is once again no consensus among the authors. Chen (2010) refers that investor prefer firms with high ROA because it is an indicator of management efficiency. Furthermore, firms with higher profitability, having greater access to credit, tend to experience less severe stock price declines than otherwise identical firms. Song et al. (2021) state that according to the perspective of shareholders and investors, the impact of a negative event on future financial performance of a firm may be greater for a firm that was more profitable than its competitors prior this negative event.

Like Leung and Lee (2006) and Song et al. (2021), we include a variable related to institutional ownership. Previous studies (e.g., Boehmer & Kelley, 2009; La Porta et al., 2002) state that institutional investors tend to be better informed than other market participants and they better focus on value maximization. Clancey-Shang and Fu (2023) argue that US market investors have more concerns over political risks with non-US-aligned political standings during war times. Leung and Lee (2005) found that institutional investors had a significantly positive effect on the performance of hospitality stocks. However, given the focus on the profitability of institutional investors and the fact that a military conflict could affect the optimal investment portfolio strategy (Liu et al., 2003), it is to be expected that institutional investors temporarily interpret the beginning of the military conflict as a high-risk event and react to it negatively, reducing the weight of their investment in “war-sensitive” industries.

Finally, we added to the cross-section regression, two dummy variables. The first dummy assumes the value of 1 for the tourism and hospitality firms located in countries that were previously part of the Warsaw Pact and/or share a common border with Russia. As mentioned in the previous two sections, the occurrence of a military conflict tends to deteriorate the destination image of neighbouring countries belonging to the same region (generalization/contagion effect) with tourists often avoiding the countries that comprise this region (Enders et al., 1992; Sönmez, 1998). The second dummy takes the value of 1 for the tourism and hospitality firms located in countries where the Russian or Ukrainian hospitality and tourism market is among the Top 5 in terms of foreign inbound tourism flows. Countries such as Turkey and Finland in which Russia is the largest source of foreign tourists to these destinations, it is expected that the military conflict will cause a drop in tourism and hospitality firms’ revenues and that they cannot be compensated with revenues from other destinations. Consequently, we expect a more negative effect on abnormal returns in these countries as a result of this military conflict.

3. Research hypotheses

We evaluate the following hypotheses:

[H1] The beginning of the military conflict between Russia and Ukraine (February 24, 2022) is associated with a significant negative stock market reaction for tourism and hospitality firms.

A significant negative stock market reaction for tourism and hospitality firms is consistent with cash flow hypothesis/asset-pricing perspective and “negative sentiment effect” reflecting that investor believe that a military conflict destroys value for listed firms. In line with Berkman and Jacobsen (2006) a strong reaction of the stock market is expected given the threat of grave damage and the involvement of a superpower on one side of the conflict. It is also expected that the negative consequences of the military conflict will extend to neighbouring countries and regions, a phenomenon identified in the literature by the term generalization/contagion effect (see Figure 1). According to the cash flow hypothesis/asset-pricing perspective, the stock market returns tend to decrease when there is an expectation of a decline in the firm’s prospective cash flows and/or an expectation of an increase in discount rates. Finally, military conflicts also impact investors’ optimal investment portfolio. Investors tend to rebalance their portfolio by shifting from “war-sensitive” industries to less risky industries.

[H2] Abnormal returns vary among tourism and hospitality listed firms and are driven by firm-specific and country-specific characteristics.

Significant differences in the cross-section of abnormal returns are evidence in favour of arguments that predict that the observed effects are associated with differential benefits across tourism and hospitality listed firms, depending on the specific characteristics of the firms and countries.

4. Data selection and event study methodology

4.1. Data

We use the date of the beginning of the military conflict between Ukraine and Russia (24 February 2022) as the event date to compute abnormal returns (ARs). The data used in the event study is collected from different sources. Tourism and hospitality stock returns were obtained from Refinitiv Eikon and computed using total return index.

Regarding the selection of tourism and hospitality listed firms, it was obtained through the following steps. First, we selected all the European tourism and hospitality listed firms. The selection of tourism and hospitality listed firms was based on the Refinitiv Eikon database. We have selected all European tourism and hospitality listed firms included in the Refinitiv’ index Hotels & Entertainment Services. This index is subdivided into the following sub-indices: (i) Leisure & Recreation; (ii) Casinos & Gambling; (iii) Restaurant & Bars; (iv) Hotels, Motels & Cruise Lines. Second, we eliminate all firms that do not have continuous quotations on the stock market and all those that became listed on the stock market after August 2021.¹ The final compilation consisted of 165 listed tourism and hospitality firms. Table 1 presents the distribution of firms by country and by industry. The UK, France, Germany, Turkey, and Sweden with 55, 25, 14, 13 and 10 tourism and hospitality listed firms, respectively, are the most represented in the table.² Finally, the distribution of tourism and hospitality listed firms by the tourism and hospitality sub-indices is the following: (i) Leisure & Recreation − 59 firms; (ii) Casinos & Gambling − 31 firms; (iii) Restaurant & Bars − 39 firms; (iv) Hotels, Motels & Cruise Lines − 36 firms.

