Did COVID-19 tourism sector supports alleviate investor fear?

The COVID-19 pandemic presented a dynamic black-swan event to which governments implemented support programmes to reduce sectoral probability of default. This research analyses investor response to such assistance, designed to mitigate the effects of the pandemic upon international aviation and tourism. Investor confidence in such support schemes is estimated through short-term abnormal returns. Results indicate significant differential behaviour, with fiscal policy found to be a dominant and largely effective mechanism generating median abnormal returns of 2.17 %. Specific assistance programmes relating to COVID-19 loan facilities, and the provision of pandemic relief packages significantly alleviated short-term investor concerns with median abnormal returns estimated between 2.87 % and 3.89 % respectively. Our empirical results offer investors and policymakers an additional layer of information.

Introduction

Externally, the world seemed to respond to the news of a ‘mystery pneumonia’ in China in late-2019, much as it would another strain of winter flu, with no obvious signs of major concerns or surprise. Internally, however, the more comprehensive internal flows of information were having effects across a range of sectors in China well before the January 2020 WHO announcement (Corbet, Efthymiou, et al., 2021; Corbet, Hou, et al., 2021). The eventual admission of the existence of a global pandemic (Conlon et al., 2020) left investors, worldwide, uncertain as to how significant the risks surrounding global transmission would be and Governments were faced with decisions about how, when and if to intervene in their respective economies. Acting too soon could generate unnecessary panic and unwarranted financial side-effects, while acting too late could lead to potential economic and social catastrophe.

While evidence suggests that China alone bore the worst of the pandemic in January 2020, it is not until the period beginning in February through May that international governments introduced several substantial packages in an attempt to mitigate the worst effects of the early stages of transmission.1 With the closure of international borders and the implementation of necessary population controls to stem the substantiative effects of the pandemic, it came as no surprise that both the aviation and tourism sectors would be amongst the most damaged in the economy.

Abnormal returns are an important barometer of idiosyncratic market performance, that is, those returns explicitly related to the company while omitting broad economic trends. It is important to note that positive abnormal returns can occur in circumstances that could be perceived, in the broader sense, to be negative in nature. We must continue to maintain sight of the fact that crises might not be specifically bad for all market participants, and while falling abnormal returns might signal diminishing corporate performance, they might also act as a signal for other, value-seeking investors to enter the market seeking profit. Such a cycle leads to resilience within pricing dynamics, and where such investors do not enter such markets, one could consider this to be a significant cause for concern and a signal that short term negative abnormal returns might not be temporary in nature. This is particularly important when considering investor evaluations in the beginning, and the subsequent contagion phases of the COVID-19 pandemic. Maneenop and Kotcharin (2020) examined the short-term impact of the pandemic on fifty-two listed airlines using an event study methodology, presenting evidence of differential impacts.

The aviation sector, which itself has significant experience with sharp negative events such as the loss of an airliner for example, has much previous experience when navigating corporations out of acute crises (Kaplanski & Levy, 2010). Therefore, persistent depressed abnormal returns can present a worrying negative signal with regards to future probability of success, therefore requiring third-party intervention such as government assistance or bailouts. However, third-party intervention can often act as a catalyst for positive sentiment, where such optimism itself could warrant the attention of value seeking investment. Such a process could potentially add further capital investment and generate positive abnormal returns, while reaffirming further signals of positivity to counteract the challenges and crisis requiring such intervention. While many governments might seek to support markets through employee assistance, it is often important to point out, that should the corporations that such employees work for not survive significant negative shocks, such employee assistance programmes could be considered futile, and very wasteful attempts to circumnavigate impending collapse. During the outbreak of the pandemic, it is important to note that staff costs were amongst the easiest corporate expenditure to reduce through furlough schemes, while fixed costs such as building and machinery for the most part, remained.2

Such research is important when considering the allocation of scarce government resources and their efficacy during exceptional crisis events, particularly within sectors that are so centrally important with regards to employment and revenue generation from international travel. Many tourist regions do not possess secondary sources of revenue generation apart from those directly tourism-related; therefore, sectoral collapse could lead to multiple secondary issues focused on the decline of population welfare without outside assistance. Governments are faced with a difficult decision, whether to provide supports to underpin employment and business viability, or should they not provide adequate support, it is most likely that collapse would occur, necessitating unemployment and regional regeneration supports. It has become quite clear in recent financial crises that the maintenance of public confidence was paramount when attempting to mitigate the effects of financial crises, and an immediate return to profitability when conditions allow. Such responses are driven primarily by government stance, and heavily dependent on systems free of corruption, inefficiency, and reduced levels of moral hazard and asymmetric information.

Understanding investor responses to the effects on the projected growth and impacts upon profitability, are vital when attempting to understand as to how influential assistance and support packages are when implemented to mitigate the effects of crises such as that of the COVID-19 pandemic. The focus of the research presented here is to consider such issues in relation to COVID-19 and the tourism and aviation sector.

Based upon a thorough review of the variety of government support-mechanisms and market intervention techniques used,3 both during the beginnings of, and throughout the development of, the COVID-19 pandemic, this research attempts to specifically identify the effects these mitigating actions had on the idiosyncratic risks of both aviation and tourism stocks in ten of the largest financial markets around the world. Effects are analysed both in terms of measured returns and volatility, in the days after the implementation of respective government actions. In general terms, we assume that the sector's expectation was for the government to attempt to alleviate the sectoral decline of confidence while underpinning job security, and reducing the probability of default of economically and strategically important sectors.

More specifically, our analysis helps answer several very important questions: First, did monetary and fiscal policies have the desired effects of calming corporate investors? Secondly, did different types of broad government supports have any effect on the stabilisation of corporate entities as separated by sub-sector and geographical region? Such government supports are categorised as monetary policy-based, through interest rate and quantitative easing manipulation, or fiscally-based, through the provision of COVID-19 loan facilities, the provision of COVID-19 relief packages, the provision of direct employee assistance programmes, the short-term manipulation of legislation to provide additional freedoms to overcome exceptional burden, and the announcement of support extensions as the effects of the COVID-19 pandemic deteriorated beyond original government expectations. Thirdly, as governments made explicit aviation and tourism support packages available, we further consider what types of packages provided the largest response? Finally, we investigate whether the response to government supports depended on institutional factors such as corporate size and age?

We specifically focus on both international schemes to which aviation and tourism companies had access to enter, and those schemes explicitly designed to both support and bail out these significantly damaged sectors. To this effect, a positive response to such government support packages is measured not only through increased short-term price movement, but also a reduction in the volatility of respective share prices, indicative of alleviation of investor concerns for the short period analysed thereafter.

Results presented here indicate that conventional and unconventional monetary policy has not been a stimulating factor for the airline and tourism sectors in the aftermath of the COVID-19 pandemic. This suggests that should governments seek to safeguard these industries, other mechanisms must be considered, such as fiscal policy, which is found to generate median abnormal returns of 2.17 %, indicating that investors observe significant alleviation of pressure after their respective implementation. Analysed government assistance programmes are found to generate significant positive abnormal returns ranging from +1.44 % to +1.75 %. Changes in legislation to alleviate corporate pressure, and employee assistance programmes such as furlough schemes, generated broadly insignificant effects. However, assistance programmes relating to COVID-19 loan facilities, and the provision of pandemic relief packages alleviated short-term investor concerns in both a specific and immediate manner across all analysed sub-sectors, with median abnormal returns estimated to be 2.87 % and 3.89 % respectively.

Airlines are found to have responded positively to tourism supports which provides a secondary process through which governments can assist, however, externalities are not identified in the reverse relationship as a result of assistance packages. Further, employee-based assistance is estimated to generate reduced median abnormal returns of approximately 2.04 % than those programmes focused on the corporation, while further AR relationship differentials are identified based on corporate size. Both of these results will be of concern to policy-makers, particular as evidence suggests that investors do not perceive such actions to be robust enough to protect smaller corporations in the same way as larger competitors.

The rest of this paper is structured as follows: Previous literature presents a concise overview of previous literature relating to the effects of the outbreak of COVID-19 on both aviation and broad tourism, while further focusing on the previous experience and resilience of both sectors to crises. Data and methodology presents an overview of data used in this analysis, while Research results presents a concise overview of the methods used to analyse corporate response to government supports and interventions to shelter the aviation and tourism sectors from the damaging effects of the COVID-19 pandemic. Discussion presents an overview and discussion of the results with associated policy implications and directions for future research. Conclusion concludes.

Previous literature

Unlike, for example, the Global Financial Crisis (GFC) of 2007/08, the spread of COVID-19 led to a global, exogenous, demand shock that impacted the international tourism and hospitality sector directly, via, for example, border travel restrictions, and indirectly via sectoral demand and supply spillovers. Given the scale of the shocks, investor responses to the pandemic were predicated on the credibility of any government responses, both in terms of employment protection and the ability to provide adequate business support. For example, the tourism sector has been particularly exposed to significant default threats due to the use of international travel restrictions and lockdowns to mitigate the effects of the pandemic. Collins-Kreiner and Ram (2021) compared six separate tourism-based, pandemic-related exit strategies to find that only 8 % of UNWTO, 2020a, UNWTO, 2020b, UNWTO, 2020c were fully implemented, where most outcomes were found to be short-term, local solutions, varying broadly from country to country. Fang et al. (2022) found that stricter government policies led to an immediate 9.2 % fall in leisure and recreation participation.

