Assessing the resilience of the financial market - a multistage approach in the context of the COVID-19 pandemic

Figures & data

Figure 1. Evolution of the BET index in the period January 2020-December 2021.

(Source: own processing).

Table 1. Separation by levels of factors influencing organizational resilience.

General level	Medium level	Deep level
Organizational competence	Organizational leadership	Equity capital
Organizational learning	Organizational structure	Organizational resources
Organizational relations	Organizational culture	Cognitive ability
Organizational communication	Passion for work	Emotional capacity

(Source: own processing)

Table 2. Authors, period analyzed, models and results for variables of interest.

Authors and period analysed	Model	Results	Variables
(H. Markowitz 1959)	Market model	Optimal portfolio selection	Share price Share rate of return Risk attached to the share
(Sharpe 1963)	Market model	Optimal portfolio selection using computer technology	Share rate of return Risk attached to the share
(Fisher and Hall 1969)	Risk measurement models and risk-return estimation for a sample of firms in different industries	Higher risk exposure (high standard deviations)→high average rates of return Exception: chemical-pharmaceutical: low risk premiums→high profit	Field of activity Rate of return on equity Risk attached to the share
(Garas and Argyrakis 2007): 1987–2004	The minimum spanning tree (MST) technique and The random matrix theory (RMT)	Analysis conducted on three different portfolios traded on the Athens Stock Exchange (ASE)→the market is affected differently by different economic sectors	Field of activity Stock exchange quotation BET/other market index BET index return/other market index
(Shalit and Yitzhaki 2009)	The mean-extended Gini model	The model quantifies the heterogeneity of risk aversion in equity markets	Risk attached to the share Type of share (offensive/defensive)
(Hasan et al. 2011)	Capital Asset Pricing Model (CAPM)	High risk→Low return (disproves CAPM hypothesis)	Share rate of return
(Renata, Ogryczak, and Grazia Speranza 2015)	Mixed Integer Linear Programming (MILP)	Review of the variety of portfolio optimization models solvable by linear programming (LP)	Share price
(Darmitha and Bagus Anom Purbawangsa 2016): 2013–2016	Saturation sampling, Single Index Model, Sharpe Index	Two indices that make up an optimal portfolio outperform the market	Share price Stock exchange quotation BET/other market index Share rate of return BET index return/other market index
(Ivanova and Dospatliev 2017): 2013–2016	Markowitz model	Bulgarian investors can select their own optimal portfolio	Share rate of return Risk attached to the share
(Bruni et al. 2017)	Stochastic dominance models	Comprehensive empirical analysis shows good practical performance of the proposed model	Risk attached to the share
(Partono et al. 2017)	Single Index Model	Optimal portfolio selection	Risk attached to the share
(Longqing 2018): 2011–2016	Monte-Carlo simulation technique	When there is a non-normality of financial assets, it is recommended to use the GH (Generalized Hyperbolic) distribution in implementing portfolio selection, which produces the lowest risk for a given target return level	Risk attached to the share
(Logubayom and Annani Victor 2019): 2011–2016	The Markowitz Mean-Variance Model	Results show how the Markowitz model can be applied to the Ghana Stock Exchange and make recommendations for the most efficient portfolio	Share price Share rate of return Risk attached to the share
(Setyantho and Hadi Wibowo 2019): 2016–2017	Single Index Model and Markowitz Model	The result shows that developing an optimal portfolio using the single-index model generates a higher expected return than the Markowitz model (in healthcare)	Field of activity
(Badea et al. 2019): 2016–2018	A Markov Regime Switching	Results show resilience of the Romanian collective investment sector to selected market risk factors	BET index return/other market index/other market index
(Trichilli, Boujelbène Abbes, and Masmoudi 2020): 2004–2016	The Markowitz, the Bayesian’s models, and the Hidden Markov model	Portfolio optimization also depends on investor sentiment	BET index return/other market index/other market index
(Oprea et al. 2020): the post-2008 crisis period	Quantile regression	Analysis of seven Eastern European countries: - resistant: Bulgaria, Slovenia; - less resistant: Croatia, Czech Republic, Hungary, Romania and Slovakia; - recovering/transforming: Bulgaria, Romania, Slovakia; -(non)recovery/ transformation: Croatia, Czech Republic, Hungary, Sloveniaa	Period (Resistance, Recovery, Transformation)
(Lestari and Sudiyono 2020): 2019	Purposive sampling technique, Single Index Model	Survey of 45 companies, members of the LQ 45 Index: investors can combine portfolios of real estate with financials or infrastructure with consumer stocks	Field of activity Stock exchange quotation BET/other market index Share rate of return Risk attached to the share
(Nisani and Shelef 2021): 2014–2017	Lorenz curve ranking method	The S&P 500 Index is not considered to be the market portfolio from the investors’ point of view, but rather is seen as another asset; investors exhibit downside risk aversion behavior	Risk attached to the share
(Komang and Made 2020): 2017–2020	Single Index Model and the Markowitz model	The results show that the optimal portfolio using the single-index model performs better than the Markowitz model, but statistically insignificant	Share price Share rate of return
(Anwar, Coviello, and Rouziou 2021): recent pandemic period	Survey, a time-lagged, multi-level study	Young IT companies are more resilient in times of crisis (perform better)	Field of activity
(Nageri 2021): Jan 2020-Dec 2020	Panel data analyses of GARCH-in-mean and Threshold GARCH	Examines how the Nigerian Stock Exchange (NSE) responds to the COVID-19 pandemic in the form of the risk-return relationship and shows that the 10 stock indices analyzed exhibit a positive risk-return relationship during COVID-19	Stock exchange quotation BET/other market index
(Shahzad, Bouri, et al. 2021): Oct 2001-Oct 2020	A quantile vector autoregressive model specifically for extreme returns	The sectoral topology did not collapse into a unified market during the pandemic	Stock exchange quotation BET/other market index
(Shahzad, Abubakr Naeem, et al. 2021): Jan 2019-Sept 2020	Networks of generalized forecast error variances by decomposition of a vector autoregressive model	The asymmetric impact of good and bad volatilities	Stock exchange quotation
(Chaweewanchon and Chaysiri 2022): jan 2015-dec 2020	Convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and the Markowitz mean-variance (MV)	Integrating stock screening can improve MV performance	Stock exchange quotation BET/other market index Share rate of return

