A multi-criteria based analytic framework for exploring the impact of Covid-19 on firm performance in emerging market

Abstract

The recent pandemic has been the greatest catastrophic event in the last century that has left its irrevocable effect on the socio-economic and cultural environment. The current work presents a combined multi-criteria decision making (MCDM) framework of logarithmic percentage-change driven objective weighting (LOPCOW) and evaluation is based on distance from average solution (EDAS) method to enfold the early impact of COVID-19 on firm performance from fast moving consumer goods and consumer durables sectors in emerging market. Five aspects such as stock performance, dividend payout capability, sales and operational performance, financial stability and economic sustainability are considered for comparing 30 firms over seven consecutive financial years (FY 2013–14 to FY 2020–21). For aggregation of year wise rankings popular methods like Borda count and Copeland methods have been applied. A comparison of results of the LOPCOW-EDAS model with other MCDM methods has been made. It is noticed that the firms which held the overall top positions prior to COVID-19 suffer more afterward than the bottom performers. However, there has not been any major effect of COVID-19 on firms’ financial health and long-term growth prospect. The result shows significant reliability and stability as revealed by the comparative ranking and sensitivity analysis. The proposed framework shall enable the organizations for detailed performance analysis. Some of the policy and managerial implications are also discussed.

1. Introduction

Since the Great Depression in the last century, the world has never been threatened with a disaster like the recent COVID-19. In the aftermath of its first reported instance in Wuhan, China, on December 08, 2019, the tiny virus has quivered the world with its fatal consequential impact on lives and livelihoods. To date, more than 60 crore people got infected with this deadly disease, while the reported figure of death across the globe is around 65 lakhs [1]. Alongside the adverse physiological and psychological effects, COVID-19 has shuttered the socio-economic and cultural environment and trades [2], [3], [4]. The global economy has not recovered from the disruptive traces of the pandemic on the stock market and business operations [5]. COVID-19 lunged at the countries as a rare momentous event that the world had never encountered in the last several decades. To combat the early jolts, the governments of almost all nations across the globe took strict measures. The countries declared a prolonged shutdown of all activities and went into lockdowns several times in 2020 and 2021. Besides the medical emergencies, the trades and businesses got severely affected, leading to a negative footfall on the firm performance. All the leading global indices have witnessed a downturn in performance and higher volatility with spill over effects [6], [7], [8], [9], [10], [11]. One recent study [12] reported that investors reacted in a usual way during the early phases of the pandemic. However, as the number of infected cases and deaths grew significantly, investment decisions were crippled by fear. As a result, the stock market started facing the adverse impact of the pandemic. Besides the downturn in stock performance, the firms have also experienced the fatal impact of the pandemic on their value chains. Due to prolonged shutdowns and uncertainty prevailing over the business environment, the organizations have suffered from operational and financial implications. The firms, irrespective of their nature and volume of operations, have reported the adverse impact of the pandemic on performance. Hence, to formulate effective strategic decisions, it is relevant to investigate the effect of COVID-19 on the firms’ financial, operational, and market performance.

The present paper demonstrates a case study on two sectors, fast-moving consumer goods (FMCG) and consumer durables (CD), in an emerging market like India. The early effect of COVID-19 on firm performance is unveiled using appropriate analytical framework. FMCG firms are an inseparable part of the household’s daily life. The sector features a higher consumption level, various products with widely varying price levels, intense intra-industry rivalry, diverse competition, and a lower entry and exit barrier. With increasing per capita consumption, disposable income, and technological advancement, CD products are essential for daily entertainment, leisure, and household use. It may be an intriguing work to explore the effect of COVID-19 on these two sectors. To carry out a comprehensive analysis, we consider five dimensions such as stock performance, dividend pay-out capability (DPC), sales and operational performance (SOP), financial stability (FS) and long-term growth prospect for economic sustainability (ES). The performance of the firms are compared during pre-and post- pandemic periods. The firm performance on each of these dimensions depend on multiple factors or criteria. Thus, the present work utilizes the multi-criteria decision making (MCDM) models. The research questions that the present paper intends to enquire are:

RQ 1. How can an effective MCDM framework be formulated and applied to discern the stock performance, DPC, SOP, FS and ES of the firms?

RQ 2. To what extent the firms differ from each other based on their stock performance, DPC, SOP, FS and ES in each period (i.e., pre and post COVID-19 phases)?

RQ 3. To what extent the firm performance in the post COVID-19 period differ from their prior performances?