Specifically, in the United States, Aharon et al. (2021) found that only the March 2020 $100 billion COVID-19 aid package is found to have had a significant impact, where political and oil uncertainty had been previously identified to be a destabilising factor for the tourism industry (Demiralay, 2020; Shahzad & Caporin, 2020; Nicolau et al., 2020), while in Australia, strong supports were found to be required to generate positive spillover effects (Pham et al., 2021). Substantial differential behaviour has also been identified throughout the hotel industry since the beginning of the pandemic, with research focusing on innovation, CSR strategies, and market valuation (He et al., 2022; Xiang et al., 2022).

The provision of subsidies is one particular tool found to have been quite productive as a mechanism through which tourism flows could be focused (Chow et al., 2021), where results indicate stronger effects in inland regions compared to those regions along the coast. Such intervention techniques were also required to reinvigorate and rejuvenate tourism in the aftermath of major disasters, such as those in the aviation industry linked to the loss of aircraft (Akyildirim, Corbet, Efthymiou, et al., 2020; Akyildirim, Corbet, Sensoy, & Yarovaya, 2020; Corbet, Efthymiou, et al., 2021; Corbet, Hou, et al., 2021), and terrorism attacks (Frey et al., 2007; Corbet et al., 2019). COVID-19 has also presented opportunities where market intervention legislation, such as that considered to counteract the effects of Airbnb may not be required (Dolnicar & Zare, 2020).

More generally, Fong et al. (2021), focusing on respondents in Macao, found that positive perceptions of government response were the leading factor fuelling an expectation of a quick recovery of tourism to the area. Such resilience is important when considering persistent negative effects experienced in local regions such as China (Wu et al., 2021), disproportionate effects upon SMEs (Hu et al., 2021), or reputational risk such as that experienced in perceived ‘superspreader’ destinations (Mayer et al., 2021). Strategic response in smaller nations, particularly exposed to the pandemic due to heavy reliance on tourism such as Vietnam, and Hong Kong, are also considered (Do et al., 2021; Zhang et al., 2021). Areas such as Hubei province, with substantial COVID-19 cases, were found to have received significantly reduced tourism flows thereafter (Li et al., 2021). China was previously identified as a country where strong political connections were influential, as state-owned enterprises with strong connections are likely to diversify into the tourism industry (Wang & Xu, 2011).

With regards to specific pandemic-related corporate effects, Kaczmarek et al. (2021) utilised eighty financial characteristics to identify that firms with low valuations, limited leverage, and high investments presented evidence of greater resilience to the COVID-19 pandemic. Wieczorek-Kosmala (2022) identified low cash-driven resilience capabilities in the Central European tourism industry, where non-resilient cases, or those distinguished by lower profitability and larger financial constraints, prevail over those identified as resilient. Proactive recovery strategies are also found to enhance such firm profitability while simultaneously reducing the attrition rate of employees (Raki et al., 2021).

Undoubtedly, the pandemic has influenced many irregular market events. Specifically, during COVID-19, Salisu et al. (2021) identified the existence of negative bidirectional return spillovers between the health and tourism sectors. The pandemic had been identified to have generated several efficiency issues, stemming from information asymmetries generated by the scale of the COVID-19 pandemic (Corbet et al., 2020), and the lethargic manner in which international markets responded to signals surrounding a ‘mystery pneumonia’ in China in November 2019 (Corbet, Efthymiou, et al., 2021; Corbet, Hou, et al., 2021). While Wut et al. (2021) provide an overview of crisis management research in the hospitality and tourism research, other relevant risks considered include geopolitical risks (Demiralay & Kilincarslan, 2019), oil price risks (Qin et al., 2021), economic policy uncertainty (Demir & Gozgor, 2018; Das & Kannadhasan, 2020), and other health-related events such as the SARS outbreak (Chen et al., 2007).

Data and methodology

Data

To present a detailed analysis both before and after the onset of the COVID-19 pandemic, we select data for the period 1 January 2017 through 31 July 2021 - a total of 1195 observations. Data are obtained from the Thomson Reuters Eikon package. To select the companies through which the effects of government intervention are analysed, we separate our analysed by TRBC sector.4

To generate a list of aviation companies, we select the TRBC sectors for Airlines; Charter & Private Air Services; Regional Airlines; and Specialised Aviation Services. To specifically analyse government effects upon the tourism sector, we focus on the sub-sectors inclusive of Casinos & Gaming; Commercial Food Services; Hotels & Motels; Hotels, Motels Cruise Lines; Leisure & Recreation; Movie Theatres & Movie Products; Professional Sports Venues; Pubs, Bars & Night Clubs; Quick Service Restaurants; Resort Operators Restaurants & Bars; and Travel Agents. Statistics based on the final sample of companies, by region and sub-sector, are presented in Table 1 , with 521 companies identified to be within the established criteria of analysis. We observe that the largest number of observations is sourced in Japan and the US, reflecting the large number of stocks that are traded in these countries. Airlines represent the largest TRBC sub-sector in the analysis based on aviation companies, while restaurants & bars are the largest tourism sector sub-sector analysed. The countries selected in this analysis are Australia, Canada, China, France, Germany, Great Britain, Italy, Japan, South Korea, and the United States.5

Table 1

Summary statistics relating to selected countries and TRBC sectors.

TRBC sector	Australia	Canada	China	France	Germany	Italy	Japan	S.Korea	UK	US	Total
Aviation sector
Airlines	3	2	5	1	2	0	3	5	4	10	35
Charter & private air services	0	0	0	0	0	0	0	0	1	1	2
Regional airlines	0	3	2	0	0	0	0	2	0	5	12
Specialised aviation services	0	0	0	0	0	0	0	0	0	1	1
Total	3	5	7	1	2	0	3	7	5	17	50
Tourism sector
Casinos	1	0	0	3	0	0	0	3	0	9	16
Casinos & gaming	8	3	0	1	1	0	4	0	3	27	47
Commercial food services	0	0	5	0	0	0	25	1	2	11	44
Hotels & motels	0	2	6	5	1	0	13	1	3	10	41
Hotels, motels & cruise lines	3	0	0	1	0	1	3	1	4	12	25
Leisure & recreation	4	4	9	2	4	0	13	1	7	29	73
Movie theatres	1	1	5	0	0	0	1	3	2	4	17
Pubs, bars & night clubs	0	0	0	0	0	0	2	0	4	2	8
Quick service restaurants	1	4	0	3	1	0	12	1	3	30	55
Resort operators	1	0	1	1	0	0	1	0	1	4	9
Restaurants & bars	1	7	0	3	0	2	64	0	14	54	145
Travel agents	4	0	8	2	1	1	7	5	3	10	41
Total	24	21	34	21	8	4	145	16	46	202	521

Note: In the above table, zero indicates that no company within the stated parameters of our analysis existed in stated sub-sector in the denoted region.

We next set out to establish the dates of government intervention through the provision of financial support, or assistance packages through which both aviation and tourism-based corporations could avail.6 This research focuses specifically on publicly traded corporations, while the news selection rule is based on the source of the data. We develop on a combined search of LexisNexis, Bloomberg, and Thomson Reuters Eikon, eliminating web news and non-reputable online news sources,7 while searching for the explicit keywords relating to government assistance packages, as per Akyildirim, Corbet, Efthymiou, et al. (2020) and Akyildirim, Corbet, Sensoy, and Yarovaya (2020). To match the identified observations with those of the stock market data, those announcements found to be made on either a Saturday or Sunday (294 announcements in total) are denoted as active on the following Monday morning. Summary statistics relating to these events are presented in Table 2 .

Table 2

Summary statistics relating type of government intervention in aviation and tourism industry post-COVID-19.

Type of market intervention	Australia	Canada	China	France	Germany	Italy	Japan	S.Korea	UK	US	Total
Aviation supports	3	3	2	5	3	3	3	1	1	4	28
COVID-19 loan facilities	5	2	4	1	1	2	6	10	3	13	47
Employee assistance	1	2	0	2	3	4	0	2	7	7	28
Interest rate change	3	3	10	0	0	0	0	1	2	0	19
Legislative change	0	8	0	2	3	6	0	2	6	6	33
Quantitative easing	2	2	0	0	0	0	1	1	2	0	8
Relief package	6	14	2	3	10	3	10	9	16	25	98
Supports extensions	1	0	2	0	0	0	1	0	3	1	8
Tourism supports	4	1	1	4	3	1	1	3	6	1	25
Total	25	35	21	17	23	19	22	29	46	57	294

Note: In the above table, zero indicates that no company within the stated parameters of our analysis existed in stated sub-sector in the denoted region.

To obtain a viable observation upon which we run our methodological analysis, a single observation must be present across each of the selected search engines, and the source itself must have been denoted to be either an international news agency, a mainstream domestic news agency, the central bank, or respective government agency making the official announcement directly. Within the searching parameters, forums, social media, blogs, and other bespoke news websites were omitted from the search. The selected observation is based solely on the confirmed news announcements being made on the same day across all of the selected sources. These results are indexed, and presented in Fig. 1 , representing both the aviation and tourism sector respectively beginning January 2020 through July 2021.

Fig. 1

Aviation and Tourism news attention indices post-COVID-19 outbreak.

Note: The above data is obtained from LexisNexis, Bloomberg, and Thomson Reuters Eikon.

