Index

Abstract

This paper investigates the influence of COVID-19 exposure and China exposure disclosed by companies in their annual reports on firm value. Research data were obtained from annual reports. The study used observations of 430 companies listed on the Indonesia Stock Exchange during 2019 and analyzed using ordinary least squares (OLS) with fixed industry effects and standard error estimation with Stata 17.0 software. The study also used the Heckman two-stage as a robustness test. The results show a positive and statistically significant relationship between COVID-19 and firm value. The same result is also shown in the relationship between China exposure and firm value. Our research provides evidence that exposure to the COVID-19 situation resulted in an increase in firm value during the period. Additionally, these results were found to be robust by the Heckman two-stage test. With these research findings, companies can identify factors that can influence an increase in firm value, including providing clear disclosure on company conditions such as COVID-19 and China exposure. Companies that comprehensively disclose their information during uncertain environmental situations will provide opportunities for them to generate a more positive performance, as stakeholders provided funding facilities for companies to pursue growth during the COVID-19 period. Therefore, the COVID-19 factor as a form of exposure for companies is expected to encourage companies to continue to adapt and innovate.

Keywords: China, COVID-19, Emergency case, Exposure, Firm value, Governance, Investor demand, Risk.

Received: 24 August 2022 / Revised: 15 March 2023/ Accepted: 8 May 2023/ Published: 18 May 2023

Contribution/ Originality

This research contributes to the literature on the effects of external factors on firm value, specifically in the context of the COVID-19 pandemic and China's economic influence. Additionally, the use of the Heckman two-stage as a robustness test provides a rigorous analysis of the results.

1. INTRODUCTION

Indonesia has been shocked by COVID-19, which is a type of infectious disease caused by a newly discovered type of coronavirus (Ramelli & Wagner, 2020). This disease first appeared in Wuhan, China, in December 2019 (Huo & Qiu, 2020; Xiong, Wu, Hou, & Zhang, 2020). But now, COVID-19 has spread to many countries, including Indonesia. Kompas (2020) reported that the Indonesian government announced the first two cases of COVID-19 on March 2, 2020. The number of patients who tested positive for COVID-19 continued to increase from March 2020 to end of December 2021. The spread of the COVID-19 outbreak in various countries and the continued increase in the number of patients who tested positive led to COVID-19 being designated as a global pandemic by the World Health Organization (WHO) on March 11, 2020 (Babuna et al., 2020). As a result, a number of countries implemented large-scale social restrictions and lockdowns, which caused changes in the entire order of life in terms of health, economy, and international relations.

The COVID-19 pandemic has become a systematic risk that is uncontrollable, unpredictable, and has an overall impact that is macro in nature (Rizwan, Ahmad, & Ashraf, 2020). This will also impact risk tolerance in decision making. Heo, Rabbani, and Grable (2021) pointed out that the risk tolerance of financial decision makers can be changed when there are shocks in economic, social, or environmental conditions. In this case, the shock in question is the COVID-19 pandemic. For firms, the COVID-19 pandemic caused changes in their business activities which affected firm income and increased company risk. A firm's ability to deal with systematic risk will greatly concern stakeholders and shareholders as they seek reassurance regarding the continuation of business operations in the midst of the COVID-19 pandemic and the various regulations that limit business activities, such as regulations for locking down in many countries and the enactment of the PSBB (social restrictions) in Indonesia.

The COVID-19 pandemic outbreak resulted in a drastic decline in firm income as they experienced problems related to cash flow, consumer demand, marketing, and supply chains, forcing many to go out of business (Donthu & Gustafsson, 2020). This led to unprecedented trade disruptions in most industrial sectors. A firm's financial condition also felt the impact of this pandemic. For example, the number of cash holdings and the amount of debt borne are essential concerns for firm survival. Joseph, Kneer, and Van Horen (2020) stated that a firm's cash holdings before the crisis had a positive impact on investment during the crisis and increased a firm's competitive advantage during the recovery phase. COVID-19 not only impacted firms' financial condition, it also had a significant impact on the stock market (Baker et al., 2020; Huo & Qiu, 2020). Investors are becoming more sensitive in capturing the signals given by firms and the issues that exist, and they increasingly consider every piece of information received before making investment decisions, e.g., on stocks. As a result, stock price volatility occurred during the COVID-19 pandemic. The emergence of COVID-19 has had a widespread impact on the economic sector and increased financial risk. The spread of the pandemic was a surprise for capital market participants (Ramelli & Wagner, 2020), especially for developing countries such as Indonesia. Various policies from companies and the government (such as the PSBB) triggered diverse investor reactions which were reflected in stock prices and firm value, so the factors that affect investor demand and firm value should be studied more deeply, especially during the COVID-19 pandemic. During the COVID-19 pandemic, the cooperative relationship between Indonesian and Chinese companies, both in export and import activities, were affected. Furthermore, other activities carried out by China also provided exposure to companies, such as the trade war between China and the United States in 2019, which caused the global economy to decline. Disclosures made by companies on perceived exposures due to activities involving China also affected investors' assessment of a firm.