The motivations of the current problem stems from the extant literature. The rare and unprecedented disruption caused by COVID-19 has garnered the immediate attention of researchers and practitioners from various domains. A plethora of work has been conducted to explore the causes of the disease, its physiological and psychological impacts, and remedial courses of action. Besides, the growing literature has contributed a sizeable number of scholarly and practical articles, cases, and reports about engineering, social science, and business management to enfold the pandemic’s effect and formulate future courses. From the literature review, it is amply evident that there has been a growing concern about probing the effects of COVID-19 on firm performance. We have noticed that the lion’s share of the previous work emphasized analyzing the impact of COVID-19 on the stock performance of the firms, i.e., market-based performance. The experts [38] believed that market-based performance measures are superior to accounting-based assessment to figure out the top-line performance and growth of the firms. The financial performance of the organizations is equally important. Some researchers argued that the performance of the firms depends on financial and operational results reflecting their effectiveness and efficiency and, thereby, their competitive positions [39], [40].

Given the above arguments, we contend that supremacy in fundamental performance and market attractiveness are the kingpins for the firms to achieve sustainable competitive advantage and show better resilience to the disruptive impact of a catastrophic and unforeseen event like COVID-19. It is also seen that the extant literature primarily uses causal and predictive models to gauge the impact of the pandemic on firm performance. The scope of the work has been mostly limited to the stock market performance only. There is a void in the literature regarding the use of prescriptive analytical models for a comprehensive assessment of firm performance based on multiple factors. Our work is motivated by the gaps mentioned earlier in the literature.

We formulate an MCDM framework comprising LOPCOW and EDAS methods to address the current issue. LOPCOW method is used to calculate the criteria weights while EDAS helps in comparative analysis of the alternatives under the influence of the criteria. The dynamic field of MCDM has been enriched with a substantial number of algorithms developed by various researchers over the last few decades. Some of the notable contributions are mentioned in Table 1.

Table 1.

List of some commonly used MCDM models.

MCDM model	Use	Reference
Simple Additive Weighting (SAW)	Ranking and criteria weights	[13]
Elimination Et Choice Translating Reality (ELECTRE)	Ranking	[14]
Analytical Hierarchy Process (AHP)	Criteria weight	[15]
Više Kriterijumska optimizacija i Kompromisno Rešenje (VIKOR)	Ranking	[16]
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)	Ranking	[17]
Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE)	Ranking	[18]
Complex Proportional Assessment (COPRAS)	Criteria weight	[19]
Analytic Network Process (ANP)	Ranking and causal relation	[20]
Multi-objective Optimization by Ratio Analysis (MOORA)	Ranking	[21]
MULTIMOORA	Ranking	[22]
Additive Ratio Assessment (ARAS)	Ranking	[23]
Step-wise Weight Assessment Ratio Analysis (SWARA)	Criteria weight	[24]
Weighted Aggregated Sum Product Assessment (WASPAS)	Ranking	[25]
Multi-Attributive Border Approximation Area Comparison (MABAC)	Ranking	[26]
Evaluation based on Distance from Average Solution (EDAS)	Ranking	[27]
Combinative Distance-based Assessment (CODAS)	Ranking	[28]
Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA)	Criteria weight	[29]
Full consistency method (FUCOM)	Criteria weight	[30]
Combined Compromise Solution (CoCoSo)	Ranking	[31]
Level Based Weight Assessment (LBWA)	Criteria weight	[32]
Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS)	Ranking	[33]
Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (RAFSI)	Ranking	[34]
Logarithm Methodology of Additive Weights (LMAW)	Criteria weight and ranking	[35]
Preference Ranking on the Basis of Ideal-Average Distance Method	Ranking	[36]
Logarithmic percentage-change driven objective weighting (LOPCOW)	Criteria weight	[37]

For any MCDM based analysis, criteria weight plays a critical role in deciding the final ranking order [41]. The alternatives are ranked subject to their performance trading off the varying influence of the criteria [42]. The selection of an appropriate algorithm given the context of the problem helps in achieving the reasonable accuracy in the result [43]. There has been a plethora of models available for deriving the criteria weights. The models are broadly classified under two categories such objective information based and subjective rating based methods. The objective information based models enjoy the advantage of lesser subjective bias and uncertainty [44]. The distribution of weights, presence of negative performance values in the decision matrix and size of the criteria and alternative sets are some of the issues that affect the true representation of the criteria influence on the firms [45]. To this end, the LOPCOW method has been developed by Ecer and Pamucar [37] for providing the following advantages over its widely applied counterpart such as Entropy method, PIPRECIA, SWARA etc.