In March 2020, there is a distinct phase where both the aviation and tourism sectors became quite pronounced topics as the deep-rooted effects of regional lockdowns began to be enforced. While some sporadic periods of attention are identified as industry leaders make statements outlining the issues to which the sectors faced, it is not again until late 2020 and early 2021 before there is a resurgence of significant media attention, with the aviation sector experiencing elevated levels of attention in comparison to the wider tourism sector.

Presenting evidence of regional, and evidence of differential of the type of assistance and support provided, we further present evidence of the timeline of market intervention by country and by type respectively in Fig. 2 . Data suggests that a large number of market assistance and interventions took place in March and April 2020 throughout all countries, while relief packages and COVID-19 loan facilities are identified as the primary tool used by governments when providing financial assistance. Monetary policy tools such as interest rate manipulation and the introduction of quantitative easing are the least utilised intervention mechanisms. Assistance and support mechanisms are sub-divided and denoted based on the manipulation and implementation of legislative change to support businesses; the manipulation of interest rates and implementation of quantitative easing; the provision of COVID-19 loan facilities; the implementation of direct relief packages and both aviation and tourism supports; the creation of employee assistance programmes; and finally, the announcement of the extensions of such support packages.

Fig. 2

Time period of government market intervention to support the aviation and tourism sectors.

Note: Summary statistics relating to these events are presented in Table 2.

Methodology

To analyse the effects of government legislation on both airlines and tourism stocks, we calculate the natural logarithm of returns and develop upon a model of the following form to estimate abnormal returns:where on day t, AR is the abnormal return and R is the daily return for each analysed company i. R represents the domestic exchange upon which analysed company i is traded, therefore eliminating market returns within our selected methodological structure. β is estimated using returns for the pre-event window [−30,−1]. We then calculate the abnormal return (arT 0) as the return for i on the event day and cumulative abnormal return for each event across a variety of windows such as [−10,+10], [−3,+3] and [−1,+1], along with a variety of windows examining returns both before and after each analysed event.8

Upon the analysed abnormal returns, we then use a GARCH(1,1) methodology to investigate the specific effects of government policies and supports on both the returns and volatility of airlines and tourism companies. The use of abnormal returns allows for our selected methodological structure to focus on those returns outside of systematic effects generated within international financial markets and broader economic influence. The use of the GARCH(1,1) framework allows us to control for the basic phenomenon that the volatility of share prices are not constant across time. High volatility tends to bunch with other periods of high volatility, while low volatility tends to be associated with other periods of low volatility. The ARCH process originally developed by Engle (1982), and later generalised by Bollerslev (1986), allows us to capture the time-series properties of the volatility process. Specification tests found that, as is typically the case with financial series, a GARCH(1,1) model provided the best fit.

To isolate external international effects, outside of those measured by domestic processes, we include the natural logarithm of the number of COVID-19 cases reported in the country in which each analysed company is located in the mean equation of our selected GARCH(1,1) methodology. Further, we also include the generated news index based on search terms relating to international government supports for both the airline and tourism sectors. This series represents the shift in focus over time towards the damaging economic side-effects to both industries, which were not at the centre of government policies in early 2020 as the healthcare sectors took priority when attempting to mitigate the initial impacts of COVID-19. The GARCH(1,1) methodology used then has the following specification

Rrepresents the lagged value of the analysed returns of the observed share, while b2C.19represents the domestic level of COVID-19 cases at time t, while b3Gov represents the news index relating to government supports for the airline and tourism sectors respectively. D represents an individual dummy variable used to analyse the specific effects of each analysed effect by type of government support scheme implemented. To separate the influence of corporate effects, and to test whether the determined price and volatility differentials varied as determined by corporate and institutional effects, we repeat the above analysis based on pre-determined groupings. These groupings are separated as per Akyildirim, Corbet, Efthymiou, et al. (2020) and Akyildirim, Corbet, Sensoy, and Yarovaya (2020), where we select corporate size, denoted as the natural logarithm of the firm's market capitalisation by the end of last quarter; and age, which is defined as the natural logarithm of the number of quarters that the company is listed on the exchange. Such analysis develops on each data point as a dependent variable, specifically investigating as to whether larger or perhaps, older organisations possessed perceived competitive advantages from the viewpoint of investors, when compared to younger, less established corporations in the aviation and tourism sectors.

Research results

Did monetary policy and fiscal stimulus alleviate investors' fears?

We first examine as to whether monetary and fiscal policies had the desired effects of calming international investors? As a primary defensive mechanism to mitigate the effects of economic headwinds, the manipulation of interest rates, along with other unconventional mechanisms such as the use of quantitative easing, remain primary tools upon which central banks rely. As presented in Fig. 2, international economies relied on interest rate reductions as the first form of defensive mechanism to counteract the effects of the escalating COVID-19 pandemic, while quantitative easing was implemented as financial markets began to fall sharply in March and April 2020 as contagion effects spread rapidly.

In Table 3 , we present the results of the GARCH(1,1) analysis based on abnormal returns in the aftermath of monetary policy as separated by the TRBC sector. It is quickly apparent that except for some Germany-based sectors, monetary policy has had a very limited effect upon both the aviation and tourism sectors. However, fiscal policy has made a significant positive impact. In the aviation sector, French, UK, and US airlines present significant positive abnormal returns ranging from +1.44 % to +1.75 %. With regards to the tourism industry, the majority of the analysed cases present significant positive returns across jurisdictions except for the casinos & gaming, the leisure & recreation, and the professional sports venues sub-sectors. Otherwise, aviation and tourism sectorbased fiscal policy implementation were largely successful when alleviating investor concerns, as evidenced in improved abnormal returns in France, Germany, Japan, the UK, and the US. All significant returns are found to be positive, ranging from 1.16 % (pubs, bars & night clubs in the UK) to 5.41 % (quick-service restaurants in Germany).

Table 3

Estimated abnormal returns based on monetary and fiscal policy packages (by TRBC sector and country).

	Australia	Canada	China	France	Germany	Italy	Japan	S. Korea	UK	US
Monetary policy
Airlines (incl regional)	0.0014	−0.0032	−0.0015	0.0012	−0.0079	–	−0.0016	0.0008	−0.0007	−0.0031
Casinos & gaming	−0.0068	−0.0077	–	0.0020	0.0154⁎	–	−0.0055	−0.0078	−0.0051	−0.0300
Commercial food services	–	–	−0.0028	–	–	–	−0.0028	0.0127⁎	−0.0030	0.0170
Hotels & motels	–	−0.0368⁎	−0.0022	0.0055	0.0250⁎	–	−0.0031	0.0159	0.0008	0.0119
Hotels, motels & cruise lines	0.0027	–	–	0.0049	–	–	−0.0107	–	0.0018	−0.0036
Leisure & recreation	0.0215⁎⁎	0.0035	−0.0041	0.0049	0.0066	–	−0.0028	0.0015	0.0072	0.0123
Movie theatres	0.0007	−0.0082	0.0036	–	–	–	−0.0029	−0.0011	0.0167	−0.0251
Professional sports venues	−0.0052	–	−0.0072	−0.0060	0.0048	0.0112	–	–	0.0044	−0.0336
Pubs, bars & night clubs	–	–	–	–	–	–	0.0056	–	−0.0162	−0.0172
Quick service restaurants	−0.0078	0.0036	–	0.0003	−0.0708	–	−0.0012	0.0006	−0.0009	−0.0119
Resort operators	−0.0035	–	−0.0058	0.0001	–	–	−0.0024	–	–	0.0315⁎
Restaurants & bars	–	−0.0017	–	0.0034⁎	–	−0.0093	−0.0025	–	−0.0131	−0.0071
Travel agents	0.0169	–	−0.0041	0.0029	0.0018⁎	−0.0149	0.0043	−0.0070	–	0.0123
Fiscal policy
Airlines (incl regional)	0.0125	0.0058	0.0101	0.0145⁎⁎	0.0032	0.0043	0.0170	0.0131	0.0175⁎	0.0144⁎
Casinos & gaming	0.0200	0.0192	–	0.0256⁎	0.0330	–	0.0196	0.0182	0.0202	−0.0070
Commercial food services	–	–	0.0185⁎⁎⁎	–	–	–	0.0220⁎⁎⁎	0.0394⁎⁎⁎	0.0349⁎⁎⁎	0.0448⁎⁎⁎
Hotels & motels	–	0.0119⁎⁎⁎	0.0220⁎⁎⁎	0.0282⁎	0.0535⁎⁎⁎	–	0.0221⁎	0.0426⁎⁎⁎	0.0300⁎⁎	0.0268⁎⁎⁎
Hotels, motels & cruise lines	0.0205	–	–	0.0340⁎⁎⁎	–	–	0.0187	–	0.0343⁎⁎⁎	0.0214⁎⁎⁎
Leisure & recreation	0.0298	0.0067	0.0203	0.0311	0.0282	–	0.0222	0.0268⁎	0.0351	0.0321
Movie theatres	0.0257⁎⁎⁎	0.0162	0.0278⁎⁎⁎	–	–	–	0.0215⁎⁎	0.0235	0.0365⁎⁎⁎	0.0059
Professional sports venues	0.0193	–	0.0184	0.0208	0.0275	0.0340	–	–	0.0228⁎	−0.0134
Pubs, bars & night clubs	–	–	–	–	–	–	0.0318⁎⁎⁎	–	0.0116⁎	0.0062
Quick service restaurants	0.0202	0.0250⁎⁎⁎	–	0.0124	0.0541⁎⁎⁎	–	0.0235⁎⁎⁎	0.0284⁎	0.0242⁎⁎⁎	0.0069
Resort operators	0.0145⁎⁎⁎	–	0.0193	0.0134	–	–	0.0204⁎⁎	–	–	0.0438⁎⁎⁎
Restaurants & bars	–	0.0241⁎⁎⁎	–	0.0283⁎⁎⁎	–	0.0165⁎⁎	0.0228	–	0.0182⁎⁎⁎	0.0209⁎⁎⁎
Travel agents	0.0344⁎⁎⁎	–	0.0211⁎⁎⁎	0.0238⁎⁎⁎	0.0485⁎⁎⁎	−0.0008	0.0347⁎⁎⁎	0.0165⁎	–	0.0360⁎⁎⁎

Note: ⁎⁎⁎, ⁎⁎ and ⁎ denote significance at the 1 %, 5 % and 10 % levels, respectively. For brevity of presentation, only the coefficients relating to the change in abnormal returns from the stated GARCH(1,1) methodologies are shown in the above table. All other estimation results, with associated pre- and post-estimation testing are available from the authors upon request.