Table 1. Distribution of tourism and hospitality listed firms by country and by industry.

Country	# Firms	Country	# Firms
Distribution by Countries
Austria	1	Netherlands	6
Belgium	1	Norway	1
Estonia	1	Poland	4
Finland	2	Portugal	7
France	25	Slovakia	1
Germany	14	Spain	6
Greece	1	Sweden	10
Ireland	2	Switzerland	3
Italy	8	Turkey	13
Lithuania	1	UK	55
Malta	3
Sub-Indices	# Firms	Sub-Indices	# Firms
Distribution by Industry
Casinos & Gambling	31	Leisure & Recreation	59
Hotels, Motels & Cruise Lines	36	Restaurant & Bars	39

This table presents the distribution of tourism and hospitality listed firms by country and by industry. # denotes the number of listed firms.

For the cross-sectional analysis we use five firm-specific variables: size, institutional ownership, liquidity, leverage, and profitability. The five firm-specific variables are calculated from the 2021 year-end accounting figures and were obtained from Refinitiv Eikon database. The Top 5 dummy variable was collected from OECD (2020)’ tourism trends and policies. Panel 6 of Table 2 provides control variables descriptive statistics from our dataset.

Table 2. Descriptive statistics of CARs and control variables and abnormal returns tests.

Variable	Mean	SD	25^th perc.	Median	75^th perc.	θ1	τ1
Panel 1: All Sample (165 firms)
CAR [−1,1]	−1.14%	4.46%	−2.16%	−1.03%	−0.04%	0.027**	0.039**
CAR [−1,5]	−1.53%	7.96%	−4.26%	−1.08%	−0.11%	0.012**	0.006**
CAR [−1,10]	−3.82%	13.79%	−8.28%	−2.27%	−0.21%	0.016**	0.018**
Panel 2: Casinos & Gambling (31 firms)
CAR [−1,1]	−0.89%	5.27%	−1.96%	−0.14%	1.13%	0.170	0.190
CAR [−1,5]	−0.92%	7.28%	−4.48%	−0.17%	2.30%	0.222	0.261
CAR [−1,10]	−1.85%	10.69%	−7.97%	−1.12%	3.07%	0.121	0.165
Panel 3: Hotels, Motels & Cruise Lines (36 firms)
CAR [−1,1]	−1.59%	4.91%	−2.87%	−1.36%	−0.29%	0.005***	0.007***
CAR [−1,5]	−3.66%	9.75%	−6.62%	−1.87%	−0.58%	0.006***	0.008***
CAR [−1,10]	−5.80%	15.58%	−9.05%	−3.58%	−0.76%	0.002***	0.006***
Panel 4: Leisure & Recreation (59 firms)
CAR [−1,1]	−1.70%	4.14%	−2.02%	−1.41%	−0.47%	0.006***	0.008***
CAR [−1,5]	−1.79%	7.63%	−3.36%	−1.66%	−0.28%	0.011**	0.015**
CAR [−1,10]	−2.90%	13.55%	−7.90%	−2.27%	−0.11%	0.015**	0.020**
Panel 5: Restaurant & Bars (39 firms)
CAR [−1,1]	−1.05%	3.93%	−1.91%	−0.94%	−0.10%	0.033**	0.040**
CAR [−1,5]	−1.67%	6.99%	−3.59%	−1.05%	−0.13%	0.027**	0.033**
CAR [−1,10]	−6.67%	14.34%	−10.95%	−2.63%	−0.15%	0.002***	0.005***
Panel 6: Control Variables – All Sample (165 firms)
SIZE	€1,723 ml	€4,855 ml	€0,453 ml	€2,201 ml	€10,560 ml
INST	24.5%	27.4%	1.2%	36.4%	46.4%
LIQ	27.0%	17.8%	12.8%	27.2%	47.2%
TLEV	10.8%	13.1%	5.2%	12.3%	31.8%
ROA	5.0%	2.6%	−0.1%	4.4%	10.2%
WP/CB	15.8%	36.5%	0	0	0
TOP5	12.8%	33.4%	0	0	0

