event study: the influence of covid-19 research and ......this research uses the event study...

Event Study: The Influence Of COVID-19 Research and Development

News on Pharmaceutical and Biotech Companies

Lydia Chuyue Zhang1

College of Literature, Science, and the Arts

University of Michigan – Ann Arbor

[email protected]

April 9, 2021

1 I thank Professor Pablo Ottonello, Professor Kathryn Dominguez, and Professor Linda Tesar for their helpful

comments.

mailto:[email protected]

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Abstract

This event study estimates the influence of COVID-19 related research and development

news on pharmaceutical and biotech companies and its impact on the stock market. Results show

that companies can grow 4% to 63.92% from their development progress, except for Johnson &

Johnson’s -2.96% loss on January 29th, 2021. Dynamics between vaccine and treatment drug

developers were collaborative initially and more competitive later. The market prefers certain

firms such as Moderna, CureVac, and Gilead, who are less likely to be defeated by others’ success

and more likely to disrupt others with their progress. Private and public benefits from vaccine and

drug developments mostly align, except when Novavax’s and Johnson & Johnson’s positive

announcements collided. Lastly, positive market sentiment seems to influence small-cap and mid-

cap firms more than large-caps, while negative events have less impact on them.

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Ⅰ. Introduction

This event study estimates the influence of COVID-19 related research and development news on

pharmaceutical and biotech companies and its impact on the stock market. During COVID-19, the

healthcare sector is attracting significant investments from governments and investors. The

pandemic outbreak severely damaged the U.S economy and stock market during March 2020,

which exacerbated domestic and external political conflicts. Therefore, the U.S government has

invested billions in vaccine companies, aiming to accelerate trials. Private investors are also paying

close attention to the pharmaceutical and biotech sectors. Even though some may argue that a

COVID-19 vaccine is far less lucrative than cancer drugs to pharmaceutical giants like Johnson &

Johnson, it can still be an attractive business due to revenues from large government contracts. For

small biotech companies, COVID-19 vaccine development can be even more rewarding. Novavax

only generated $18.66 million in revenue during 2019, while Johnson & Johnson gained $82.1

billion for the same calendar year2. If Novavax can successfully distribute an effective COVID-19

vaccine, it will bring colossal capital gain for its early investors.

However, the vaccine race can be risky and competitive. Accidents such as the severe

adverse reactions among trial participants have already happened, which could hinder research

progress and decrease company stock price by more than 10%3. Meanwhile, due to vaccine trials’

competitive nature, one company’s progress can bring considerable losses to its competitors.

Nevertheless, some also argue that the market already anticipates such risk. Hiatus in research can

also show that companies have taken necessary safety precautions, which increases the public trust

towards vaccines4. Some media also pointed out that the COVID-19 vaccine trials are highly

2 Numbers retrieved from published financial reports of each company mentioned. 3 Observed from FactSet stock price charts and AI price-news analysis function 4 Argued by the Wall Street Journal and several media

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cooperative compared to other vaccines5. Diestre and Nandini (2012) discovered that new biotech

firms often select pharmaceutical firms as research allies. Thus, it is debatable whether the vaccine

race is truly a zero-sum game. Therefore, it is crucial to measure and quantify the influence of trial

news on pharmaceutical and biotech stocks.

This research focuses on four events. Event 1 is the official confirmation that the former

President Trump used Regeneron’s antibody cocktail for COVID-19 treatment, at 4:11 pm EST6,

October 2nd, 2020. By the time of announcement, there were no approved treatments for COVID-

19. This news shifted Regeneron’s stock price from 564.80 dollar per share on October 2nd to

605.08 on October 5th, with a 7.13% increase.

Event 2 is Pfizer announcing its vaccine to be 90% effective at 06:45 am, November 9th,

2020. It disclosed that its study enrolled 43,538 participants, with 42% being racially diverse, and

no serious safety concerns have emerged. Pfizer is the first major pharmaceutical company to

achieve such high efficacy, which exceeds the 50% threshold that the US Food and Drug

Administration (FDA) requires for a coronavirus vaccine’s emergency use approval. This news is

a milestone for COVID-19 vaccine development.

Event 3 is Moderna’s announcement at 6:56 am, November 16th, 2020 that its COVID-19

vaccine mRNA-1273 has met statistical criteria with an efficacy of 94.5%, higher than expected

by experts. Its phase-3 study, which enrolled more than 30,000 participants in the U.S, also

displayed no safety concerns. This event is also profound since Moderna’s vaccine outperformed

Pfizer’s in terms of efficacy, storage conditions, and shelf life.

Event 4 happened at 8:03 am EST, January 29th, 2021. Johnson & Johnson claimed that its

vaccine candidate is 72% effective in the U.S and 66% effective overall at preventing moderate to

5 Argued by the Wall Street Journal and several media 6 All time mentioned in this research are EST time.

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severe COVID-19 syndromes. Meanwhile, it is 85% effective overall in preventing severe disease.

Novavax also announced at 4:05 pm, January 28th, 2020 that its vaccine is 89.3% effective. Both

events influenced the market at 9:30 am, January 29th, 2021, due to trading hour constraints.

This research uses the event study methodology to estimate the influence of COVID-19

related research and development news on pharmaceutical and biotech companies, and its impact

on the stock market as a whole. It also discusses whether private gains from research and

development align with social benefits. The main results are that companies generally profited

from their own development progress, except for Johnson & Johnson’s Johnson’s -2.96% loss on

January 29th, 2021. Positive cumulative abnormal returns can be as high as 63.92% in Novavax’s

case. Dynamics between vaccine and treatment drug developers were collaborative during the first

two events, when one company's progress was mostly good news for all firms. Later the market

seems to prefer certain firms such as Moderna, CureVac, and Gilead, who are less likely to be

influenced by others’ success and more likely to disrupt others with their progress. Lastly, 75% of

the events had a positive impact on the stock market, which indicates private and public benefits

from vaccine and drug developments align most of the time, except when Novavax’s and Johnson

& Johnson’s positive announcements collided.

Ⅱ. Literature Review: Reaction Patterns of Healthcare Stocks and Proof of Shock

A large amount of literature discussed the event study methodology and the use of intraday data.

