Winners and losers from Pfizer and Biontech's vaccine announcement: Evidence from S&P 500 (Sub)sector indices.

This study explores how the US stock market reacted to the news of a successful development of vaccine by Pfizer and Biontech on November 9, 2020. In particular, the study analyses the effect of the vaccine announcement on 11 sector indices and 79 subsector indices. A key contribution of the present study is to provide a deeper subsector level of analysis lacking in existing literature. An event study approach is applied in identifying abnormal returns due to the November 9th vaccine announcement. Several event periods (-1, 0, 1, 2, 3, 0-1, 0-3) are analysed to provide a more complete picture of the effects. Based on analysis, it is established that there are considerable inter and intra sectoral variations in the impact of the vaccine news. The results show that the impact follows a clear pattern. The sectors that were hit hardest by the pandemic such as energy, financials, as well as subsectors like hotels and casinos, benefited the most from positive vaccine news. Subsectors that gained from the pandemic such as airfreight, household appliances and computers and electronics retail were depressed the most by the news. These findings suggest that while the availability of vaccines is expected to help steer economies gradually to normalcy, the re-adjustment is likely to be asymmetric across subsectors. While some subsectors expect to expand as these industries recover from the contraction inflicted by the COVID-19 environment, other subsectors expect adjustment losses as these industries shed off the above average gains driven by the COVID-19 environment.

1 Introduction

The COVID-19 pandemic has affected countries and businesses in many different ways. The implementation of government restrictions on commercial activity and social distancing measures have caused extreme volatility in global financial markets that is unmatched in recent decades [1-3]. For example, the Dow Jones Industrial Average (DIJA) index lost more than a quarter of its value in only four trading days in March 2020 [4]. Although markets picked up and share prices rose to even above the pre-pandemic levels, the recovery in the US stock market was mainly confined to stocks of technology and tech-enabled companies such as Facebook, Apple, Amazon, Netflix, and Google, that witnessed a considerable surge in demand due to lockdown policies [5].

As expected, stock markets showed some improvement on the announcement of success in phase-3 study of Pfizer and Biontech vaccine candidate against COVID-19 on November 9, 2020. The study indicated that the vaccine candidate reduced the risk of infection by around 90 percent [6]. However, there are striking sectoral disparities on the impact of the vaccine news on the share prices of various sectors [4, 5]. According to [7], the spread between the best and worst-performing sectors increased from 27 percentage points in mid-March to 80 percentage points over the year.

It is widely accepted that share prices reflect available information and adjust swiftly to current news and events [8, 9], though other studies acknowledge (some) markets are less than fully efficient [10-12]. Understanding how different sectors and subsectors are impacted by the news will help investors and policy makers to formulate optimal responses. This requires an in-depth analysis at both sector and subsector levels to examine the variation in the impact of the news on share prices. However, the focus of existing literature on the impact of vaccine development [13-15] is on the aggregate market level, not on subsector impact.

The current study delves deeper to provide a better understanding of the impact of vaccine development news. The aim of the study is twofold. First it examines the effect of the Pfizer and Biontech November 9, 2020, announcement. Thus, it contributes to the growing body of research on the impact of the COVID-19 pandemic on financial markets by exploring the COVID-19 vaccines’ impact on stock market. The second objective of the current research is to disaggregate the analysis to both sectoral and sub-sectoral levels. By focusing on sectoral/sub-sectoral level, this paper expands the existing literature that currently is mainly focused on aggregate equity markets [1, 16–24] and therefore ignore the heterogenous effects at the subsector level.

The results of the study suggest that the announcement generates optimism in a wide range of sectors and subsectors, though the impact has been disparate. Interestingly while some sectors exhibit no significant immediate effect overall, the sub-sectors do show some (sometimes diverse) effects. The findings from this study reveal the impact of the vaccine announcement that is opposite to that exhibited by the market at the onset of the COVID-19 pandemic found by [4].

The rest of this study is structured as follows. Section 2 discusses the literature review; Section 3 presents the data; Section 4 explains the methodology; Section 5 presents empirical results and discussion; and Section 6 concludes and presents policy recommendations.

2 Literature review

With the outbreak of the coronavirus (COVID-19) pandemic, an increasing number of researchers have examined the impact of the pandemic on stock markets. [1] document that no previous infectious disease outbreak, including the Spanish Flu, has impacted the stock market as forcefully as the COVID-19 pandemic due to strict government restrictions on commercial activity and voluntary social distancing. Exploring the direct effects and spillovers of COVID-19, [19] find that COVID-19 has a negative but short-term impact on stock markets of affected countries. By using a large sample of 63 stock markets covering all key markets, [16] find that the Wuhan lockdown induces negative spillover effects on markets in Europe, North America and other global markets. This is mainly attributed to fear and uncertainty as these markets had yet to introduce domestic restrictions and had minimal infections at the time. The rapid transmission of cases outside China particularly in Europe and the introduction of containment measures result in severe market decline which highlights the need for quick, globally coordinated response to contagious diseases.

Controlling for traditional market drivers (such as investor sentiment, credit risk, liquidity risk, safe-haven asset demand and the price of oil), [23] conclude that the daily total count of confirmed COVID-19 cases is a leading factor in influencing equity prices. Using panel data analysis, [25] estimate that both the daily growth in confirmed cases and number of deaths caused by COVID-19 have significant negative effects on returns in Chinese stock market. [26] finds that stock markets react more strongly to the growth in number of confirmed cases as compared to the growth in number of deaths. [4] examine the US stock market during the crash of March 2020. They estimate that approximately 90% of the S&P 1500 stocks generate asymmetrically distributed large negative returns. The consensus from this emerging literature on COVID-19 suggests that stock markets respond negatively and significantly to COVID-19. Individual stock responses may vary, however, depending on several factors.

There are few studies documenting the role of vaccine development on stock markets. [15] using data for 66 markets, show that mass vaccinations significantly decreased the stock market volatility, even after controlling for both the pandemic and government policy interventions. [27] examine the volatility of the energy stocks in fifty-eight countries. They document that vaccination programs assist in decreasing the volatility of energy stocks around the world. Interestingly both [15, 27] find that the stabilizing effect of vaccination on volatility is more pronounced in developed markets than in emerging ones. [28] study the role of the vaccine initiation rate in mitigating the stock market volatility on a global scale. They highlight the positive effect of the vaccine initiation rate in stabilizing the international stock markets. Consistent with earlier studies, they also find a stronger impact of vaccinations in developed markets.

[14] focus on the impact of vaccine news announcements by leading vaccine companies on the financial and commodity markets from January to December 2020. They show that the vaccine announcements of Johnson and Johnson, Moderna, Oxford-AstraZeneca, and Pfizer-BioNTech have impacted the stock markets in the U.S. and Europe, while stocks markets in Asia and Australia are unaffected. The impact is also varied in the commodities markets. Transportation commodities (crude oil and gasoline) react to the announcements while metal and construction commodities (gold, copper, and lumber) remain unmoved. Overall, they conclude that the effect of the announcements is statistically significant on the stock prices, interest rates, commodity currencies, and transportation commodities.

In another study, [29] show that global stock markets react positively to different phases of human clinical trials for COVID-19 vaccine candidates. After controlling for both the pandemic itself and investor sentiment, they find a positive abnormal return in forty-nine countries on the first day of clinical trials. Phase I and Phase II of the human clinical trials of vaccine candidates are more effective on the stock market.

[13] develop an asset-pricing model to forecast the value of medicine by employing a vaccine progress indicator. They estimate that the stock market improves by around 8.6 percent if the expected time to vaccine development decreases by a year. [30] develops a model of pandemic risk management that is integrated into an asset-pricing structure. They conclude that the quick arrival of vaccines lowers the duration of the pandemic and minimizes the impact of shocks.

All the studies mentioned have focused on the effect of vaccine announcement on the aggregate stock market. To the best of the author’s knowledge, the effect of vaccinations on the stock prices at the sectoral and sub-sectoral level remain unexplored. Some studies such as [31, 32] have analysed the energy companies, but their interest is on the impact of fiscal pressure.

3 Data

This study covers the daily stock prices of 11 sector and 79 sub-sector indices of the S&P 500. The 11 sectors, as per the Global Industry Classification Standard (GICS), are as follows: Information Technology, Health Care, Financials, Consumer Discretionary, Communication Services, Industrials, Consumer Staples, Energy, Utilities, Real Estate, and Materials. Data is obtained from Thomson Reuters Eikon for the period 23 June 2020 to 12 November 2020, for a total of 139 daily observations per index. This period includes the announcement date, on November 9, 2020, by Pfizer and Biontech that their vaccine candidate was successful in Phase-3. The S&P 500 index measures the stock performance of 500 large companies listed on stock exchanges in the U.S. and is widely used as an indicator of the performance of global equities. For each index, daily return is calculated as the natural logarithmic first difference of the daily closing price multiplied by 100.

Table 1 reports the descriptive statisticsal of the sectoral indices considered in this study over the period from 23 June 2020 to 12 November 2020. According to the table, all energy indices, biotechnology from health care sector, food retail and drug retail from consumer staples sector, internet services and infrastructure from information technology sector and gas utilities from utilities sector give negative average return for investors. Most of the series are moderately skewed having positive or negative skewness. Majority of the indices have high kurtosis which indicates fat tails in the distributions.

Table 1

Descriptive statistics.