This paper investigates the effect of COVID-19 and China exposure disclosed by firms in annual reports on firm value. Using 430 firm-year observations from firms listed on the Indonesia Stock Exchange in 2019, which were analyzed using OLS regression with Stata 17.0 software, the results showed a positive and statistically significant relationship between COVID-19 and firm value. The same results are also shown in the relationship between China's exposure and firm value. The Heckman two-stage test confirmed the robustness of these results. Thus, firms that disclose exposure to the COVID-19 pandemic and activities carried out by China can increase firm value. Furthermore, this study also shows that disclosure of COVID-19 and China's exposure to firms with high leverage levels can further increase firm value.An essential contribution is expected from the results of this study. First, this research makes a practical contribution to firms. With the results of this study, firms can determine what factors can increase firm value, including the provision of clear disclosures on firm conditions such as COVID-19 and China exposure. These factors can then be applied in policies to increase firm value. Second, the results of this study enrich the literature on related topics, especially firm performance during the pandemic. Third, the outcome can contribute to future studies to develop research on related topics and refine the limitations of this study. This paper consists of several sections. After the introduction, the literature review is presented, followed by the research method. The results and discussion of the tests that have been carried out are in the fourth section. The fifth section contains the limitations, and the final section presents the conclusions.

2. LITERATURE REVIEW

The COVID-19 pandemic has brought significant changes to various aspects of life in terms of health, economy, and international relations. The COVID-19 outbreak has resulted in a drastic decline in firm income and caused problems related to cash flow, consumer demand, marketing, supply chains, and has even forced many to go out of business (Donthu & Gustafsson, 2020). The impact of the pandemic has also had a significant impact on the stock market (Baker et al., 2020; Huo & Qiu, 2020). This is supported by signaling theory, which describes the signal given by a firm to users of financial statements (Spence, 1973). Signals arise when the sender of information tries to provide relevant information that can be used by the recipient of the information. The signal in the form of information is then received by the recipient and adjusted to the recipient's understanding. The signal given can be either good or bad. Disclosure of disease terms such as coronavirus, COVID-19, Sars-Cov, Mers-Cov, and other terms in firm reports is considered a negative tone of language (Spence, 1973). Not only the disclosure of terms related to disease but the mention of trade relations with China (the country where COVID-19 was first discovered) in the midst of the COVID-19 pandemic can also be a negative tone of language. This will allow investors to interpret information as a bad signal that will affect their investment decisions. In a health emergency, investors will increasingly consider all information received before making investment decisions. Baker et al. (2020); Bose, Shams, Ali, and Mihret (2022); Hassan (2020); Li, Li, Yang, and Zhao (2021); Ramelli and Wagner (2020) argue that the COVID-19 pandemic reduced firm value. Based on signaling theory, companies can provide good news or bad news to investors which will ultimately affect their investment decisions. Many aspects need to be considered by investors, one of which is the disclosure made by the company. Several studies (Baker et al., 2020; Bose et al., 2022; Hassan, 2020; Li et al., 2021; Ramelli & Wagner, 2020) have stated the COVID-19 pandemic decreased firm value. However, this study provides another perspective on the disclosure of COVID-19 exposure. In addition to having a negative effect, there is a possibility that this disclosure will have a positive impact on firm value. This is because the disclosures made regarding the actual conditions of the firm can be a plus for investors. Of course, damaging disclosures will generally be followed by disclosure of promising firm prospects. In addition, investors are not always able to see the disclosures made by a firm. Instead, they will see or consider the fundamental aspects of the firm and other firms’ activities. Not only disclosure of COVID-19 exposure, but disclosure of China's exposure can also increase firm value with the same perspective. The activities carried out by China, especially in 2019, are in the world's spotlight because this is where the coronavirus (COVID-19) began to spread. But, on the other hand, China also has power in world trade. Moreover, the conflict or cold war between the United States and China has profoundly impacted the global economy.

H1: There is a positive relationship between COVID-19 exposure and firm value.
H2: There is a positive relationship between China's exposure and firm value.

3. RESEARCH METHODS

3.1. Data and Sample Selection

The population of this study consists of all companies listed on the Indonesia Stock Exchange during the year 2019. The research period was chosen because that year marked the beginning of the COVID-19 pandemic outbreak that originated in China and impacted various countries, including Indonesia. The sample selection was conducted using purposive sampling technique by first establishing several criteria according to the analysis needs. We excluded companies in the financial industry sector (standard industrial classification (SIC) 6) and missing data to ensure the accuracy and reliability of the testing. The final sample consisted of 430 yearly observations of companies. The sample distribution by industry is described in Table 1. Meanwhile, Table 3 shows the summary statistics consisting of the mean, standard deviation, first quartile, median, third quartile, maximum, and minimum values of the data used as the research sample. The research data was obtained from the annual reports issued by the companies, OSIRIS database (a financial data center from around the world), and TMAILC (software for conducting data mining from company financial reports).

Table 1 contains sample distributions based on eight SICs in the 430 firm-year observations. Panel A shows that the sample of this study is dominated by manufacturing firms (SIC 2), with 109 firms or 26.67% of the entire sample. In this study, 80.70% (347 firms) disclosed their exposure to the COVID-19 pandemic in their annual reports. The sample distribution for China exposure is presented in Panel B of Table 1. The manufacturing industry comprises the most significant number in the research sample. As many as 84.88% or 365 firms disclosed exposure to the firm's activities and their relationship with China in their annual report.