(Source: own processing)

Figure 2. Distribution of the sample analysed by field of activity.

(Source: own processing)

Table 3. List of identified variables and their description.

Variable	Variable symbol	Value
Field of Activity	FA	Manufacturing Industry
		Trade
		Chemical-Pharmaceutical
		Tourist Services
		Other Services
		Financial Services
		Building
		Energy-Oil
The Share Price-Company	SP_COMP	Continuous Values
The Share Price-BET	SP_BET	Continuous Values
The Rate of Return on the Share-Company	RRS_COMP	Continuous Values
The Rate of Return on the Share-BET	RRS_BET	Continuous Values
Average Rate of Return-Company	ARR_COMP	1-Low 2-High
Chance to Win-Company	CW_COMP	1-Low 2-High
Risk-Company	R_COMP	1-Low 2-High
Share Type-Company	ST_COMP	1-Defensive 2-Offensive
Period	PERIOD	1-01_2020 - 02_2020 – Resistance_BET 2-03_2020 - 10_2020 – Recovery_BET 3-11_2020 - 12-2021 – Transformation_BET

(Source: own processing)

Table 4. Indicators of descriptive statistics on the BET index.

Indicator	Value (%)	Interpretation
Mean: RRS_BET	1.19	The Rate of Return on the Share-BET
Median: CW_BET	1.07	Chance to Win-BET
Std. Deviation: R_BET	6.11	Risk-BET

RRS_BET means The Rate of Return on the Share-BET (Mean), CW_BET means Chance to Win-BET (Median), R_BET means Risk-BET (Std. Deviation).

(Source: own processing)

Table 5. The mean, median and standard deviation of shares for the sampled entities.