Index	Mean	Standard Deviation	Minimum	Maximum	Skewness	Kurtosis	Jarque-Bera Test Statistics
Financials	0.11	1.66	-4.43	7.85	0.71	6.71	66.03
Banks	0.07	2.39	-6.32	12.48	1.18	8.77	162.2
Insurance	0.10	1.46	-3.22	5.48	0.29	3.72	3.55
Capital Markets	0.07	1.35	-3.88	3.14	-0.52	3.33	5.10
Consumer Finance	0.16	2.74	-6.16	15.12	1.41	10.51	268.1
Diversified Financial Services	0.22	1.28	-3.03	5.87	0.62	6.07	45.75
Energy	-0.20	2.60	-5.7	13.3	1.37	8.87	175
Oil& Gas Exploration and Production	-0.17	3.25	-7.40	15.13	0.89	6.57	66.34
Oil & Gas Equipment Services	-0.07	3.63	-8.23	17.01	0.87	6.69	69.54
Oil & Gas Drilling	-0.35	4.50	-11.23	16.05	0.18	3.96	4.43
Energy Equipment and Services	-0.04	3.44	-8.87	17.14	0.88	8.17	124.8
Real Estate	0.04	1.31	-2.95	2.54	-0.17	2.32	2.44
Equity Real Estate Investment Trusts	0.04	1.30	-2.97	2.61	-0.17	2.31	2.498
Real Estate Mng.and Dev.	0.18	2.86	-7,34	15.53	1.84	11.61	365.5
Communication Services	0.12	1.52	-4.59	4.16	-0.44	3.75	5.56
Wireless Telecommunication Services	0.17	1.68	-4.63	6.27	0.27	5.13	20.04
Interactive Media and Services	0.16	2.12	-6.89	6.63	-0.32	4.18	7.60
Broadcasting	0.04	2.15	-4.68	4.29	-0.26	2.52	2.12
Interactive Home Entertainment	-0.01	1.83	-4.43	3.75	-0.44	2.98	3.33
Media and Entertainment	0.14	1.75	-5.05	5.07	-0.31	3.69	3.57
Integrated Telecommunication Services	0.02	0.98	-2.84	3.13	0.28	4.51	10.87
Diversified Telecommunication Services	0.02	0.99	-2.82	3.07	0.26	4.34	8.64
Health Care	0.10	1.14	-3.28	4.35	0.14	4.57	10.58
Biotechnology	-0.08	1.56	-3.87	7.46	0.97	7.33	93.81
Health Care Equipment and Supplies	0.16	1.29	-4.02	2.85	-0.31	3.68	3.57
Health Care Distributors	0.08	1.77	-3.42	5.28	0.42	2.99	2.94
Heath Care Facility	0.35	2.54	-6.03	10.12	0.50	4.75	16.97
Health Care Technology	0.04	1.48	-3.43	3.74	-0.03	3.07	0.04
Life Sciences Tools and Services	0.21	1.58	-5.01	3.51	-0,57	3.75	7.83
Pharmaceuticals	0.05	1.07	-3.01	4.16	0.11	4.53	9.98
Consumer Discretionary	0.15	1.47	-3.63	3.16	-0.26	2.67	1.547
Auto Components	0.25	2.15	-5.69	4.89	-0.11	2.80	0.35
Automobiles	0.32	2.10	-4.93	4.93	0.004	2.63	0.57
Hotels	0.19	3.35	-7.29	17.03	1.43	9.05	187
Casinos and Gaming	0.16	3.23	-7.24	13.90	1.08	5.78	51.86
Restaurants	0.17	1.14	-3.69	2.78	-0.45	3.43	4.29
Household Durables	0.21	1.94	-4.94	4.22	-0.17	2.73	0.81
Home Building	0.21	2.48	-7.10	6.51	-0.02	3.06	0.02
Household Appliances	0.36	2.23	-10.96	7.69	-0.91	9.18	173.4
Consumer Electronics	0.15	1.48	-4.34	5.49	0.20	4.45	9.54
Internet and Direct Marketing Retail	0.12	2.41	-5.31	6.94	0.25	2.90	1.13
Multiline Retail	0.18	1.29	-3.81	6.19	0.53	7.03	72.41
Distributors	0.15	1.88	-6.10	5.05	-0.31	3.96	5.53
General Merchandise Stores	0.18	1.31	-3.81	6.36	0.60	7.40	86.7
Specialty Retail	0.12	1.26	-3.77	2.66	-0.39	2.97	2.65
Computers and Electronics Retail	0.28	2.05	-10.31	7.55	-1.19	10.12	235.2
Home Furnishing Retail	0.12	2.87	-15.05	7.61	-1.31	9.69	214.9
Textiles, Apparel and Luxury Goods	0.22	1.81	-7.02	6.47	-0.31	5.71	32.09
Consumer Staples	0.12	0.91	-2.96	1.97	-0.51	3.79	7.13
Food and Staples Retailing	0.17	1.11	-2.57	3.12	0.05	3.11	0.10
Drug Retail	-0.05	2.31	-8.07	6.42	0.12	4.04	4.74
Food Retail	-0.002	1.62	-6.73	4.34	-0.80	5.67	40.52
Hypermarkets and Super Centers	0.21	1.26	-3.33	4.32	0.23	4.19	6.81
Brewers	0.11	2.21	-5.25	7.15	0.43	3.68	5.00
Soft Drinks	0.12	1.16	-3.80	3.40	-0.25	3.97	4.96
Food Products	0.06	1.08	-3.33	2.41	-0.44	3.55	4.59
Agricultural Products	0.22	0.98	-2.35	3.38	0.29	3.62	2.98
Packaged Foods and Meats	0.05	1.09	-3.38	2.47	-0.49	3.53	5.33
Household Products	0.15	0.99	-3.82	1.87	-0.98	5.43	40.85
Personal Products	0.24	1.62	-6.80	4.20	-0.81	6.30	56.48
Tobacco	0.016	1.43	-4.16	4.05	0.08	4.09	5.06
Industrials	0.19	1.44	-3.58	3.29	-0.35	3.06	2.12
Airlines	0.11	3.42	-7.97	13.73	0.79	5.24	31.65
Railroads	0.22	1.55	-3.06	3.35	-0.32	2.18	4.48
Transportation Infrastructure	0.18	1.74	-4.26	7.03	0.35	4.91	17.33
Air Freight and Logistics	0.43	1.93	-7.94	8.02	-0.02	8.10	108.8
Building products	0.31	1.56	-4.09	4.03	-0.31	3.58	3.15
Aerospace and Defense	0.008	1.98	-4.35	6.81	0.40	3.87	5.87
Electrical Equipment	0.21	1.67	-4.94	4.58	-0.21	3.39	1.38
Industrial Conglomerates	0.18	1.68	-4.65	4.35	-0.42	3.29	3.27
Machinery	0.28	1.55	-4.03	3.85	-0.34	3.14	2.09
Information Technology	0.14	1.77	-6.00	3.76	-0.71	3.78	11.02
Communications Equipment	-0.08	1.59	-9.32	2.86	-2.13	13.00	492.5
IT Services	0.06	1.45	-4.41	3.49	-0.54	3.43	5.75
IT Consulting and Other Services	0.09	1.39	-3.78	4.62	-0.19	4.35	8.20
Data Processing and Outsourcing	0.06	1.56	-4.63	4.15	-0.42	3.38	3.54
Internet Services and Infrastructure	-0.034	1.72	-6.75	5.02	-0.80	5.61	39.16
Semiconductors and Equipment	0.18	1.92	-6.42	3.96	-0.49	3.58	5.57
Software	0.09	2.02	-5.89	4.73	-0.33	3.32	2.34
Technology Hardware and Storage	0.25	2.62	-8.15	9.62	-0.12	4.70	12.33
Materials	0.18	1.47	-3.47	3.97	-0.22	2.91	0.87
Chemicals	0.16	1.54	-3.44	4.25	-0.11	2.88	0.25
Construction Materials	0.18	2.14	-9.09	5.22	-0.84	6.01	49.5
Containers and Packaging	0.25	1.49	-3.11	3.26	-0.15	2.62	0.98
Metals and Mining	0.22	1.94	-5.58	4.72	-0.46	3.72	5.78
Utilities	0.14	1.14	-2.98	3.08	-0.03	2.63	0.59
Electric Utilities	0.14	1.19	-2.78	3.34	0.08	2.50	1.17
Gas Utilities	-0.001	1.39	-4.62	4.65	0.46	5.19	23.67
Water Utilities	0.25	1.30	-4.59	3.13	-0.24	3.72	3.19
Multi Utilities	0.11	1.21	-3.42	2.74	-0.24	3.09	0.99

This table reports descriptive statistics of indices. Data is obtained from Thomson Reuters Eikon for the period from 23 June 2020 to 12 November 2020, for a total of 139 daily observations per index.

4 Methodology

Event study methodology is one of the most frequently used analytical tools in financial research. The objective of an event study is to assess whether there are any abnormal or excess returns earned by security holders accompanying specific events (e.g., earnings announcements, merger announcements, stock splits) where an abnormal or excess return is the difference between observed return and that expected return given a particular return generating model. In this study, we have exploited market-model event-study methodology to identify abnormal returns resulting from the announcement of the vaccine.

4.1 Event study methodology

The market model is the most frequently used expected return model. For any stock market index i, the market model can be presented as Eq 1: where R represents return of a sectoral or sub-industry indices on day t which belongs to estimation window, R denotes the return of the S&P 500 Index on day t belonging to the same period. and are the parameters of the market model. The expected return E(Ri) is then calculated as in Eq 2 and AR which represents the abnormal return of any sectoral or sub-industry indices on day t is determined from Eq 3. while;

To measure the total impact of an event right after the announcement, the “cumulative abnormal return (CAR)” for the event window of [0, 1] and [0, 3] is calculated as follows in Eq 4: where t1 and t2 represent the start and end of event window.

Finally, after identifying all abnormal returns and cumulative abnormal returns over chosen event windows, their statistical significance are tested with t-test. The null and alternative hypotheses are stated as in Eq 5. The critical value for the null hypothesis rejection is ± 2.576, 1.96 and 1.645 with the confidence level of 99%, 95% and 90%, respectively.

Fig 1 below presents the event timeline. Estimation window of 90 days dating from 23 June 2020 to 28 October 2020 is selected. The main reason for choosing a short estimation window is to exclude the early period of the pandemic where S&P 500 experienced high volatility. In case there is an information leakage in the market before the announcement, the data dating from 29 October 2020 to 6 November 2020, i.e. 7 days preceding the event from the estimation window is excluded (see Fig 1 below). Six different event windows: [-1], [0], [1], [2], [3], [0, 1] and [0, 3] are considered to measure the instantaneous effect of the announcement.

Fig 1

Event timeline.

5 Empirical results and discussion

Uncertainty and fear have been a feature of the COVID-19 pandemic environment especially during the early period. The uncertainty and non-pharmaceutical interventions such as social distancing, travel restrictions and lockdowns to limit the spread were the key factors driving the socio-economic impacts of the pandemic. There are several channels through which a vaccine announcement, even prior to its actual rollout, would benefit the economy. An affirmation that a vaccine was imminent would reduce the uncertainty and fear, and generally boost optimism in markets as economic actors anticipate a rollback of restrictive interventions.

The basic results (Table 2) show the vaccine announcement on November 9, 2020, has disparate effects on the market. The (sub)sectors that were hardest hit by the pandemic [4, 5, 33] benefit the most from positive vaccine news, while sectors that gained from the pandemic were depressed by the news. Financials and energy sectors exhibit clear positive effects on [0], [0, 1] and [0, 3]. The financial sector and subsectors show significant gains with the largest gains registered by consumer finance and banks at 13.94 percent and 11.46 percent on [0], respectively. Financial industry benefits from positive economic news and a healthy economic environment that the availability of a vaccine is expected to promote. Some indicators such as the US 10-year treasury bond yields increased to 1.675% after the vaccine announcement which is the highest level since March 2020. The vaccine understandably provides an opportunity for the economy to begin recovering.

Table 2

Market model abnormal return and cumulative abnormal return for different event windows-estimation period: 23 June 2020–28 October 2020.