This table presents descriptive statistics of CARs and control variables and the results of abnormal returns tests. All figures of firm-specific control variables are calculated from the 2021 year-end accounting figures. The firm-specific and country-specific variables are the following: SIZE_i is the total assets (natural logarithm of total assets, millions of Euro) for tourism or hospitality firm i; INST_i is the percentage of stock that are in possession of institutional investors (%) for tourism or hospitality firm i; LIQ_i is the ratio of current assets to total assets (%) for tourism or hospitality firm i; TLEV_i is the ratio of total debt to total assets (%) for tourism or hospitality firm i; ROA_i is the ratio of operating income to total average assets (%) for tourism or hospitality firm i; WP/CB_i is a dummy variable that assumes the value of 1 for the tourism and hospitality firms located in countries that were previously part of the Warsaw Pact and/or share a common border with Russia and 0 otherwise; TOP5i is a dummy variable that takes the value of 1 for the tourism and hospitality firms located in countries where the Russian or Ukrainian hospitality and tourism market is among the Top 5 in terms of foreign inbound tourism flows and 0 otherwise. θ₁ and τ₁ are the p-values of Brown and Warner (1980, 1985) t-test statistics and z-statistic for the sign test, respectively (see Serra, 2004, for more details). *, ** and *** denote statistical significance at the 10%, 5% and 1% level, respectively.

The descriptive statistics reveal that the average size of listed firms in the sample is €1,723 million. Given the heterogeneity of subsectors within the industry, it is noteworthy that in the top quartile, mostly consisting of casinos, the average firm size is five times higher than the overall average. Another noteworthy statistic is the low ROA of firms, with an average value of 5%. It is important to remember that the sector has recently experienced another negative event, COVID-19, which negatively influenced this indicator. Lastly, it is important to mention that 15.8% of the firms are located in countries near the military conflict and 12.8% of firms are in countries where Russia is among the largest sources of foreign tourists to these destinations.

4.2. Event study methodology

We employ the event study methodology t to measure the magnitude of stock price reaction to the onset of the military conflict between Russia and Ukraine. The market model³ was used to calculate the expected rate of return, as follows:

(2) $\mathit{E}\left(R_{\mathit{it}}\right)=\widehat{a_i}+\widehat{b_i}{R}_{\mathit{mt}}$(2)

where, E(R_it) represents the expected return rate of the tourism or hospitality stock i on the trading day t; R_mt is the country’s total return market index; a_i and b_i are the regression coefficients of the daily return rate of the tourism or hospitality stock i and the market return rate (STOXX Europe 600 index), respectively.

We use the date of the beginning of the military conflict between Ukraine and Russia (24 February 2022) as the event date to compute abnormal returns (ARs), which are obtained by the difference between observed returns of stock i on day t and the normal return generated by the market model, as follows:

(3) $\mathit{A}{R}_{\mathit{it}}=R_{\mathit{it}}-\mathit{E}\left(R_{\mathit{it}}\right)$(3)

The date of the beginning of the military conflict between Ukraine and Russia is designated as day t = 0. Daily returns are collected for the period (t = −140 to 20). The estimation and event periods were defined respectively as [−140, −21] and [−1, 10]. By cumulating the ARs over a particular time interval, we obtain the cumulative abnormal returns (CARs) as follows:

(4) $\mathit{CAR}\left[t_1,t_2\right]={\sum }_{t_1}^{t_2}\mathit{A}{R}_t$(4)

In the present study t₁=-1 and t₂ = 1, 5 or 10. In the analysis of the impact of the beginning of the military conflict between Ukraine and Russia on the stock market, three different time intervals for the CARs were considered: [−1,1], [−1,5] and [−1,10]. The descriptive statistics for CARs are shown in Panels 1 to 5 of Table 2.

We employ both parametric and non-parametric tests to assess the statistical significance of average abnormal returns. The parametric test statistic examined is Brown and Warner (1980, 1985) without crude dependence adjustment. The non-parametric test statistic is the sign test. For more details about the tests see Serra (2004).

4.3. Cross-sectional analysis

To estimate the impact of country- and firm-specific characteristics on the cross-sectional variation of abnormal returns, we estimate the following equation by OLS:

(5) $\mathit{CA}{R}_i={\beta }_0+{\beta }_{1}ln\left(\mathit{SIZ}{E}_i\right)+{\beta }_2\mathit{INS}{T}_i+{\beta }_3\mathit{LI}{Q}_i+{\beta }_4\mathit{TLE}{V}_i+{\beta }_5\mathit{RO}{A}_i+{\beta }_6\mathit{WP}/\mathit{C}{B}_i+{\beta }_7\mathit{TOP}{5}_i+{\epsilon }_i$(5)

where, CAR_i are the cumulative abnormal returns for tourism or hospitality firm i; SIZE_i is the total assets (natural logarithm of total assets, millions of Euro) for tourism or hospitality firm i; INST_i is the percentage of stock that are in possession of institutional investors (%) for tourism or hospitality firm i; LIQ_i is the ratio of current assets to total assets (%) for tourism or hospitality firm i; TLEV_i is the ratio of total debt to total assets (%) for tourism or hospitality firm i; ROA_i is the ratio of operating income to total average assets (%) for tourism or hospitality firm i; WP/CB_i is a dummy variable that assumes the value of 1 for the tourism and hospitality firms located in countries that were previously part of the Warsaw Pact and/or share a common border with Russia and 0 otherwise; TOP5_i is a dummy variable that assumes the value of 1 for the tourism and hospitality firms located in countries where the Russian or Ukrainian hospitality and tourism market ranks among the Top 5 in terms of foreign inbound tourism flows and 0 otherwise; ε_i is an i.i.d. error term.