Table 1. Sample distribution by industry.

Panel A: COVID-19 exposure

SIC

Industry

Disclosed exposure to COVID-19

Did not disclose exposure to COVID-19

Total

n

%

n

%

n

%

0

Agriculture, forestry and fishery

13

86.67

2

13.33

15

3.19%

1

Mining Industries

51

79.69

13

20.31

64

14.78%

2

Construction Industries

88

80.73

21

19.27

109

26.67%

3

Manufacturing

52

75.36

17

24.64

69

15.65%

4

Transportation, communications, electric, gas, & sanitary services

68

87.18

10

12.82

78

19.42%

5

Wholesale & retail

30

76.92

9

23.08

39

8.70%

7

Services Industries

38

84.44

7

15.56

45

9.28%

8

Health, Legal, and Educational Services and Consulting

7

63.63

4

36.37

11

2.32%

Total

 

347

80.70

83

19.30

430

100%

Panel B: China exposure

SIC

Industry

Disclosed exposure and relationship with China

Did not disclose exposure and relationship with China

Total

n

%

n

%

n

%

0

Agriculture, forestry and fishery

15

100

0

0%

15

3.19%

1

Mining Industries

57

89.06

7

10.94

64

14.78%

2

Construction Industries

95

87.16

14

12.84

109

26.67%

3

Manufacturing

57

82.61

12

17.39

69

15.65%

4

Transportation, communications, electric, gas, & sanitary services

67

85.90

11

14.10

78

19.42%

5

Wholesale & retail

32

82.05

7

17.95

39

8.70%

7

Services Industries

33

73.33

12

26.67

45

9.28%

8

Health, Legal, and Educational Services and Consulting

9

81.81

2

18.19

11

2.32%

Total

 

365

84.88

65

15.12

430

100%

Table 1 shows the sample distribution based on the eight standard industrial classification (SIC) codes for firms that disclosed and did not disclose exposure to COVID-19 in their annual report (Panel A). Meanwhile, Panel B shows the sample distribution of firms that disclosed and did not disclose their exposure and relationship with China.

3.2. Variables

Our research uses firm value as the dependent variable. Firm value in this study was measured using the market-to-book value (MTB) proxy (Ramelli & Wagner, 2020). There are two independent variables in this study. First, we analyze the impact of firm disclosure and the use of various COVID-19 terms at the beginning of the pandemic or in the 2019 report. The COVID-19 exposure variable (COVDUM) is measured using a dummy variable with a value of 1 if the firm discloses the term COVID-19 in its annual report (Hassan, 2020). In addition, based on research by Ramelli and Wagner (2020) with several adjustments, the China exposure variable is measured by dummy variable DUMCHINA, with a value of 1 if the firm has and discloses cooperation with China or is affected by activities and policies (such as import and export activities) made by China and 0 otherwise. China exposure is used to see whether the disclosures in the firm's annual report for cooperation and the impact felt by the firm due to Chinese activities have a relationship with the value of the firm during the COVID-19 pandemic. Specifically for the COVID-19 and China exposure variables, data were obtained by searching for terms for COVID-19 (such as COVID-19, coronavirus, sars-cov-2) and China (such as Tiongkok and China). These two variables were collected using the help of TMAILC software, which was developed by the Center for Political Economy and Business Research (CPEBR) team at Airlangga University.

To limit the influence of other variables not examined in this study, we also used several control variables to refer to previous research (Bose et al., 2022; Chen & Wang, 2011; Hassan, 2020; Ramelli & Wagner, 2020; Wang, Yu, & Chan, 2021). These control variables are number of directors (DIRSIZE); number of commissioners (COMSIZE); number of the audit committee (AUCOM); the existence of a risk management committee (RMC); firm age (FIRMAGE); firm size (FSIZE); firm leverage (LEV); the amount of cash owed by the firm (CASH); sales growth (GROWTH); plant property equipment ratio (PPE); net cash flow from operating activities (NCFO); and research and development value (RND). The variable definitions are summarized in Table 2.

Table 2. Variable definitions.

Variable

Explanation

Source

Dependent:

MTB

Book value of equity divided by market valuation

OSIRIS

Independent:

COVDUM

Dummy variable, 1 if the firm discloses the term COVID-19 in its annual report

Annual report and TMAILC

DUMCHINA

Dummy variable, 1 if the firm has and discloses cooperation with China or is affected by activities and policies (Such as import and export activities) made by China and 0 otherwise

Annual report and TMAILC

Control:

DIRSIZE

Total number of directors on the board

Annual report

COMSIZE

Total number of commissioners on the board

Annual report

AUCOM

Total number of members on the audit committee

Annual report

RMC

Total number of members on the risk management committee

Annual report

LNFIRMAGE

Natural logarithm of the number of years since the firm was founded

Annual report

FSIZE

Natural logarithm of total assets

OSIRIS

LEV

Total debt divided by total assets

OSIRIS

LNCASH

Natural logarithm of total cash and cash equivalent

OSIRIS

GROWTH

Proportional change of sales (the difference between total sales minus lag total sales scaled by lag total sales)

OSIRIS

PPE

Plant and property equipment divided by total assets

OSIRIS

NCFO

Net cash flow from operating activities

OSIRIS

RND

Total of research and development

OSIRIS

3.3. Research Design

This study uses an ordinary least squares (OLS) regression model with industry fixed effects and clustered standard errors to control for differences in industry characteristics (Petersen, 2008). To test the hypotheses, we used two research models. The first model is used to examine the relationship between COVID-19 exposure and firm value, and the second model is used to examine the relationship between China exposure and firm value. We also conducted a robustness test to overcome the problem of endogeneity.