Armitage (1995) discussed widely used methods of estimating abnormal returns, including but not

limited to the market model, index model, average return model, and Fama-MacBeth model. He

claimed that the market model is the most commonly used and has no better alternative. Marshall,

Nguyen, and Visaltanachoti (2019) discussed the benefits of using intraday data for event studies

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such as stronger confidence in robustness. Aktas (2008) further suggested that when using intraday

data to measure abnormal stock returns, simple procedures like mean adjusted returns do not

perform worse than more sophisticated approaches. Meanwhile, Marshall, Nguyen, and

Visaltanachoti (2019) suggested that correcting beta estimates via complicated models often have

trivial effect on improving estimation accuracy.

Many works studied the pharmaceutical and biotech industry’s innovation process,

industry development, market power, profits and social impact. Lakdawalla (2018) reviewed

extensive economics literature on the pharmaceutical industry and discovered that cost of

pharmaceutical research and development is rising, but it is unclear whether there is more medical

innovation. Spitz, Janet, and Wickham (2012) revealed that pharmaceuticals have enjoyed profits

of 3 to 37 times higher than all-industry average from year 1988 to 2009, while the U.S life

expectancy has not grown significantly. They suggested that many companies are abusing their

market power to maximize profits, urging government control for drug pricing.

Pérez-Rodríguez and Valcarcel (2012) studied the price change of pharmaceutical stocks

after product innovation and R&D news. They found that 40% of large abnormal market-adjusted

daily returns are not linked with any plausible cause. Furthermore, for the remaining 40%, only 6%

are related to FDA approvals. They also discovered that only 0.58% of FDA approvals of new

drugs lead to significant abnormal returns. Lastly, they uncovered that the influence of negative

news is more extensive than positive ones.

De Schrijver (2013) used an event study to analyze the stock return reaction of NASDAQ

and NYSE listed healthcare stocks towards FDA and EMEA announcements. He used both the

market model and the Fama-French model and discovered significant negative abnormal returns

after negative FDA news. Like Pérez-Rodríguez and Valcarcel (2012), he found that company

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stocks react more to negative news. However, he did not find significant cumulative abnormal

returns after the announcement dates of both positive and negative news he selected. Nevertheless,

he showed that the Fama-French model outperforms the market model.

Except for the pharmaceutical and biotech industry, there are large amount of literature

using event studies to investigate the effects of important events and news in other areas. Brown

and Cliff (2004) investigated market aggregate investor sentiment and its relation to near-term

market return, and their results opposed the conventional viewpoint that sentiment primarily

affects individual investors and small stocks. Li and Yang (2017) analyzed the cross-section and

time-series effects of investor sentiment on stock prices. Contrary to Brown and Cliff (2004), they

discovered that individual stock sentiment influences small-cap firms more than large-caps,

especially during stock market downturn.

Naubert and Tesar (2018) used an event study approach to estimate the burden of

Systemically Important Financial Institution (SIFI) designation. They found significant abnormal

returns on the date of ruling and estimated SIFI designation cost using company financials.

Although their focus is not on pharmaceutical companies, their study shows that one can prove

chosen announcements to be a shock by using Google search trends and that it is possible to assign

a dollar value to news items.

This research differs from previous literature for its focus on COVID-19 related research

and development news. It also primarily concerns middle to large-cap (valuation is at least two

billion dollars) companies who are in leading positions in the vaccine and drug development race.

Meanwhile, for the selection of events, I found that selected companies’ stock price movements

are more correlated with development progress than negative incidents such as severe adverse

reactions. Therefore, the research mainly studies positive announcements.

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Ⅲ. Methodology

This research adopts the event study approach, and the following equations are from Armitage

(1995) and Naubert and Tesar (2018). The primary model is the market model, which considers

the focal firm's risk by multiplying the market return with the firm individual β factor.

E(Ri, t) = αi + βi * RM, t

where Ri, t is the minute return of the stock of observation i on day t, RM, t is the minute return of

the reference market on day t. The firm individual βi factor measures the sensitivity of Ri, t on the

reference market. The abnormal return is calculated as follows:

ARi, t = Ri, t − E(Ri, t)

The cumulative abnormal return during period [t1, t2] is calculated by the following:

CARi, t [t1, t2] = ∑ ARi, t

As the formula above generates different values when t changes, this research uses

endogenous windows of 15 and 60 minutes to offer concrete estimations. End of event window is

set after no significant abnormal return is observed for 15 or 60 consecutive minutes with a 95%

confidence level.

Notably, even though the market model is widely accepted as the standard model, there is

also criticism towards its accuracy. The model assumes that the risk-free interest rate is constant,

which conflicts with the reality that market returns vary. However, results from Table 1 to 4

confirm that the market model works well for this research. Therefore, this research will not

expand to other models, such as the Market Adjusted or Garch model.

Lastly, to estimate each news's monetary impact, this research multiplies cumulative

abnormal returns with the corresponding stock or market index's latest market capitalization.

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Ⅳ. Data Analysis

To measure the impact of COVID-19 vaccine news, this research uses the intraday stock and index

trade price data and related news data from Bloomberg. The following data are involved: the index

price of S&P 500 (SPX), S&P Midcap 400 (MID), Russell 2000 (RTY), and the stock price of

seven companies: Pfizer (PFE), Moderna (MRNA), Johnson & Johnson (JNJ), Novavax (NVAX),

CureVac (CVAC), Gilead (GILD), and Regeneron (REGN). The first five are COVID-19 vaccine

developers, and the rest are focusing on therapeutics of COVID-19. The rationale for this selection

is that all of them are in phase three of the vaccine trial or leading in therapeutics, therefore more

sensitive to research and development news. And S&P 500 is a commonly used index for the event

study method (De Schrijver, 2013). Meanwhile, S&P 500 market index's price, S&P Midcap 400,

Russell 2000 represents large-cap, mid-cap, and small-cap companies accordingly.

This research gathers Google search trend and stock price movement data to prove events

are unexpected by the market, shown in Figure 1 to Figure 13. For Google search popularity trend,

numbers represent search interest for the given region and time. A value of 100 is the peak

popularity; 50 means that the term is half as popular; 0 means there was not enough data. Figure 1

to 4 show that Google search popularity for selected events is nearly unobservable before the

announcement and hits the highest popularity level afterward. Thus, each event should be a shock

to the market. From the stock price change shown in Figure 4 to Figure 13, we can also see sharp

spikes at the selected announcement time. Under the assumption of event study, leakage of

information will be instantly absorbed by the market and reflected on the stock price. Therefore,

the observations above show selected events are unexpected. They also prove that the exact time

of announcement used in this research is correct, as movements happened precisely at 9:30 am,

the first effective trading minute for all events.