Equation (5)(5) $\mathit{CA}{R}_i={\beta }_0+{\beta }_{1}ln\left(\mathit{SIZ}{E}_i\right)+{\beta }_2\mathit{INS}{T}_i+{\beta }_3\mathit{LI}{Q}_i+{\beta }_4\mathit{TLE}{V}_i+{\beta }_5\mathit{RO}{A}_i+{\beta }_6\mathit{WP}/\mathit{C}{B}_i+{\beta }_7\mathit{TOP}{5}_i+{\epsilon }_i$(5) can suffer from specification problems if unobservable country characteristics that influence tourism and hospitality stock returns are ignored. We use clustered robust standard errors to solve this problem. We group the standard errors by country and consider intra-cluster correlations or unobserved country heterogeneity in the equation. According to Wooldridge (2003), this procedure is equivalent to modelling country-specific random effects for the intercept.

5. Results

5.1. Abnormal returns

Panels 1 to 5 of Table 2 show the cumulative abnormal returns for tourism and hospitality firms around the beginning of the military conflict between Russia and Ukraine. We observe a negative and statistically significant stock price reaction around the beginning of the military conflict between Russia and Ukraine. The parametric and non-parametric tests show that there is a level of statistical significance of at least 5%. The only exception is the Casino & Gambling sub-index which, despite having negative abnormal returns, are not statistically significant. This outcome can be easily understood due to the nature of the business, in which much of the revenue is generated by customers who do not even need to travel to European countries, since most of the revenue is generated through online platforms.

The other results, for the total sample and the other three sub-indices: Hotel, Motels & Cruise Lines; Leisure & Recreation and Restaurants & Bars, are consistent with investor sentiment and asset-pricing hypotheses This is because it is expected that stock market responds negatively to the beginning of the military conflict between Russia and Ukraine, reflecting the expectation of a decline in the firm’s prospective cash-flows and/or an expectation of an increase in discount rate. It is important to remember that this military conflict encompasses all the factors likely to result in a conflict with a high impact on the stock market: (i) the conflict started with violence and human losses; (ii) it involves severe values threats and (iii) a military superpower is directly involved. According to Berkman and Jacobsen (2006) and Karolyi and Martell (2010), military conflicts with these characteristics are typically associated with substantial losses in firms’ market values. In addition, firms in the tourism and hospitality industry are among the industry’s most negatively exposed to this type of events (e.g., Cam, 2008).

The decrease of prospective cash-flows and/or the expectation of an increase in discount rate leads investors to rebalance their investment portfolios (Liu et al., 2003). According to the authors, it is expected that in times of uncertainty investors shift their holdings from risky investments (such as tourism and hospitality firms, which are among those most at risk) to relatively safe investments (such as bonds). In the context of a military conflict, this theory implies a shift from “war-sensitive” industries, such as the tourism and hospitality industry, to less risky industries.

Finally, the results reveal more negative abnormal returns for the longer time window – time window [−1; 10]. We believe this can be attributed to the initial perception that the conflict would be of short duration. In fact, the market eventually expected a Blitzkrieg campaign,⁴ i.e., a surprise and intense military attack intended to bring about a swift victory. However, this was not the case. What is happening is a military conflict that tends to last a long time, with threats of nuclear war. This likely amplified investor anxiety and led to a greater-than-expected negative impact on riskier stock prices.

In short, the results obtained for tourism and hospitality CARs lead us to reject the null hypothesis of no significant aggregate market reaction to the beginning of the military conflict between Russia and Ukraine. As expected, the beginning of the military conflict between Russia and Ukraine is associated with a negative and significant impact on stock prices. The only exception is the Casino & Gambling sub-index.

5.2. Cross-sectional analysis

We regress cumulative abnormal returns (CAR [−1,1]; CAR [−1,5] and CAR [−1,10]) against a set of firm-specific and country-specific variables as proposed by the empirical specification in (5). Cross-sectional estimates are made for the entire sample (165 firms) and for each of the four sub-indices considered: (i) Leisure & Recreation; (ii) Casinos & Gambling; (iii) Restaurant & Bars; (iv) Hotels, Motels & Cruise Lines, since the threats to firms that constitute each of these sub-indices seem to be different. The results are presented in Table 3.

Table 3. Cross-sectional analysis of CARs for the European tourism and hospitality industry.