Fig. 3 presents a comparison of all individual, significant estimated abnormal returns in the aftermath of events separated as monetary and fiscal policy-based assistance. Considering all observations, fiscal policies are found to generate median abnormal returns of 2.17 % for both aviation and tourism companies analysed, while monetary policy events generate abnormal returns of −0.11 %. It is quickly evident that reliance upon economic stimulus through the form of monetary policy in isolation is not a pathway through which the safeguarding of these two key economic sectors can be guaranteed.

Fig. 3

Box plots of estimated abnormal returns during monetary and fiscal policy events.

Note: The above figures present the estimated corporate abnormal returns at the time at which each of the stated type of government assistance programme was implemented. Only significant results are included in the above presentation.

Did investors respond to the type of implemented government support scheme?

Next, we set out to establish whether investors differentiated between the type of government support packages that were implemented. In Table 4 , we present the GARCH-estimated, international-factor adjusted abnormal returns by TRBC sector for assistance packages relating to COVID-19 loan facilities; explicit pandemic-related relief packages; legislative changes to provide pressure alleviation; employee assistance programmes (including furlough schemes); and finally, dates relating to the extensions of any implemented schemes, which are identified to have had significant secondary effects through the alleviation of sectoral-based economic fears.

Table 4

Estimated abnormal returns denoted by type of government support package, by TRBC sector.

	Austr.	Canada	China	France	Germany	Italy	Japan	S. Korea	UK	US
COVID-19 loan facilities
Airlines (incl regional)	0.0115⁎⁎⁎	0.0143⁎	0.0143⁎⁎⁎	0.0134⁎⁎	0.0188⁎	–	0.0195⁎⁎	0.0112⁎	0.0159⁎	0.0224⁎⁎⁎
Casinos & gaming	0.0359⁎⁎⁎	0.0260⁎⁎⁎	–	0.0239⁎⁎⁎	0.0116⁎	–	0.0349⁎⁎⁎	0.0345⁎⁎⁎	0.0303⁎⁎⁎	0.0670⁎⁎⁎
Commercial food services	–	–	0.0303⁎⁎⁎	–	–	–	0.0263⁎⁎⁎	0.0213	0.0285⁎⁎⁎	−0.0112
Hotels & motels	–	0.0619⁎⁎⁎	0.0305⁎⁎⁎	0.0206⁎	0.0021	–	0.0248⁎⁎⁎	0.0089	0.0211⁎⁎⁎	0.0136⁎⁎⁎
Hotels, motels & cruise lines	0.0118⁎⁎	–	–	0.0186⁎	–	–	0.0355⁎⁎⁎	–	0.0199⁎⁎⁎	0.0284⁎⁎⁎
Leisure & recreation	−0.0034	0.0492⁎⁎⁎	0.0308⁎⁎⁎	0.0193⁎⁎⁎	0.0148⁎	–	0.0275⁎⁎⁎	0.0192⁎⁎⁎	0.0116⁎	0.0151⁎⁎⁎
Movie theatres	0.0205⁎⁎	0.0256⁎⁎⁎	0.0220⁎	–	–	–	0.0268⁎⁎⁎	0.0241⁎⁎⁎	0.0033	0.0411⁎⁎⁎
Professional sports venues	0.0448⁎⁎⁎	–	0.0326⁎⁎⁎	0.0307⁎⁎⁎	0.0193⁎⁎⁎	0.0103⁎	–	–	0.0218⁎⁎⁎	0.0551⁎⁎⁎
Pubs, bars & night clubs	–	–	–	–	–	–	0.0214⁎⁎⁎	–	0.0329⁎⁎⁎	0.0239⁎⁎⁎
Quick service restaurants	0.0320⁎⁎⁎	0.0224⁎⁎⁎	–	0.0315⁎⁎⁎	0.0606⁎⁎⁎	–	0.0258⁎⁎⁎	0.0268⁎⁎⁎	0.0334⁎⁎⁎	0.0404⁎⁎⁎
Resort operators	0.0281⁎⁎	–	0.0327⁎⁎⁎	0.0098⁎	–		0.0285⁎⁎⁎	–	–	0.0016
Restaurants & bars	–	0.0228⁎⁎⁎		0.0255⁎⁎⁎	–	0.0347⁎⁎⁎	0.0268⁎⁎⁎	–	0.0358⁎⁎⁎	0.0341⁎⁎⁎
Travel agents	0.0022	–	0.0303	0.0207⁎⁎⁎	0.0689⁎⁎⁎	0.0351⁎⁎⁎	0.0203⁎⁎⁎	0.0266⁎⁎⁎	–	0.0286⁎⁎⁎
Relief package
Airlines (incl regional)	0.0192⁎⁎⁎	0.0190⁎⁎	0.0181⁎	0.0215⁎⁎⁎	0.0215⁎⁎⁎	–	0.0262⁎⁎⁎	0.0156⁎⁎⁎	0.0140⁎	0.0329⁎⁎⁎
Casinos & gaming	0.0434⁎⁎⁎	0.0360⁎⁎⁎	–	0.0294⁎⁎⁎	0.0282⁎	–	0.0407⁎⁎⁎	0.0428⁎⁎⁎	0.0383⁎⁎	0.0833⁎⁎⁎
Commercial food services	–	–	0.0380⁎⁎⁎	–	–	–	0.0373⁎⁎⁎	0.0258⁎⁎⁎	0.0323⁎⁎⁎	0.0307⁎⁎⁎
Hotels & motels	–	0.0736⁎⁎⁎	0.0363⁎⁎⁎	0.0276⁎⁎⁎	−0.0062	–	0.0376⁎⁎⁎	0.0159⁎⁎⁎	0.0343⁎⁎⁎	0.0331⁎⁎⁎
Hotels, motels & cruise lines	0.0351⁎⁎⁎	–	–	0.0328⁎⁎⁎	–	–	0.0409⁎⁎⁎	–	0.0325⁎	0.0419⁎⁎⁎
Leisure & recreation	0.0264⁎⁎⁎	0.0530⁎⁎⁎	0.0396⁎⁎⁎	0.0328⁎⁎⁎	0.0377⁎⁎⁎	–	0.0371⁎⁎⁎	0.0327⁎⁎⁎	0.0313⁎⁎	0.0313⁎⁎⁎
Movie theatres	0.0380⁎⁎⁎	0.0395⁎⁎⁎	0.0335⁎⁎	–	–	–	0.0365⁎⁎⁎	0.0353⁎⁎⁎	0.0380⁎⁎⁎	0.0568⁎⁎⁎
Professional sports venues	0.0390⁎⁎⁎	–	0.0405⁎⁎⁎	0.0379⁎⁎⁎	0.0295	0.0264⁎⁎⁎	–	–	0.0309⁎⁎⁎	0.0835⁎⁎⁎
Pubs, bars & night clubs	–	–	–	–	–	–	0.0297⁎⁎⁎	–	0.0465⁎⁎⁎	0.0582⁎⁎⁎
Quick service restaurants	0.0383⁎⁎⁎	0.0334⁎⁎⁎		0.0372⁎⁎⁎	0.1010⁎⁎⁎	–	0.0364⁎⁎⁎	0.0288⁎⁎⁎	0.0343⁎⁎⁎	0.0588⁎⁎⁎
Resort operators	0.0406⁎⁎⁎	–	0.0393⁎⁎⁎	0.0514⁎⁎⁎	–	–	0.0374⁎⁎⁎	–	–	0.0074
Restaurants & bars	–	0.0380⁎⁎⁎	–	0.0437⁎⁎⁎	–	0.0420⁎⁎⁎	0.0367⁎⁎⁎	–	0.0431⁎⁎⁎	−0.0081
Travel agents	0.0234⁎⁎⁎	–	0.0369⁎⁎⁎	0.0314⁎⁎⁎	0.1222⁎⁎⁎	0.0541⁎⁎⁎	0.0292⁎⁎⁎	0.0424⁎⁎⁎	–	0.0312⁎⁎⁎
Legislative change
Airlines (incl regional)	−0.0020	0.0052	0.0009	−0.0021	0.0159⁎⁎⁎	–	−0.0005	−0.0015	0.0004	0.0056⁎⁎⁎
Casinos & gaming	0.0101	−0.0105	–	−0.0056	0.0100⁎⁎⁎	–	0.0077	0.0129	0.0009	0.0294⁎⁎⁎
Commercial food services	–	–	0.0012	–	–	–	0.0001	−0.0241	−0.0059	−0.0594
Hotels & motels	–	0.0837⁎⁎⁎	0.0026	−0.0024	0.0371⁎⁎⁎	–	−0.0018	0.0201⁎⁎⁎	−0.0005	0.0193⁎⁎⁎
Hotels, motels & cruise lines	0.0182⁎	–	–	0.0021	–	–	0.0096	–	0.0025	0.0070
Leisure & recreation	−0.0170	0.0150⁎⁎⁎	0.0011	−0.0091	0.0038	–	0.0029	−0.0016	−0.0029	−0.0069
Movie theatres	0.0003	−0.0007	−0.0046	–	–	–	−0.0070	0.0040	−0.0040	0.0121
Professional sports venues	0.0152⁎	–	0.0047	0.0042	−0.0070	−0.0116	–	–	−0.0025	0.0503⁎⁎⁎
Pubs, bars & night clubs	–	–	–	–	–	–	−0.0099	–	0.0103	0.0253
Quick service restaurants	0.0019	−0.0032	–	0.0196⁎⁎⁎	0.0464⁎⁎⁎	–	−0.0056	−0.0069	0.0097	0.0297
Resort operators	0.0027	–	0.0034	−0.0033	–	–	0.0008	–	–	−0.0392⁎⁎⁎
Restaurants & bars	–	−0.0048⁎	–	−0.0031	–	0.0124⁎⁎⁎	−0.0012	–	−0.0022	0.0190⁎⁎⁎
Travel agents	0.0240	–	0.0047	0.0063	0.0024	0.0117	−0.0029	0.0088	–	−0.0053
Employee assistance
Airlines (incl regional)	−0.0012	0.0000	0.0001	−0.0007	−0.0016	–	0.0029	0.0010	0.0000	0.0028
Casinos & gaming	0.0073	−0.0011	–	−0.0003	−0.0098	–	0.0070	0.0030	−0.0022	0.0079⁎
Commercial food services	–	–	−0.0008	–	–	–	0.0035	0.0106⁎⁎⁎	−0.0003	−0.0062
Hotels & motels	–	0.0058	−0.0009	−0.0127	−0.0549	–	−0.0001	−0.0033	0.0060	0.0027
Hotels, motels & cruise lines	−0.0039	–	–	0.0021	–	–	0.0068	–	0.0006	0.0063
Leisure & recreation	−0.0181	0.0086	0.0029	−0.0012	−0.0102	–	0.0046	−0.0011	0.0005	−0.0093
Movie theatres	0.0021	−0.0065	−0.0016	–	–	–	0.0071⁎⁎⁎	0.0000	0.0011	0.0110
Professional sports venues	0.0010	–	0.0059	0.0043	−0.0201	−0.0062	–	–	−0.0070	0.0256⁎⁎⁎
Pubs, bars & night clubs	–	–	–	–	–	–	−0.0011	–	−0.0007	−0.0117
Quick service restaurants	0.0045	−0.0052		−0.0160	0.0351	–	0.0050	−0.0028	−0.0001	0.0182
Resort operators	0.0028	–	0.0002	0.0039	–	–	0.0021	–	–	−0.0049
Restaurants & bars	–	−0.0011	–	0.0123	–	0.0016	0.0046	–	0.0023	0.0263⁎⁎⁎
Travel agents	−0.0119	–	−0.0043	−0.0002	0.0399	0.0016	0.0042	0.0075⁎⁎⁎	–	0.0350⁎⁎⁎
Supports extensions
Airlines (incl regional)	−0.0044	0.0042	0.0010	−0.0017	0.0073	–	0.0026	0.0018	0.0030	0.0081
Casinos & gaming	0.0116⁎⁎⁎	0.0050	–	−0.0041	0.0089⁎⁎⁎	–	0.0051	0.0098⁎⁎⁎	0.0037	0.0915⁎⁎⁎
Commercial food services	–	–	−0.0032	–	–	–	0.0051⁎⁎⁎	0.0000	0.0290⁎⁎⁎	0.0369⁎⁎⁎
Hotels & motels	–	0.0246⁎⁎⁎	0.0011	0.0099⁎⁎⁎	0.0373⁎⁎⁎	–	0.0005	−0.0019	0.0049	−0.0107
Hotels, motels & cruise lines	0.0130⁎⁎⁎	–	–	0.0093	–	–	0.0120⁎⁎⁎	–	0.0080	0.0093⁎⁎⁎
Leisure & recreation	0.0240⁎⁎⁎	0.0174	0.0048	0.0094	−0.0036	–	0.0024	0.0063	0.0059	0.0332⁎⁎⁎
Movie theatres	−0.0081	0.0137⁎⁎⁎	−0.0024	–	–	–	0.0013	0.0037	−0.0076	0.0275⁎⁎⁎
Professional sports venues	−0.0092	–	−0.0008	0.0012	0.0113⁎⁎⁎	−0.0084⁎⁎⁎	–	–	0.0158⁎⁎⁎	0.0344⁎
Pubs, bars & night clubs	–	–	–	–	–	–	−0.0014	–	0.0067	0.0194
Quick service restaurants	0.0099⁎⁎⁎	−0.0055	–	0.0303⁎⁎⁎	0.0450⁎⁎⁎	–	0.0010	−0.0045	−0.0019	0.0202
Resort operators	−0.0038	–	0.0075⁎⁎⁎	0.0136⁎⁎⁎	–	–	−0.0006	–	–	0.0460⁎⁎⁎
Restaurants & bars	–	0.0072	–	0.0197⁎⁎⁎	–	0.0170⁎⁎⁎	0.0032	–	0.0112⁎⁎⁎	0.0502⁎⁎⁎
Travel agents	0.0289⁎⁎⁎	–	0.0066	−0.0045	0.1279	−0.0050	0.0111⁎⁎⁎	0.0101⁎⁎⁎	–	−0.0179