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Ⅴ. Effects of News on Pharmaceutical and Biotech Companies

Event 1: President Trump Used Regeneron’s Unproved Cocktail Treatment (04:11 pm, October

2nd, 2020)7

Figure 14 and Table 5 show the cumulative abnormal returns of seven selected firms around the

first event. Most companies reacted positively towards the news, except for Novavax and Pfizer

in the first 10 minutes and Moderna in the first two hours. For Regeneron, a significant cumulative

abnormal return of 8% happened at 9:46 am, and then decreased to around 4% to 6%, which lasted

for more than one trading day. Notably, Regeneron had previously disclosed positive results of

remdesivir, but the stock price only surged until former President Trump’s treatment.

Among all firms, Regeneron, CureVac, Novavax, Gilead and Moderna had the highest

cumulative abnormal returns, all larger than 1%. However, because of Johnson & Johnson’s large

market capitalization, it had the most prominent capital gain of 3.1 billion, even more than

Regeneron’s 2.53 billion gain. Meanwhile, despite the competition between Gilead’s remdesivir

and Regeneron’s REGN-COV2 antibody cocktail, Gilead also gained 1.82 billion in capitalization.

Those results show that the vaccine race was not a zero-sum game, as the market confidence in

Regeron’s COVID-19 antibody cocktail benefited almost all vaccine and therapeutics developers.

Results might also suggest that investors predicted there would be a greater demand than

supply for COVID-19 related prevention and treatment methods for an extensive period, especially

since President Trump’s acceptance of treatment signals the severity of COVID-19 and can

increase public trust towards COVID-19 healthcare.

7 This event happened after regular trading hours, so I pick 9:30 am, October 5th, 2020, the nearest regular trading time, as the effective announcement time.

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Event 2: Pfizer announced its vaccine to be 90% effective (06:45 am, November 9th, 2020) 8

Figures 7, 15 and Table 5 show that the Pfizer stock surged immediately after the announcement

with large positive abnormal returns of about 12% within the first five minutes. It raised Pfizer’s

capitalization by a colossal amount of 17.32 million. Other firms such as Moderna, Johnson &

Johnson, and CureVac also reacted positively towards the news. Especially, Johnson & Johnson

gained 9.76 billion. Overall, the positive reactions showed strong market confidence, especially

since the 90% efficacy rate outperformed prior expectations.

Contrary to event 1, event 2 did not lead to gains for all. Novavax and Regeneron suffered

significant losses, while Gilead had a moderate loss within the trading day but surged the day after.

Novavax’s drastic drop in stock price is likely due to its lag in development, small size, and

questionable history in vaccine production, since it never delivered any vaccine to market for its

33-year history. Novavax was also overpriced - it was a popular bet on the vaccine race and had a

more than 2000% price growth since March 2020. Consequently, it can be risky to keep investing

in it.

For the two drugmakers, Regeneron and Gilead, the reason for their drop might be lower

projected revenues. Since Pfizer is a repudiable pharmaceutical company with strong government

support and established supply and logistics chains, the Pfizer vaccine's distribution will likely

decrease COVID-19 infections, thus decreasing the need for treatment in the mid-to-long term. In

comparison, Gilead suffered less loss, which can imply that investors have higher expectations of

Gilead's Veklury than Regeneron's cocktail treatment, although President Trump chose the latter

over Gilead.

8 Both announcements happened before regular trading hours, so I pick 9:30 am, the opening of regular hours, as the announcement time for this research.

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For Moderna, its positive returns can result from similar technology with Pfizer, as both

are using a new approach of Messenger RNA (mRNA). Moderna is only 12% the size of Pfizer.

Therefore, its capital gains from the Pfizer announcement suggest that small biotech companies

are not less trusted than their big pharmaceutical counterparts, as investors still showed faith in the

former despite the latter's advancement. Some might have seen the unique opportunity to invest in

smaller biotech companies like Moderna, since they do have expertise in producing vaccines and

yield a higher return once succeeding. Figure 9 and the following section demonstrate this

strategy's success, as the disclosure of the Moderna vaccine's efficacy raised its stock from as low

as 74 dollars per share (6 days before the announcement) to 100 dollars after the announcement.

Event 3: Moderna announced vaccine efficacy of 94.5% and long shelf life at refrigerated

temperatures (06:56 am, November 16th, 2020)

Figure 9, 16 and Table 5, 6 show that Moderna attained colossal gains from its vaccine

development, with a 9.47% cumulative abnormal return and a 3.23 billion market capitalization

increase. This result is coherent since Moderna’s vaccine is superior to Pfizer in many ways. It has

a higher efficacy rate, a longer shelf life, and most importantly, a more attainable storage

temperature. Pfizer’s vaccine requires a minus 75 degrees Celsius condition, which cannot be met

by many doctor’s offices or pharmacies, and a short shelf life of seven days. Meanwhile,

Moderna’s vaccine merely requires a minus 20 degrees Celsius environment and can last for up to

30 days. Therefore, it is reasonable that Pfizer had a 10.18 billion drop in market capitalization

and -4.75% cumulative abnormal returns.

Moderna’s success also disrupted other companies, such as Johnson & Johnson, Novavax,

and Regeneron. The first two were lagging in the process of development compared to Moderna

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and Pfizer. Simultaneously, as both Moderna and Pfizer showed higher-than-expected efficacy

rates, it raised the bar for upcoming vaccines. The rationales behind Regeneron's loss can be similar

to that of event 2, which is lowered projected revenues. By comparing its stock market

performance with Gilead, it shows that investors continued to favor Gilead than Regeneron.

Results on event 3 demonstrated investors' increasing trust in mRNA vaccines. Unlike most

companies selected, the small German biotech company CureVac surged after Moderna's

announcement. CureVac could potentially demonstrate a high efficacy rate and even longer shelf

life than Moderna, adopting the same mRNA approach and supposedly more advanced technology

for degradation preventions. It is also sponsored by the German government and can become the

leading vaccine supplier for the European market. Therefore, it is reasonable that CureVac stock

had 3.70% cumulative abnormal returns and a 0.49 billion market capitalization increase.