	Panel 1: Entire Sample			Panel 2: Hotel, Motel & Cruise Lines			Panel 3: Leisure & Recreation			Panel 4: Restaurants & Bars			Panel 5: Casino & Gambling
	[−1,1]	[−1,5]	[−1,10]	[−1,1]	[−1,5]	[−1,10]	[−1,1]	[−1,5]	[−1,10]	[−1,1]	[−1,5]	[−1,10]	[−1,1]	[−1,5]	[−1,10]
Constant	−0.051 (−0.680)	−0.082 (−1.490)	−0.129 (−1.677)	−0.040 (−1.463)	−0.010 (−0.145)	0.034 (0.267)	−0.016 (−1.568)	−0.159 (−1.516)	−0.229 (−0.651)	−0.017 (−0.201)	−0.062 (−0.401)	−0.183 (−1.030)	−0.122 (−0.450)	−0.049 (−0.619)	−0.086 (−0.899)
Ln(SIZE)	0.033** (2.133)	0.052** (2.465)	0.065** (2.450)	0.024** (2.234)	0.021** (2.121)	0.037** (2.175)	0.058** (2.525)	0.074** (2.563)	0.073** (2.442)	0.037* (1.722)	0.050** (2.477)	0.059** (2.522)	0.094** (2.695)	0.089** (2.416)	0.080** (2.382)
INST	−0.081** (−2.500)	−0.033** (−2.220)	−0.064** (−2.429)	−0.062** (−2.511)	−0.086** (−2.441)	−0.072** (−2.180)	−0.092** (−2.533)	−0.081** (−2.238)	−0.069* (−1.882)	−0.059** (−2.221)	−0.089** (−2.674)	−0.079** (−2.621)	−0.084** (−2.409)	−0.075** (−2.317)	−0.074** (−2.391)
LIQ	0.025* (1.773)	0.025* (1.700)	0.030* (1.860)	0.047*** (3.623)	0.090*** (4.050)	0.153** (5.981)	0.037* (1.985)	0.096* (1.942)	0.162*** (2.755)	0.025* (1.699)	0.028* (1.925)	0.032* (1.879)	0.029* (1.809)	0.022* (1.750)	0.027* (1.844)
TLEV	−0.011 (−1.631)	−0.002 (−1.358)	−0.001 (−0.571)	0.003 (1.510)	0.003 (1.373)	0.008 (1.411)	0.003 (0.834)	0.006 (1.569)	0.009 (1.555)	0.006 (0.807)	0.005 (0.542)	0.007 (0.935)	0.013 (1.563)	0.008 (0.584)	0.009 (0.799)
ROA	−0.043* (−1.888)	−0.037* (−1.826)	−0.058* (−1.914)	−0.061** (−2.343)	−0.082** (−2.383)	−0.097** (−2.611)	−0.093** (−2.173)	−0.088* (−1.892)	−0.062** (−2.435)	−0.045** (−2.201)	−0.059** (−2.215)	−0.095** (−2.611)	−0.040* (−1.822)	−0.046* (−1.911)	−0.058** (−2.203)
WP/CB	−0.018** (−2.488)	−0.018*** (−2.988)	−0.036*** (−3.706)	−0.011** (−2.235)	−0.019** (−2.260)	−0.015** (−2.343)	−0.014** (−2.410)	−0.019** (−2.585)	−0.015** (−2.521)	−0.028** (−2.277)	−0.033** (−2.320)	−0.037** (−2.449)	−0.013** (−2.197)	−0.036** (−2.266)	−0.044** (−2.358)
TOP5	−0.055** (−2.477)	−0.024** (−2.379)	−0.040** (−2.405)	−0.074** (−2.391)	−0.060** (−2.442)	−0.081** (−2.543)	−0.053** (−2.310)	−0.082** (−2.333)	−0.075** (−2.221)	−0.059** (−2.211)	−0.070** (−2.208)	−0.077** (−2.311)	−0.058** (−2.333)	−0.055** (−2.166)	−0.060** (−2.458)
# Obs.	165	165	165	36	36	36	59	59	59	39	39	39	31	31	31
Adj. R^2	0.115	0.167	0.132	0.598	0.678	0.638	0.572	0.579	0.611	0.537	0.508	0.510	0.479	0.406	0.411

Our results indicates that the effect of liquidity is positive and statistically significant, aligning with the perspective that liquidity serves as a premium that provides investors with insurance against risks that are otherwise challenging to hedge (e.g., Almeida et al., 2004; Bates et al., 2009). As has been highlighted earlier, the military conflict may depress firms’ sales and revenues, therefore, companies with an adequate level of liquidity tend to be better prepared to fulfil their recurring cash obligations. Identical result was found by Carter et al. (2022) and Song et al. (2021)

Regarding profitability, our findings suggest that past firm’s profitability is negatively related with abnormal returns. Thus, the notion that the impact of a negative event on a firm’s future financial performance is greater for a firm that was more profitable than its competitors prior to this negative event seems to prevail (Song et al., 2021).