• (a)The distribution of the criteria weights gets less influenced by the variations in the performance values of the alternatives and provide a reasonable accurate result.
• (b)Ability to work with negative performance values of the alternatives
• (c)Does not suffer from the effect of a large number of alternatives and criteria

EDAS is a distance based MCDM model that compares the attractiveness of the alternatives based on two distances such as PDA (positive distance from the average) and NDA (negative distance from the average) with respect to the average solution point as benchmark. The method developed by Keshavarz Ghorabaee et al. [27] provides a number of advantages such as stability and reliability of the results subject to presence of variations in the performance values in the decision matrix and a large number of alternatives and criteria and no impact of rank reversal phenomenon among others [46].

The main contributions of the present paper are as follows. First, the paper provides a new application of the hybrid analytical model utilizing LOPCOW and EDAS for evaluation of firm performance. The proposed model can be leveraged for obtaining reliable and stable outcome for solving various complex real-life issues. Secondly, the current work is a novel attempt to discern the firm performance vis-à-vis COVID-19 considering both market based and accounting based measures in a MCDM framework. Third, the present study provides a multi-period performance assessment using MCDM algorithms and aggregation techniques.

The rest of the paper is exhibited in the following manner. In Section 2, a brief summary of some of the recent work related to the effect of COVID-19 on firm performance and stock market is presented while highlighting the research gap and contributions of the present paper. Section 3 elaborates the research framework. Section 4 provides the description of the criteria used for the comparative performance assessment of the firms under study. In Section 5, the procedural steps of the methods are mentioned. Section 6 highlights the key findings of the data analysis while Section 7 includes discussions on the results and mentions some of the implications of the research. Section 8 concludes the paper with a direction toward some of the future work.

2. Related work

In this section we summarize some of the related work in the stated field. In relation with the impact of the COVID-19, the extant literature shows two broad strand of contributions. The first major strand focuses on the effect of the COVID-19 on the firms reflected in their stock market performance. There has been a plethora of work conducted in this work in no time after the spread of COVID-19. For instance, Rao et al. [47] studied the impact of COVID 19 on the financial market. Daily performance of Nifty 50 indexed companies was associated with the cases and death reported due to COVID 19 from the period of March 2020 to November 2020. The paper also examined the impact of COVID-19 across10 sectors of Nifty using panel regression techniques. The test revealed that the daily return of the stock market has been negatively impacted due to COVID-19 exposure. The sectors like Pharma, Telecom, and FMCG have shown a significant positive impact over other sectors was also illustrated in the study. Mazur et al. [48] examined the immediate impact of the outbreak of COVID-19 and revenue shock by observing the stock behavior market. S&P 1500 firms were studied across sectors to understand the differential price reaction and investigate the implications for stock price volatility. The authors had adopted the event study methodology. The study concluded that the outbreak of the pandemic showed abnormally high returns in healthcare, food, natural gas, and software sectors, whereas firms operating in crude petroleum, real estate, entertainment, and hospitality sectors plummet considerably losing more than 70% of their market capitalizations.

Kumar and Kumara [49] have attempted to study the performance of the Nifty 50 before and after the outbreak of COVID-19. The study was made by comparing the closing indexes of Nifty for January, April & June 2020. They conclude that in the pre-COVID era, the market was witnessing new highs in January. The market started to fall at the end of March’20 and crashed in April’20 when the country went into a nationwide lockdown. With the restart of economic operations, the stock indexes also started picking up their upward pace as per the data of June 2020. Sun et al. [50] investigated how the individual investor’s sentiments on returns during COVID-19 have impacted the Chinese stock market. They conducted an event study on 1914 sample companies listed under the China Securities Regulatory Commission for the period starting from 25th July 2019 to 31st March 2020. To sentiment of investors is measured in GuhaSenti, established by the International Institute of Big Data in Finance, BNU. The study was analyzed using the Panel regression model where the expected return was derived using Fama–French Model. The study concluded that the volatility in the stock market during the pandemic was not purely because of economic losses, the widespread negative sentiment of panic and uncertainty was also found to be associated with the same.