BET & Comp	Mean	ARR Type	Median	Chance to Win	Std. Deviation	Risk	Coef Beta	Share Type
BET	1,19	By comp. BET	1,07	By comp. BET	6,11	By comp. BET	Linear Regression	β > 1-Offensive β < 1-Defensive
AAG	1,07	Low	0,31	Low	4,71	Low	0,163	defensive
ALR	0,58	Low	2,29	High	13,39	High	1,101	offensive
ALT	-1,15	Low	-2,19	Low	13,06	High	0,814	defensive
ALU	4,00	High	4,98	High	8,60	High	0,564	defensive
ARM	-4,10	Low	-3,03	Low	5,77	Low	-0,176	defensive
ARS	2,71	High	0,23	Low	14,52	High	1,365	offensive
ARTE	2,32	High	2,92	High	10,31	High	0,225	defensive
ATB	0,64	Low	0,18	Low	4,05	Low	0,223	defensive
BCM	0,03	Low	0,31	Low	6,78	High	0,656	defensive
BIO	3,90	High	3,48	High	7,49	High	0,368	defensive
BNET	0,61	Low	-1,34	Low	12,30	High	0,857	defensive
BRD	1,23	High	1,69	High	8,41	High	1,071	offensive
BRK	7,18	High	-0,27	Low	24,55	High	1,382	offensive
BRM	3,06	High	1,57	High	10,30	High	0,233	defensive
BVB	0,16	Low	0,00	Low	3,24	Low	0,267	defensive
CAOR	1,06	Low	0,00	Low	9,27	High	-0,296	defensive
CBC	12,76	High	0,00	Low	58,41	High	-0,320	defensive
CEON	3,64	High	2,32	High	8,22	High	0,531	defensive
CMCM	1,04	Low	0,00	Low	12,96	High	0,416	defensive
RCMF	0,94	Low	1,91	High	9,29	High	0,568	defensive
CMP	-0,63	Low	0,00	Low	7,90	High	1,022	offensive
CNTE	-0,20	Low	0,00	Low	7,38	High	0,693	defensive
COMI	1,34	High	-2,50	Low	14,22	High	0,839	defensive
COS	6,18	High	0,00	Low	26,50	High	0,152	defensive
COTE	0,23	Low	-0,26	Low	6,91	High	0,657	defensive
CRC	13,62	High	1,62	High	48,69	High	-0,228	defensive
DAFR	-1,44	Low	0,00	Low	7,80	High	0,796	defensive
DIGI	1,06	Low	1,67	High	6,26	High	0,554	defensive
ECT	1,17	Low	0,00	Low	10,75	High	0,806	defensive
EFO	1,48	High	0,68	Low	10,27	High	0,656	defensive
EL	0,17	Low	0,16	Low	7,07	High	0,612	defensive
ELGS	-3,22	High	-0,28	Low	10,71	High	0,177	defensive
ELJ	1,51	High	0,00	Low	13,39	High	0,708	defensive
ELMA	-1,45	Low	-0,77	Low	7,88	High	0,471	defensive
ENP	0,13	Low	0,00	Low	1,56	Low	-0,058	defensive
EVER	-1,11	Low	-1,52	Low	5,45	Low	0,644	defensive
FP	1,91	High	1,58	High	6,05	Low	0,671	defensive
IARV	-1,05	Low	-1,61	Low	9,53	High	1,002	offensive
IMP	4,85	High	1,76	High	10,17	High	0,653	defensive
M	6,79	High	5,46	High	11,77	High	1,051	offensive
MCAB	6,53	High	3,77	High	13,26	High	0,363	defensive
MECE	7,80	High	-0,28	Low	27,64	High	1,076	offensive
MECF	-0,44	Low	-1,06	Low	8,87	High	-0,457	defensive
NAPO	-0,70	Low	-1,54	Low	19,35	High	-0,027	defensive
OIL	-0,57	Low	-0,89	Low	12,85	High	1,110	offensive
PBK	0,23	Low	1,00	Low	7,26	High	0,926	defensive
PPL	10,64	High	3,68	High	17,39	High	0,149	defensive
PREB	4,79	High	2,41	High	12,08	High	-0,110	defensive
PREH	5,51	High	0,26	Low	19,91	High	1,052	offensive
PTR	1,37	High	-0,29	Low	15,51	High	0,835	defensive
RMAH	3,25	High	2,66	High	10,30	High	0,107	defensive
ROCE	3,47	High	0,44	Low	11,54	High	0,685	defensive
RPH	-0,71	Low	-1,32	Low	6,13	High	0,279	defensive
RRC	-0,05	Low	0,55	Low	6,01	Low	0,609	defensive
SCD	-1,00	Low	-1,14	Low	3,59	Low	0,308	defensive
SFG	0,04	Low	0,54	Low	10,11	High	1,104	offensive
SIF1	-0,43	Low	-0,61	Low	6,80	High	0,685	defensive
SIF3	-0,68	Low	0,55	Low	8,06	High	0,971	defensive
SIF4	2,81	High	1,30	High	11,57	High	1,146	offensive
SIF5	-1,65	Low	-0,93	Low	5,93	Low	0,619	defensive
SNG	0,49	Low	0,12	Low	7,72	High	0,987	defensive
SNN	5,38	High	5,77	High	9,51	High	0,750	defensive
SNO	2,71	High	0,00	Low	17,27	High	1,384	offensive
SNP	1,06	Low	2,69	High	9,48	High	1,387	offensive
SOCP	0,25	Low	0,00	Low	7,79	High	0,151	defensive
STZ	1,91	High	0,41	Low	12,00	High	0,408	defensive
TBK	4,07	High	1,73	High	9,06	High	0,537	defensive
TBM	-1,79	Low	-0,88	Low	10,46	High	1,099	offensive
TEL	0,75	Low	1,20	High	7,81	High	0,699	defensive
TGN	-1,19	Low	-1,98	Low	7,36	High	0,926	defensive
TLV	1,24	High	2,63	High	8,57	High	1,217	offensive
TRP	8,62	High	4,01	High	15,73	High	0,728	defensive
TUFE	-0,24	Low	-1,19	Low	5,88	Low	0,450	defensive
UAM	-0,06	Low	-1,35	Low	10,10	High	-0,252	defensive
UZT	0,72	Low	0,00	Low	11,19	High	-0,690	defensive
VESY	-1,32	Low	-2,52	Low	19,38	High	0,457	defensive
VNC	1,52	High	1,24	High	6,28	High	0,440	defensive