Equation 1:

Equation 1 shows the dependent variable, namely firm value (MTB), and the independent variable, COVID-19 exposure (COVDUM). Additionally, several control variables are included in the model. We expect that COVDUM will have a positive effect on MTB.

Equation 2:

Equation 2 presents the dependent variable, which is firm value (MTB), and the independent variable, which is exposure to the COVID-19 situation in China on the global business environment (DUMCHINA). In addition, several control variables are also included in the model to enhance the testing and analysis outcomes. We expect that DUMCHINA will have a positive effect on MTB.

4. RESULTS AND DISCUSSION

4.1. Summary Statistic

Table 3 shows a statistical summary of the total sample in this study. The results show that the mean firm value of this research sample is 2.304, with a range between -6.186 and 35.693. Of the entire sample, 80.7% disclosed exposure to the COVID-19 pandemic in their annual reports. Meanwhile, 84.9% disclosed exposure to activities carried out by China in the form of impacts on imports and exports, and the trade war with the United States that occurred until 2019. The whole sample has an average of four members on the board of directors, ranging between 2–11 members, four members on the board of commissioners with a range of 2–10 members, and three members on the audit committee with a range between 1–4 members in its corporate governance system.

Table 3. Summary statistics.

 Variable

N

Mean

SD

p25

Median

p75

Min.

Max.

 MTB

430

2.304

4.771

0.606

1.12

2.223

-6.186

35.693

 COVDUM

430

0.807

0.395

1

1

1

0

1

 DUMCHINA

430

0.849

0.359

1

1

1

0

1

 DIRSIZE

430

4.437

1.806

3

4

5

2

11

 COMSIZE

430

3.923

1.696

3

3

5

2

10

 AUCOM

430

2.981

0.327

3

3

3

1

4

 RMC

430

0.198

0.399

0

0

0

0

1

 NFIRMAGE

430

3.273

0.640

2.89

3.401

3.738

1.099

4.771

 FSIZE

430

28.436

1.742

27.298

28.376

29.58

23.723

32.261

 LEV

430

0.51

0.369

0.296

0.47

0.635

0.02

2.629

 LNCASH

430

25.469

2.285

23.888

25.596

26.953

19.932

30.482

 GROWTH

430

320.958

2462.505

-143.683

154.814

1254.666

-9602.08

9310.479

 PPE

430

0.393

0.254

0.196

0.378

0.604

0.001

.918

 NCFO

430

7.310e+11

2.085e+12

1.110e+09

7.683e+10

4.502e+11

-9.725e+11

1.236e+13

 RND

430

1.447e+09

7050629515.198

0

0

0

0

4.751e+10

Only 19.8% of the sampled firms also have a risk management committee. All firms in the sample, on average, have been established for more than three years, the average firm size is between 1.099 and 4.771, and the firms have an average leverage of 0.51, and cash of 25.469. The sample includes 430 firm-year observations from firms listed on the Indonesia Stock Exchange in 2019. This analysis uses data after winsorizing at 1% and 99%.

4.2. Univariate Analysis

We have performed two univariate analyses, namely the two-sample independent t-test presented in Table 4, and the pearson correlation test in Table 5. The two-sample independent t-test test showed differences in the characteristics of the mean of MTB, DIRSIZE, COMSIZE, AUCOM, RMC, LNFIRMAGE, FSIZE, LNCASH, GROWTH, PPE, NCFO, and RND between firms that disclose and not disclose COVID-19 exposure (Panel A) and firms that disclose and not disclose exposures or relationships with China (Panel B). Panel A shows that firms that disclose and do not disclose COVID-19 exposures have differences even though they are not statistically significant. Panel B documents that firms that disclose China's exposure statistically significant have a higher number of members of the board of directors and commissioners, have been established longer, have larger firm size, have more cash and cash equivalents, and net cash flow from operating activities.

Table 4. Two samples independent t-test.

Panel A: For COVDUM

Variable

COVDUM

 

 

Mean = 1

Mean = 0

Coeff.

T-value

MTB

2.473

1.597

0.876

1.504

DIRSIZE

4.421

4.506

-0.085

-0.386

COMSIZE

3.911

3.976

-0.065

-0.314

AUCOM

2.983

2.976

0.007

0.170

RMC

0.205

0.169

0.036

0.737

LNFIRMAGE

3.279

3.244

0.035

0.446

FSIZE

28.500

28.167

0.333

1.569

LEV

0.511

0.509

0.001

0.026

LNCASH

25.538

25.179

0.358

1.285

GROWTH

355.016

178.571

176.444

0.586

PPE

0.390

0.405

-0.015

-0.482

NCFO

6.945e+11

8.837e+11

-1.892e+11

-0.742

RND

1.326e+09

1.951e+09

-6.244e+08

-0.724

Panel B: For DUMCHINA

 Variables

DUMCHINA

Coeff.