Event 4: Novavax vaccine demonstrated 89.3% efficacy, Johnson & Johnson announced 66%

efficacy (4:05 pm, January 28th, 2021 & 08:03 am, January 29th, 2021)

The two events happened at different time, but both influenced the market at 9:30 am, January 29th,

2021. Figure 17 and Table 5 and 6 show that Novavax was the primary beneficiary from the

announcements, with 63.92% cumulative abnormal returns and 4.93 billion growth in market

capitalization. Most other firms also reacted positively, except for Pfizer and Johnson & Johnson.

The latter suffered -2.96% cumulative abnormal returns and a 14.20 billion loss in market

capitalization, despite its research advancement. This contrast is plausible since Novavax has

outperformed Johnson & Johnson in both efficacy and distribution.

The results revealed investors’ collective opinions: Moderna and CureVac are the favorite

players in the vaccine race; therapeutics such as Gilead and Regeneron still have growth potentials;

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while Pfizer and Johnson & Johnson are likely to have limited revenues due to increased

competitions from Moderna and Novavax. It also showed that Johnson & Johnson’s unique

advantage of a single shot does not compensate for its low efficacy in the market’s viewpoint.

Ⅵ. Effects of News on the Market

Table 7 shows that among all events selected, the approval of Pfizer’s vaccine has the most

profound positive effect on the market and firms of all sizes. S&P 500 increased by more than 3%,

and its capitalization grew by 971 billion within one hour of the announcement. Small-cap and

mid-cap firms also had high returns of about 5% and increased market capitalization between

$88.96 billion to 128.42 billion. As Pfizer’s is the first approved American vaccine, the results

show strong positive market sentiment and the high value of a COVID-19 vaccine.

Both event 1 and event 3 also yield positive returns for the market and all size sectors. The

market gains after President Trump’s physical acknowledgement of Regeneron’s cocktail

treatment demonstrate correlation between market optimism and increased credibility of the

treatment drug candidate. And the market growth after Moderna’s vaccine announcement

highlights the value of an effective COVID-19 vaccine again. However, by comparing results of

event 2 and event 3 on Table 3, it is notable that the market as a whole seems less excited over

Moderna’s vaccine than Pfizer’s. It can be that investors have anticipated Moderna’s research

progress after Pfizer’s announcement.

Event 4 is the only case where positive announcements generated overall negative market

reactions, as the market dropped by 0.37% within an hour. A possible explanation is that the

disappointment in Johnson & Johnson might have outweighed the positive sentiments towards

Novavax. Table 7 together with Table 5 and 6 support the hypothesis that the private and public

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benefits of COVID-19 vaccines and treatment drugs generally align, as only event 4 yielded

negative market reactions.

Results of Table 7 also partly support the claim of Li and Yang (2017) that individual stock

sentiment influences small-cap firms more than large-caps. Both Russell 2000 and S&P Midcap

400 had higher returns than S&P 500 during all four events. However, they are also generally less

impacted during event 4, when the announcements caused overall negative effectives.

Ⅶ. Conclusions

Results of the four events show that COVID-19 vaccines and treatment drugs can be highly

lucrative for companies, as progress in development can lead to billions of growths in market

capitalization and return rate as high as 63.92%. Meanwhile, the competition between developers

is increasingly fierce. Initially any development progress will bring collective gains for the whole

sector, but the market gradually developed preference for certain companies, such as Moderna and

Gilead. Their progress is more likely to have a negative impact on others, while they are less likely

to be negatively affected. However, the vaccine race is still not a zero-sum game, as some

companies with similar technology and overlapping market can mutually benefit from each other’s

positive news, such as Moderna and CureVac. Results also show that the private and public

benefits of COVID-19 vaccines and treatment drugs generally align, except for the case of event

4. Lastly, positive market sentiment seems to influence small-cap and mid-cap firms more than

large-caps, while negative events have less impact on them.

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Appendix

Figure 1: Google Search Popularity (%) for "Trump Regeneron Cocktail" in the U.S

September 25, 2020 - October 7, 2020

Source: Google Trends.

Figure 2: Google Search Popularity (%) for "Pfizer Vaccine Effective " in United States

October 15, 2020 - December 16, 2020

Source: Google Trends.

0

10

20

30

40

50

60

70

80

90

100

Event 1

0

10

20

30

40

50

60

70

80

90

100

10

/1/2

0

10

/3/2

0

10

/5/2

0

10

/7/2

0

10

/9/2

0

10/1

1/2

0

10/1

3/20

10/1

5/2

0

10/1

7/2

0

10/1

9/2

0

10/2

1/2

0

10/2

3/2

0

10/2

5/2

0

10/2

7/2

0

10/2

9/2

0

10/3

1/2

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11

/2/2

0

11

/4/2

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11

/6/2

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11

/8/2

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11/1

0/2

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11/1

2/2

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11/1

4/2

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11/1

6/2

0

Event 2

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Figure 3: Google Search Popularity (%) for "Moderna Vaccine Effective" in the U.S

November 16, 2020 - November 20, 2020

Source: Google

Figure 4: Google Search Popularity (%) for "Johnson & Johnson vaccine" and "Novavax

Vaccine" in the U.S

January 10, 2021 - February 2, 2021

Source: Google

0

10

20

30

40

50

60

70

80

90

100

Event

0

20

40

60

80

100

Johnson & Johnson Vaccine Novavax Vaccine

Event 4

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Figure 5: Regeneron Stock Price Around Event 1


Source: Bloomberg.

Figure 6: S&P500 Index Price Around Event 1


Source: Bloomberg.