Note: ⁎⁎⁎, ⁎⁎ and ⁎ denote significance at the 1 %, 5 % and 10 % levels, respectively. For brevity of presentation, only the coefficients relating to the change in abnormal returns from the stated GARCH(1,1) methodologies are shown in the above table. All other estimation results, with associated pre- and post-estimation testing are available from the authors upon request.

From the results, it is clear that both legislative changes (such as the change of taxation due dates, or the easing of regulatory positions for example), and employee assistance programmes generated little in the way of optimistic perception for the future of the aviation and tourism industry. Some legislative changes in Germany and the US are found to have had positive impacts, but broadly, there is little evidence of widespread improvements in conditions. However, when considering assistance programmes relating to COVID-19 loan facilities, and the provision of pandemic relief packages, evidence suggests that both facilities alleviated investor concerns in both a specific and immediate manner across all analysed sub-sectors. Almost all sectors are found to have presented significant, positive abnormal returns in the days after the implementation of these facilities. Abnormal returns in the aviation sector range from +1.12 % to +2.24 %, and + 1.40 % to +3.29 % in the aftermath of loan facilities and relief packages respectively. With regards to tourism-based TRBC sub-sectors, hotels, motels & cruise lines; movie theatres; restaurants & bars; quick-service restaurants; and resort operators all present significant positive short-term effects in the aftermath of specific government interventions.

Further, in Fig. 4 , we present box plots based on the types of assistance packages implemented. Similar to results presented in Table 4, evidence suggests significant disparity of corporate response, where median responses to legislative changes, employee assistance programmes, and support extensions, despite being significant, are all close to 0 %. However, the estimated abnormal return response to loan facilities and relief packages are found to be significantly positive, with median abnormal returns estimated to be 2.87 % and 3.89 % respectively, indicating their position as assistance mechanisms with the largest direct impacts upon the targeted sectors and sub-sectors. It is interesting to note that the announcement of extensions of available support packages presents significant positive outcomes when focusing on broad analyses, when considering only significant, individual corporate response, such effects appear to be little more effective than that presented by legislative changes, or the announcement of employee assistance programmes.

Fig. 4

Box plots of estimated abnormal returns denoted by type of government support package.

Note: The above figures present the estimated corporate abnormal returns at the time at which each of the stated type of government assistance programme was implemented. Five separate types of government support package are considered: the introduction of COVID-19 loan facilities; the provision of sectoral relief packages; the introduction of legislative changes to provide corporate assistance or regulatory alleviation; the provision of explicit employee assistance programmes; and the announcement of support extensions of any type. Only significant results are included in the above presentation.

Considering the above results, policy-makers focusing on underpinning the aviation and tourism sectors should consider that loan facilities and relief packages appear to be the most successful mechanism in alleviating investors' fears for the future. It is also important to note, that while the ambitions of such packages should not focus on the growth of share prices as a barometer of success, they should consider the reduction of volatility to present a signal of pressure alleviation. It is extreme volatility events that have presented very negative outcomes for all corporate stakeholders, including that of employees who can have their futures placed at risk through no fault of their own.9

Have explicit aviation and tourism support packages mitigated the sectoral damage generated by COVID-19?