T-value

Mean = 1

Mean = 0

MTB

2.330

2.153

0.177

0.275

DIRSIZE

4.556

3.769

0.787***

3.272

COMSIZE

3.989

3.554

0.435*

1.912

AUCOM

2.981

2.985

-0.004

-0.086

RMC

0.200

0.185

0.015

0.286

LNFIRMAGE

3.300

3.118

0.182**

2.120

FSIZE

28.628

27.357

1.271***

5.612

LEV

0.516

0.479

0.037

0.742

LNCASH

25.701

24.166

1.535***

5.135

GROWTH

307.928

394.125

-86.197

-0.260

PPE

0.394

0.388

0.006

0.182

NCFO

8.095e+11

2.903e+11

5.192e+11*

1.855

RND

1.414e+09

1.628e+09

-2.137e+08

-0.225

Note:

Table 4 reports two independent sample t-tests on the characteristics of firms that disclosed and did not disclose COVID-19 exposure (Panel A). In addition, this table also reports the characteristics of firms that disclosed and did not disclose China exposure in their annual reports (Panel B). COVDUM and DUMCHINA are used as treatment variables to divide the sample into two sub-samples. This analysis used winsorized data at 1% and 99% levels. Tests show: * p < 0.1 (or * t > 1.645), ** p > 0.05 (or ** t > 1.960), *** p > 0.01 (or *** t > 2.326), significance at 10%, 5% and 1%, respectively.

The results of the Pearson correlation test in Table 5 show the relationship between variables. The results report that the COVDUM and DUMCHINA variables are positively related to firm value but are not statistically significant, while the GROWTH, PPE, and RND variables proved to be significantly positively related to firm value with respective coefficient values of 0.112 (t = 0.020), 0.091 (t = 0.060), and 0.112 (t = 0.021). It can be concluded that when a company has high sales growth, a high plant property and equipment ratio, and a high research and development value, its firm value will increase. On the other hand, increasing the RMC and LNFIRMAGE variables significantly reduced firm value with coefficient values of -0.085 (t = 0.077) and -0.097 (t = 0.044). When a company has a risk management committee and has been established for a long time, there is a possibility of value.

Table 5. Pearson correlation.

Panel A: From MTB to LNFIRMAGE

Variable

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[1] MTB

1.000

 

 

 

 

 

 

 

[2] COVDUM

0.073
(0.133)

1.000

 

 

 

 

 

 

[3] DUMCHINA

0.013
(0.783)

0.287***
(0.000)

1.000

 

 

 

 

 

[4] DIRSIZE

-0.002
(0.963)

-0.019
(0.700)

0.156***
(0.001)

1.000

 

 

 

 

[5] COMSIZE

-0.015
(0.755)

-0.015
(0.753)

0.092*
(0.057)

0.529***
(0.000)

1.000

 

 

 

[6] AUCOM

0.060
(0.213)

0.008
(0.865)

-0.004
(0.931)

0.144***
(0.003)

0.187***
(0.000)

1.000

 

 

[7] RMC

-0.085*
(0.077)

0.036
(0.461)

0.014
(0.775)

0.135***
(0.005)

0.071
(0.143)

-0.007
(0.877)

1.000

 

[8] LNFIRMAGE

-0.097**
(0.044)

0.022
(0.656)

0.102**
(0.035)

0.209***
(0.000)

0.266***
(0.000)

0.015
(0.760)

0.013
(0.783)

1.000

[9] FSIZE

-0.044
(0.360)

0.076
(0.117)

0.262***
(0.000)

0.531***
(0.000)

0.544***
(0.000)

0.155***
(0.001)

0.113**
(0.019)

0.239***
(0.000)

[10] LEV

-0.058
(0.230)

0.001
(0.980)

0.036
(0.458)

-0.063
(0.190)

-0.006
(0.902)

-0.129***
(0.007)

0.074
(0.123)

0.126***
(0.009)

[11] LNCASH

-0.032
(0.506)

0.062
(0.199)

0.241***
(0.000)

0.470***
(0.000)

0.475***
(0.000)

0.157***
(0.001)

0.085*
(0.078)

0.185***
(0.000)

[12] GROWTH

0.112**
(0.020)

0.028
(0.558)

-0.013
(0.795)

0.062
(0.202)

0.011
(0.818)

0.085*
(0.078)

-0.048
(0.323)

-0.195***
(0.000)

[13] PPE

0.091*
(0.060)

-0.023
(0.630)

0.009
(0.856)

0.018
(0.709)

0.020
(0.678)

0.017
(0.730)

-0.013
(0.783)

0.024
(0.622)

[14] NCFO

0.071
(0.143)

-0.036
(0.458)

0.089*
(0.064)

0.379***
(0.000)

0.404***
(0.000)

0.109**
(0.023)

0.018
(0.703)

0.143***
(0.003)

[15] RND

0.112**
(0.021)

-0.035
(0.469)

-0.011
(0.822)

0.255***
(0.000)

0.183***
(0.000)

0.120**
(0.013)

0.043
(0.377)

0.169***
(0.000)