The SIZE variable exhibits a positive and statistically significant coefficient, in line with the literature, which indicates that larger firms appear to be less affected by negative events like the current military conflict, than smaller ones (e.g., Carter et al., 2022; Song et al., 2021). Titman and Wessels (1988) refer that large firms tend to diversify their businesses more efficiently and are less susceptible to bankruptcy. In general, larger firms possess greater resources and the ability to raise funds when necessary, resulting in a positive market valuation from investors.

The coefficient associated with the INST variable is negative and statistically significant, aligning with existing the literature. As previously mentioned, institutional investors tend to be better informed compared to other market participants, and they have a greater focus on value maximization (e.g., Boehmer & Kelley, 2009; La Porta et al., 2002).

While debt and hence leverage is reported to have a significant impact on tourism and hospitality firm’s value during negative events, such as COVID-19 (e.g., Carter et al., 2022; Song et al., 2021), we find that TLEV variable has no significant effect on abnormal returns at the beginning of the military conflict between Russia and Ukraine. As discussed in the literature review, there are two contradictory signal arguments between leverage and abnormal returns. If, on the one hand, there are those who refer to the existence of a negative relationship between leverage and CARs (e.g., Carter et al., 2022), on the other hand, there are those who argue that firms with more leverage are more resilient to the emergence of negative events, due to tax benefits from leverage (e.g., Song et al., 2021. In the present study, these two opposing arguments appear to eliminate each other, leading to the non-statistical significance of the TLEV variable in the abnormal returns.

Finally, regarding the two country-specific dummies variables – WP/CB and TOP5, we observe negative and statistically significant coefficients. Regarding the dummy variable WP/CB, as mentioned before, the military conflict between Russia and Ukraine tends to deteriorate the destination image of neighbouring countries within the same region (generalization/contagion effect) leading tourists to often avoid the countries that comprise this region (Enders et al., 1992; Sönmez, 1998). The negative coefficients found for the second dummy variable (TOP5) are attributable to the expectation that the military conflict will result in reduced revenue for firms in the tourism and hospitality industry located in countries where Russia and Ukraine are the principal sources of inbound foreign tourists for those destinations. It is not an easy task for these destinations to replace the flows of Russian or Ukrainian tourists with other alternative tourist markets, in order to mitigate the negative effects caused by the decrease in the expected revenues of firms in the tourism and hospitality industry.⁵

5.3. Robustness check

We conduct an additional robustness check to assess the sensitivity of our findings to the addition of two new variables concerning the level of volatility (VOL) and past average returns (PAST) in the cross-sectional regressions by sub-sector. We find positive and statistically significant coefficients for the VOL variable across all subsectors. This relationship means that sub-sector CARs are higher for more volatile stocks, as suggested by Duffee (1995). An identical result was found by Pandey and Kumar (2023) in their study on the impact of the Russia-Ukraine war on the tourism sector. Regarding the PAST variable, there is no statistically significant impact on sub-sector CARs. The results are displayed in Table 4.

Table 4. Cross-sectional analysis of CARs for subsectors of the European tourism and hospitality industry: robustness check results.