Event Windows	[-1]		[0]		[1]		[2]		[3]		[0, 1]		[0, 3]
Indices	AR	T-Stats.	AR	T-Stats.	AR	T-Stats.	AR	T-Stats.	AR	T-Stats.	CAR	T-Stats.	CAR	T-Stats.
Financials	-0.73%	-0.70	6.91%	6.58***	0.90%	0.86	-1.10%	-1.05	-0.77%	-0.73	7.81%	5.2***	5.94%	2.83***
Banks	-1.66%	-0.97	11.46%	6.70***	0.09%	0.05	-1.56%	-0.91	-1.07%	-0.63	11.55%	4.77***	8.92%	2.61***
Insurance	0.12%	0.12	4.63%	4.58***	2.28%	2.26**	-1.49%	-1.48	-0.54%	-0.53	6.91%	4.84***	4.88%	2.41**
Capital Markets	-0.74%	-1.07	2.12%	3.07***	0.07%	0.10	0.34%	0.49	-0.89%	-1.29	2.19%	2.24***	1.64%	1.19
Consumer Finance	-1.31%	-0.68	13.94%	7.19***	-0.51%	-0.26	-3.92%	-2.02**	0.53%	0.27	13.43%	4.90***	10.04%	2.59***
Diversified Financial Services	0.62%	0.75	5.02%	6.05***	3.12%	3.76***	-1.07%	-1.29	-0.58%	-0.70	8.14%	6.93***	6.49%	3.91***
Energy	-1.71%	-0.92	12.45%	6.73***	3.07%	1.66*	-1.25%	-0.68	-1.93%	-1.04	15.52%	5.93***	12.34%	3.34***
Oil & Gas Exploration and Production	-2.63%	-1.11	13.87%	5.85***	3.55%	1.50	-1.42%	-0.60	-2.33%	-0.98	17.42%	5.20***	13.67%	2.88***
Oil & Gas Equipment Services	-1.82%	-0.66	15.70%	5.67***	1.18%	0.43	-2.34%	-0.84	-1.19%	-0.43	16.88%	4.31***	13.35%	2.41**
Oil & Gas Drilling	-1.89%	-0.49	14.80%	3.86***	0.93%	0.24	-1.76%	-0.46	-2.56%	-0.67	15.73%	2.90***	11.41%	1.49
Energy Equipment and Services	-1.66%	-0.64	15.92%	6.17***	0.31%	0.12	-2.92%	-1.13	-0.38%	-0.15	16.23%	4.45***	12.93%	2.51**
Real Estate	-0.33%	-0.35	1.71%	1.84*	0.69%	0.74	0.25%	0.27	-0.18%	-0.19	2.40%	1.82*	2.47%	1.33
Equity Real Estate Investment Trusts	-0.31%	-0.33	1.52%	1.62	0.78%	0.83	0.29%	0.31	-0.17%	-0.18	2.30%	1.73*	2.42%	1.29
Real Estate Mng.and Dev.	-1.26%	-0.53	9.32%	3.92***	-2.72%	-1.14	-1.29%	-0.54	-0.79%	-0.33	6.60%	1.96**	4.52%	0.95
Communication Services	-0.06%	-0.08	-1.54%	-2.00**	-0.17%	-0.22	-0.28%	-0.36	0.62%	0.81	-1.71%	-1.57	-1.37%	-0.89
Wireless Telecommunication Services	5.29%	4.34***	-1.60%	-1.31	-0.72%	-0.59	2.16%	1.77	2.20%	1.80	-2.32%	-1.34	2.04%	0.84
Interactive Media and Services	-0.26%	-0.21	-3.62%	-2.92***	-1.54%	-1.24	-0.12%	-0.10	1.04%	0.84	-2.32%	-1.34	-4.24%	-1.71
Broadcasting	-4.23%	-2.32**	0.78%	0.43	3.11%	1.71*	-0.15%	-0.08	-3.54%	-1.95**	3.89%	1.51	0.20%	0.05
Interactive Home Entertainment	-2.44%	-1.68*	-5.37%	-3.70***	-0.06%	-0.04	1.30%	0.90	0.73%	0.50	-5.43%	-2.65***	-3.40%	-1.17
Media and Entertainment	-0.26%	-0.28	-2.17%	-2.33**	-0.57%	-0.61	-0.30%	-0.32	0.71%	0.76	-2.74%	-2.08**	-2.33%	-1.25
Integrated Telecommunication Services	0.33%	0.42	2.18%	2.79***	2.14%	2.74***	-0.53%	-0.68	-0.14%	-0.18	4.32%	3.92***	3.65%	2.34**
Diversified Telecommunication Services	0.28%	0.35	2.15%	2.72***	2.22%	2.81***	-0.55%	-0.70	-0.13%	-0.16	4.37%	3.91***	3.69%	2.34**
Health Care	0.21%	0.34	-0.11%	-0.18	0.49%	0.79	-0.70%	-1.13	0.38%	0.61	0.38%	0.43	0.06%	0.05
Biotechnology	-0.36%	-0.34	-2.31%	-2.20**	2.14%	2.04**	-0.34%	-0.32	0.18%	0.17	-0.17%	-0.11	-0.33%	-0.16
Health Care Equipment and Supplies	0.62%	0.64	0.92%	0.95	0.67%	0.69	-1.00%	-1.03	0.22%	0.23	1.59%	1.16	0.81%	0.42
Health Care Distributors	-0.34%	-0.24	4.06%	2.92***	2.95%	2.12**	-3.64%	-2.62***	0.48%	0.35	7.01%	3.56***	4.41%	0.96
Heath Care Facility	0.19%	0.09	5.40%	2.52**	3.40%	1.59	-4.85%	-2.27**	-0.16%	-0.07	8.80%	2.91**	3.74%	0.53
Health Care Technology	0.86%	0.76	0.30%	0.27	1.33%	1.18	-0.74%	-0.65	-0.08%	-0.07	1.63%	1.02	0.81%	0.36
Life Sciences Tools and Services	1.93%	2.03**	-6.20%	-6.53***	-3.19%	-3.36***	1.21%	1.27	1.47%	1.55	-9.39%	-6.99***	-6.71%	-3.53***
Pharmaceuticals	0.34%	0.33	1.36%	1.31	0.79%	0.76	-0.91%	-0.88	-0.24%	-0.23	2.28%	2.37**	1.32%	0.38
Consumer Discretionary	-0.32%	-0.49	-2.91%	-4.48***	-1.05%	-1.62	0.61%	0.94	-0.21%	-0.32	-3.96%	-4.31***	-3.56%	-2.74***
Auto Components	-0.69%	-0.41	1.24%	0.74	2.05%	1.23	0.43%	0.26	-2.03%	-1.22	3.29%	1.39	1.69%	0.51
Automobiles	-0.70%	-0.40	2.59%	1.47	3.34%	1.90*	-1.12%	-0.63	-1.96%	-1.11	5.93%	2.38**	2.85%	0.81
Hotels	2.87%	1.01	6.74%	2.37**	10.04%	3.54***	-2.05%	-0.72	-1.90%	-0.67	16.78%	4.18***	23.97%	2.54**
Casinos and Gaming	0.64%	0.23	12.77%	4.64***	-1.16%	-0.42	-4.25%	-1.55	-1.82%	-0.66	11.61%	2.99***	5.54%	1.01
Restaurants	0.04%	0.05	0.33%	0.43	-1.01%	-1.33	0.96%	1.26	-1.44%	-1.89	-0.68%	-0.63	-0.51%	-0.20
Household Durables	-2.26%	-1.60	-6.17%	-4.38***	4.01%	2.84***	0.23%	0.16	-1.23%	-0.87	-2.16%	-1.08	-3.16%	-1.12
Home Building	-3.64%	-1.94*	-8.41%	-4.47***	6.51%	3.46***	0.96%	0.51	-1.53%	-0.81	-1.90%	-0.71	-9.21%	-2.00**
Household Appliances	1.10%	0.67	-12.37%	-7.59***	1.06%	0.05	0.08%	0.05	-0.12%	-0.07	-11.31%	-4.91***	-11.35%	-3.48***
Consumer Electronics	1.72%	1.77	-0.88%	-0.91	-0.82%	-0.85	-0.06%	-0.06	-0.08%	-0.08	-1.70%	-1.24	-1.84%	-0.95
Internet and Direct Marketing Retail	-0.20%	-0.12	-5.52%	-3.41***	-3.50%	-2.16**	1.98%	1.22	0.31%	0.19	-9.02%	-3.94***	-0.95%	-2.08**
Multiline Retail	1.39%	1.26	-4.55%	-4.14***	0.83%	0.75	0.38%	0.35	0.02%	0.02	-3.72%	-2.39**	-3.32%	-1.51
Distributors	-1.37%	-0.94	-4.99%	-3.42***	3.57%	2.45**	-1.81%	-1.24	-1.24%	-0.85	-1.42%	-0.69	-4.47%	-1.53
General Merchandise Stores	1.38%	1.23	-4.54%	-4.05***	0.83%	0.74	0.38%	0.34	0.01%	0.01	-3.71%	-2.34**	-3.32%	-1.48
Specialty Retail	-0.53%	-0.71	-3.09%	-4.12***	1.81%	2.41**	-0.59%	-0.79	0.09%	0.12	-1.28%	-1.21	-1.78%	-1.19
Computers and Electronics Retail	-0.22%	-0.15	-11.55%	-7.97***	1.33%	0.92	0.80%	0.55	0.12%	0.08	-10.22%	-4.99***	-9.30%	-3.21***
Home Furnishing Retail	-2.36%	-0.95	0.75%	0.30	1.68%	0.68	-4.06%	-1.64	-1.06%	-0.43	2.43%	0.69	-2.69%	-0.54
Textiles, Apparel and Luxury Goods	-1.16%	-0.72	1.08%	0.67	-0.69%	-0.43	-1.29%	-0.80	-0.41%	-0.25	0.39%	0.17	-1.31%	-0.40
Consumer Staples	0.43%	0.84	-1.22%	-2.39**	2.02%	3.96***	0.35%	0.69	0.39%	0.76	0.80%	1.11	1.54%	1.51
Food and Staples Retailing	0.47%	0.56	0.72%	0.86	-2.29%	-2.73***	1.47%	1.75	0.28%	0.33	-0.82%	-0.69	0.67%	0.80
Drug Retail	-0.25%	-0.14	5.90%	3.19***	6.67%	3.61***	-2.41%	-1.30	-0.59%	-0.32	12.57%	4.80***	9.57%	2.59***
Food Retail	1.39%	0.97	-7.05%	-4.93***	2.95%	2.06**	0.32%	0.22	0.11%	0.08	-4.01%	-2.03**	-3.67%	-1.28
Hypermarkets and Super Centers	0.78%	0.75	-4.11%	-3.95***	0.89%	0.86	1.11%	1.07	0.82%	0.79	-3.22%	-2.19**	-1.29%	-0.62
Brewers	0.38%	0.22	6.62%	3.78***	4.17%	2.38**	-1.30%	-0.74	0.39%	0.22	10.79%	4.36***	9.88%	2.82***
Soft Drinks	-1.04%	-1.42	0.11%	0.15	1.86%	2.56***	2.45%	2.56***	0.01%	0.01	4.31%	4.17***	0.10%	0.14
Food Products	0.45%	0.66	-1.65%	-2.43**	1.77%	2.60***	0.77%	1.13	0.19%	0.28	0.12%	0.12	1.08%	0.79
Agricultural Products	-0.33%	-0.34	0.04%	0.04	2.14%	2.18**	-0.78%	-0.80	-0.65%	-0.66	2.18%	1.57	0.75%	0.38
Packaged Foods and Meats	0.44%	0.64	-1.92%	-2.78***	1.79%	2.59***	0.93%	1.35	0.31%	0.45	-0.13%	-0.13	1.11%	0.80
Household Products	0.60%	0.95	-4.54%	-7.21***	1.78%	2.83***	0.88%	1.40	0.61%	0.97	-2.76%	-3.09***	-1.27%	-1.00
Personal Products	0.68%	0.52	2.12%	1.62	0.48%	0.37	-0.33%	-0.25	1.20%	0.92	2.60%	1.40	3.47%	1.32
Tobacco	-0.34%	-0.27	1.41%	1.10	4.18%	3.27***	0.14%	0.11	-0.15%	-0.12	5.59%	3.09***	5.58%	2.72***
Industrials	0.11%	0.13	2.20%	2.50**	1.85%	2.10**	-1.61%	-1.83	-0.32%	-0.36	4.05%	3.25***	2.12%	1.20
Airlines	4.46%	1.44	1.79%	0.58	10.06%	3.25***	0.01%	0.00	-4.46%	-1.44	11.85%	2.70***	20.36%	1.98**
Railroads	-1.02%	-0.89	2.24%	1.96**	2.36%	2.05**	-1.61%	-1.40	0.53%	0.46	4.60%	2.83***	3.52%	1.53
Transportation Infrastructure	-0.98%	-0.84	5.93%	5.07***	0.55%	0.47	-2.27%	-1.94	-1.09%	-0.93	6.48%	3.91***	3.12%	1.33
Air Freight and Logistics	1.27%	0.82	-4.75%	-3.08***	0.71%	0.46	-0.46%	-0.30	-0.94%	-0.61	-4.04%	-1.86*	-5.44%	-1.77*
Building products	0.87%	0.84	-1.97%	-1.91*	-0.70%	-0.68	-0.78%	-0.76	0.27%	0.26	-2.67%	-1.83*	-3.18%	-1.54
Aerospace and Defense	-0.07%	-0.05	5.92%	4.00***	3.71%	2.51**	-2.61%	-1.76	-0.46%	-0.31	9.63%	4.60***	6.56%	2.22**
Electrical Equipment	-0.01%	-0.01	3.49%	3.17***	0.81%	0.74	-2.49%	-2.26**	-0.45%	-0.41	4.30%	2.76***	1.36%	0.62
Industrial Conglomerates	0.54%	0.45	3.30%	2.73***	3.00%	2.48**	-1.41%	-1.17	-0.48%	-0.40	6.30%	3.68***	4.41%	1.82*
Machinery	-0.12%	-0.11	1.55%	1.48	1.50%	1.43	-1.77%	-1.69	-0.22%	-0.21	3.05%	2.05**	1.06%	0.51
Information Technology	0.32%	0.44	-2.27%	-3.11***	-1.78%	-2.44**	1.37%	1.88	0.37%	0.51	-4.05%	-3.92***	-2.31%	-1.58
Communications Equipment	1.40%	1.05	1.52%	1.14	1.06%	0.80	1.29%	0.97	-0.48%	-0.36	2.58%	1.37	3.39%	1.27
IT Services	0.06%	0.10	2.35%	3.79***	-0.11%	-0.18	-0.19%	-0.31	-0.55%	-0.89	2.24%	2.55**	1.50%	1.21
IT Consulting and Other Services	0.21%	0.24	0.62%	0.71	0.89%	1.02	0.20%	0.23	-0.95%	-1.09	1.51%	1.23	0.76%	0.44
Data Processing and Outsourcing	0.05%	0.07	2.97%	4.13***	-0.33%	-0.46	-0.31%	-0.43	-0.48%	-0.67	2.64%	2.59***	1.85%	1.28
Internet Services and Infrastructure	-0.75%	-0.60	-2.86%	-2.29***	-1.87%	-1.50	0.19%	0.15	0.71%	0.57	-4.73%	-2.67***	-3.83%	-1.53
Semiconductors and Equipment	1.36%	1.33	-3.38%	-3.31***	-3.36%	-3.29***	2.63%	2.58***	0.13%	0.13	-6.74%	-4.67***	-3.98%	-1.95
Software	0.27%	0.24	-3.78%	-3.35***	-3.44%	-3.04***	1.61%	1.42	0.68%	0.60	-7.22%	-4.52***	-4.93%	-2.18**
Technology Hardware and Storage	-0.35%	-0.20	-3.81%	-2.15**	-0.18%	-0.10	1.44%	0.81	1.14%	0.64	-3.99%	-1.59	-1.41%	-0.40
Materials	0.12%	0.14	1.10%	1.25	1.18%	1.34	-2.14%	-2.43**	-1.35%	-1.53	2.28%	1.83*	-1.21%	-0.69
Chemicals	0.23%	0.24	1.88%	1.94*	1.38%	1.42	-2.72%	-2.80***	-1.50%	-1.55	3.26%	2.38**	-0.96%	-0.49
Construction Materials	-1.97%	-1.13	0.25%	0.14	2.42%	1.39	-0.18%	-0.10	-1.32%	-0.76	2.67%	1.09	1.17%	0.34
Containers and Packaging	-0.10%	-0.10	-0.41%	-0.41	1.30%	1.29	-0.81%	-0.80	-1.88%	-1.86	0.89%	0.62	-1.80%	-0.89
Metals and Mining	0.50%	0.34	-1.33%	-0.92	-0.52%	-0.36	-1.01%	-0.70	0.03%	0.02	-1.85%	-0.90	-2.83%	-0.98
Utilities	-0.30%	-0.31	1.17%	1.21	1.40%	1.44	-0.10%	-0.10	-1.35%	-1.39	2.57%	1.87*	1.12%	0.58
Electric Utilities	-0.20%	-0.20	0.88%	0.88	1.11%	1.11	0.31%	0.31	-1.26%	-1.26	1.99%	1.41	1.04%	0.52
Gas Utilities	-1.36%	-1.26	4.08%	3.78***	4.79%	4.44***	-1.82%	-1.69	-1.61%	-1.49	8.87%	5.81***	5.44%	2.52**
Water Utilities	1.66%	1.44	-0.01%	-0.01	0.61%	0.53	-0.03%	-0.03	-1.52%	-1.32	0.60%	0.37	-0.95%	0.41
Multi Utilities	-0.53%	-0.49	1.75%	1.62	2.00%	1.85*	-0.92%	-0.85	-1.38%	-1.28	3.75%	2.45**	1.45%	0.67

Note: This table shows the Market Model Abnormal Returns and Cumulative Abnormal Returns for different event windows. The null hypothesis of H0: AR = 0 orCAR = 0 is tested with t-test.

***,**,* indicate 1%, 5%, and 10% significance,respectively. Data is obtained from Thomson Reuters Eikon for the period 23 June 2020 to 12 November 2020, for a total of 139 daily observations per index.