Panel B: From FSIZE to RND

Variable

[9]

[10]

[11]

[12]

[13]

[14]

[15]

[9] FSIZE

1.000

 

 

 

 

 

 

[10] LEV

-0.027
(0.579)

1.000

 

 

 

 

 

[11] LNCASH

0.809***
(0.000)

-0.159***
(0.001)

1.000

 

 

 

 

[12] GROWTH

0.094*
(0.052)

-0.314***
(0.000)

0.125***
(0.009)

1.000

 

 

 

[13] PPE

0.064
(0.183)

0.104**
(0.031)

-0.135***
(0.005)

0.026
(0.585)

1.000

 

 

[14] NCFO

0.551***
(0.000)

0.005
(0.917)

0.512***
(0.000)

0.031
(0.517)

0.047
(0.333)

1.000

 

[15] RND

0.233***
(0.000)

0.001
(0.985)

0.209***
(0.000)

0.098**
(0.043)

0.000
(0.993)

0.236***
(0.000)

1.000

Note:

The Pearson correlation analysis results comprise the 430 firm-year observations that were used in this study. This analysis used winsorized data at 1% and 99% levels. Tests show: * p < 0.1 (or * t > 1.645), ** p > 0.05 (or ** t > 1.960), *** p > 0.01 (or *** t > 2.326), significance at 10%, 5% and 1%.

4.3. Multivariate Analysis

The results of the OLS regression testing on the primary regression with 430 firm-year observations from firms listed on the Indonesia Stock Exchange during 2019 are documented in Table 6, column 1 (for model 1) and column 2 (for model 2). The test results are in line with hypotheses 1 and 2, which were built in this study. Table 6 column 1 shows that COVID-19 exposure statistically has a positive relationship with firm value. This opinion is supported by the coefficient value of 1.213 (t = 2.76) and is significant at the 1% level in column 1. Therefore, disclosure in a firm's annual report regarding exposure to the COVID-19 pandemic that impacted the firm can actually increase its value.

This disclosure is likely to be seen as a good signal. Investors will view it as a form of disclosing information presented by the firm to users of firm reports, especially annual reports. This provides a clear explanation of a firm's performance or the reasons behind poor performance.

This positive effect is also because investors not only look at whether a firm discloses COVID-19 exposure but also how the firm performs in other areas, such as its corporate social responsibility activities (CSR) (Qiu, Jiang, Liu, Chen, & Yuan, 2021) and how it disclosed environmental, social, and governance (ESG) information in the previous year (Feng & Wu, 2021). Therefore, firms that still had good performance during the COVID-19 pandemic will also have been seen as being good value by investors. However, this result is different from Baker et al. (2020); Bose et al. (2022); Hassan (2020); Li et al. (2021); Ramelli and Wagner (2020), who argue that the COVID-19 pandemic reduced firm value.

The relationship between China exposure and firm value in column 2 of Table 6 also shows a positive and statistically significant relationship, which is in line with hypothesis 2. This statement is supported by the DUMCHINA coefficient value of 0.869 (t = 1.65) and is significant at the 10% level. Thus, disclosures made by firms in their annual reports on firm activities in the export–import sector and disclosures due to the impact felt by China's activities are statistically proven to increase firm value. This is in line with the results for when firms disclosed their COVID-19 exposure.

Investors can consider this as a positive signal because firms disclosed all conditions that impacted their performance, one of which is the impact of China's activities. In 2019, aside from China being the origin of the pandemic, there was a trade war between the US and China. However, several firms in this research sample expressed their optimism over their prospects that the trade war will end soon to recover the global economy. This, of course, is also a positive signal for investors.

Table 6 shows significant results according to the analysis test. This is different from Ramelli and Wagner (2020), who stated that China's exposure and the international orientation of companies are negatively related to stock returns during the incubation period and the COVID-19 outbreak.

Other correlations between the independent variables are generally low and do not cause multicollinearity issues for further analysis. The variance inflation factor (VIF) produced a value of 2.64 for the first model and 2.63 for the second model.

4.4. Robustness Test

We used a Heckman two-stage as a robustness test to address the possibility of endogeneity problems, specifically for the omitted variables in this research model. In the Heckman two-stage regression, we use the instrument variable that has a relationship with the dependent variable only through an interested variable (Harymawan, Putra, Rizki, & Nasih, 2022; Nasih, Harymawan, Putra, & Qotrunnada, 2019). We use one instrumental variable in each model: the average value of COVID-19 exposure (AVECOV) and the average value of China exposure (AVECHIN).

The selection of this instrument was based on the idea that, on average, firms do the same thing when exposed to both COVID-19 and China.

The empirical model for the Heckman two-stage regression in the relationship between COVID-19 exposure and firm value is set out in Equation 3 below.

Equation 3:


Table 6. Main regression.