	Panel 1: Hotel, Motel & Cruise Lines			Panel 2: Leisure & Recreation			Panel 3: Restaurants & Bars			Panel 4: Casino & Gambling
	[−1,1]	[−1,5]	[−1,10]	[−1,1]	[−1,5]	[−1,10]	[−1,1]	[−1,5]	[−1,10]	[−1,1]	[−1,5]	[−1,10]
Constant	−0.027 (−1.354)	−0.022 (−0.422)	0.024 (0.172)	−0.013 (−1.434)	−0.223 (−1.616)	−0.245 (−0.711)	−0.055 (−0.322)	−0.044 (−0.325)	−0.243 (−1.145)	−0.159 (−0.589)	−0.121 (−0.888)	−0.111 (−0.995)
Ln(SIZE)	0.024** (2.230)	0.022** (2.155)	0.035** (2.144)	0.052** (2.499)	0.072** (2.511)	0.075** (2.455)	0.039* (1.744)	0.048** (2.401)	0.057** (2.509)	0.090** (2.601)	0.086** (2.332)	0.078** (2.311)
INST	−0.060** (−2.425)	−0.085** (−2.422)	−0.069** (−2.155)	−0.089** (−2.489)	−0.079** (−2.202)	−0.070* (−1.899)	−0.061** (−2.230)	−0.085** (−2.611)	−0.081** (−2.645)	−0.085** (−2.415)	−0.077** (−2.389)	−0.070** (−2.309)
LIQ	0.047*** (3.555)	0.087*** (3.895)	0.145** (5.865)	0.037* (1.980)	0.095* (1.922)	0.166** (2.766)	0.028* (1.712)	0.029* (1.941)	0.033* (1.903)	0.029* (1.815)	0.022* (1.758)	0.027* (1.849)
TLEV	0.003 (1.483)	0.003 (1.390)	0.007 (1.343)	0.003 (0.825)	0.006 (1.555)	0.008 (1.533)	0.006 (0.812)	0.005 (0.555)	0.007 (0.944)	0.012 (1.544)	0.008 (0.611)	0.009 (0.812)
ROA	−0.057** (−2.222)	−0.078** (−2.212)	−0.092** (−2.533)	−0.089** (−2.111)	−0.085* (−1.872)	−0.059** (−2.387)	−0.044** (−2.188)	−0.055** (−2.175)	−0.090** (−2.533)	−0.038* (−1.801)	−0.044* (−1.898)	−0.055** (−2.145)
WP/CB	−0.011** (−2.245)	−0.018** (−2.234)	−0.015** (−2.334)	−0.013** (−2.389)	−0.018** (−2.533)	−0.015** (−2.515)	−0.027** (−2.265)	−0.031** (−2.275)	−0.033** (−2.388)	−0.013** (−2.188)	−0.036** (−2.272)	−0.043** (−2.333)
TOP5	−0.070** (−2.345)	−0.058** (−2.377)	−0.080** (−2.535)	−0.051** (−2.292)	−0.079** (−2.285)	−0.074** (−2.218)	−0.057** (−2.200)	−0.068** (−2.188)	−0.075** (−2.289)	−0.057** (−2.301)	−0.054** (−2.154)	−0.058** (−2.411)
VOL	0.008** (2.101)	0.009** (2.121)	0.009** (2.133)	0.008** (2.071)	0.009** (2.143)	0.009** (2.243)	0.006** (2.066)	0.006** (2.089)	0.006** (2.101)	0.008** (2.244)	0.008** (2.231)	0.008** (2.210)
PAST	0.003 (1.345)	0.003 (1.325)	0.004 (1.388)	0.004 (1.411)	0.005 (1.569)	0.005 (1.543)	0.004 (1.211)	0.004 (1.208)	0.003 (0.935)	0.004 (1.300)	0.005 (1.420)	0.005 (1.411)
# Obs.	36	36	36	59	59	59	39	39	39	31	31	31
Adj. R^2	0.608	0.682	0.643	0.577	0.584	0.615	0.542	0.514	0.516	0.484	0.411	0.415

This table presents the estimation results of the robustness tests for the subsector CARs of the European tourism and hospitality industry. Panels 1, 2, 3 and 4 present market reactions for Hotel, Motels & Cruise Lines; Leisure & Recreation; Restaurants & Bars and Casino & Gambling sub-indices, respectively. The dependent variables are the tourism and hospitality CARs for three different time windows: [−1,1]; [−1,5] and [−1,10]. The firm-specific and country-specific variables are the following: SIZE_i is the total assets (natural logarithm of total assets, millions of Euro) for tourism or hospitality firm i; INST_i is the percentage of stock that are in possession of institutional investors (%) for tourism or hospitality firm i; LIQ_i is the ratio of current assets to total assets (%) for tourism or hospitality firm i; TLEV_i is the ratio of total debt to total assets (%) for tourism or hospitality firm i; ROA_i is the ratio of operating income to total average assets (%) for tourism or hospitality firm i; WP/CB_i is a dummy variable that assumes the value of 1 for the tourism and hospitality firms located in countries that were previously part of the Warsaw Pact and/or share a common border with Russia and 0 otherwise; TOP5i is a dummy variable that takes the value of 1 for the tourism and hospitality firms located in countries where the Russian or Ukrainian hospitality and tourism market is among the Top 5 in terms of foreign inbound tourism flows and 0 otherwise; VOL_i is the standard deviation stock return of firm i, 20 days before the event; PAST_i is the CARs of the last 20 days before the event for firm i. *, ** and *** denote statistical significance at the 10%, 5% and 1% level, respectively. t-values from country-clustering robust errors are used in parentheses. # Obs. denotes the number of observations used in the estimation.

6. Conclusions

This paper analyses the stock market impact of the beginning of the military conflict between Russia and Ukraine within a sample of 165 publicly listed firms in the tourism and hospitality industry. According to the asset-pricing perspective and investor sentiment hypothesis is expected a negative and significant impact on stock prices with the beginning of the military conflict. The emergence of panic due to the military conflict and the resultant travel-related fears are likely to lead to a decline in demand within the tourism and hospitality industry and an increase of business risk (Cam, 2008; Campbell, 2000; Zopiatis et al.; Zopiatis et al., 2019), resulting in a decrease in the firms’ market value. This decline in market value of tourism and hospitality firms is not confined to the countries directly involved in the conflict, but tends to spread throughout Europe, as the military conflict tends to deteriorate the destination image of neighbouring countries, through a phenomenon described in the literature by the term generalization/contagion effect (Enders et al., 1992; Sönmez, 1998). Our findings confirm this hypothesis.

