Introduction

AJA

Arab J Adm

The Arab Journal of Administration

Arab J Admin

1110-5453 2663-4473

League of Arab States, Arab Organization for Administrative Development

2 El Hegaz Street, Roxy, Heliopolis, Cairo, Arab Republic of Egypt; P.O. 2692, El Horreya Post Office

10.21608/aja.2023.166514.1330

862

Accounting Disclosure Capital markets COVID-19

Studying the Impact of Voluntary Disclosure in Light of the COVID-19 Pandemic on the Risk of Future Stock Price Collapse in the Egyptian Market: Evidence from Companies Listed in the EGX30 Index

Abdel-Maqsoud

Osama El-Sayed

1 Beni Suef University Faculty of Commerce Department of Accounting Egypt 2 King Faisal University College of Business Administration Department of Accounting Saudi Arabia sa.edu.kfu@ofathebab

01 10 2022

01 01 2023

26 01 2026

46 1 107 122 01102022 01012023 26012026

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).

The study aimed to examine the impact of voluntary disclosure during the COVID-19 pandemic on the risk of future stock price collapse in the Egyptian market, applying to companies listed in the EGX30 index on the Egyptian Stock Exchange. The researcher used the deductive method to derive hypotheses from prior literature and the inductive method by collecting and analyzing data. The study relied on EGX30 constituent companies with ordinary shares traded on 31 December 2021, over the period 2019–2021. Results indicate a statistically significant relationship between voluntary disclosure during the pandemic and the risks of future stock price collapse in the Egyptian market. Accordingly, the study recommends emphasizing voluntary disclosure of non-mandatory information by companies listed on the Egyptian Exchange because of its substantial importance in reducing the future crash risk of stock prices in the Egyptian market, especially under COVID-19 conditions.

Voluntary disclosure Future stock price crash risk EGX30 COVID-19 Egyptian Exchange

Introduction

The COVID-19 pandemic became an unprecedented event in modern history, with still-unknown ramifications. The pandemic caused massive job losses, threatening widespread loan defaults and bankruptcies among firms and individuals, which in turn jeopardize financial systems (Zamil & Ehrentraud, 2020). Companies are at the epicenter of this unfolding economic storm, needing sufficient capital to absorb credit losses.

The importance of voluntary disclosure lies in its contribution to report quality, which positively reflects on lowering the cost of capital by attracting more investment. Voluntary disclosure yields numerous benefits, chiefly enhancing the company’s image and reputation among shareholders and the broader community. Hence, assessing the level of voluntary disclosure and its reflection on financial statement disclosure quality became necessary (Kabra & Saha, 2020).

Investors in capital markets face stock price crash risk. Jeon (2019) defines stock price crash risk as the risk arising from sharp, sudden declines in stock prices that affect investor welfare. Crash risk is a key attribute influencing stock returns and has important implications for portfolio theory and asset pricing models (Zhang & Kim, 2014). Information asymmetry between insiders and outsiders increases crash risk because it allows managers to withhold bad news for extended periods to protect their positions and reduce litigation risk; when accumulated bad news is finally released, prices fall persistently, leading to crashes (Tasnia et al., 2021).

Some prior studies (Tian & Yin, 2017; Fang et al., 2020; Chae et al., 2020) agree that stock price crash risk is the probability of a sharp drop in a firm’s market value. Crash risk is among the most important risks for investors and firms because it affects decision-making. At the market level, it explains part of the variation in equity; at the firm level, it is a key determinant of expected returns in the firm’s sector.

Theoretical framework and prior studies Voluntary disclosure Definition

Vurro & Romito (2021) define voluntary disclosure as providing additional information that meets users’ needs and aids decision-making without a legal requirement.

Determinants

Studies (Tran et al., 2021; Putri, 2020; Soliman & Khlif, 2019) show voluntary disclosure level is affected by firm size and age, leverage, board functions and independence, growth, overall profitability, governance mechanisms, and auditor size.