Variable

(1)
MTB

(2)
MTB

COVDUM

1.213***
(2.76)

 

DUMCHINA

 

0.869*
(1.65)

DIRSIZE

0.033
(0.20)

0.005
(0.03)

COMSIZE

-0.002
(-0.01)

-0.000
(-0.00)

AUCOM

0.710
(1.49)

0.762
(1.54)

RMC

-0.852**
(-2.20)

-0.797**
(-2.15)

LNFIRMAGE

-0.652
(-1.61)

-0.626
(-1.54)

FSIZE

-0.347
(-1.17)

-0.329
(-1.11)

LEV

-0.212
(-0.27)

-0.239
(-0.31)

LNCASH

-0.044
(-0.20)

-0.064
(-0.28)

GROWTH

0.000
(0.65)

0.000
(0.71)

PPE

2.055*
(1.71)

1.940
(1.63)

NCFO

0.000**
(2.11)

0.000**
(2.03)

RND

0.000
(1.39)

0.000
(1.38)

Intercept

10.950**
(2.01)

11.063**
(2.02)

Industry fixed effect

Included

Included

R2

0.087

0.081

Adjusted R2

0.043

0.037

N

430

430

Note:

Column 1 in this table shows the OLS test results between COVID-19 exposure and firm value. Column 2 shows the OLS test results between China exposure and firm value. The sample includes 430 firm-year observations from firms listed on the Indonesia Stock Exchange in 2019. This analysis uses winsorized data at 1% and 99% levels. These tests include industry fixed effects. Tests show: * p < 0.1 (or * t > 1.645), ** p > 0.05 (or ** t > 1.960), *** p > 0.01 (or *** t > 2.326), significance at 10%, 5% and 1%.

Equation 4:

Equations 3 and 5 are used as first stage probit regression, while Equations 4 and 6 are the second stage regressions. The MILLS variable is the inverse mills ratio, resulting from the probit regression between interested and instrumental variables in Equations 3 or 5. To fulfill the ideal Heckman two-stage test, the MILLS value of each model must be insignificant, and our results show that the MILLS variable is not significant. The results of the Heckman two-stage test are reported in Table 7.

Table 7.  Heckman two-stage test results.

Panel A: First stage

Variable

-1
COVDUM

-2
DUMCHINA

COVDUM 

AVECOV

3.073
(1.31)

DUMCHINA

AVECHIN

1.600
(0.10)

DIRSIZE

-0.058
(-1.24)

0.083
(1.51)

COMSIZE

-0.037
(-0.73)

-0.063
(-0.95)

AUCOM

0.128
(0.53)

-0.237
(-0.98)

RMC

0.110
(0.59)

-0.124
(-0.63)

LNFIRMAGE

0.139
(1.10)

0.068
(0.51)

FSIZE

0.164**
(2.01)

0.165*
(1.87)

LEV

0.029
(0.14)

0.252
(1.22)

LNCASH

0.013
(0.22)

0.117*
(1.96)

GROWTH

0.000
(0.82)

-0.000
(-0.21)

PPE

-0.402
(-1.26)

-0.073
(-0.21)

NCFO

-0.000*
(-1.84)

-0.000
(-0.75)

RND

-0.000
(-1.00)

-0.000**
(-2.01)

Intercept

-6.964***
(-2.87)

-7.724
(-0.60)

Industry fixed effect

Included

Included

r2_p

0.049

0.123

N

431

416

Panel B. Second stage

Variable

-1
MTB

-2
MTB

COVDUM

1.182***
(2.78)

 

DUMCHINA

0.910*
(1.72)

MILLS

12.307
(0.71)

4.547
(1.03)

DIRSIZE

-0.257
(-0.82)

0.121
(0.71)

COMSIZE

-0.196
(-0.51)

-0.092
(-0.44)

AUCOM

1.397
(1.50)

0.472
(0.98)

RMC

-0.309
(-0.41)

-1.035***
(-2.71)

LNFIRMAGE

0.061
(0.06)

-0.567
(-1.29)

FSIZE

0.421
(0.42)

-0.069
(-0.15)

LEV

-0.079
(-0.10)

0.138
(0.18)

LNCASH

0.038
(0.15)

0.147
(0.60)

GROWTH

0.000
(0.98)

0.000
(0.50)

PPE

0.262
(0.10)

1.870
(1.53)

NCFO

-0.000
(-0.13)

0.000
(0.97)

RND

0.000
(0.55)

0.000
(0.70)

Intercept

-18.076
(-0.47)

-2.136
(-0.14)

Industry fixed effect

Included

Included

Adjusted R2

0.043

0.035

N

430

415

Note:

Table 7 reports the results of the two-stage Heckman test on 430 firm-year observations for COVID-19 exposure and 415 firm-year observations for China exposure. This test uses the instrument variables AVECOV and AVECHIN. This analysis uses winsorized data at 1% and 99%. Panel A shows the results of the first stage test and Panel B shows the second stage test. Tests show: * p < 0.1 (or * t > 1.645), ** p > 0.05 (or ** t > 1.960), *** p > 0.01 (or *** t > 2.326), significance at 10%, 5% and 1%.

The results of the Heckman two-stage test report that the results from the main regression are robust. COVID-19 and China exposure have a positive and statistically significant relationship with firm value. This is indicated by the coefficient value of COVDUM of 1.182 (t = 2.78), which is significant at the 1% level, and the coefficient of DUMCHINA of 0.910 (t = 1.72), which is significant at the 10% level. It can be concluded that there is a high probability that firm value will increase when a company discloses its exposure to the COVID-19 pandemic as well as the perceived exposure to China's activities regarding exports, imports, and its activities with other countries.