Mean is Average Rate of Return for BET and Company, Median is Chance to Win for BET and Company and Std. Deviation is Risk for BET and Company, and have as attributes: Low and High by comparison with BET; Beta is the regression coefficient (β >1-Offensive shares β < 1-Defensive shares).

(Source: own processing)

Table 6. Regression model parameter estimates.

Variables included in the model	β	Stand β	t	Sig
RRS_BET	.571	.228	10.524	.000
Constant	.011		3.321	.001

R2 is 0,057; N=77.

Regression model (ST_COMP): RRS_COMP_i=β₀+β₁RRS_BET+ε_i.

(Source: own processing)

Table 7. Assessment of the resilience of BSE-listed companies compared to the BET index.

Nr. crt.	BET & COMP	SRes	RRes (Jan 20 – Feb 20)	SRec	RRec (Mar 20 – Oct 20)	STrans	RTrans (Nov 20 – Dec 21)
*	BET	−0.1800		0.0144		0.0203
1	AAG	−0.1800	1	0.0343	1	0.0690	1
2	ALR	−0.6350	0	0.0647	1	−0.0263	0
3	ALT	−0.0045	1	0.0005	0	−0.0007	0
4	ALU	−0.0220	1	−0.0032	0	0.0657	1
5	ARM	−0.0050	1	−0.0054	0	−0.0022	0
6	ARS	−1.3200	0	−0.0143	0	0.4016	1
7	ARTE	0.3500	1	−0.0792	0	0.1624	1
8	ATB	0.0120	1	−0.0065	0	0.0077	0
9	BCM	−0.0165	1	−0.0011	0	0.0010	0
10	BIO	−0.0100	1	0.0005	0	0.0265	1
11	BNET	−0.0253	1	0.0001	0	−0.0081	0
12	BRD	−2.7000	0	0.1936	1	0.3495	1
13	BRK	−0.0118	1	−0.0004	0	0.0147	0
14	BRM	0.0400	1	0.0055	0	0.0214	1
15	BVB	−0.4000	0	−0.0726	1	0.1066	1
16	CAOR	0.0713	1	−0.0151	0	0.0219	1
17	CBC	−2.9000	0	0.2976	1	1.6119	1
18	CEON	−0.0180	1	0.0021	0	0.0345	1
19	CMCM	−0.0400	1	0.0003	0	0.0039	0
20	CMF	−0.3900	0	−0.0020	0	0.0068	0
21	CMP	−0.2020	0	−0.0077	0	0.0045	0
22	CNTE	−7.0000	0	0.3464	1	−0.5877	0
23	COMI	−0.0020	1	−0.0002	0	0.0002	0
24	COS	−0.0200	1	0.0027	1	0.0024	0
25	COTE	−6.6000	0	−1.0738	0	−0.6787	0
26	CRC	−0.2381	0	−0.1377	0	1.7346	1
27	DAFR	−0.0038	1	−0.0003	0	0.0000	0
28	DIGI	−2.6000	0	−0.2298	0	0.4545	1
29	ECT	0.0001	1	0.0003	0	0.0007	0
30	EFO	−0.0150	1	0.0041	0	0.0048	0
31	EL	−0.6600	0	0.2679	1	−0.2778	0
32	ELGS	−0.2550	0	0.0030	0	−0.3240	0
33	ELJ	−0.0380	1	0.0033	0	0.0022	0
34	ELMA	−0.0040	1	−0.0048	0	−0.0015	0
35	ENP	0.0050	1	0.0045	0	−0.0036	0
36	EVER	−0.1350	1	−0.0222	0	−0.0065	0