We further investigate whether there existed differential behaviour between the targeted sector of the support package, whether it be aviation or tourism supports, or as to whether targeted packages focusing on employees or the corporate entity itself were deemed to be more important from the viewpoint of investors. Results are presented in Tables 5 & 6 respectively. In Table 5, we observe that not only did the airline industry respond positively to airline-based assistance packages, but the sector also presented significant positive abnormal returns in the aftermath of tourism-centralised support. Particularly strong effects are identified through Australian, Canadian, European, Korean, and US-driven supports. US-based supports are found to generate the broadest range of sectoral influence, with abnormal returns estimated to be more than 8 % across several sub-sectors.

Table 5

Estimated abnormal returns based on government introduced COVID-19 assistance programmes (by TRBC sector and country).

	Australia	Canada	China	France	Germany	Italy	Japan	S.Korea	UK	US
Aviation supports
Airlines (incl regional)	0.0374⁎⁎⁎	0.0419⁎⁎⁎	0.0173⁎⁎⁎	0.0260⁎⁎⁎	0.0263⁎⁎⁎	–	0.0193⁎⁎⁎	0.0308⁎⁎⁎	0.0113⁎⁎⁎	0.0264⁎⁎⁎
Casinos & gaming	0.0077	0.0069	–	−0.0063	−0.0074	–	0.0056	0.0045	−0.0012	0.0817⁎⁎⁎
Commercial food services		–	−0.0031	–	–	–	0.0070	0.0050	0.0289⁎⁎⁎	0.0455⁎⁎⁎
Hotels & motels	–	−0.0074	0.0162	−0.0160	0.0274⁎⁎⁎	–	0.0053	0.0201⁎⁎⁎	−0.0025	−0.0003
Hotels, motels & cruise lines	0.0061	–	–	0.0049	–	–	0.0140⁎	–	−0.0061	0.0048
Leisure & recreation	0.0205⁎⁎	0.0293⁎⁎	0.0052	0.0016	0.0048	–	0.0032	−0.0109	−0.0037	−0.0167
Movie theatres	−0.0032	0.0044	−0.0013	–	–	–	−0.0025	0.0047	0.0019	0.0093
Professional sports venues	0.0257⁎	–	−0.0131	−0.0020	−0.0191	−0.0025	–	–	−0.0053	0.0259
Pubs, bars & night clubs		–	–	–	–	–	−0.0096	–	−0.0191	0.0533⁎⁎⁎
Quick service restaurants	−0.0009	0.0182⁎	–	−0.0099	0.0371⁎⁎⁎	–	−0.0013	0.0024	−0.0005	−0.0171
Resort operators	−0.0108	–	−0.0117	−0.0117	–	–	−0.0047	–	–	0.0312⁎⁎⁎
Restaurants & bars	–	0.0159⁎	–	0.0181⁎	–	0.0207⁎	0.0049	–	−0.0006	0.0819⁎⁎⁎
Travel agents	−0.0129	–	0.0173	−0.0021	−0.0056	0.0294⁎⁎⁎	0.0257	−0.0082	–	0.0150⁎⁎⁎
Tourism supports
Airlines (incl regional)	0.0189⁎⁎⁎	0.0212⁎⁎⁎	0.0088	0.0131⁎	0.0133⁎⁎	–	0.0097	0.0155⁎⁎⁎	0.0057	0.0133⁎⁎⁎
Casinos & gaming	0.0133⁎⁎⁎	0.0241⁎		0.0063	−0.0074	–	0.0100	0.0055	−0.0018	0.0391⁎⁎⁎
Commercial food services	–	–	−0.0062	–	–	–	0.0081	−0.0012	0.0123	0.0450⁎⁎⁎
Hotels & motels	–	−0.0171	0.0140	−0.0131	−0.0225	–	0.0074	0.0200⁎⁎⁎	−0.0088	−0.0209
Hotels, motels & cruise lines	−0.0099	–	–	−0.0069	–	–	0.0267⁎⁎⁎	–	−0.0124	0.0099
Leisure & recreation	0.0315⁎⁎⁎	0.0594⁎⁎⁎	0.0000	−0.0170	0.0019	–	0.0016	−0.0003	−0.0227	−0.0002
Movie theatres	−0.0047	0.0517⁎⁎⁎	−0.0038	–	–	–	−0.0018	0.0137	−0.0331	0.0445⁎⁎⁎
Professional sports venues	0.0414⁎⁎⁎	–	−0.0119	−0.0047	−0.0287	−0.0120		–	0.0035	0.0518⁎⁎⁎
Pubs, bars & night clubs		–	–	–	–	–	−0.0309	–	−0.0463	0.0140
Quick service restaurants	−0.0077	0.0212	–	0.0618⁎⁎⁎	0.1586⁎⁎⁎	–	−0.0069	−0.0158	0.0105	−0.0041
Resort operators	0.0170	–	0.0201⁎⁎	0.0000	–	–	−0.0107	–	–	0.0266⁎⁎⁎
Restaurants & bars	–	−0.0156	–	0.0216⁎⁎⁎	–	0.0307⁎⁎⁎	0.0037	–	0.0004	0.0118
Travel agents	−0.0280	–	0.0184	0.0048	0.1102⁎⁎⁎	−0.0097	0.0350⁎⁎⁎	−0.0079	–	0.0491⁎⁎⁎

Note: ⁎⁎⁎, ⁎⁎ and ⁎ denote significance at the 1 %, 5 % and 10 % levels, respectively. For brevity of presentation, only the coefficients relating to the change in abnormal returns from the stated GARCH(1,1) methodologies are shown in the above table. All other estimation results, with associated pre- and post-estimation testing are available from the authors upon request.

In Table 6 , we observe that employee-based assistance programmes, such as furlough schemes introduced across several countries, had quite a limited response. The majority of examined sectors, despite associated corporations being entitled to enter such programmes, present little evidence of significant outcomes except for moderate positively significant results for US tourism sub-sectors. However, company-based support schemes, targeted specifically at aviation and tourism corporations, present a broader range of significantly positive outcomes, indicating relative success when achieving targeted assistance.

Table 6

Estimated abnormal returns based on staff-based and corporate-based programmes (by TRBC sector and country).

	Australia	Canada	China	France	Germany	Italy	Japan	S.Korea	UK	US
Employee assistance programmes - analysed corporations eligible to partake
Airlines (incl regional)	0.0336⁎⁎⁎	0.0377⁎⁎⁎	0.0156⁎⁎⁎	0.0234⁎⁎⁎	0.0236⁎⁎⁎	–	0.0173⁎⁎⁎	0.0277⁎⁎⁎	0.0102⁎	0.0237⁎⁎⁎
Casinos & gaming	−0.0068	0.0099	–	0.0049	−0.0024	–	−0.0021	−0.0008	0.0005	0.0223⁎⁎⁎
Commercial food services	–	–	0.0072	–	–	–	0.0009	0.0143⁎⁎⁎	−0.0151⁎	0.0100⁎⁎⁎
Hotels & motels	–	0.0051	0.0011	0.0079	0.0165⁎⁎	–	0.0032	−0.0019	−0.0077	0.0015
Hotels, motels & cruise lines	0.0161⁎⁎⁎	–	–	−0.0125	–	–	−0.0006	–	−0.0113	0.0033⁎⁎
Leisure & recreation	0.0154⁎⁎⁎	0.0045	0.0006	−0.0032	−0.0031	–	0.0004	0.0006	−0.0114	0.0147⁎⁎⁎
Movie theatres	−0.0028	0.0079	−0.0008	–	–	–	0.0047⁎	−0.0002	−0.0196⁎⁎	0.0019
Professional sports venues	−0.0020	–	0.0034	0.0023	0.0118	0.0008	–	–	0.0096	−0.0011
Pubs, bars & night clubs	–	–	–	–	–	–	−0.0026	–	0.0073	0.0094⁎⁎
Quick service restaurants	0.0025	0.0039	–	0.0204⁎⁎⁎	0.0330⁎⁎⁎	–	0.0017	0.0024	−0.0034	−0.0029
Resort operators	0.0089⁎	–	0.0023	0.0036	–	–	0.0028	–	–	0.0058⁎⁎
Restaurants & bars	–	−0.0002	–	−0.0141	–	0.0009	0.0010	–	0.0017	0.0504⁎⁎⁎
Travel agents	0.0044	–	0.0020	0.0039	0.0509⁎⁎⁎	0.0199	−0.0110	0.0024	–	0.0070⁎
Direct corporate assistance programmes - analysed corporations eligible to partake
Airlines (incl regional)	0.0179⁎⁎⁎	0.0200⁎⁎⁎	0.0083	0.0124⁎⁎⁎	0.0126⁎⁎⁎	–	0.0092	0.0147⁎⁎⁎	0.0213⁎⁎⁎	0.0126⁎⁎⁎
Casinos & gaming	0.0215*	0.0086	–	0.0272	0.0362⁎⁎⁎	–	0.0210	0.0215⁎⁎⁎	0.0306⁎⁎⁎	0.0537⁎⁎⁎
Commercial food services	–	–	0.0218⁎⁎	–	–	–	0.0162	0.0268⁎	0.0135	0.0470⁎
Hotels & motels	–	0.0411⁎⁎⁎	0.0097	0.0359⁎⁎⁎	0.0432⁎⁎⁎	–	0.0154	0.0069	0.0319	0.0274⁎⁎⁎
Hotels, motels & cruise lines	0.0096	–	–	0.0296⁎⁎⁎	–	–	0.0114	–	0.0403⁎⁎⁎	0.0210⁎
Leisure & recreation	0.0394⁎⁎⁎	0.0442⁎⁎⁎	0.0183	0.0274⁎⁎⁎	0.0277	–	0.0203	0.0324⁎⁎⁎	0.0471⁎⁎⁎	0.0278**
Movie theatres	0.0295	0.0050	0.0278	–	–	–	0.0216	0.0205	0.0427	0.0018
Professional sports venues	0.0403⁎⁎⁎	–	0.0306	0.0267	0.0307	0.0253⁎	–	–	0.0281	0.0257⁎⁎⁎
Pubs, bars & night clubs	–	–	–	–	–	–	0.0355⁎	–	0.0441⁎⁎⁎	0.0255⁎⁎⁎
Quick service restaurants	0.0239	0.0069	–	0.0160	−0.0079	–	0.0230	0.0276⁎⁎⁎	0.0325	0.0411⁎⁎⁎
Resort operators	0.0244	–	0.0337	0.0321⁎⁎⁎	–	–	0.0301⁎	–	–	−0.0162
Restaurants & bars	–	0.0430⁎⁎⁎	–	0.0133	–	0.0047	0.0176⁎⁎	–	0.0259	0.0674⁎⁎⁎
Travel agents	0.0432⁎⁎⁎	–	0.0091	0.0266	0.0564	0.0319	0.0042	0.0305⁎⁎⁎	–	0.0424⁎⁎⁎