The energy sector gains 12.45 percent and 12.34 percent on [0] and [0, 3] respectively. The four sub-sectors all show gains of over 13 percent on [0]. The S&P energy sector is by far the worst performing index of the eleven sectors during the pandemic [4]. It is unsurprising that the energy sector and oil and gas subsectors show a positive response. Demand for energy would be expected to increase with the easing of restrictions and return to economic normalcy enabled by the vaccine rollout. In fact IMF projected a global growth of 6.0 percent in 2021 due to vaccine rollout and stimuli [34]. Though the real estate management response is muted, the real estate management and development subsector is more upbeat about the vaccine with a gain of 9.32 percent on [0], and 6.60 percent over [0, 1]. The subsector has been hit hard by a global work-from-home trend. A vaccine raises the prospect of people returning to the office. The real estate development would also benefit from a growing economy. The communications services sector experiences a moderate loss of 1.54 percent. Subsectors that offer news, broadcasting and entertainment services indicate loss, possibly because it is expected that home consumption of these services would decline once economic activity fully resume. However, more integrated/diversified telecommunication services that are linked to the overall economic performance, show some gain.

Overall, the health care sector does not show significant reaction to the announcement, but some subsectors such as biotechnology and life sciences tools, engaged mainly in the research, development, and manufacturing, register losses. This likely reflects the lost competitive edge of firms that are in competition with the developers of Pfizer. In addition, health care providers such as distributors and operators of health care facilities indicate a positive response to the announcement. COVID-19 pandemic has significantly disrupted services for noncommunicable diseases [35]. A return to economic normalcy would allow health care providers to meet this pent-up demand. Furthermore, health care providers would benefit if involved in the vaccine rollout.

The consumer discretionary sector records a negative impact of 2.91 percent and 3.56 percent at [0] and [0, 3] respectively. But the subsector reactions vary significantly. Hotels register a substantial gain of 23.97 percent over [0, 3] window. [4] show that entertainment and hospitality were negatively affected by the COVID-19 pandemic. These subsectors would benefit from elimination of restrictions. Home building, household appliances and computers and electronics retail subsectors lost 9.21 percent, 11.35 percent and 9.3 percent over [0, 3] window, respectively as vaccination would reverse the work-from-home trend. The announcement triggered significant disparate effects within the subsectors of consumer staples. On the event day, food retail and hypermarket sub-sectors lost. So do packaged foods, and household products. This can be attributed to several reasons. One of the main drivers is the anticipated reduced work-from-home when the COVID-19 pandemic is controlled by widespread vaccination. Also, with the economic activity expected to go back to normal, consumer uncertainty, fear and thus panic buying would decrease. Literature shows that perceived scarcity drives panic buying [36, 37]. Furthermore studies [38, 39] relate panic buying to social media use. In fact, some literature [40] have found that social influence can produce a stronger impact on panic buying than perceived scarcity. With the announcement of the vaccine, social media likely would have more upbeat messages. Leisure good providers like brewers gained 9.88 percent over [0, 3].

The airline industry which stands to benefit from resumption of travel show substantial gains of 20.36 percent over [0, 3]. [41] show that airline industry stocks were affected more than the market average by the pandemic. Railroad and transportation infrastructure also show (more moderate) gains. Increase in e-commerce spurred by COVID-19 restrictions, and the requirement for essential medical supplies by governments to meet the challenges of COVID-19 stimulated air freight demand. This explains the 5.44 percent loss over [0, 3] by air freight business on vaccine announcement.

Return to normal work environment would limit demand for internet, software and other products needed to operate from home or remote work locations. These subsectors show negative reaction to the announcement. However, IT services such as consulting and information management, and system integration (IT services), as well as data processing rely more on the performance of the overall economy. This explains why they show a positive reaction to the announcement. The effect on materials sector is muted with chemicals subsector having a modest gain of 1.88 percent on [0]. Utilities sector also does not display significant reaction except for the gas utilities subsector that gains 5.44 percent over [0, 3]. Gas utilities comprise primarily companies that distribute natural and manufactured gas. Low prices that encouraged switching from coal to gas for power generation, and strong demand from China and Asia-Pacific region in 2020 are the main reasons that affect gas stocks positively. Anticipated improvement in the general economy due to a vaccine rollout would also positively affect demand for gas.

5.1 Robustness analysis

As the period chosen from June to October 2020 represents the recovery phase after COVID-19 crisis, we choose an additional pre-pandemic estimation period between 10 September 2019 to 16 January 2020 for a robustness analysis following [42, 43]. The results obtained (see Table 3) closely mirror the results in Table 2 indicating the results are robust to the choice of estimation period. The financial sector gains with consumer finance and banks gaining the most. Likewise the gains by energy sector are also large. The real estate management and development subsector shows a gain of 9.06 percent over [0], and 6.46 percent over [0, 1]. Overall, the communications services sector lost 1.71 percent. As in Table 2, subsectors that offer news, broadcasting and entertainment services indicate loss, while more integrated/diversified telecommunication services that are linked to the overall economic performance, show some gain. Reactions in consumer discretionary show strong gains by hotels and casinos, but losses by direct retail, internet, computers and electronics. As in Table 2 food retail and hypermarket show losses, while brewers and drug retail gained. Airline industry and transportation show substantial gains of 12.54 and 5.93 percent on [0], while airfreight register losses. All in all, the results in Table 3 closely mimic the results in Table 2.

Table 3

Market model abnormal return and cumulative abnormal return for different event windows-estimation period: 10 September 2019–16 January 2020.