Benefits

From the firm’s perspective: Improving corporate image via modern disclosure channels; attracting investors; lowering financing costs; improving accounting disclosure quality (Kumar & Chauhan, 2019).

From investors’ perspective: Improving shareholders’ ability to interpret and process data; helping investors assess future risks and opportunities; improving stock liquidity (Kumar & Chauhan, 2018).

Motives for voluntary disclosure

Firms disclose voluntarily to inform users, shape perceptions, pre-empt potential regulation, lower cost of capital (Hoelscher, 2019), enhance price efficiency, reduce information asymmetry, provide accurate information for valuation, and increase market activity and liquidity (Akhtaruddin & Rouf, 2018).

Future stock price crash risk Concept

Zhu (2016) frames crash risk as the probability of a large, abrupt, non-recurring drop in a firm’s stock price. Dang et al. (2018) see it as severe market value deterioration leading to a sharp fall in firm value and owners’ wealth. Cheng (2019) describes it as a phenomenon where a firm’s share price plunges sharply within a short period. Kim et al. (2019) view it as the probability of a large stock price drop observed through negative skewness in return distributions.

Determinants

Determinants include accounting conservatism, earnings management, corporate social responsibility, financial reporting quality and comparability, tax avoidance (Andreou et al., 2021), accrual quality, short-term solvency, audit quality, meeting analysts’ forecasts (Chae et al., 2020; Habib et al., 2018).

Explanations

Explanations include managerial withholding of bad news, opportunistic behavior, blocking information flows, default risk, firm size and activity (Hunjra et al., 2020; Jeon, 2019).

Measurement

Common measures: (1) Likelihood of a substantial drop in weekly returns in the following year (CRASHdummy). (2) Negative skewness of weekly returns (NCSKEW). (3) Down-to-up volatility of weekly returns (DUVOL) (Hunjra, 2020).

Prior studies on voluntary disclosure

Mahmoud et al. (2022) found a significant inverse effect of regulating non-financial voluntary disclosures on information asymmetry. Mohamed (2021) reported EGX-listed firms, especially EGX30, pay attention to voluntary disclosure and that levels increased after IFRS adoption. Gerged et al. (2021) found a positive relationship between voluntary environmental disclosure and return on assets. Riyad (2020) noted weak voluntary disclosure. Ali (2020) showed voluntary disclosure provides valuable information on R&D activities aiding investor evaluation. Ramesh & Charumathi (2020) found a positive link between voluntary disclosure and firm value. Hussainey & Enache (2020) observed product and governance voluntary disclosure in early product life has no performance effect. Ana & Kamran (2020) found voluntary disclosure improves performance. Harjoto & Rossi (2020) showed non-financial voluntary disclosure positively affects performance. Wibowo & Krisdayanti (2019) found no significant impact of voluntary disclosure on ROA or ROE. Siueia et al. (2019) found positive voluntary disclosures improve bank financial performance more than negative ones.

Prior studies on crash risk

Al-Sawy (2022) found financial report ambiguity is positively related to future crash risk for Egyptian listed firms. Abdel Rahim (2022) highlighted earnings management as a mediator between CEO power and crash risk. Al-Wakeel (2021) showed sustainability performance disclosure increases information asymmetry and crash risk. Metwally (2021) found audit committee effectiveness and financial reporting quality reduce crash risk. Murniati & Butar (2021) linked accounting conservatism to lower crash risk. Kong et al. (2021) found complex, unclear language in annual reports increases crash risk by reducing readability. Harmadji et al. (2020) showed sustainability reporting disclosure raises stock prices. Hunjra et al. (2020) found active CSR reduces crash risk. Li et al. (2020) found shifting annual report timing to low-attention periods raises crash risk. Karami & Moradi (2020) found information asymmetry does not affect the relation between trade credit financing and crash risk. Diao & Qi (2020) found crash risk rises when firms shift from specialized to diversified operations.