Note: ⁎⁎⁎, ⁎⁎ and ⁎ denote significance at the 1 %, 5 % and 10 % levels, respectively. For brevity of presentation, only the coefficients relating to the change in abnormal returns from the stated GARCH(1,1) methodologies are shown in the above table. All other estimation results, with associated pre- and post-estimation testing are available from the authors upon request.

Confirmation of these identified results is presented in Fig. 5 based on individual corporate response statistics. While median abnormal returns are found to be 0.29 % higher for tourism-based support packages when compared to airline-based support packages, the similarity of the interquartile range indicates little to differentiate. However, when focusing on the differentials of stock market behaviour based on whether assistance packages were focused on the employee or the corporation, there is clear evidence to suggest that investors responded far more to corporate assistance. Median abnormal returns in the aftermath of corporate assistance is estimated to be 2.22 %, while considering employee-based assistance, mediate returns are 0.18 %. Governments must therefore consider whether they should support the corporations with the ambition of maintaining employee welfare, perhaps through attached employee-centralised caveats to enter such assistance programmes, as through the eyes of the international investment community, the targeting of employees in isolation is found to offer little to benefit the corporations through which they are employed.

Fig. 5

Box plots of estimated abnormal returns denoted by type of government support package.

Note: The above figures present the estimated corporate abnormal returns at the time at which each of the stated type of government assistance programme was implemented. Results are separated based on the type of sector to which the support package was targeted, and corporate access, as identified whether the programme is aimed at employees or the aviation/tourism company itself. Only significant results are included in the above presentation.

Has government assistance in response to COVID-19, supported all aviation and tourism companies in the same manner?

Finally, we investigate whether the response to government supports depended on institutional factors such as corporate age and size? In Table 7 , we first separate our methodological process by TRBC sector, while in Table 8 we separate our analysis by geographical region to identify key results of interest. First, we observe that there exist few significant results when focusing on corporate age when considered in terms of the TRBC sector. Interestingly, professional sports venues are found to present significant reductions in abnormal returns during the analysed government assistance programmes, a result that can be largely explained by the long-term reluctance to allow spectators to return to full capacity for sporting and music events in a non-restrictive manner.

Table 7

Estimated abnormal returns based on age and size of the analysed corporations (separated by TRBC sector).

	Mon Pol	Fisc Pol	Av Supp	Tour Supp	Staff Sch.	Comp Eligible
Age
Airlines (incl regional)	0.0011	0.0019	0.0049	0.0103⁎	0.0174⁎	0.0089
Casinos & gaming	0.0143	0.0016	0.0289	0.0469	−0.0096	−0.0387
Commercial food services	−0.0047	−0.0069	−0.0126	−0.0188	0.0044	0.0098
Hotels & motels	0.0003	0.0010	0.0024	0.0073	0.0022	−0.0050
Hotels, motels & cruise lines	−0.0008	−0.0035	0.0159⁎	0.0310⁎	0.0005	0.0147⁎
Leisure & recreation	−0.0065	−0.0054	0.0018	0.0004	0.0014	−0.0052
Movie theatres & movie products	0.0033	0.0027	−0.0054	−0.0095	0.0000	0.0075
Professional sports venues	−0.0414	−0.0418	0.0250	0.0676	0.0024	−0.0076
Pubs, bars & night clubs	0.0053	0.0011	0.0247	−0.0037	−0.0215	0.0033
Quick service restaurants	−0.0013	−0.0002	−0.0022	−0.0039	0.0007	0.0008
Resort operators	−0.0079	−0.0068	−0.0109	0.0017	0.0035	0.0165
Restaurants & bars	0.0068	0.0048	0.0490	0.0305	−0.0318	−0.0180
Travel agents	−0.0085	−0.0038	0.0012	0.0270	−0.0054	−0.0113
Market capitalisation
Airlines (incl regional)	0.0050	0.0077⁎⁎⁎	0.0187⁎⁎⁎	0.0109⁎⁎⁎	0.0152⁎⁎⁎	0.0107⁎⁎⁎
Casinos & gaming	0.0032	0.0045⁎	0.0178⁎⁎⁎	0.0281⁎⁎⁎	0.0063⁎⁎⁎	0.0183⁎⁎⁎
Commercial food services	−0.0010	−0.0020	0.0041⁎⁎	0.0154⁎⁎⁎	−0.0015⁎	0.0075⁎⁎
Hotels & motels	−0.0014	−0.0002	0.0013	0.0059⁎⁎⁎	0.0001	0.0025⁎⁎⁎
Hotels, motels & cruise lines	−0.0008	−0.0008	0.0015	0.0035⁎	−0.0003	−0.0014
Leisure & recreation	0.0030	0.0022⁎⁎	0.0022	0.0006	−0.0008	−0.0003
Movie theatres & movie products	−0.0017	−0.0009	0.0016	0.0026	0.0004	−0.0019
Professional sports venues	0.0149	0.0105⁎⁎⁎	−0.0207⁎⁎⁎	−0.0202⁎⁎⁎	−0.0016	−0.0036⁎⁎
Pubs, bars & night clubs	0.0033	0.0035	−0.0064	−0.0058	−0.0030	0.0089⁎
Quick service restaurants	0.0061	0.0073⁎⁎⁎	0.0076⁎⁎⁎	0.0084⁎⁎⁎	0.0004⁎⁎⁎	0.0063⁎
Resort operators	−0.0009	−0.0021	−0.0034	−0.0034	−0.0013	0.0032
Restaurants & bars	0.0008	0.0001	0.0176⁎	0.0159⁎⁎⁎	0.0109⁎⁎⁎	0.0100⁎⁎⁎
Travel agents	−0.0018	−0.0002	−0.0002	0.0083⁎⁎	0.0022⁎	0.0050⁎

Note: ⁎⁎⁎, ⁎⁎ and ⁎ denote significance at the 1 %, 5 % and 10 % levels, respectively. For brevity of presentation, only the coefficients relating to the change in abnormal returns from the stated GARCH(1,1) methodologies are shown in the above table. All other estimation results, with associated pre- and post-estimation testing are available from the authors upon request.

Table 8

Estimated abnormal returns based on age and size of the analysed corporations (separated by country).

	Mon Pol	Fisc Pol	Av Supp	Tour Supp	Staff Sch.	Comp Eligible
Age
Australia	−0.0047	0.0004	0.0027	0.0101	−0.0057	−0.0006
Canada	−0.0031	−0.0050	0.0200	0.0303	0.0010	−0.0227
China	0.0021	0.0038	0.0114	0.0208	0.0011	−0.0134
France	0.0028	0.0135	0.0574⁎⁎⁎	−0.0229	0.0275	0.0316
Germany	0.0087	0.0008	0.0378⁎⁎⁎	0.0519⁎⁎	−0.0169	−0.0265
Italy	−0.0011	−0.0017	−0.0014	0.0011	0.0013	−0.0005
Japan	0.0028	0.0026	0.0001	0.0001	0.0017	0.0015⁎
South Korea	0.0008	0.0000	−0.0013	0.0061	0.0025	−0.0009
United Kingdom	−0.0014	−0.0018	−0.0023	0.0010	0.0001	0.0012
United States	0.0071	0.0004	0.0327⁎	0.0469⁎	0.0152⁎⁎	0.0229⁎⁎
Market capitalisation
Australia	−0.0034	0.0003	0.0028⁎	0.0037	0.0046⁎⁎⁎	−0.0001
Canada	0.0045	0.0046⁎⁎	0.0069⁎⁎	0.0107⁎⁎	0.0089⁎	0.0066⁎
China	0.0007	0.0014	−0.0008	−0.0006	−0.0007	0.0023
France	0.0108	0.0088⁎	0.0029	−0.0158	0.0005	0.0008
Germany	0.0015	0.0216⁎⁎	0.0338⁎⁎⁎	0.0140⁎⁎⁎	0.0021⁎	0.0034⁎
Italy	−0.0001	0.0005	0.0011	−0.0002	−0.0007⁎	−0.0005
Japan	−0.0007	−0.0002	0.0004	0.0002	0.0002	−0.0007
South Korea	−0.0023	−0.0009	0.0014	−0.0007	−0.0007	−0.0004
United Kingdom	0.0009	0.0002	−0.0005	0.0002	−0.0016	0.0018
United States	0.0013	0.0193⁎⁎	0.0221⁎⁎⁎	0.0134⁎⁎⁎	0.0207⁎⁎⁎	0.0316⁎⁎⁎

Note: ⁎⁎⁎, ⁎⁎ and ⁎ denote significance at the 1 %, 5 % and 10 % levels, respectively. For brevity of presentation, only the coefficients relating to the change in abnormal returns from the stated GARCH(1,1) methodologies are shown in the above table. All other estimation results, with associated pre- and post-estimation testing are available from the authors upon request.