Event Windows	[-1]		[0]		[1]		[2]		[3]		[0, 1]		[0, 2]
Indices	AR	T-Stats.	AR	T-Stats.	AR	T-Stats.	AR	T-Stats.	AR	T-Stats.	CAR	T-Stats.	CAR	T-Stats.
Financials	-0.73%	-0.70	6.50%	15.85***	0.89%	2.17**	-1.39%	-3.39***	-0.53%	-1.29	7.39%	12.75***	5.47%	6.67***
Banks	-1.80%	-2.47**	10.91%	14.94***	-0.01%	-0.01	-1.97%	-2.70***	-0.88%	-1.21	10.90%	10.56***	8.05%	5.51***
Insurance	0.17%	0.38	4.40%	9.78***	2.36%	5.24***	-1.62%	-3.60***	-0.25%	-0.56	6.76%	10.62***	4.89%	5.43***
Capital Markets	-0.80%	-2.16**	1.74%	4.70***	0.04%	0.11	0.07%	0.19	-0.69%	-1.86*	1.78%	3.40***	1.16%	1.57
Consumer Finance	-1.32%	-2.28**	13.64	23.52	-0.49	-0.84	-4.13	-7.12	0.76	1.31	13.15	16.03	9.78	8.43
Diversified Financial Services	0.69%	1.50	4.74%	10.30***	3.22%	7.00***	-1.24%	-2.70***	-0.22%	-0.48	7.96%	12.24***	6.50%	7.07***
Energy	-1.98%	-2.83***	12.86%	18.37***	2.73%	3.90***	-1.08%	-1.54	-2.76%	-3.94***	15.59%	15.75***	11.75%	8.39***
Oil& Gas Exploration and Production	-3.03%	-1.99**	16.52%	10.87***	2.86%	1.88*	0.21%	0.14	-5.22%	-3.34***	19.38%	9.02***	14.37%	4.73***
Oil & Gas Equipment Services	-1.97%	-1.17	18.13%	10.79***	0.79%	0.47	-0.78%	-0.46	-3.44%	-2.05**	18.92%	7.96***	14.70%	4.38***
Oil & Gas Drilling	-2.61%	-1.13	16.83	7.25	-0.04	-0.02	-0.65	-0.28	-5.53	-2.38	16.79	5.12	10.61	2.29
Energy Equipment and Services	-1.73%	-1.40	18.19%	14.66***	0.02%	0.02	-1.44%	-1.16	-2.37%	-1.91*	18.21%	10.38***	14.40%	5.81***
Real Estate	-0.36%	-0.46	2.10%	2.69***	0.68%	0.87	0.66%	0.85	-0.72%	-0.92	2.78%	2.52**	2.72%	1.74*
Equity Real Estate Investment Trusts	-0.36%	-0.46	2.10%	2.69***	0.68%	0.87	0.66%	0.85	-0.72%	-0.92	2.78%	2.52**	2.72%	1.74*
Real Estate Mng.and Dev.	-1.17%	-1.14	9.06%	8.80***	-2.60%	-2.52**	-1.43%	-1.39	-0.42%	-0.41	6.46%	4.43***	4.61%	2.24**
Communication Services	-0.12%	-0.35	-1.71%	-5.02***	-0.22%	-0.65	-0.41%	-1.21	0.66%	1.94*	-1.93%	-4.01***	-1.68%	-2.47**
Wireless Telecommunication Services	5.02%	9.30***	-1.22%	-2.25**	-1.05%	-1.94*	2.32%	4.29***	1.42%	2.63***	-2.27%	-2.97***	1.47%	1.36
Interactive Media and Services	-0.45%	-0.71	-4.28	-6.79***	-1.69	-2.68	-0.63	-1.00	1.22	1.94	-5.97	-6.70	-5.38	-4.27
Broadcasting	-3.96%	-5.42***	1.04%	1.42	3.39%	4.64***	0.11%	0.15	-3.25%	-4.45***	4.43%	4.29***	1.28%	0.88
Interactive Home Entertainment	-2.55%	-3.40***	-4.84%	-6.45***	-0.23%	-0.31	1.62%	2.16**	0.10%	0.13	-5.07%	-4.78***	-3.35%	-2.23**
Media and Entertainment	-0.36%	-0.90	-2.61%	-6.53***	-0.63%	-1.58	-0.63%	-1.58	0.90%	2.25**	-3.24%	-5.73***	-2.97%	-3.71***
Integrated Telecommunication Services	0.46%	0.88	2.98%	5.73***	2.20%	4.23***	0.05%	0.10	-0.57%	-1.10	5.18%	7.04***	4.66%	4.48***
Diversified Telecommunication Services	0.40%	0.83	2.92%	6.08***	2.28%	4.75***	-0.0001%	-0.0004	-0.54%	-1.13	5.20%	7.66***	4.66%	4.85***
Healthcare	0.14%	0.31	0.07%	0.16	0.40%	0.89	-0.60%	-1.33	0.12%	0.27	0.47%	0.74	-0.01%	-0.01
Biotechnology	-0.44%	-0.75	-2.35%	-3.98***	2.05%	3.47***	-0.40%	-0.68	0.06%	0.10	-0.30%	-0.36	-0.64%	-0.54
Health Care Equipment and Supplies	0.75%	2.14**	0.73%	2.09**	-0.79%	-2.26**	-0.59%	-1.69*	0.25%	0.71	-0.06%	-0.12	-0.40%	-0.57
Health Care Distributors	-0.94%	-0.68	3.87%	2.78***	2.32%	1.67*	-3.97%	-2.86***	-0.44%	-0.32	6.19%	3.15***	1.78%	0.64
Heath Care Facility	0.32%	0.50	6.85%	10.70***	3.40%	5.31***	-3.84%	-6.00***	-1.12%	-1.75	10.25%	11.32***	5.29%	4.13***
Health Care Technology	0.66%	0.94	0.95%	1.36	1.05%	1.50	-0.38%	-0.54	-0.97%	-1.39	2.00%	2.02**	0.65%	0.46
Life Sciences Tools and Services	2.07%	3.83***	-5.71%	-10.57***	-3.08%	-5.71***	1.58%	2.93***	1.32%	2.44**	-8.79%	-11.51***	-5.89%	-5.45***
Pharmaceuticals	0.20%	0.48	1.38%	3.28***	0.64%	1.52	-0.95%	-2.26**	-0.51%	-1.21	2.02%	3.40***	0.56%	0.67
Consumer Discretionary	-0.19%	-0.53	-2.90%	-8.05***	-0.91%	-2.53**	0.66%	1.83*	0.02%	0.05	-3.81%	-7.48***	-3.13%	-4.35***
Auto Components	0.15%	0.13	1.69%	1.50	2.94%	2.60***	1.01%	0.89	-0.85%	-0.75	4.63%	2.90***	4.79%	2.12**
Automobiles	0.07%	0.10	2.66%	3.86***	4.17%	6.04***	-0.82%	-1.18	-0.62%	-0.90	6.83%	6.99***	5.39%	3.91***
Hotels	0.18%	0.30	15.99%	26.21***	-1.73%	-2.84***	-2.47%	-4.04***	-1.03%	-1.69*	14.26%	16.53***	10.76%	8.82***
Casinos and Gaming	0.56%	0.80	12.10%	17.29***	-1.18%	-1.69*	-4.71%	-6.73***	-1.42%	-2.03**	10.92%	11.03***	4.79%	3.42***
Restaurants	-0.01%	-0.02	-0.20%	-0.43	-1.01%	-2.20**	0.59%	1.28	-1.09%	-2.37**	-1.21%	-1.86*	-1.71%	-1.86*
Household Durables	-2.42%	-3.84***	-5.30%	-8.41***	3.75%	5.95***	0.75%	1.19	-2.23%	-3.54***	-1.55%	-1.74*	-3.03%	-2.40**
Home Building	-4.01%	-5.01***	-6.97%	-8.71***	5.98%	7.47***	1.79%	2.24**	-3.37%	-4.21***	-0.99%	-0.88	-2.57%	-1.61
Household Appliances	1.25%	2.02**	-12.72%	-20.51***	1.25%	2.02**	-0.10%	-0.16	0.44%	0.71	-11.47%	-13.08***	-11.13%	-8.98***
Consumer Electronics	1.91%	4.15***	-0.64%	-1.39	-0.63%	-1.37	0.16%	0.35	0.06%	0.13	-1.27%	-1.95*	-1.05%	-1.14
Internet and Direct Marketing Retail	-0.09%	-0.08	-6.05%	-5.35***	-3.33%	-2.95***	1.67%	1.48	0.94%	0.83	-9.38%	-5.87***	-6.77%	-3.00***
Multiline Retail	1.78%	1.66*	-3.06%	-2.86***	1.12%	1.05	1.50%	1.40	-0.49%	-0.46	-1.94%	-1.28	-0.93%	-0.43
Distributors	-0.79%	-1.05	-4.05%	-5.40***	4.11%	5.48***	-0.99%	-1.32	-0.96%	-1.28	0.06%	0.06	-1.89%	-1.26
General Merchandise Stores	1.83%	1.54	-3.12%	2.62***	1.18%	0.99	1.47%	1.24	-0.35%	-0.29	-1.94%	-1.15	-0.82%	-0.34
Specialty Retail	-0.45%	-1.25	-2.95%	-8.19***	1.88%	5.22***	-0.47%	-1.31	0.11%	0.31	-1.07%	-2.10**	-1.43%	-1.99**
Computers and Electronics Retail	-0.09%	-0.10	-10.33%	-11.61***	1.36%	1.53	1.66%	1.87*	-0.64%	-0.71	-8.97%	-7.13***	-7.95%	-4.47***
Home Furnishing Retail	-2.38%	-1.90*	1.07%	0.86	1.63%	1.30	-3.85%	-3.08***	-1.35%	-1.08	2.70%	1.53	-2.50%	-1.00
Textiles, Apparel and Luxury Goods	-0.97%	-1.59	1.47%	2.41**	-0.53%	-0.87	-0.97%	-1.59	-0.39%	-0.64	0.94%	1.09	-0.42%	-0.34
Consumer Staples	0.45%	0.94	-0.96%	-2.00**	2.02%	4.21***	0.53%	1.10	0.21%	0.44	1.06%	1.56	1.80%	1.88*
Food and Staples Retailing	0.98%	2.72***	-1.64%	-4.56***	1.69%	4.69***	0.79%	2.19**	0.61%	1.69*	0.05%	0.10	1.45%	2.01**
Drug Retail	-0.22%	-0.12	7.67%	4.24***	6.54%	3.61***	-1.22%	-0.67	-1.98%	-1.09	14.21%	5.55***	11.01%	3.04***
Food Retail	1.46%	1.74*	-5.89%	-7.01***	2.91%	3.46***	1.12%	1.33	-0.71%	-0.85	-2.99%	-2.51***	-2.57%	-1.53
Hypermarkets and Super Centers	1.07%	2.33**	-3.77%	-9.20***	1.19%	2.59***	1.44%	3.13***	1.08%	2.35**	-2.58%	-3.97***	-0.06%	-0.06
Brewers	-0.11%	-0.14	6.52%	8.58***	3.64%	4.79***	-1.53%	-2.01**	-0.43%	-0.57	10.16%	9.45***	8.20%	5.39***
Soft Drinks			1.90%	2.97***	2.32%	3.63***	0.01%	0.02	-0.14%	-0.22	4.22%	4.66***	4.09%	3.20***
Food Products	0.40%	0.57	-0.62%	-0.89	1.63%	2.33**	1.44%	2.06**	-0.74%	-1.06	1.01%	1.02	1.71%	1.22
Agricultural Products	-0.33%	-0.34	-1.43%	-1.86*	2.65%	3.44***	-2.98%	-3.87***	1.77%	2.30**	1.22%	1.12	0.01%	0.006
Packaged Foods and Meats	0.34%	0.47	-0.90%	-1.23	1.59%	2.18**	1.57%	2.15**	-0.70%	-0.96	0.69%	0.67	1.56%	1.07
Household Products	0.68%	1.00	-4.52%	-6.65***	1.86%	2.73***	0.91%	1.34	0.73%	1.07	-2.66%	-2.76***	-1.02%	-0.75
Personal Products	0.50%	0.68	1.64%	2.24**	0.33%	0.45	-0.71%	-0.97	1.26%	1.73*	1.97%	1.91*	2.52%	1.73*
Tobacco	-0.49%	-0.77	1.19%	1.86*	4.03%	6.29***	-0.06%	-0.09	-0.25%	-0.39	5.22%	5.77***	4.91%	3.84***
Industrials	0.15%	0.38	2.15%	5.38***	1.90%	4.75***	-1.63%	-4.08***	-0.21%	-0.53	4.05%	7.16***	2.21%	2.76***
Airlines	-1.89%	-2.20**	12.54%	14.58***	0.15%	0.17	-4.87%	-5.66***	-1.63%	-1.90*	12.69%	10.43***	6.19%	3.60***
Railroads	-1.02%	-0.89	2.16%	4.59***	2.42%	5.15***	-1.65%	-3.51***	0.68%	1.45	4.58%	6.89***	3.61%	3.84***
Transportation Infrastructure	-0.99%	-1.90*	5.93%	11.40***	0.54%	1.04	-2.27%	-4.37***	-1.11%	-2.13**	6.47%	8.80***	3.09%	2.97***
Air Freight and Logistics	1.67%	2.88***	-4.32%	-7.45***	1.12%	1.93*	-0.04%	-0.07	-0.56%	-0.97	-3.20%	-3.90***	-3.80%	-3.28***
Building products	1.25%	4.17***	-2.00%	-6.66***	-0.29%	-0.97	-0.67%	-2.23**	0.98%	3.27***	-2.29%	-5.40***	-1.98%	-3.30***
Aerospace and Defense	-0.44%	-0.50	6.04%	6.86***	3.29%	3.74***	-2.65%	-3.01***	-1.23%	-1.40	9.33%	7.50***	5.45%	3.10***
Electrical Equipment	0.19%	0.48	3.56%	8.90***	1.03%	2.58***	-2.37%	-5.93***	-0.14%	-0.35	4.59%	8.11***	2.08%	2.60***
Industrial Conglomerates	0.55%	0.71	2.40%	3.08***	3.09%	3.96***	-2.00%	-2.56***	0.26%	0.33	5.49%	4.98***	3.75%	2.40**
Machinery	0.27%	0.84	1.53%	4.78***	1.93%	6.03***	-1.65%	-5.16***	0.50%	1.56	3.46%	7.65***	2.31%	3.61***
Information Technology	0.27%	1.08	-2.64%	-10.56***	-1.80%	-7.20***	1.11%	4.44***	0.57%	2.28**	-4.44%	-12.56***	-2.76%	-5.52***
Communications Equipment	1.27%	2.65***	0.83%	1.73*	0.97%	2.02**	0.79%	1.65*	-0.17%	-0.35	1.80%	2.65***	2.42%	2.52**
IT Services	-0.10%	-0.28	1.85%	5.14***	-0.25%	-0.69	-0.58%	-1.61	-0.44%	-1.22	1.60%	3.14***	0.58%	0.81
IT Consulting and Other Services	0.34%	0.85	0.67%	1.68*	1.03%	2.58***	0.27%	0.68	-0.76%	-1.90*	1.70%	3.00***	1.21%	1.51
Data Processing and Outsourcing	-0.18%	-0.46	2.32%	5.95***	-0.52%	-1.33	-0.82%	-2.10**	-0.37%	-0.95	1.80%	3.26***	0.61%	0.78
Internet Services and Infrastructure	-1.44%	-1.87*	-3.63%	-4.71***	-2.54%	-3.30***	-0.55%	-0.71	0.09%	0.12	-6.17%	-5.66***	-6.63%	-4.30***
Semiconductors and Equipment	1.60%	2.02**	-3.17%	-4.01***	-3.12%	-3.94***	2.85%	3.61***	0.38%	0.48	-6.29%	-5.63***	-3.06%	-1.94*
Software	0.20%	0.74	-4.35%	-16.11***	-3.46%	-12.81***	1.21%	4.48***	1.03%	3.81***	-7.81%	-20.45***	-5.57%	-10.31***
Technology Hardware and Storage	-0.52%	-0.74	-4.40%	-6.29***	-0.31%	-0.44	0.99%	1.41	2.31%	1.87*	-4.71%	-4.76***	-2.41%	-1.72*
Materials	0.38%	0.53	1.47%	2.04**	1.44%	2.00**	-1.81%	-2.51**	-1.18%	-1.64*	2.91%	2.86***	-0.08%	-0.06
Chemicals	0.49%	0.60	2.09%	2.58***	1.65%	2.04**	-2.49%	-3.07***	-1.19%	-1.47	3.74%	3.26***	0.06%	0.04
Construction Materials	-1.50%	-1.40	1.08%	1.01	2.86%	2.67***	0.53%	0.50	-1.14%	-1.07	3.94%	2.60***	3.33%	1.56
Containers and Packaging	0.20%	0.24	0.01%	0.01	1.59%	1.87*	-0.42%	-0.49	-1.66%	-1.95*	1.60%	1.33	-0.48%	-0.28
Metals and Mining	0.60%	0.64	-0.23%	-0.24	-0.53%	-0.56	-0.25%	-0.27	-0.07%	-0.75	-0.76%	-0.57	-1.71%	-0.91
Utilities	-0.44%	-0.71	1.70%	2.74***	1.19%	1.92	0.02%	0.32	-2.04%	-3.29***	2.89%	3.30***	1.05%	0.85
Electric Utilities	-0.37%	-0.57	1.37%	2.11**	0.88%	1.35	0.57%	0.88	-1.97%	-3.03***	2.25%	2.45**	0.85%	0.65
Gas Utilities	-1.75%	-2.50**	4.64%	6.63***	4.31%	6.18***	-1.58%	-2.25**	-2.78%	-3.97***	8.95%	9.04***	4.59%	3.28***
Water Utilities	1.48%	1.49	0.72%	0.72	0.35%	0.35	0.39%	0.39	-2.43%	-2.45**	1.07%	0.76	-0.97%	-0.49
Multi Utilities	-0.65%	-1.12	2.31%	3.98***	1.82%	3.14***	-0.58%	-1.00	-2.07%	-3.57***	4.13%	5.04***	1.48%	1.28

Note: This table shows the Market Model Abnormal Returns and Cumulative Abnormal Returns for different event windows. The null hypothesis of H0: AR = 0 orCAR = 0 is tested with t-test.

***,**,* indicate 1%, 5%, and 10% significance,respectively. Data is obtained from Thomson Reuters Eikon for the period 23 June 2020 to 12 November 2020, for a total of 139 daily observations per index.

As a further robustness check, a battery of t-tests are carried out to test if the magnitude of the market reaction (ARs and CARs) is different across the 11 sectors and 79 subsectors. First, in Tables 4 to 14, the t-tests are carried out at subsector level for each industry over [0] and [1]. The average effect for each subsector is averaged over [0], and [1] and compared. Overall, substantial differences are detected in subsector AR responses. Table 4 for example shows that within the finance sector, over [0] and [1], the average AR for the capital markets is significantly different from the insurance subsector and diversified finance service subsector. In the communication services (Table 7), the magnitude of AR on wireless telecommunication services is significantly lower than integrated telecommunication services and diversified telecommunication services, while AR for interactive media is smaller than for broadcasting. In consumer discretionary industry (Table 9), hotel subsector is one of the subsectors showing a relatively high positive response. It is found that the AR for hotel subsector over [0] and [1] is significantly different (higher) from some of the other subsectors such as the internet and direct retail and multiline retail. Auto components and automobiles are also significantly different form several sub sectors. Brewers in consumer staples (Table 10) is different from food and staples. Several subsectors also show differences in information technology (Table 12). The tests shows that a considerable number of subsector ARs display significant differences.

Table 4

Financials industry.

	Banks	Insurance	Capital Markets	Consumer Finance	Diversified Fin.Serv.
Banks		0.70	0.50	0.65	0.78
Insurance	0.70		0.04	0.69	0.22
Capital Market	0.50	0.04		0.53	0.02
Consumer Finance	0.65	0.69	0.53		0.75
Diversified Fin.Serv.	0.78	0.22	0.02	0.75

The table reports the p values of t-test checking the equality of means of AR(0) and AR(1) values. The null hypothesis states that the difference in group means is zero. An alternate hypothesis states that the difference in group means is different from zero. Data is obtained from Thomson Reuters Eikon for the period 23 June 2020 to 12 November 2020, for a total of 139 daily observations per index.

Table 14

Utilities industry.

	Electric	Gas	Water	Multi
Electric		0.49	0.04	0.85
Gas	0.49		0.37	0.36
Water	0.04	0.37		0.39
Multi	0.85	0.36	0.39

The table reports the p values of t-test checking the equality of means of AR(0) and AR(1) values. The null hypothesis states that the difference in group means is zero. An alternate hypothesis states that the difference in group means is different from zero. Data is obtained from Thomson Reuters Eikon for the period 23 June 2020 to 12 November 2020, for a total of 139 daily observations per index.

Table 7

Communication services industry.