37	FP	−0.1600	1	0.0196	1	0.0336	1
38	IARV	−4.4000	0	−0.1690	0	0.0111	0
39	IMP	−0.0158	1	0.0016	0	0.0332	1
40	M	−0.8833	0	0.3034	1	1.2625	1
41	MCAB	0.0011	1	0.0050	0	0.0206	1
42	MECE	−0.5000	0	0.0625	1	2.5633	1
43	MECF	0.0150	1	−0.0087	0	0.0004	0
44	NAPO	−1.2000	0	0.2277	1	−0.1724	0
45	OIL	−0.0780	1	−0.0036	0	−0.0014	0
46	PBK	−0.0190	1	0.0005	0	0.0000	0
47	PPL	−0.1577	1	0.3873	1	0.6764	1
48	PREB	0.1250	1	0.0334	1	0.0012	0
49	PREH	−0.0550	1	0.1152	1	0.0524	1
50	PTR	−0.0620	1	−0.0097	0	−0.0076	0
51	RMAH	−0.0280	1	0.0188	1	−0.0331	0
52	ROCE	−0.0110	1	0.0020	0	0.0105	0
53	RPH	−0.0080	1	−0.0153	0	−0.0013	0
54	RRC	−0.0033	1	0.0001	0	0.0001	0
55	SCD	−0.0600	1	−0.0421	0	−0.0375	0
56	SFG	−5.3000	0	−0.3530	0	0.0623	1
57	SIF1	−0.2300	0	−0.0817	0	0.0215	1
58	SIF3	−0.0710	1	0.0002	0	0.0008	0
59	SIF4	−0.1760	1	0.0022	0	0.0685	1
60	SIF5	−0.3500	0	−0.0750	0	−0.0113	0
61	SNG	−4.8500	0	−0.3851	0	0.5629	1
62	SNN	−0.1600	1	−0.0571	0	2.2722	1
63	SNO	−0.6000	0	−0.0479	0	0.0914	1
64	SNP	−0.0650	1	0.0021	0	0.0087	0
65	SOCP	−0.0600	1	0.0066	0	0.0046	0
66	STZ	−0.0760	1	−0.0010	0	0.0158	0
67	TBK	−0.5000	0	0.3655	1	1.3354	1
68	TBM	−0.1260	1	0.0032	0	−0.0059	0
69	TEL	−2.2000	0	0.9964	1	−0.4143	0
70	TGN	−57.000	0	0.8452	1	−5.6802	0
71	TLV	−0.4331	0	0.0470	1	0.0504	1
72	TRP	−0.0104	1	0.0272	1	0.0640	1
73	TUFE	−0.0380	1	−0.0003	0	0.0005	0
74	UAM	0.2000	1	0.0219	1	−0.0315	0
75	UZT	0.1400	1	−0.1042	0	0.0053	0
76	VESY	0.0220	1	0.0002	0	−0.0034	0
77	VNC	−0.0075	1	−0.0005	0	0.0043	0

Note: SRes, SRec, STrans represents the slope for stages of resiliences: Resistance, Recovery, Transformation, and RRes, RRec RTrans represents the resilience of the three phases (1 - indicates resilience through resistance, recovery or transformation; 0 - indicates no resilience on all three phases).

(Source: own processing)

Figure 3. Association of RRes, RRec, RTrans and activity field by BET.

(Source: own processing using MCFA)

Figure 4. Association of ARR_COMP, CW_COMP, FA, R_COMP and ST_COMP.

(Source: own processing)

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