Research gap

Prior studies addressed components of this study but did not examine the impact of voluntary disclosure during COVID-19 on future stock price crash risk in the Egyptian market for EGX30 companies. Most studies were conducted in contexts different from Egypt, limiting their applicability. This motivates the present study to fill the gap and tailor the analysis to the Egyptian environment.

Research problem

Voluntary disclosure involves providing information beyond market requirements (Fadhilla & Hapsoro, 2017). Despite many countries adopting voluntary disclosure, there is a pressing need for standards on form and content aligned with market needs for timeliness, relevance, and reliability. Such standards would enhance understanding and comparability across firms (Nicolo et al., 2020).

Several studies (Harper et al., 2020; Mamun Al et al., 2020; Shahab et al., 2020; Karan & Yildiz, 2020) attribute crash risk to withholding bad news. This study assesses the impact of voluntary disclosure level on future stock price crash risk in the Egyptian market for EGX30 firms. Adequate disclosure reduces uncertainty and narrows information asymmetry between management and users, enabling clearer assessment of firms and influencing investment decisions. Therefore, the study asks: What is the effect of voluntary disclosure during COVID-19 on the future stock price crash risk in the Egyptian market for EGX30-listed companies?

Objectives

The main objective is to determine the effect of voluntary disclosure during COVID-19 on future stock price crash risk in the Egyptian market for EGX30-listed companies.

Hypothesis

Based on prior literature, the alternative hypothesis is: Voluntary disclosure during COVID-19 affects the future stock price crash risk in the Egyptian market.

Methodology

The study adopts deductive and inductive approaches. Deductively, it reviews literature on voluntary disclosure during COVID-19 and crash risk to build a model linking voluntary disclosure to crash risk. Inductively, it applies the model using data from annual reports of EGX30 companies for 2019–2021.

Population and sample

The sample comprises EGX30 firms with ordinary shares traded, with fiscal year ending 31 December, over 2019–2021. EGX30 represents the 30 most liquid and active firms; free float must be at least 15%. The final sample includes 79 firm-year observations, excluding banks, firms with non-December year-ends, and firms lacking required data.

Table 1. Sample distribution by sector

Sector	2019	2020	2021	Total observations
Basic resources	2	2	2	4
Healthcare and pharmaceuticals	2	2	3	5
Industrial products, services, and automobiles	6	3	3	3
Real estate	7	7	7	14
Tourism and entertainment	1	1	1	2
Telecommunications, media, and IT	6	3	3	3
Food, beverages, and tobacco	6	7	7	14
Building materials	2	4	4	8
Total	24	27	28	79

Data sources

Data were collected from quarterly and annual reports of EGX30 firms and daily stock prices from company websites, Mubasher (information.mubasher), Misr for Information Dissemination (www.egypt.com.eg), and the Egyptian Exchange website.

Variables: description and measurement

Independent variable: Voluntary disclosure (VD) during COVID-19. A 36-item index across five groups: (1) General information (GIVD): firm age, organizational structure, business description, incorporation date, activity type. (2) Strategic information (SIVD): management goals, vision, objectives, strategy, future plans, expansion, capital expenditures. (3) Human resources (HRIVD): headcount, training, incentives, other employee services, human rights, internal training programs, scholarships, talent development. (4) Social responsibility (SRIVD): public health, sports support, recreational projects, donations, community development programs. (5) Financial performance (FPIVD): financial position analysis, competitor analysis, earnings forecasts, cash flow rates, return on equity, cost-to-net-income ratio, earnings per share, liquidity ratios, debt-to-equity, dividends per share, sales forecasts. Each item scored 1 if disclosed, 0 otherwise; VD = disclosed items / 36.

Dependent variable: Stock price crash risk (CRASH). Measured by: (1) CRASHdummy: indicator of at least one extreme weekly return drop next year. (2) NCSKEW: negative coefficient of weekly return skewness. (3) DUVOL: down-to-up volatility of weekly returns.

Control variables: Seven controls: return standard deviation (SIGMA), average returns (RET), return kurtosis (KURT), firm size (SIZE = ln total assets), market-to-book equity (MTB), leverage (LEV = total debt / total assets), return on assets (ROA = net income / average total assets).