	Wire. Tel. Serv.	Inter. Media	Broadcasting	Interactive Home Ent.	Media and Ent.	Integ. Telecomm.	Div. Telecom.
Wireless Tel. Serv.		0.25	0.15	0.61	0.66	0.09	0.08
Interactive Media	0.25		0.02	0.95	0.12	0.14	0.13
Broadcasting	0.15	0.02		0.20	0.07	0.89	0.87
Interactive Home Ent.	0.61	0.95	0.20		0.60	0.32	0.31
Media and Entertainment	0.66	0.12	0.07	0.60		0.15	0.13
Integrated Telecomm.	0.09	0.14	0.89	0.32	0.15		0.73
Diversified Telecom.	0.08	0.13	0.87	0.31	0.13	0.73

The table reports the p values of t-test checking the equality of means of AR(0) and AR(1) values. The null hypothesis states that the difference in group means is zero. An alternate hypothesis states that the difference in group means is different from zero. Data is obtained from Thomson Reuters Eikon for the period 23 June 2020 to 12 November 2020, for a total of 139 daily observations per index.

Table 9

Consumer discretionary industry.

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17
Auto Comp.		0.01	0.12	0.67	0.32	0.66	0.78	0.45	0.09	0.06	0.37	0.65	0.37	0.46	0.46	0.08	0.46
Automobiles	0.01		0.15	0.77	0.20	0.55	0.68	0.40	0.06	0.05	0.28	0.52	0.28	0.33	0.41	0.03	0.27
Hotels	0.12	0.15		0.81	0.17	0.22	0.35	0.22	0.11	0.06	0.18	0.06	0.06	0.22	0.10	0.19
Casinos	0.67	0.77	0.81		0.51	0.67	0.72	0.56	0.52	0.42	0.57	0.67	0.57	0.62	0.57	0.65	0.53
Restaurants	0.32	0.20	0.17	0.51		0.92	0.95	0.60	0.60	0.24	0.73	0.95	0.73	0.94	0.62	0.40	0.24
Household Durables	0.66	0.55	0.22	0.67	0.92		0.97	0.22	0.97	0.80	0.73	0.80	0.89	0.21	0.71	0.87
Home Building	0.78	0.68	0.35	0.72	0.95	0.97		0.10	0.99	0.68	0.88	0.95	0.88	0.96	0.15	0.81	0.91
Household App.	0.45	0.40	0.22	0.56	0.60	0.22	0.10		0.60	0.87	0.52	0.29	0.52	0.45	0.30	0.47	0.58
Consumer Electr.	0.09	0.06	0.11	0.52	0.60	0.97	0.99	0.60		0.17	0.77	0.98	0.77	0.94	0.63	0.13	0.46
Internet Ret.	0.06	0.06	0.03	0.42	0.24	0.55	0.68	0.87	0.17		0.36	0.45	0.36	0.23	0.93	0.06	0.24
Multiline Retail	0.37	0.28	0.06	0.57	0.73	0.80	0.88	0.52	0.77	0.36		0.60	0.50	0.12	0.54	0.40	0.67
Distributors	0.65	0.52	0.18	0.67	0.95	0.73	0.95	0.29	0.98	0.45	0.60		0.60	0.98	0.29	0.70	0.89
General Merch.Stores	0.37	0.28	0.06	0.57	0.73	0.80	0.88	0.52	0.77	0.36	0.50	0.60		0.12	0.55	0.40	0.67
Specialty Retail	0.46	0.33	0.06	0.62	0.84	0.89	0.96	0.45	0.94	0.23	0.12	0.98	0.12		0.46	0.52	0.84
Electronics Retail	0.46	0.41	0.22	0.57	0.62	0.21	0.15	0.30	0.63	0.93	0.54	0.29	0.55	0.46		0.48	0.60
Home Retail	0.08	0.03	0.10	0.65	0.40	0.71	0.81	0.47	0.13	0.06	0.40	0.70	0.40	0.52	0.48		0.59
Textiles Goods	0.46	0.27	0.19	0.53	0.24	0.87	0.91	0.58	0.46	0.24	0.67	0.89	0.67	0.84	0.60	0.59

The table reports the p values of t-test checking the equality of means of AR(0) and AR(1) values. The null hypothesis states that the difference in group means is zero. An alternate hypothesis states that the difference in group means is different from zero. 1 refers to Auto Components; 2 Automobiles;3 Hotels; 4 Casinos and Gaming; 5 Restaurants; 6 Household Durables; 7 Home Building; 8 Household Appliances; 9 Consumer Electronics; 10 Internet and Direct Marketing Retail; 11 Multiline Retail; 12 Distributors; 13 General Merchandise Stores; 14 Specialty Retail; 15 Computers and Electronics Retail; 16 Home Furnishing Retail, 17 Textiles, Apparel and Luxury Goods. Data is obtained from Thomson Reuters Eikon for the period 23 June 2020 to 12 November 2020, for a total of 139 daily observations per index.

Table 10

Consumer staples industry.

	Food&Staples Retailing	Drug Retail	Food Retail	Hypermarkets	Brewers	Soft Drinks	Food Prod.	Agricultural Prod.	Packaged Foods.	Household Prod.	Personal Prod.	Tobacco
Food and Staples Retailing		0.17	0.88	0.87	0.03	0.59	0.84	0.60	0.87	0.92	0.20	0.43
Drug Retail	0.17		0.32	0.17	0.68	0.06	0.13	0.08	0.14	0.22	0.15	0.18
Food Retail	0.88	0.32		0.89	0.44	0.60	0.64	0.57	0.64	0.78	0.67	0.41
Hypermarkets and Super Centers	0.87	0.17	0.89		0.31	0.36	0.28	0.31	0.25	0.79	0.54	0.16
Brewers	0.03	0.68	0.44	0.31		0.28	0.32	0.31	0.33	0.37	0.06	0.50
Soft Drinks	0.59	0.06	0.60	0.36	0.28		0.47	0.66	0.48	0.49	0.88	0.17
Food Products	0.84	0.13	0.64	0.28	0.32	0.47		0.36	0.55	0.50	0.71	0.08
Agricultural Products	0.60	0.07	0.57	0.31	0.31	0.66	0.36		0.39	0.45	0.93	0.12
Packaged Foods and Meats	0.87	0.14	0.64	0.25	0.33	0.48	0.55	0.39		0.50	0.70	0.10
Household Products	0.92	0.22	0.78	0.79	0.37	0.49	0.50	0.45	0.50		0.62	0.26
Personal Products	0.20	0.15	0.67	0.54	0.06	0.77	0.71	0.93	0.70	0.62		0.62
Tobacco	0.43	0.18	0.41	0.16	0.50	0.17	0.08	0.12	0.10	0.26	0.62

The table reports the p values of t-test checking the equality of means of AR(0) and AR(1) values. The null hypothesis states that the difference in group means is zero. An alternate hypothesis states that the difference in group means is different from zero. Data is obtained from Thomson Reuters Eikon for the period 23 June 2020 to 12 November 2020, for a total of 139 daily observations per index.

Table 12

Information technology industry.

	Comm. Equip.	IT Services	IT Consulting	Data Processing	Internet Serv. and Inf.	Semiconductors and Equip.	Software	Tech. Hardware and Storage
Communications Equipment		0.89	0.38	0.99	0.12	0.03	0.05
IT Services	0.89		0.83	0.72	0.29	0.17	0.18	0.49
IT Consulting and Other Services	0.38	0.83		0.80	0.07	0.02	0.00	0.35
Data Processing and Outsourcing	0.99	0.72	0.80		0.34	0.22	0.23	0.51
Internet Services and Infrastructure	0.12	0.29	0.07	0.34		0.29	0.16	0.83
Semiconductors and Equipment	0.03	0.17	0.02	0.22	0.29		0.37	0.59
Software	0.05	0.18	0.00	0.23	0.16	0.37		0.51
Technology Hardware and Storage	0.35	0.49	0.35	0.51	0.83	0.59	0.51

The table reports the p values of t-test checking the equality of means of AR(0) and AR(1) values. The null hypothesis states that the difference in group means is zero. An alternate hypothesis states that the difference in group means is different from zero. Data is obtained from Thomson Reuters Eikon for the period 23 June 2020 to 12 November 2020, for a total of 139 daily observations per index.

Finally, Table 15 shows tests of equality of mean abnormal returns averaged over the subsectors by industry on [0], while Table 16 follows the same approach to test for equality of average mean CAR (averaged over subsectors) by industry for [0, 3] interval. The results in Table 15 indicate the mean ARs for several industries differ (see the P-values reflected in the tables). In particular, the mean AR for the subsectors within the financial sector included in the analysis is significantly different from the means of several other sectors such as energy, consumer services, health care, information technology, and materials. Similarly, the average performance of energy sector which exhibited one of the highest positive reactions is different from nearly all the other sectors. Table 16 which tests for equality of the CARs for [0, 3] shows a similar trend. Financials, energy and to a lesser degree the real estate subsectors have CARs that significantly differ from other industries. Financials for example differ from energy, communication services, information technology and materials. While this supports the earlier results that there are differences in sector reactions to the vaccine announcement, the sector mean averages out some subsector differences, and might hide important differences at this level. Hence it is important to consider this part of the results with that obtained in Tables 4 to 14 at the subsector level above. Overall, the t-tests clearly show that there are significant differences in ARs and CARs at subsector and industry levels. This conforms with the results contained in Tables 2 and 3.

Table 15

All industries.

	Financials	Energy	Real Estate	Comm. Serv.	Healthcare	Cons. Discr.	Cons. Stapl.	Ind.	Inf. Tech.	Materials	Utilities
Financials		0.09	0.78	0.03	0.09	0.33	0.14	0.14	0.03	0.07	0.12
Energy	0.09		0.30	0.00	0.00	0.03	0.01	0.01	0.00	0.00	0.00
Real Estate	0.78	0.20		0.35	0.23	0.47	0.35	0.52	0.50	0.53	0.42
Comm. Serv.	0.03	0.00	0.35		0.50	0.54	0.65	0.01	0.74	0.13	0.13
Healthcare	0.09	0.00	0.23	0.50		0.70	0.88	0.63	0.54	0.48	0.88
Cons. Discr.	0.33	0.03	0.47	0.54	0.70		0.59	0.49	0.86	0.17	0.25
Cons. Stapl.	0.14	0.01	0.35	0.65	0.88	0.59		0.34	0.69	0.66	0.28
Industrials	0.14	0.01	0.52	0.01	0.63	0.49	0.34		0.20	0.60	0.90
Inf. Tech.	0.03	0.00	0.50	0.74	0.54	0.86	0.69	0.20		0.17	0.79
Materials	0.07	0.00	0.53	0.13	0.48	0.17	0.66	0.60	0.17		0.26
Utilities	0.12	0.00	0.42	0.1	0.88	0.25	0.28	0.90	0.79	0.26

The table reports the p values of t-test checking the equality of means of AR(0) values. The null hypothesis states that the difference in group means is zero. An alternate hypothesis states that the difference in group means is different from zero. Data is obtained from Thomson Reuters Eikon for the period 23 June 2020 to 12 November 2020, for a total of 139 daily observations per index.

Table 16

All industries.

	Financials	Energy	Real Estate	Comm. Serv.	Healthcare	Cons. Discr.	Cons. Stapl.	Ind.	Inf. Tech.	Materials	Utilities
Financials		0.03	0.46	0.02	0.09	0.96	0.35	0.59	0.03	0.05	0.12
Energy	0.03		0.08	0.00	0.00	0.50	0.03	0.25	0.00	0.00	0.00
Real Estate	0.46	0.08		0.47	0.09	0.24	0.71	0.54	0.70	0.00	0.87
Comm. Serv.	0.02	0.00	0.47		0.79	0.52	0.43	0.22	0.76	0.90	0.32
Healthcare	0.09	0.00	0.09	0.79		0.50	0.52	0.45	0.39	0.17	0.86
Cons. Discr.	0.96	0.50	0.24	0.52	0.50		0.50	0.55	0.57	0.18	0.36
Cons. Stapl.	0.35	0.02	0.71	0.43	0.52	0.50		0.64	0.22	0.34	0.81
Industrials	0.59	0.25	0.54	0.22	0.45	0.55	0.64		0.11	0.30	0.57
Inf. Tech.	0.03	0.00	0.70	0.76	0.39	0.57	0.22	0.11		0.06	0.93
Materials	0.05	0.00	0.00	0.90	0.17	0.18	0.34	0.30	0.06		0.04
Utilities	0.12	0.00	0.87	0.32	0.86	0.36	0.81	0.57	0.93	0.04

The table reports the p values of t-test checking the equality of means of CAR(0,3) values. The null hypothesis states that the difference in group means is zero. An alternate hypothesis staTes that the difference in group means is different from zero. Data is obtained from Thomson Reuters Eikon for the period 23 June 2020 to 12 November 2020, for a total of 139 daily observations per index.

6 Conclusion and policy recommendations

COVID-19 pandemic caused an unprecedented level of uncertainty and fear. The announcement of a successful phase 3 trial of Pfizer vaccine on November 9 marks a major milestone in the fight to control the pandemic. Previous literature on the impact of vaccine development [13-15] has focused on the aggregate market level, and not at the sectoral level. In this study, the effect of the Pfizer vaccine announcement on S&P 500 11 sectors and a total of 79 subsectors is analysed. The results follow an interesting pattern. The results clearly indicate the announcement generates optimism in a range of subsectors, while some other subsectors are significantly depressed. The (sub)sectors that were hardest hit by the pandemic [4, 5] show the most gains from the vaccine news, while sectors that gained from the pandemic are depressed by the news. For example, financial sector, particularly consumer finance, energy, airlines, hotels, and casinos gain. Subsectors that gained from the COVID-19 environment and likely to lose from a return to normalcy lost. Examples include airfreight, home building, household appliances and computers and electronics retail. These results are in line with [33] who provide evidence that not all sectors comprising the Nasdaq-100 have reacted the same way.