535

545

555

565

575

585

595

605

615

6259/

16

/20

9:43

9/1

6/2

0 1

2:1

39

/16

/20

14

:44

9/1

7/2

0 1

0:3

79

/17

/20

13

:11

9/1

7/2

0 1

5:4

59

/18

/20

11

:48

9/1

8/2

0 1

4:2

29

/21

/20

10

:18

9/2

1/2

0 1

2:5

49

/21

/20

15

:40

9/2

2/2

0 1

1:4

49

/22

/20

14

:28

9/2

3/2

0 1

0:2

99

/23

/20

13

:23

9/2

4/2

0 9:

329

/24

/20

12

:26

9/2

4/2

0 1

5:3

79

/25

/20

11

:45

9/2

5/2

0 1

4:1

79

/28

/20

10

:10

9/2

8/2

0 1

2:5

79

/28

/20

15

:40

9/2

9/2

0 1

1:5

39

/29

/20

15

:26

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0/2

0 1

1:2

29

/30

/20

13

:47

10/1

/20

9:39

10

/1/2

0 1

2:0

51

0/1

/20

14

:34

10

/2/2

0 1

0:2

91

0/2

/20

12

:57

10

/2/2

0 1

5:2

41

0/5

/20

11

:16

10

/5/2

0 1

3:3

9

3190

3240

3290

3340

3390

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3490

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43

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0 12

:18

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/20

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:53

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:32

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:21

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0 14

:58

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:01

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0 13

:48

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0 10

:00

9/22

/20

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1

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:32

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:43

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:41

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:59

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:37

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:19

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:52

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/20

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:01

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:13

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:45

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:59

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:29

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:57

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/20

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7

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/20

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/20

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1

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5

10/2

/20

15:3

6

10/5

/20

11:3

3

10/5

/20

14:0

2

Zhang

21

Figure 7: Pfizer Stock Price Around Event 2

August 10, 2020 - December 8, 2020

Source: Bloomberg.

Figure 8: S&P 500 Index Price Around Event 2

August 10, 2020 - December 8, 2020

Source: Bloomberg.

34

35

36

37

38

39

40

41

42

43

448/

10

/20

9:30

8/1

2/2

0 12

:08

8/1

4/2

0 14

:46

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0 10

:54

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:32

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408/

28/2

0 12

:18

9/1

/20

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69/

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0 11

:03

9/9

/20

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19/

14

/20

9:48

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0 12

:26

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0 15

:04

9/2

3/2

0 11

:12

9/2

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0 13

:50

9/3

0/2

0 9:

5810

/2/2

0 12

:36

10/6

/20

15:1

410

/9/2

0 11

:23

10/1

3/2

0 1

4:01

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0:09

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0/20

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4710

/22/

20

15:

2610

/27/

20 1

1:34

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/20

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011

/5/2

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:58

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/20

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711

/12/

20

11:

4511

/16/

20

14:

2311

/19/

20

10:

3111

/23/

20

13:

0911

/25/

20

15:

4712

/1/2

0 14

:55

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/20

11:0

212

/8/2

0 13

:40

3034

3134

3234

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8/1

0/2

0 9:

308/

12

/20

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58/

14

/20

15:4

08/

19

/20

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58/

21

/20

15:2

08/

26

/20

11:5

58/

28

/20

15:0

09/

2/2

0 11

:35

9/4/

20

14:3

99/

10

/20

11:1

39/

14

/20

14:1

89/

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0 10

:53

9/2

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0 13

:58

9/2

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0 10

:33

9/28

/20

13:3

810

/1/2

0 10

:13

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/20

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810

/8/2

0 9:

5410

/12/

20

12:

5910

/15/

20

9:3

410

/19/

20 1

2:39

10/2

1/2

0 1

5:45

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0 1

2:20

10/2

8/20

15:

2511

/2/2

0 12

:00

11/4

/20

15:0

511

/9/2

0 11

:41

11/1

1/2

0 1

4:46

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0 1

1:21

11/1

8/20

14:

2611

/23/

20

11:

0111

/25/

20

14:

0612

/1/2

0 13

:41

12/4

/20

10:1

512

/8/2

0 13

:20

Zhang

22

Figure 9: Moderna Stock Price Around Event 3


Source: Bloomberg.



Source: Bloomberg.

70

75

80

85

90

95

100

11/6

/20

15:3

111

/9/2

0 10

:39

11/9

/20

12:1

811

/9/2

0 13

:57

11/9

/20

15:3

611

/10/

20

10:

4411

/10/

20 1

2:23

11/1

0/20

14:

0211

/10/

20 1

5:41

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0 1

0:49

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0 1

5:46

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0:54

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0 1

2:33

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0 1

4:12

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0 1

5:51

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0:59

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0 1

2:38

11/1

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0 1

4:17

11/1

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0 1

5:56

11/1

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0 1

1:04

11/1

6/2

0 1

2:43

11/1

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0 1

4:22

11/1

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0 1

6:29

11/1

7/2

0 1

1:08

11/1

7/2

0 1

2:47

11/1

7/2

0 1

4:26

11/1

8/2

0 9

:34

11/1

8/20

11:

1311

/18/

20 1

2:52

11/1

8/20

14:

3111

/19/

20

9:3

911

/19/

20

11:

1811

/19/

20

12:

57

3500

3520

3540

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3580

3600

3620

3640

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3:55

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4:37

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0 1

5:19

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0 1

6:01

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:58

11/1

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0:40

11/1

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0 1

1:22

11/1

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2:04

11/1

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0 1

2:46

11/1

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13:

2811

/12/

20

14:

1011

/12/

20

14:

5211

/12/

20 1

5:34

11/1

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0 9

:31

11/1

3/2

0 1

0:13

11/1

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10:

5511

/13/

20

11:

3711

/13/

20

12:

1911

/13/

20

13:

0111

/13/

20

13:

4311

/13/

20

14:

2511

/13/

20

15:

0711

/13/

20

15:

4911

/16/

20

9:4

611

/16/

20

10:

2811

/16/

20

11:

1011

/16/

20

11:

5211

/16/

20

12:

3411

/16/

20

13:

1611

/16/

20

13:

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/16/

20

14:

4011

/16/

20

15:

2211

/16/

20 1

6:04

11/1

7/2

0 1

0:01

11/1

7/2

0 1

0:43

Zhang

23

Figure 11: Novavax Stock Price Around Event 4

January 29, 2020 - Feburary 4, 2021

Source: Bloomberg.

Figure 12: Johnson & Johnson Stock Price Around Event 4


Source: Bloomberg.