Table 2. Study variables: description, measurement, and sources

Type	Variable	Measurement	Sources
Independent	Voluntary disclosure (VD)	36-item index; VD = disclosed items / 36	Elfeky (2017); Harjoto & Rossi (2020); Charumathi (2020)
Dependent (crash risk)	CRASHdummy	Indicator for substantial weekly return drop next year	Habib et al. (2018); Chang et al. (2017); Li et al. (2020); Jung & Park (2017)
	NCSKEW	Negative skewness of weekly returns	Arianwuri et al. (2017); Zhang et al. (2017); Kim et al. (2016)
	DUVOL	Down-to-up volatility of weekly returns	Hunjra et al. (2020); Zhang & Fu (2019); Kim et al. (2019); Wang & Hu (2018)
Controls	SIGMA	Std. dev. of daily returns	Dang et al. (2018); Lim et al. (2016); Zhu (2016)
	RET	Average daily returns	Dang et al. (2018)
	KURT	Kurtosis of daily returns	Dang et al. (2018)
	SIZE	ln(total assets)	Zhu (2016)
	MTB	Close price / book value per share	Zhu (2016)
	LEV	Total debt / total assets	Zhu (2016)
	ROA	Net income / average total assets	Zhu (2016)

Proposed empirical model

The study tests three alternative crash-risk measures. The general regression specification is:

CRASH = β0 + β1 VD + β2 SIGMA + β3 RET + β4 KURT + β5 SIZE + β6 MTB + β7 LEV + β8 ROA + ε

Figure 1. Proposed empirical model of the study Statistical techniques

Multiple linear regression (OLS) was used to test hypotheses. Descriptive statistics employed mean, standard deviation, minimum, maximum, and range. Durbin–Watson tested autocorrelation. Kolmogorov–Smirnov tested normality. Multicollinearity was assessed via VIF. Analyses were run in SPSS v22.

Results Descriptive statistics

Table 3. Descriptive statistics

Variable	n	Min	Max	Mean	Std. dev.
VD	79	0.469	0.952	0.875	0.266
CRASHdummy	79	0.000	1.000	0.501	0.488
NCSKEW	79	-0.981	0.902	0.089	0.325
DUVOL	79	-23.701	12.476	2.019	2.001
SIGMA	79	0.009	0.045	0.016	0.009
RET	79	-0.008	0.006	0.000	0.005
KURT	79	-0.288	13.661	4.022	2.919
SIZE	79	2.912	5.213	3.918	0.277
MTB	79	0.431	4.010	1.981	0.790
LEV	79	0.054	0.911	0.507	0.320
ROA	79	-0.056	0.921	0.801	0.220

Correlation

Table 4. Pearson correlations among variables

Variable	VD	CRASHdummy	NCSKEW	DUVOL	SIGMA	RET	KURT	SIZE	MTB	LEV	ROA
VD	1	0.59	0.54	0.51	0.18	0.26	0.05	-0.31	-0.21	0.06	0.17
CRASHdummy	0.59	1	0.77	0.71	0.12	0.49	0.05	-0.11	-0.03	0.09	-0.13
NCSKEW	0.54	0.77	1	0.69	0.15	0.25	0.19	0.20	0.31	0.22	0.15
DUVOL	0.51	0.71	0.69	1	0.17	0.20	0.04	-0.18	0.11	0.06	0.16
SIGMA	0.18	0.12	0.15	0.17	1	0.11	0.23	0.16	0.22	0.27	0.31
RET	0.26	0.49	0.25	0.20	0.11	1	-0.19	0.18	0.31	0.32	0.29
KURT	0.05	0.05	0.19	0.04	0.23	-0.19	1	0.27	0.16	0.17	-0.18
SIZE	-0.31	-0.11	0.20	-0.18	0.16	0.18	0.27	1	0.22	0.11	0.29
MTB	-0.21	-0.03	0.31	0.11	0.22	0.31	0.16	0.22	1	0.17	0.18
LEV	0.06	0.09	0.22	0.06	0.27	0.32	0.17	0.11	0.17	1	0.15
ROA	0.17	-0.13	0.15	0.16	0.31	0.29	-0.18	0.29	0.18	0.15	1

Model diagnostics

Durbin–Watson values ranged 1.712–2.111, indicating no autocorrelation. Kolmogorov–Smirnov p-values ranged 0.195–0.504, confirming normality. VIF values ranged 1.166–2.905, indicating no multicollinearity.