Significant positive outcomes are identified for airlines and the hotel, motel and cruise line sector when considering explicit aviation supports, explicit tourism supports, staff support schemes, and direct schemes to provide corporate support. In each case, markets responded more positively to older companies, however, the lack of significance across the breadth of the results would indicate that there was little differential of investor behaviour when separating both older and younger corporations. However, when considering market capitalisation, indicative of corporate size effects, results indicate that larger airlines, casinos, commercial food services, sports venues, restaurants, and travel agents all present evidence that larger companies exhibit more positive abnormal returns in the aftermath of government assistance and support packages. Explicit aviation and tourism support packages, along with staff support schemes, generated the largest positive responses from investors in the form of elevated share prices in the aftermath of the announcement of these programmes.

In Table 8 we present methodological structures defining geographical separation. Age effects are identified predominantly in the German and US aviation and tourism sectors, indicating that old, more experienced corporations were perceived to possess improved future aspirations in the aftermath of such specific government assistance packages. In terms of corporate size, broad effects are identified in Australia, Canada, Germany, and the US, where positive abnormal returns are more prevalent for larger corporations in the aftermath of government intervention to stem the damage generated by the COVID-19 pandemic. These results indicate that the age of the companies analyses was largely irrelevant, however, larger companies presented significantly elevated returns, indicating an alleviation of investor fears. While such an outcome would be desired by governments before the implementation of such assistance, the reduced effects identified for smaller, perhaps more local corporations would be a cause for concern. While attempting to reduce the impact of the pandemic on the analysed sectors, one potential side-effect could be the unintentional monopolisation of certain geographical locations or sectors, as larger companies obtain added corporate buffer through a premium in the form of elevated share prices.

Discussion

Practical implications of this work

In the aftermath of the international financial crises that followed the subprime debt collapse, if any lessons were of a priority, it would be that maintaining public confidence within the system, and a viable path towards a return to profitability during crises should be of a primary target (Gofman, 2017). The results presented here, in terms of the response of international investors in the aviation and tourism sectors to the spread of COVID-19, in general terms, support this view. However, there are some important and often subtle differences between the responses, at the regional or national levels, which relate to the effectiveness of different types of mitigating practices, monetary or fiscal, which may reflect both technical issues of lags in implementation and/or left- and rightwing government ambitions. Political panic is a factor upon which such support packages can be implemented in very inefficient manners, however, the strongest governments and sovereign states with the least amount of wastage and corruption should see a smoother transition of supports to the areas most at risk.

It is important to note, that despite the stresses that developed within financial markets during the time of the escalation of the COVID-19 pandemic, investors did respond positively to attempts to restore credibility within the aviation and tourism sectors. This result provides further verification of the efficient market hypothesis and shows that investors remain vigilant and have responded rationally to extremely fluid situations.

The aviation and tourism sectors are typically highly labour intensive sectors and it may seem that the main support mechanism during COVID-19 should focus on protecting their employment via wage subsidies. However, over time, the unique responses to the COVID-19 shock, including border lock-downs, ultimately threatened the financial viability of the actual companies employing such workers. To maintain long-term investor confidence in these firms, governments began to realise attention should include investment and cost support for aviation and tourism businesses via fiscal and tax policies.

Directions for future research

The focus of the research presented here was to consider how investor expectations in the aviation and tourism sector were influenced by any government responses to the spread of COVID-19. The mechanism that we relied upon was the presence of abnormal returns at the firm level in response to actual dates specific government policy changes were implemented. There are several ways our presented research could be expanded and improved including the addition of an empirical focus on rumours and leaks of forthcoming assistance programmes, and as to whether such initial outflow of information mitigated some of the impacts of forthcoming ‘official’ announcements.10 However, empirically it has been shown that the majority of the share price premium would occur in such circumstances on the dates surrounding the official implementation of such types of assistance programmes (Corbet, Efthymiou, et al., 2021; Corbet, Hou, et al., 2021).

Other potential directions for future research could also include analysis of other types of social media and multi-lingual analysis of results. The use of surveys, or questionnaire-based data as provided from policy-markets, stakeholders, and market participants, would present a deeper understanding with regards to ambitions of governments to underpin these important industries, but also as to what those closest to the industries believe is necessary to do so. It would likely be beneficial should future work also focus on outstanding hypotheses surrounding the influence of firm characteristics on the presented results within this research, that is, the effects of variables such as firm size, profitability, leverage, or other corporate characteristics upon corporate response to the COVID-19 pandemic.

Conclusion

The international aviation and tourism sectors have dealt with several black-swan events in recent times, but the onset and rapid contagion associated with the COVID-19 pandemic has presented a very different type of challenge to those previously faced. The aviation sector, recently navigating damaging issues surrounding the Boeing 737-MAX disasters and mounting pressures centred on the global environmental challenge, has found itself particularly vulnerable. Many international governments attempted to mitigate the worst effects of the COVID-19 challenge, however, correctly had to prioritise medical sectors. Shortly after, while recognising the deeprooted damage that broad loss of employment and competitiveness within both the aviation and tourism sectors, a variety of different, yet ambitious assistance packages were announced around the world. This research investigates whether such supports delivered upon their desired ambitions, namely, the alleviation of the sectoral decline of confidence while underpinning job security and reducing the probability of default of economically and strategically important sectors. In this research, investor confidence is defined as the stock market response to the physical introduction of these government assistance packages.

A number of key findings are identified in this research. First, conventional, and unconventional central bank-initiated monetary policy, while possessing significant influence upon broad market returns, has had a very limited effect upon the abnormal returns of both the aviation and tourism sectors. This result firmly indicates that should governments seek to support these industries, other mechanisms must be considered, such as fiscal policy, to which our adapted GARCH(1,1) methodological structure incorporating international effects identifies the existence of positive significant effects, and largely successful outcomes when attempting to alleviate investor concerns about the aviation and tourism sectors due to the COVID-19 pandemic. Such government assistance programmes are found to generate positive abnormal returns ranging from +1.44 % to +1.75 %. When comparing both types of common government assistance mechanisms, fiscal policies are found to generate median abnormal returns of 2.17 %, while monetary policy events generate abnormal returns of −0.11 %.

Government reliance on monetary policy in isolation is not a pathway through which the safeguarding of these two key economic sectors can be guaranteed. When separating estimated results by type of government assistance package, changes in legislation to alleviate corporate pressure and employee assistance programmes such as furlough schemes generated broadly insignificant effects. Assistance programmes relating to COVID-19 loan facilities, and the provision of pandemic relief packages alleviated short-term investor concerns in both a specific and immediate manner across all analysed sub-sectors, with median abnormal returns estimated to be 2.87 % and 3.89 % respectively, producing the largest direct impacts upon the targeted sectors and sub-sector analysed. Further, not only does the airline industry respond positively to airline-based assistance packages, but the sector also presented significant positive abnormal returns in the aftermath of tourism-centralised support. This result indicates the existence of a secondary channel through which sectoral assistance can be provided.

Results further indicate that assistance programmes that were focused on the employee generate reduced median abnormal returns of approximately 2.04 % than those programmes focused on the corporation. Explicit aviation and tourism support packages, along with staff support schemes generated the largest positive responses from investors in the form of elevated share prices in the aftermath of the announcement of these programmes.

When investigating whether corporate factors such as company age and size influence investor perceptions in the aftermath of COVID-19 government assistance programmes, results indicate that while age had no significant influence, corporate size did present evidence of positive abnormal returns, indicative of the alleviation of investor fears about the future of the analysed sectors when considering the ongoing development and contagion relating to the pandemic. This latter finding will be somewhat concerning for policy-makers. While attempting to mitigate the sectoral effects of the pandemic, such assistance packages might be creating an uneven response, with perceptions of the unequal response being observed by investors in the aftermath of major support implementation. Such a signal should serve as a warning that market participants do not perceive such supports to be adequate to underpin the survival of smaller, most likely, more local orientated businesses, to which some packages were explicitly targeted.

If lessons are to be learned in terms of governments' responses to the COVID-19 pandemic, they would include a primary target of maintaining public confidence within the system, and supporting a viable path towards a return to profitability during crises (Gofman, 2017). High-level monetary policies, including interest rate reductions and quantitative easing, are less likely to have positive effects on the corporate sector than targeted fiscal policies, which allow businesses to remain trading. Wage support and furlough policies may support demand and employment in the short term, but the efficacy of support, in the longer term, requires that businesses see a credible return to profitability.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.