100

150

200

250

30012

/29/

20 1

1:33

12/3

0/20

10:

25

12/3

0/2

0 1

5:49

12/3

1/2

0 1

4:41

1/4

/21

13:3

3

1/5

/21

12:3

0

1/6

/21

11:2

3

1/7

/21

10:1

5

1/7

/21

15:3

9

1/8/

21

14:3

2

1/1

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1 13

:26

1/1

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:17

1/1

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:10

1/1

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:03

1/1

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:26

1/1

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1 14

:26

1/1

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1 13

:18

1/20

/21

12:1

0

1/21

/21

11:0

4

1/2

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1 10

:02

1/2

2/2

1 15

:26

1/2

5/2

1 14

:20

1/2

6/2

1 13

:11

1/2

7/2

1 12

:07

1/2

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1 10

:59

1/2

9/2

1 9:

51

1/29

/21

15:1

4

2/1

/21

14:0

6

2/2

/21

12:5

8

2/3

/21

11:5

0

2/4

/21

10:4

2

150

155

160

165

170

175

12/2

9/2

0 1

5:06

12/3

0/2

0 1

3:56

12/3

1/2

0 1

2:46

1/4

/21

11:3

61/

5/2

1 10

:26

1/5

/21

15:4

91/

6/2

1 14

:39

1/7

/21

13:2

91/

8/2

1 12

:19

1/1

1/2

1 11

:09

1/1

2/2

1 9:

591/

12

/21

15:2

21/

13

/21

14:1

21/

14

/21

13:0

21/

15

/21

11:5

21/

19

/21

10:4

21/

20

/21

9:32

1/2

0/2

1 14

:55

1/2

1/2

1 13

:45

1/2

2/2

1 12

:35

1/2

5/2

1 11

:25

1/26

/21

10:1

51/

26

/21

15:3

81/

27

/21

14:2

81/

28

/21

13:1

81/

29

/21

12:0

82/

1/2

1 10

:58

2/2

/21

9:48

2/2/

21

15:1

12/

3/2

1 14

:01

2/4

/21

12:5

1

Zhang

24



Source: Bloomberg.

Figure 14: CAR of Selected Companies After Event 1

October 5, 2020 - October 6, 2020

Source: Bloomberg & by calculation.

3600

3650

3700

3750

3800

3850

39001/

25

/21

13:5

81/

25

/21

15:2

91/

26

/21

10:1

51/

26

/21

11:4

61/

26

/21

13:1

71/

26/2

1 14

:48

1/2

7/2

1 9:

341/

27

/21

11:0

51/

27

/21

12:3

61/

27

/21

14:0

71/

27

/21

15:3

81/

28

/21

10:2

41/

28

/21

11:5

51/

28

/21

13:2

61/

28

/21

14:5

71/

29

/21

9:43

1/2

9/2

1 11

:14

1/29

/21

12:4

51/

29

/21

14:1

61/

29

/21

15:4

72/

1/2

1 10

:33

2/1

/21

12:0

42/

1/2

1 13

:35

2/1

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15:0

62/

2/2

1 9:

522/

2/2

1 11

:23

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12:5

42/

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:25

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15:5

62/

3/2

1 10

:42

2/3

/21

12:1

32/

3/2

1 13

:44

2/3

/21

15:1

5

-2.00%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

10/5

/20

9:30

AM

10/5

/20

9:46

AM

10/5

/20

10:0

3 A

M

10/5

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10:1

9 A

M

10/5

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9 A

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M

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8 A

M

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4 PM

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6 PM

10/5

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PM

10/5

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PM

10/5

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1:49

PM

10/5

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2:12

PM

10/5

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2:42

PM

10/5

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3:07

PM

10/5

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3:25

PM

10/5

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3:44

PM

10/6

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9:30

AM

10/6

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9:46

AM

10/6

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10:0

2 A

M

10/6

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10:1

9 A

M

10/6

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5 A

M

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10:5

1 A

M

10/6

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11:0

8 A

M

10/6

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11:2

7 A

M

10/6

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11:4

3 A

M

10/6

/20

12:0

3 PM

10/6

/20

12:2

4 PM

10/6

/20

12:4

5 PM

CVAC_CAR GILD_CAR JNJ_CAR MRNA_CAR

NVAX_CAR PFE_CAR REGN_CAR

Zhang

25







-15.00%

-10.00%

-5.00%

0.00%

5.00%

10.00%

15.00%

20.00%

11/9

/20

9:30

AM

11/9

/20

9:46

AM

11/9

/20

10:0

2 A

M

11/9

/20

10:2

1 A

M

11/9

/20

10:3

8 A

M

11/9

/20

10:5

9 A

M

11/9

/20

11:2

0 A

M

11/9

/20

11:3

8 A

M

11/9

/20

11:5

7 A

M

11/9

/20

12:1

6 PM

11/9

/20

12:3

7 PM

11/9

/20

12:5

8 PM

11/9

/20

1:17

PM

11/9

/20

1:37

PM

11/9

/20

1:56

PM

11/9

/20

2:12

PM

11/9

/20

2:30

PM

11/9

/20

2:48

PM

11/9

/20

3:05

PM

11/9

/20

3:23

PM

11/9

/20

3:40

PM

11/9

/20

3:56

PM

11/1

0/2

0 9

:42

AM

11/1

0/2

0 9

:59

AM

11/1

0/2

0 1

0:15

AM

11/1

0/2

0 1

0:34

AM

11/1

0/2

0 1

0:52

AM

11/1

0/20

11:

08 A

M

11/1

0/2

0 1

1:24

AM

11/1

0/2

0 1

1:45

AM

11/1

0/2

0 1

2:02

PM

CVAC_CAR GILD_CAR JNJ_CAR MRNA_CAR

NVAX_CAR PFE_CAR REGN_CAR

-15.00%

-10.00%

-5.00%

0.00%

5.00%

10.00%

15.00%

11/1

6/2

0 9

:30

AM

11/1

6/20

9:4

6 A

M

11/1

6/2

0 1

0:02

AM

11/1

6/2

0 1

0:20

AM

11/1

6/2

0 1

0:43

AM

11/1

6/2

0 1

1:06

AM

11/1

6/2

0 1

1:27

AM

11/1

6/2

0 1

1:45

AM

11/1

6/2

0 1

2:13

PM

11/1

6/2

0 1

2:39

PM

11/1

6/20

1:0

5 PM

11/1

6/2

0 1

:35

PM

11/1

6/2

0 1

:56

PM

11/1

6/2

0 2

:18

PM

11/1

6/2

0 2

:37

PM

11/1

6/2

0 2

:57

PM

11/1

6/2

0 3

:19

PM

11/1

6/2

0 3

:37

PM

11/1

6/2

0 3

:53

PM

11/1

7/2

0 9

:39

AM

11/1

7/2

0 9

:55

AM

11/1

7/2

0 1

0:11

AM

11/1

7/2

0 1

0:27

AM

11/1

7/20

10:

43 A

M

11/1

7/2

0 1

1:00

AM

11/1

7/2

0 1

1:18

AM

11/1

7/2

0 1

1:35

AM

11/1

7/2

0 1

1:55

AM

11/1

7/2

0 1

2:15

PM

11/1

7/2

0 1

2:38

PM

11/1

7/2

0 1

2:57

PM

MRNA_CAR PFE_CAR NVAX_CAR JNJ_CAR

CVAC_CAR GILD_CAR REGN_CAR

Zhang

26




-10.00%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

1/2

9/2

1 9

:30

AM

1/2

9/2

1 9

:46

AM

1/2

9/2

1 10

:02

AM

1/2

9/2

1 10

:18

AM

1/2

9/2

1 10

:35

AM

1/2

9/2

1 10

:52

AM

1/2

9/2

1 11

:08

AM

1/2

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1 11

:24

AM

1/2

9/2

1 11

:41

AM

1/29

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12:0

0 PM

1/2

9/2

1 12

:17

PM

1/2

9/2

1 12

:34

PM

1/2

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1 12

:50

PM

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1 1:

08 P

M

1/29

/21

1:24

PM

1/2

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1 1:

41 P

M

1/2

9/2

1 1:

58 P

M

1/2

9/2

1 2:

16 P

M

1/2

9/2

1 2:

35 P

M

1/29

/21

2:52

PM

1/2

9/2

1 3:

08 P

M

1/2

9/2

1 3:

24 P

M

1/2

9/2

1 3:

40 P

M

1/2

9/2

1 3:

56 P

M

2/1/

21

9:42

AM

2/1

/21

9:5

8 A

M

2/1

/21

10

:14

AM

2/1

/21

10

:30

AM

2/1

/21

10

:48

AM

2/1

/21

11

:06

AM

2/1

/21

11

:23

AM

MRNA_CAR PFE_CAR NVAX_CAR JNJ_CAR

CVAC_CAR GILD_CAR REGN_CAR

Zhang

27

Table 1: Market Models for Selected Firms Estimated Before Event 1

Dependent variables

PFE JNJ MRNA CVAC NVAX GILD REGN

(1) (2) (3) (4) (5) (6) (7)

SPX 0.349*** 0.355*** 1.585*** 1.151*** 1.829*** 0.440*** 0.915***

(0.010) (0.010) (0.010) (0.103) (0.010) (0.014) (0.016)

Constant -0.00001 -0.00000 0.00000 0.00004 -0.00002 -0.00001 -0.00001

(0.00001) (0.00001) (0.00002) (0.00001) (0.00004) (0.00001) (0.00001)

Obs. 8,029 8,029 8,029 8,029 8,029 8,029 8,029

R2 0.124 0.142 0.218 0.015 0.096 0.112 0.281

Adjusted R2 0.123 0.142 0.218 0.015 0.096 0.112 0.281

R. S. E. 0.001 0.001 0.002 0.007 0.004 0.001 0.001

F Statistic 1,131.827*** 1,333.425*** 2,240.060*** 124.038*** 848.956*** 1,015.983*** 3,141.848***

Note: R. S. E. is residual standard error

***p<0.01


Dependent variables


(1) (2) (3) (4) (5) (6) (7)

SPX 0.500*** 0.504*** 1.352*** 1.068*** 1.668*** 0.530*** 0.867***

(0.008) (0.007) (0.023) (0.058) (0.038) (0.010) (0.014)

Constant -0.00000 -0.00000 0.00000 0.00002 -0.00003 -0.00001 -0.00001

(0.00001) (0.00001) (0.00002) (0.00004) (0.00003) (0.00001) (0.00001)

Obs. 15,684 15,684 15,684 15,684 15,684 15,684 15,684

R2 0.194 0.239 0.182 0.021 0.111 0.157 0.204

Adjusted R2 0.194 0.239 0.182 0.021 0.111 0.157 0.204

R. S. E. 0.001 0.001 0.002 0.005 0.003 0.001 0.001

F Statistic 3,780.563*** 4,914.783*** 3,481.897*** 336.186*** 1,956.309*** 2,927.371*** 4,008.621***


***p<0.01

Zhang

28


Dependent variables


(1) (2) (3) (4) (5) (6) (7)

SPX 0.442*** 0.446*** 1.390*** 1.017*** 1.669*** 0.513*** 0.891***

(0.009) (0.008) (0.024) (0.064) (0.041) (0.010) (0.015)

Constant -0.00000 -0.00000 0.00000 0.00002 -0.00003 -0.00001 -0.00000

(0.00001) (0.00001) (0.00002) (0.00004) (0.00003) (0.00001) (0.00001)

Obs. 14,636 14,636 14,636 14,636 14,636 14,636 14,636

R2 0.155 0.190 0.184 0.017 0.105 0.144 0.201

Adjusted R2 0.155 0.190 0.184 0.017 0.104 0.144 0.201

R. S. E. 0.001 0.001 0.002 0.005 0.003 0.001 0.001

F Statistic 2,676.227*** 3,430.616*** 3,295.135*** 255.084*** 1,707.849*** 2,465.206*** 3,675.463***


***p<0.01


Dependent variables


(1) (2) (3) (4) (5) (6) (7)

SPX 0.861*** 0.606*** 1.348*** 1.129*** 1.675*** 0.479*** 0.632***

(0.010) (0.006) (0.026) (0.044) (0.030) (0.007) (0.011)

Constant -0.00000 0.00000 0.00002 0.00004 -0.00000 -0.00000 -0.00001

(0.00001) (0.00000) (0.00002) (0.00003) (0.00002) (0.00000) (0.00001)