These results suggest that even though the availability of vaccines is expected to help steer economies gradually back to normalcy, the re-adjustment is not going to be homogeneous for all subsectors. While some subsectors expect a recovery from the COVID-induced contraction, other subsectors face adjustment losses as these industries shed off the above average gains driven by the COVID-19 environment. These results are relevant for governments and policymakers in managing the transition back to normalcy. Policy makers may have to provide support packages for industries expected to be negatively affected during the recovery phase. Furthermore, the results are relevant for portfolio managers. It suggests that portfolio diversification should consider the effects of external shocks such as pandemics, as sectors and sub-sectors are likely to be affected differently.

As an extension of this study, future work should focus on examining the underlying factors that explain a considerable inter and intra sectoral differences in the impact of vaccine announcement. For this purpose, the sectoral determinants such as asset size, leverage and profitability ratios of the sectors/subsectors can be regressed on AR or CARs to estimate the likely determinants of different reactions.

Although studies [15, 44, 45] show that vaccination has a stabilising impact on the capital markets, a high vaccination coverage rate is necessary to reap full benefits of vaccination programs. However, vaccine hesitancy is considered a serious problem [46, 47] that needs to be addressed, especially in the developing countries [48]. More research is needed to devise optimal ways to counter vaccine hesitancy and to identify key factors promoting the slow uptake of COVID-19 vaccines in some communities and countries.

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Reviewer #1: Dear Author/s

I would like to thank the author(s) for your submission and appreciate the opportunity to read and review your manuscript. I enjoyed reading it. My comments concern only with clarity, text, methodology used and interpretation of results. I would recommend publication, after the issues below are addressed.

Changes which must be made before publication:

Minor changes:

1. Abstract: please expand the abstract and add the methodology used.

2. The Keywords: the keywords does not accurately reflect the content of the study, additional 2-3 keywords should be added.

3. The introduction section: I think it is good.

4. The literature review section: please expand the literature review and add some latest references.

5. The methodology section: It's okay, but it could be better explained and motivated further. Please reference all numbered equations in text. Currently, numbered equations [1, 2, 3, 4] in the manuscript have not been cited in text.

6. The empirical results and discussion section: the results and discussion section should be better analyzed and developed further.

7. Conclusion section: It's okay, but it could be better explained and motivated with a few more minor adjustments, please try to augment its quality with more in-depth investigation and analysis:

a. I recommend to change the section title to (Conclusion and Policy Recommendations).

b. "limitations, and further research" could be added

8. The language of the paper: the language of the paper needs a careful editing by a native speaker.

Additional comments: the author/s are recommended to do the following to increase the paper readability.

1. To start with, the paper should more clearly and more explicitly spell out its objectives.

2. Develop the literature review section of the article to include 3-5 latest journal references (2021-2022) and relevant extracts from them.

3. Please, avoid placing tables or figures before their first mention in the text, and the analysis should always be below the figure or the table.

4. Please avoid the following word/s; "our analysis" (please see p. 5), "our results" (please see p.8) , "our earlier results" (please see p.9). Please replace with "the present study………" or "the current study………"…etc.

5. Please be careful in using the abbreviations and abbreviated words throughout the paper.

6. Please be careful in using "Punctuating", e.g. (comma, full stop, etc…) throughout the paper.

Reviewer #2: Recommendations for Manuscript PONE-D-22-14074 „Winners and Losers from Pfizer and Biontech's Vaccine Announcement: Evidence from S&P 500 (Sub)Sector Indices” for the PLOS ONE Journal.

General Comments

From my point of view, it is a very interesting topic and simultaneously it seems that to the best of my knowledge is the first empirical research which explores how various sectors and subsectors of the US stock market react to the news of successful development of vaccine by Pfizer and Biontech on November 9, 2020. Based on eleven sector and eighty subsector indices of S&P 500, the authors establish that there are considerable inter and intra sectoral variations in the impact of the vaccine news, and that the impact follows a clear pattern.

The paper contains the following sections: Introduction, Literature Review, Data, Methodology, Empirical Results and Discussion and Conclusion.

However, I find some recommendations:

1. The abstract must contain the main purpose of the paper, the research method used in the research and the main contributions.

2. It would be very useful to add in the "Introduction" section the purpose, objectives and hypothesis of the research. I consider that a weak point of the paper is that the authors did not show the novelty of the paper compared to other works. That is why, I consider that the introduction should specify the novelty of the paper compared to other papers published in this area.

3. The research is well based on science and the results are in agreement with the theoretical part. The model applied to the analyzed data is correctly used in the analysis undertaken, it is a strength point of this paper.

4. At the same time, the authors are required to present Descriptive Statistics, Correlation matrix with all tests and indicators: standard deviation, Jarqe-Berra, Skewness and Kurtosis interpretation, Jarqe-Berra with probabilities analysis, etc.

5. It is important to present the VIF test on multicollinearity between independent variables. Heteroskedasticity and endogeneity tests are also important in this study. All these aspects that are not found in the paper represent weaknesses of the research.

6. I think that the literature needs to be improved with other recent works, refers to the companies listed on the stock market. That is why I recommend the authors to refer to other recent works indexed in Web of Science, Scopus, Emerald and Cambrige Journals. We suggest that the authors cite papers published in Web of Science Journals, such as:

1. Batrancea, L.; Rus, M.I.; Masca, E.S.; Morar, I.D. Fiscal Pressure as a Trigger of Financial Performance for the Energy Industry: An Empirical Investigation across a 16-Year Period. Energies 2021, 14, 3769. https://doi.org/10.3390/en14133769

2. Batrancea, L., Rathnaswamy, M.K. & Batrancea, I. A Panel Data Analysis on Determinants of Economic Growth in Seven Non-BCBS Countries. J Knowl Econ (2021). https://doi.org/10.1007/s13132-021-00785-y

3. Batrancea, L. (2021) An Econometric Approach Regarding the Impact of Fiscal Pressure on Equilibrium: Evidence from Electricity, Gas and Oil Companies Listed on the New York Stock Exchange, Mathematics 9, no. 6: 630. https://doi.org/10.3390/math9060630.ISSN:2227-7390

7. Based on the data obtained, the conclusions must be extended.

In conclusion, the article should be improve. It should also be enhanced with a review of the literature adequate to the subject and a broader interpretation and commentary of the research results.

Reviewer #3: The authors are able to provide his readers a solid, clear and adequate justification of the problem, the authors' should need a keen review of the whole paper. Paper is good and can be considered after making improvements described in review report.

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Reviewer #2: No

Reviewer #3: No

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22 Aug 2022

Answer to the Referee Report – PLOS One

August 17, 2022

First of all, we would like to express our gratitude for your careful reading and comments on

our manuscript. They have helped to clarify important aspects of the manuscript that were unclear in the previous version. We worked on these comments and believe our work benefited as a result. In this document, we provide our response to each of the points raised in your referee report.

Reviewer 1

I would like to thank the author(s) for your submission and appreciate the opportunity to read and review your manuscript. I enjoyed reading it. My comments concern only with clarity, text, methodology used and interpretation of results. I would recommend publication, after the issues below are addressed.

Changes which must be made before publication:

Minor changes:

1. Abstract: please expand the abstract and add the methodology used.

Answer: Abstract has been rewritten including the methodology used.

2. The Keywords: the keywords does not accurately reflect the content of the study, additional 2-3 keywords should be added.

Answer: We have added COVID-19 pandemic, Pfizer and Biontech vaccine, Event Study and Overreaction.

3. The introduction section: I think it is good.

4. The literature review section: please expand the literature review and add some latest references.

Answer: This section has been reviewed and recently published papers added.

5. The methodology section: It's okay, but it could be better explained and motivated further. Please reference all numbered equations in text. Currently, numbered equations [1, 2, 3, 4] in the manuscript have not been cited in text.

Answer: We referenced all numbered equations in the text.

6. The empirical results and discussion section: the results and discussion section should be better analyzed and developed further.

Answer: Discussion section has been rewritten including the robustness section.

7. Conclusion section: It's okay, but it could be better explained and motivated with a few more minor adjustments, please try to augment its quality with more in-depth investigation and analysis:

a. I recommend to change the section title to (Conclusion and Policy Recommendations).

b. "limitations, and further research" could be added

Answer: Title is changed as recommended. Limitations and Further research has been improved.

8. The language of the paper: the language of the paper needs a careful editing by a native speaker.

Answer: Paper is proofread.

Additional comments: the author/s are recommended to do the following to increase the paper readability.

1. To start with, the paper should more clearly and more explicitly spell out its objectives.

Answer: Objective section is rewritten.

2. Develop the literature review section of the article to include 3-5 latest journal references (2021-2022) and relevant extracts from them.

Answer: Recently published papers about the topic are added.

3. Please, avoid placing tables or figures before their first mention in the text, and the analysis should always be below the figure or the table.

Answer: Tables and figures are placed after they are mentioned. As there are many tables and some in the landscape format, majority of the tables are placed at the end of the paper as “Supporting Information.”

4. Please avoid the following word/s; "our analysis" (please see p. 5), "our results" (please see p.8) , "our earlier results" (please see p.9). Please replace with "the present study………" or "the current study………"…etc.

Answer: All changes are made.

5. Please be careful in using the abbreviations and abbreviated words throughout the paper.

Answer: Abbreviations are removed. Only AR(Abnormal Return) and CAR (Cumulative Abnormal Return) are used throughout the paper.

6. Please be careful in using "Punctuating", e.g. (comma, full stop, etc…) throughout the paper.

Answer: Paper is proofread.

Reviewer 2

From my point of view, it is a very interesting topic and simultaneously it seems that to the best of my knowledge is the first empirical research which explores how various sectors and subsectors of the US stock market react to the news of successful development of vaccine by Pfizer and Biontech on November 9, 2020. Based on eleven sector and eighty subsector indices of S&P 500, the authors establish that there are considerable inter and intra sectoral variations in the impact of the vaccine news, and that the impact follows a clear pattern.

The paper contains the following sections: Introduction, Literature Review, Data, Methodology, Empirical Results and Discussion and Conclusion.

However, I find some recommendations:

1. The abstract must contain the main purpose of the paper, the research method used in the research and the main contributions.

Answer: Abstract has been rewritten.

2. It would be very useful to add in the "Introduction" section the purpose, objectives and hypothesis of the research. I consider that a weak point of the paper is that the authors did not show the novelty of the paper compared to other works. That is why, I consider that the introduction should specify the novelty of the paper compared to other papers published in this area.

Answer: Introduction section has been rewritten including the points mentioned.

3. The research is well based on science and the results are in agreement with the theoretical part. The model applied to the analyzed data is correctly used in the analysis undertaken, it is a strength point of this paper.

4. At the same time, the authors are required to present Descriptive Statistics, Correlation matrix with all tests and indicators: standard deviation, Jarqe-Berra, Skewness and Kurtosis interpretation, Jarqe-Berra with probabilities analysis, etc.

Answer: Table 1 reports descriptive statistics (mean, standard deviation, minimum, maximum, skewness, kurtosis) and Jarque Berra Test statistics. In the Data section, there is interpretation of the statistics. Supporting information document is submitted separately reporting the Pearson Correlation Coefficients (requested) among main industries, and sub-sectors under each industry. However, note that our analysis is carried out using the market model and the computation of the abnormal returns is done sector (subsector) by sector (subsector). The correlation matrix of sectors (subsectors) does not therefore provide much addition information.

5. It is important to present the VIF test on multicollinearity between independent variables. Heteroskedasticity and endogeneity tests are also important in this study. All these aspects that are not found in the paper represent weaknesses of the research.

Answer:

On VIF: The event study analysis approach (market model) that we use involves the use of one independent variable (market index) at a time. Hence there is no element of multicollinearity. We do not therefore need to carry out VIF.

On endogeneity, our primary (and only) independent variable is market index. To test for endogeneity requires a good IV. As most literature generally show, it is not easy to get a good IV, and in particular it is a challenge to get a good instrument for market index. It is part of the reason event studies (market model) do not generally provide tests for endogeneirty. For this reason we do not provide endogeneity tests.

To check the heteroskedasticity, we have applied the Breusch-Pagan test on the residuals from our market model. The table below shows the p value of some of the tests. The null hypothesis of the tests states that there is generally a constant variance (homescedasticity). This indicates that generally there is no heteroskedasticity in market model regression and confirms the findings of our methodology.