The decrease of prospective cash-flows and/or the expectation of an increase in discount rates leads investors to rebalance their investment portfolios (Liu et al., 2003). According to the authors, it is expected that in times of uncertainty, investors tend to shift their holdings from risky investments or “war-sensitive” industries (such as tourism and hospitality firms, which are among the most vulnerable) to relatively safe investments (such as bonds). Therefore, price movements should reflect the restructuring of investors’ equity portfolios in response to threat of war.

Our findings also provide insights into which firm-specific characteristics emerge as value drivers during the military conflict between Russia and Ukraine, our results indicate that European tourism and hospitality listed firms with past characteristics of higher liquidity and size along with lower profitability and institutional ownership, demonstrate greater resilience to stock declines, as a result of the beginning of the military conflict between Russia and Ukraine compared to their counterparts in the same industry. We also find that firms located in: (i) countries where Russia and Ukraine are the largest source of inbound foreign tourists, and (ii) countries formerly occupied by the Soviet Union and/or that share a common border with Russia, tend to show more negative abnormal returns. Firms located in these countries face heightened risk and more substantial revenue decrease due to the heightened fear among tourists and the negative sentiment of investors.

Our findings carry significant implications for both tourism and hospitality firms’ management and governments. Firstly, the presented results offer insights for investors seeking to minimise war-related risk through industry diversification. Given the risk exposure of the tourism and hospitality sector, this industry is among the industries whose weight tends to decrease in investors’ investment portfolios when a military conflict arises. Secondly, at the destination level, our findings hold significance for tourism policymakers striving to alleviate the adverse effects of such events. Establishing a consensus on specific measures that mitigate a country’s risk exposure and ensure the sustainability of tourism and hospitality industry can facilitate the development of more effective governmental responses in the future. The development of pre- and post-event strategies such events and implementing specific actions by the relevant institutions will reinforce the confidence of current and potential investors, safeguarding the industry´s financial interests. Thirdly, tourism organizations may undertake proactive measures in strategic market diversification, namely when one of the countries involved in the military conflict serves as the main source of tourists to that country. Finally, tourism and hospitality firms should aim to be more resilient to these negative events in the future. This involves paying closer attention to firm-specific characteristics related to liquidity, size, profitability, and institutional ownership, in order to mitigate the decline in market value caused by military conflicts.

Acknowledgments

This paper is financed by Portuguese national funds through FCT – Fundação para a Ciência e a Tecnologia, I.P., projects numbers UIDB/00685/2020 (António Martins); UIDB/04007/2020 (Susana Cró) and UIDB/04470/2020 (Pedro Correia).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Fundação para a Ciência e a Tecnologia [UIDB/04470/2020]; Fundação para a Ciência e a Tecnologia [UIDB/00685/2020]; Fundação para a Ciência e a Tecnologia [UIDB/04007/2020].

Notes on contributors

António Miguel Martins

Dr. António Miguel Martins is an assistant professor at the Faculty of Social Sciences of the University of Madeira, Portugal. He holds a PhD in Management at the University of Porto, Portugal. His current research activities include finance, banking, real estate, hospitality management and tourism.

Pedro Correia

Dr. Pedro Correia is an assistant professor at the Faculty of Social Sciences of the University of Madeira, Portugal. He holds a PhD in Management at the University of Vic, Spain. His current research activities include digital management, performance measurement and management in hospitality and tourism.

Susana Cró

Dr. Susana Cró is an assistant professor at University of Évora, Portugal. She holds a PhD in Tourism at the Institute of Geography and Spatial Planning (IGOT) of the University of Lisbon, Portugal. Her current research activities include crisis and disasters management, performance measurement and management in hospitality and tourism.

Notes

¹ The date of beginning of forecast interval.

² Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Hungary, Luxembourg, Romania, Russia, Serbia, Slovenia and Ukraine do not present tourism and hospitality industry’s listed firms eligible for the present study.

³ For more details, please see MacKinlay (1997) and Serra (2004).

⁴ https://voxukraine.org/en/five-failures-of-putin-s-blitzkrieg-in-ukraine/; https://ecfr.eu/article/view-from-kyiv-putins-failed-blitzkrieg-and-the-future-of-europe/.

⁵ In this regard, see for example: https://www.aljazeera.com/news/2022/5/2/lack-of-russian-visitors-to-turkey-contributes-to-tourism-slump; https://www.dw.com/en/turkeys-tourism-sector-battling-fallout-from-the-war-in-ukraine/a-61698095.