4.5. Additional Analysis

This study uses the moderating role of high leverage in the sample firms for the relationship between COVID-19 and China's exposure to firm value. The HIGHLEV variable is a dummy variable, with a value of 1 if the firm has a leverage value above the median and 0 if it has a leverage below the median. This model is carried out using moderate regression analysis testing using Stata 17.0 software. This additional analysis uses the following empirical model:

The results of the moderate regression analysis using Stata 17.0 show that a high level of leverage can strengthen the relationship between COVID-19 and firm value. In Table 8, the coefficient value of COVDUM*HIGHLEV is 1.784 (t = 2.99) and is significant at the 1% level, and the DUMCHINA*HIGHLEV variable shows a value of 1.920 (t = 3.17) and is significant at the 10% level. The same results are also shown in the relationship between China exposure and firm value. Disclosure of exposure to COVID-19 as well as activities and collaborations carried out with China in firms with a high level of leverage are statistically proven to increase firm value. A high level of leverage is one of the advantages that investors can get when trading forex. High leverage allows traders to buy investments with small capital and earn significant profits. However, leverage can backfire for traders because the higher the leverage, the higher the risk for the firm.

Table 8. The moderating role of high leverage.

Variable

(1)
MTB

(2)
MTB

COVDUM*HIGHLEV

1.784***
(2.99)

 

DUMCHINA*HIGHLEV

 

1.920***
(3.17)

COVDUM

0.354
(0.93)

 

DUMCHINA

 

0.043
(0.09)

LEV

-1.318*
(-1.82)

-1.537**
(-2.08)

DIRSIZE

0.086
(0.51)

0.089
(0.52)

COMSIZE

0.001
(0.00)

-0.018
(-0.10)

AUCOM

0.746
(1.52)

0.825*
(1.66)

RMC

-0.854**
(-2.25)

-0.776**
(-2.13)

LNFIRMAGE

-0.647
(-1.61)

-0.552
(-1.37)

FSIZE

-0.555*
(-1.81)

-0.572*
(-1.87)

LNCASH

0.021
(0.09)

-0.004
(-0.02)

GROWTH

0.000
(0.61)

0.000
(0.64)

PPE

1.901
(1.62)

1.793
(1.54)

NCFO

0.000**
(2.21)

0.000**
(2.23)

RND

0.000
(1.42)

0.000
(1.41)

Intercept

15.297***
(2.64)

16.133***
(2.71)

Industry fixed effect

Included

Included

R2

0.105

0.102

Adjusted R2

0.059

0.056

N

430

430

Note:

Table 8 reports the moderating role of high leverage on the relationship between COVID-19 exposure and firm value and the relationship between China exposure and firm value. HIGHLEV is a dummy variable with a value of 1 if the firm has a leverage value above the median and 0 if the leverage value is below the median. The sample includes 430 firm-year observations from firms listed on the Indonesia Stock Exchange during 2019. This analysis uses winsorized data at 1% and 99% levels. These tests include industry fixed effects. Tests show: * p < 0.1 (or * t > 1.645), ** p > 0.05 (or ** t > 1.960), *** p > 0.01 (or *** t > 2.326), significance at 10%, 5% and 1%.

5. LIMITATIONS

First, this research is limited by the year of the study, which is 2019, so the sample is small. The year 2019 was chosen because the COVID-19 pandemic began to enter Indonesian territory and resulted in a global economic downturn. Future research can update the research year. Future research can also make comparisons before and during the COVID-19 pandemic. Second, this research is also limited to discussions related to the relationship between COVID-19 and China exposure, so there is an opportunity for future research to examine firms' disclosures and other performance against firm value in greater depth. Third, this research is limited to the proxies used to measure COVID-19 and China exposure, which only uses dummy variables. Future research may be able to use other proxies, such as analyzing the content of annual reports or using the number of mentions of COVID-19 and China in the annual reports.

6. CONCLUSIONS

This paper investigates the effect of COVID-19 and China exposure disclosed by firms in annual reports on firm value using 430 firm-year observations from firms listed on the Indonesia Stock Exchange in 2019, which were analyzed using OLS regression with Stata 17.0 software. The main regression results showed a positive and statistically significant relationship between COVID-19 and firm value. The same results are also shown in the relationship between China's exposure and firm value. Furthermore, these results are robust in the Heckman two-stage test. Thus, firms that disclose exposure to the COVID-19 pandemic and activities carried out by China can increase firm value. Furthermore, this study also shows that disclosure of COVID-19 and China's exposure to firms with high leverage levels can further increase firm value. The results of this study provide important implications for firms, investors, and future research. An essential contribution is undoubtedly expected from the results of this study. First, this research makes a practical contribution to firms. Based on the results of this study, firms can determine what factors will affect an increase in firm value, including providing clear disclosures on firm conditions such as COVID-19 and China exposure. These factors can be applied to firm policies to increase firm value. Second, the results of this study contribute to enriching the literature on related topics, especially those related to firm performance during the pandemic. Third, the results of this study contribute to the development of future research on related topics and overcome the limitations found in this study.

Funding: This study received no specific financial support.  

Competing Interests: The authors declare that they have no competing interests.

Authors’ Contributions: All authors contributed equally to the conception and design of the study.

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