Regression results

Model 1 (CRASHdummy): Regression is significant (F = 32.013, p < 0.001). VD has a negative, significant coefficient (B = -10.533, p < 0.001), indicating higher voluntary disclosure reduces crash probability. RET is positive and significant (B = 8.007, p < 0.001). SIZE is negative and significant (B = -1.164, p = 0.039). KURT and MTB are negative but not significant; SIGMA positive but not significant; LEV positive not significant; ROA negative not significant. R² = 0.787.

Table 5. OLS results: CRASHdummy

Predictor	B	t	p
Constant	34.703	—	—
VD	-10.533	8.551	0.000
SIGMA	4.853	2.934	0.087
RET	8.007	8.137	0.000
KURT	-0.018	0.007	0.934
SIZE	-1.164	3.840	0.039
MTB	-0.054	0.031	0.861
LEV	0.540	0.947	0.331
ROA	-11.923	0.952	0.329
R = 0.887; R² = 0.787; F = 32.013; p < 0.001

Model 2 (NCSKEW): Regression is significant (F = 33.540, p < 0.001). VD is negative and significant (B = -8.201, p < 0.001), reducing negative skewness. SIGMA and RET are positive and significant. Other controls are not significant. R² = 0.812.

Table 6. OLS results: NCSKEW

Predictor	B	t	p
Constant	29.101	—	—
VD	-8.201	11.209	0.000
SIGMA	0.119	5.950	0.000
RET	33.701	143.051	0.000
KURT	0.001	0.132	0.895
SIZE	-0.001	0.140	0.889
MTB	-0.002	0.824	0.411
LEV	0.002	0.319	0.750
ROA	-0.019	0.484	0.629
R = 0.901; R² = 0.812; F = 33.540; p < 0.001

Model 3 (DUVOL): Regression is significant (F = 28.931, p < 0.001). VD is negative and significant (B = -11.764, p < 0.001). SIGMA, KURT, SIZE are positive and significant. LEV and ROA are negative and significant. RET and MTB are not significant. R² = 0.863.

Table 7. OLS results: DUVOL

Predictor	B	t	p
Constant	38.601	—	—
VD	-11.764	17.005	0.000
SIGMA	10.003	6.405	0.000
RET	10.261	0.551	0.582
KURT	0.306	2.558	0.011
SIZE	1.162	2.829	0.005
MTB	0.227	0.540	0.132
LEV	-1.278	2.750	0.007
ROA	-8.139	2.633	0.009
R = 0.929; R² = 0.863; F = 28.931; p < 0.001

Conclusions

Across all three models, voluntary disclosure during COVID-19 significantly reduces future stock price crash risk for EGX30 firms. Explanatory power is high: R² = 0.787 (CRASHdummy), 0.812 (NCSKEW), and 0.863 (DUVOL).

Recommendations and future research

Emphasize voluntary disclosure of non-mandatory information by Egyptian Exchange–listed firms to reduce future crash risk, especially under pandemic conditions.

Add a market-level index to measure voluntary disclosure for Egyptian listed firms to prepare for future crises.

Include voluntary disclosure topics in undergraduate accounting curricula to raise awareness.

Encourage management to adopt measures that mitigate future crash risk.

Corporate reports should include detailed information on COVID-19 crisis management and its economic, social, technological impacts and risks to inform users’ decisions.

Conduct further empirical studies on voluntary disclosure and market value in Egypt and other contexts.

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