Dependant Variable Independent Variable P value

Communication Services Index Market Index 0.1678

Utilities Index Market Index 0.0781

Energy Index Market Index 0.1055

Financials Index Market Index 0.0835

Real Estate Index Market Index 0.9275

Consumer Staples Index Market Index 0.2561

Consumer Discretionary Index Market Index 0.1785

Industrials Index Market Index 0.3066

Information Technology Index Market Index 0.4503

6. I think that the literature needs to be improved with other recent works, refers to the companies listed on the stock market. That is why I recommend the authors to refer to other recent works indexed in Web of Science, Scopus, Emerald and Cambrige Journals. We suggest that the authors cite papers published in Web of Science Journals, such as:

1. Batrancea, L.; Rus, M.I.; Masca, E.S.; Morar, I.D. Fiscal Pressure as a Trigger of Financial Performance for the Energy Industry: An Empirical Investigation across a 16-Year Period. Energies 2021, 14, 3769. https://doi.org/10.3390/en14133769

2. Batrancea, L., Rathnaswamy, M.K. & Batrancea, I. A Panel Data Analysis on Determinants of Economic Growth in Seven Non-BCBS Countries. J Knowl Econ (2021). https://doi.org/10.1007/s13132-021-00785-y

3. Batrancea, L. (2021) An Econometric Approach Regarding the Impact of Fiscal Pressure on Equilibrium: Evidence from Electricity, Gas and Oil Companies Listed on the New York Stock Exchange, Mathematics 9, no. 6: 630. https://doi.org/10.3390/math9060630.ISSN:2227-7390

Answer: The references mentioned have been added.

7. Based on the data obtained, the conclusions must be extended.

In conclusion, the article should be improve. It should also be enhanced with a review of the literature adequate to the subject and a broader interpretation and commentary of the research results.

Answer: Conclusion section has been rewritten and limitations of the research has been added.

Reviewer 3

In the underlined research topic “Winners and Losers from Pfizer and Biontech's Vaccine Announcement: Evidence from S&P 500 (Sub)Sector Indices” is a novel part to the existing literature. The title of the article is remarkable, the presentation is good and the article may provide several scientific justifications. Below are several suggestions which will increase writer input to technical research and probability.

1. The paper needs to be improved in all parts (justification of the contribution, conceptual background, the method). I hope you find the reviewers' comments helpful in developing the paper further.

2. A single paragraph serves as the article's assessment of the literature, and the commentary is disorganized and illogical. I'd advise the writers to restructure the literature review section and make the logical connections clearer.

Answer: This part has been rewritten.

3. Globally, the manuscript needs a revision of the English and there are paragraphs and sentences written in a very confusing way.

Answer: Paper is proofread.

4. The technique used in the study is appropriate for the findings, and but the authors should review it again to make is more attractive.

Answer: This section has been improved.

5. At the bottom of the table, provide a note identifying the data source.

Answer: The data source is mentioned under each table.

6. The author should expand on the empirical findings.

Answer: Empirical parts have been rewritten.

7.To make the paper statistically strong, the authors should need to add more innovative techniques to make the results much clear.

Answer: This section has been improved.

8.The results follow the methodology but the interpretation of tables and graphs need more quality presentation.

Answer: Tables have been improved.

9.The results and discussion of the study is quite lengthy despite the importance of the information but if possible to eliminate some of the secondary information that does not disturb the quality of the research flow.

Answer: Discussion section is improved.

10. A separate set of data is needed for the author.

Answer: Data set will be submitted to the journal.

11.To write an equation, the author must adhere to a prescribed format.

Answer: Equations are rewritten and referenced in the text.

12."Illustration from simulation" is the desired outcome of the simulation research.

Answer: This is removed from the paper.

13. The author must distinguish the inference from the conclusion in order to avoid confusion.

Answer: This is taken into account in the discussion and conclusion sections.

14. Referencing should follow a standard structure that is consistent with the references mentioned in journal authors’ guidelines.

Answer: References section is updated in line with the journal requirement.

15. Discussions should add some latest references of 2021 and 2022 to prove the results.

Answer: Latest references are added in Introduction and Discussion sections.

16. More explicitly stated restrictions and future proposals should be included in this article.

Answer: Limitations and further research are added to the Conclusion.

Finally, it’s a nice attempt, the topic is of interest and has a wide range of policy implementations. Bases on the above-mentioned comments, I suggest “major revision” of the article for more practicability before “publication”.

Submitted filename: Answer to the Referee Report-PLOS One.pdf

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6 Sep 2022

Submitted filename: Referee_report_PONE-D-22-14074-R2.docx

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17 Sep 2022

September 7, 2022

First of all, we would like to express our gratitude for your careful reading and comments on

our manuscript. They have helped to clarify important aspects of the manuscript that were unclear in the previous version. We worked on these comments and believe our work benefited as a result. In this document, we provide our response to each of the points raised in your referee report.

Minor changes:

1. The introduction section: Some of the terminology used in the manuscript needs to be unified. For instance, the use of two different words that have the same meaning interchangeably could confuse some readers. Dear author(s)please see the introduction section, p. 3 line 16-17 the author(s) mentioned that " "Section 5 presents empirical findings and discussion, and Section 6 concludes", while in p. 9 line 9 the author(s)used another words "5 Empirical results and Discussion", then in p. 14 line 1 the author(s)used another words "6 Conclusion and Policy Recommendations".

Answer: This is corrected. In Page 3, we mentioned as this: Section 2 discusses the literature review; Section 3 presents the data; Section 4 explains the methodology; Section 5 presents empirical results and discussion; and Section 6 concludes and presents policy recommendations.

2. The Literature review section: In the first paragraph the author(s) mentioned that "With the outbreak of the coronavirus (COVID-19) pandemic, an increasing number of researchers have examined the impact of the pandemic on international financial markets. Towards this end, recent studies have established that the pandemic impacted stock markets (Acharya et al., 2021; Al-Awadhi et al., 2020; Baek et al., 2020; Engelhardt et al., 2021; Kapar et al., 2021; Kucher et al., 20221; Rouatbi et al., 2021), bond markets (Augustin et al., 2022; Chen et al., 2021; Falato et al., 2021; Haddad et al., 2021), gold markets (Corbert et al., 2020; Gharib et al. 2021; Mensi et al., 2020), exchange rate (Li et al., 2021; Aquilante et al., 2022; Jamal and Bhat, 2022), crude oil markets (Sharif et al., 2020; Mensi et al., 2020; Shaikh, 2021; Gharib et al., 2021; Wang et al., 2022), cryptocurrency markets (Corbert et al., 2020; Conlon and McGee, 2020; Yarovaya et al., 2020; Vidal-Tomás, 2021; Hong and Yoon, 2022), real estate markets (Balemi et al., 2021; Ling et al., 2020; Milcheva et al., 2022), and other asset classes". It contains too a large number of references, I would suggest you devote some effort to discuss such previous studies and – more importantly – their relationships and the major factors responsible for differences in results from same study conducted by different researchers.

Answer: We have updated this paragraph and discuss the papers that talks about the effect of COVID-19 only on stock markets in details as our paper is related to the stock market. The additional two paragraphs are as below:

With the outbreak of the coronavirus (COVID-19) pandemic, an increasing number of researchers have examined the impact of the pandemic on stock markets. Baker et al. (2020) document that no previous infectious disease outbreak, including the Spanish Flu, has impacted the stock market as forcefully as the COVID-19 pandemic due to strict government restrictions on commercial activity and voluntary social distancing. Exploring the direct effects and spillovers of COVID-19, He et al. (2020a) find that COVID-19 has a negative but short-term impact on stock markets of affected countries. By using a large sample of 63 stock markets covering all key markets, Kapar et al. (2021) find that the Wuhan lockdown induces negative spillover effects on markets in Europe, North America and other global markets. This is mainly attributed to fear and uncertainty as these markets had yet to introduce domestic restrictions and had minimal infections at the time. The rapid transmission of cases outside China particularly in Europe and the introduction of containment measures result in severe market decline which highlights the need for quick, globally coordinated response to contagious diseases.

Controlling for traditional market drivers (such as investor sentiment, credit risk, liquidity risk, safe-haven asset demand and the price of oil), O’Donnell et al. (2021) conclude that the daily total count of confirmed COVID-19 cases is a leading factor in influencing equity prices. Using panel data analysis, Al-Awadhi et al. (2020) estimate that both the daily growth in confirmed cases and number of deaths caused by COVID-19 have significant negative effects on returns in Chinese stock market. Ashraf (2020) finds that stock markets react more strongly to the growth in number of confirmed cases as compared to the growth in number of deaths. Mazur et al. (2021) examine the US stock market during the crash of March 2020. They estimate that approximately 90% of the S&P 1500 stocks generate asymmetrically distributed large negative returns. The consensus from this emerging literature on COVID-19 suggests that stock markets respond negatively and significantly to COVID-19. Individual stock responses may vary, however, depending on several factors.

3. The methodology section: The description of the methods is much clearer now. But I would suggest the author(s)to add a new subtitle after the methodology section title. Please add a subtitle (4.1 Event Study Methodology) then add a single paragraph discussing the theoretical basis of this methodology to help the reader to follow the section better.

Answer: 4.1 Event Study Methodology subtitle is included under Methodology section. A paragraph is added to discuss the theoretical basis of this methodology.

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26 Sep 2022

Winners and Losers from Pfizer and Biontech's Vaccine Announcement: Evidence from S&P 500 (Sub)Sector Indices

PONE-D-22-14074R2

Dear Dr. Burcu Kapar ,

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Reviewers' comments:

6 Oct 2022

PONE-D-22-14074R2

Winners and Losers from Pfizer and Biontech's Vaccine Announcement: Evidence from S&P 500 (Sub)Sector Indices

Dear Dr. Kapar:

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Table 5

Energy industry.

	Oil& Gas Exloration and Production	Oil & Gas Equipment Services	Oil & Gas Drilling	Energy Equipment and Services
Oil& Gas Exloration and Production		0.92	0.72	0.86
Oil & Gas Equipment Services	0.92		0.33	0.66
Oil & Gas Drilling	0.72	0.33		0.82
Energy Equipment and Services	0.86	0.66	0.82

The table reports the p values of t-test checking the equality of means of AR(0) and AR(1) values. The null hypothesis states that the difference in group means is zero. An alternate hypothesis states that the difference in group means is different from zero. Data is obtained from Thomson Reuters Eikon for the period 23 June 2020 to 12 November 2020, for a total of 139 daily observations per index.

Table 6

Real estate industry.

	Equity Real Estate Investment Trusts	Real Estate Mng.and Dev.
Equity Real Estate Investment Trusts		0.77
Real Estate Mng.and Dev.	0.77

The table reports the p values of t-test checking the equality of means of AR(0) and AR(1) values. The null hypothesis states that the difference in group means is zero. An alternate hypothesis states that the difference in group means is different from zero. Data is obtained from Thomson Reuters Eikon for the period 23 June 2020 to 12 November 2020, for a total of 139 daily observations per index.

Table 8

Health care industry.

	Biotechnology	Health Care Equipment and Supplies	Health Care Distributors	Heath Care Facility	Health Care Technology	Life Sciences Tools and Services	Pharmaceuticals
Biotechnology		0.77	0.42	0.40	0.69	0.09	0.72
Health Care Equipment and Supplies	0.77		0.10	0.15	0.98	0.18	0.33
Health Care Distributors	0.42	0.10		0.29	0.24	0.16	0.07
Heath Care Facility	0.40	0.15	0.29		0.25	0.17	0.13
Health Care Technology	0.69	0.98	0.24	0.25		0.11	0.80
Life Sciences Tools and Services	0.09	0.18	0.16	0.17	0.11		0.19
Pharmaceuticals	0.72	0.33	0.07	0.13	0.80	0.19

The table reports the p values of t-test checking the equality of means of AR(0) and AR(1) values. The null hypothesis states that the difference in group means is zero. An alternate hypothesis states that the difference in group means is different from zero. Data is obtained from Thomson Reuters Eikon for the period 23 June 2020 to 12 November 2020, for a total of 139 daily observations per index.

Table 11

Industrials industry.

	Airlines	Railroads	Trans. Inf.	Air Freight&Log.	Building prod.	Aerospace	Elect.Eq.	Ind. Congl.	Machinery
Airlines		0.54	0.76	0.11	0.29	0.87	0.62	0.63	0.48
Railroads	0.54		0.79	0.35	0.10	0.28	0.93	0.15	0.07
Transportation Infrastructure	0.76	0.79		0.51	0.40	0.50	0.57	0.98	0.64
Air Freight and Logistics	0.11	0.35	0.51		0.80	0.33	0.49	0.32	0.42
Building products	0.29	0.10	0.40	0.80		0.18	0.33	0.11	0.14
Aerospace and Defense	0.87	0.28	0.50	0.33	0.18		0.06	0.33	0.20
Electrical Equipment	0.62	0.93	0.57	0.49	0.33	0.06		0.55	0.72
Industrial Conglomerates	0.63	0.15	0.98	0.32	0.11	0.33		0.55	0.05
Machinery	0.48	0.07	0.64	0.42	0.14	0.20	0.72	0.05

The table reports the p values of t-test checking the equality of means of AR(0) and AR(1) values. The null hypothesis states that the difference in group means is zero. An alternate hypothesis states that the difference in group means is different from zero. Data is obtained from Thomson Reuters Eikon for the period 23 June 2020 to 12 November 2020, for a total of 139 daily observations per index.

Table 13

Materials industry.

	Chemicals	Construction Materials	Containers and Packaging	Metals and Mining
Chemicals		0.86	0.48	0.16
Construction Materials	0.86		0.16	0.19
Containers and Packaging	0.48	0.16		0.20
Metals and Mining	0.16	0.19	0.20

The table reports the p values of t-test checking the equality of means of AR(0) and AR(1) values. The null hypothesis states that the difference in group means is zero. An alternate hypothesis states that the difference in group means is different from zero. Data is obtained from Thomson Reuters Eikon for the period 23 June 2020 to 12 November 2020, for a total of 139 daily observations per index.