Obs. 29,503 29,503 29,503 29,503 29,503 29,503 29,503

R2 0.198 0.287 0.086 0.022 0.097 0.134 0.095

Adjusted R2 0.198 0.287 0.086 0.022 0.097 0.134 0.095

R. S. E. 0.001 0.001 0.003 0.005 0.003 0.001 0.001

F Statistic 7,285.808*** 11,891.310*** 2,760.949*** 659.687*** 3,179.148*** 4,679.189*** 3,111.062***


***p<0.01

Zhang

29

Table 5: Cumulative Abnormal Returns by Event9

PFE MRNA JNJ NVAX CVAC GILD REGN

Event 1

Endogenous

Window 15

minutes10

-0.05% 2.23% 0.16% 2.97% 2.88% 1.44% 5.10%

Endogenous

Window 60

minutes11

0.53% 1.76% 0.80% 2.76% 1.39% 2.33% 4.17%

Event 2

Endogenous

Window 15

minutes

5.11% 4.70% 1.00% -3.71% 2.84% -0.05% -6.32%

Endogenous

Window 60

minutes

8.57% 2.34% 2.61% -13.49% 5.71% -0.25% -4.61%

Event 3

Endogenous

Window 15

minutes

-4.39% 6.06% -0.23% -9.38% 4.23% -0.19% -1.35%

Endogenous

Window 60

minutes

-4.75% 9.47% -1.12% -11.13% 3.70% 0.59% -3.03%

Event 4

Endogenous

Window 15

minutes

2.28% 10.68% -2.73% 48.42% 7.11% 1.76% 0.82%

Endogenous

Window 60

minutes

-4.09%12 0.33% -2.96% 63.92% 13.85% 1.44% 1.14%

9 Event 1: Trump Used Regeneron’s Unproved Cocktail Treatment (4:11 pm, October 2nd, 2020).

Event 2: Pfizer announced its vaccine to be 90% effective (06:45 am, November 9th, 2020)

Event 3: Moderna announced vaccine efficacy of 94.5% (06:56 am, November 16th, 2020)

Event 4: Novavax vaccine demonstrated 89.3% efficacy, Johnson & Johnson announced 66% efficacy (4:05 pm,

January 28th, 2021 & 08:03 am, January 29th, 2021)

10 End of event window is set after no significant abnormal return is observed for 15 consecutive minutes with a

95% confidence level.

11 End of event window is set after no significant abnormal return is observed for 60 consecutive minutes a 95%

confidence level.

12 Observed 58.6 hours after start of event 4.

Zhang

30

Table 6: Change of Company Market Capitalization by Event (Billions USD) 13

PFE MRNA JNJ NVAX CVAC GILD REGN

Event 1

Endogenous

Window 15

minutes -0.10 0.58 0.61 0.18 0.24 1.12 3.10

Endogenous

Window 60

minutes 1.08 0.46 3.10 0.16 0.12 1.82 2.53

Event 2

Endogenous

Window 15

minutes 10.32 1.30 3.76 -0.19 0.25 -0.04 -3.93

Endogenous

Window 60

minutes 17.32 0.65 9.76 -0.70 0.51 -0.19 -2.87

Event 3

Endogenous

Window 15

minutes -9.42 2.06 -0.91 -0.52 0.57 -0.15 -0.82

Endogenous

Window 60

minutes -10.18 3.23 -4.40 -0.62 0.49 0.45 -1.85

Event 4

Endogenous

Window 15

minutes 4.54 7.05 -12.15 3.73 1.15 1.43 0.45

Endogenous

Window 60

minutes -8.1614 0.22 -13.20 4.93 2.24 1.17 0.62






14 Observed 58.6 hours after start of event 4.

Zhang

31

Table 7: Market Return and Capitalization Change (Billions USD) by Event15

RTY

Return

RTY

Capitalization

Change

MID

Return

MID

Capitalization

Change

SPX

Return

SPX

Capitalization

Change

Event 1 15 minutes 1.78% 39.94 1.46% 25.93 0.93% 268.21

60 minutes 1.79% 40.18 1.45% 25.81 1.14% 329.51

End of

Trading

Day 2.75% 61.80 2.28% 40.46 1.78% 516.52

Event 2 15 minutes 5.45% 130.76 6.36% 120.48 3.27% 993.06

60 minutes 5.35% 128.42 4.70% 88.96 3.20% 971.35

End of

Trading

Day 3.80% 91.06 2.82% 53.41 1.23% 375.05

Event 3 15 minutes 1.58% 40.20 1.20% 23.75 0.71% 219.66

60 minutes 1.81% 45.94 1.49% 29.49 0.96% 296.25

End of

Trading

Day 2.35% 59.84 2.00% 39.57 1.20% 372.13

Event 4 15 minutes -0.09% -2.97 -0.80% -17.61 -1.05% -350.97

60 minutes 0.69% 21.57 -0.10% -2.27 -0.37% -122.92

End of

Trading

Day -1.59% -50.18 -2.01% -44.53 -1.98% -660.85






Zhang

32

Table 8: Event16 Impact by Group: Announcing Company(s), Other Selected Companies,

and the Market17

Announcing

Company(s)

Other Selected

Companies in Average The Market (SPX)

Return

Capitalization

Change Return

Capitalization

Change Return

Capitalization

Change

Event 1

15

minutes 5.10% 3.10 1.61% 0.44 0.93% 268.21

REGN

60

minutes 4.17% 2.53 1.60% 1.12 1.14% 329.51

Event 2

15

minutes 5.11% 10.32 -0.26% 0.19 3.27% 993.06

PFE

60

minutes 8.57% 17.32 -1.28% 1.19 3.20% 971.35

Event 3

15

minutes 6.06% 2.06 -1.89% -1.88 0.71% 219.66

MRNA

60

minutes 9.47% 3.23 -2.63% 2.69 0.96% 296.25

Event 4

15

minutes 45.69%18 -8.4219 4.53% 2.92 -1.05% -350.97

NVAX,

JNJ

60

minutes 60.96% -8.27 2.53% 0.85 -0.37% -122.92






17 Announcing companies are noted in the first column; capitalizations measured in billions USD.

18 Calculated by summing return of NVAX and JNJ. NVAX outweighed JNJ.

19 Calculated by summing capitalization changes of NVAX and JNJ. JNJ outweighed NVAX

event study: the influence of covid-19 research and ......this research uses the event study...

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