Agustin [51] explored the impact of rising cases and deaths due to COVID-19 along with the implementation of social restrictions on the performance of the Islamic stocks listed under the Indonesia Stock Exchange. Panel regression was applied to the daily stock price of the companies listed under the Jakarta Islamic Index from December 2019 to November 2020. The study took Market to book ratio and Market Capitalization of the selected 26 companies as the controlled variables. The results suggested that there was a negative impact of the said event on the stability of the stock market. The market became more volatile with the increasing cases and tightening of social restrictions as investors reacted more to these than the growth in the number of deaths. The study also concluded that some sectors like- consumer goods, mining, and trading performed better than other sectors. Bing [52] examined the relationship between the retail investor’s behavior and their ability to forecast the market during the COVID-19. The bivariate Vector Auto Regression Model was used to analyze by comparing the retail investor’s flows and returns during the pandemic. The sample for the study was collected for the period January 2019 to March 2020 from the Chinese Stock Market. The study concludes that positive feedback trading during COVID-19 was weaker than in the pre-COVID-19 period which followed the negative returns during the COVID-19 which indicates the panic trading by retail investors.

Kusumahadi and Permana [53] studied the impact of COVID-19 on the volatility of stock returns of 15 countries. Daily data from January 2019 to June 2020 was analyzed using Generalised Autoregressive Conditional Heteroskedasticity Regression which revealed that stock return volatility of all the selected countries was affected except in the case of the United Kingdom. The study also concluded that the stock return of all the selected countries was highly volatile, especially during March 2020. Further, an analysis was also made to understand the fundamental factors affecting the stock returns and it was observed that the exchange rate was one of the significant factors that negatively affected the stock returns. Yong et al. [54] studied time-series data from the FTSE Malaysia KLCI Index and FTSE Straits Time Index to analyze the stock volatility and performance before and during the COVID-19 pandemic using the daily closing prices of indices. The data was examined from July 2019 to August 2020 using various GARCH models with different probability distributions in the log-likelihood function. The analysis determined that the stock returns in both the exchanges are very persistent in the pre-Covid era, which declined during the pandemic.

Lee et al. [55] studied the interrelationship between the COVID-19 outbreak, macroeconomic variables (exchange rate, interest rate, market returns), and hospitality industry returns in China. The analysis was made using the SVAR framework on the data collected from January 13, 2020, to May 11, 2020. The transmission of structural shock due to pandemic and macroeconomic fluctuation was quantified using impulse response analysis. The study concluded that the shock of COVID-19 has a slight and insignificant influence as there was a transitory increase in the exchange rate (currency depression), a persistent decrease in the interest rate, and a short-lived decrease in hospitality industry return. The study also employed Variance Decomposition, which showed that the percentage contribution of COVID-19 to variation of hospitality industry return was increasing since the pandemic continues to spread. Bora and Basistha [56] empirically examined the impact of COVID-19 on volatility and returns earned at the stock prices in India. The study was divided into Pre-COVID Phase (September 3, 2019, to January 29, 2020) and COVID Period (January 30, 2020, to July 7, 2020) to compare the volatility and returns in both periods. The study employed the Generalised Auto Regression Conditional Heteroscedasticity Model in daily closing prices of Nifty 50 and Sensex. The results concluded that the Indian Stock Market did experience volatility in the wake of the pandemic and that the Sensex was more sensitive than NSE. It also added that the returns were higher in the pre-COVID period than during the COVID-19 period. However, the market from April 2020 started to show a positive trend. The authors have also conducted Ljung Box Q and ARCH LM test which concluded that the models used for the study are performed correctly.

Verma et al. [57] analyzed the short-the term impact of the pandemic on the Indian Stock Market. Nifty-50 was chosen as a proxy for the stock market and its 13 constituent sectors were also examined using 3 models- constant return, market, and adjusted market models of the event study method. The results showed that at the onset of the pandemic, the market declined by 15%–17%, but the impact was noted as temporary as the stock market’s performance was relatively better. The sector-wise study showed that 4 sectors had an average abnormal return out of which IT, Pharma, and Consumer Goods have a positive or limited impact whereas Financial Services are impacted the worst. Mittal and Sharma [58] explored the impact of COVID-19 on Indian Healthcare and Pharma stock returns. The daily closing price of the stocks listed on BSE was considered for 233 trading days from May 15, 2019 – to April 24, 2020. The analysis was made using the Event Study Method to compare the abnormal & cumulative returns of the different sectors of BSE. The results showed that the significant abnormal positive returns were seen in only two sectors — BSE FMCG & BSE Healthcare whereas sectors like- Consumer Discretionary, Banks and Real estate sectors were worst affected.

Utomo and Hanggraeni [59] examined the impact of COVID-19 and lockdown policies on the stock market returns in Indonesia. Fixed Effects Panel Data Regression was used to study the impact of COVID-19 spread, mortalities, and the lockdown policies on the stock returns of 272 firms listed on the Indonesian Stock Exchange between March 2, 2020, and November 27, 2020. It was concluded from the study that COVID-19 had a mixed impact on the Indonesian Stock Market. The daily growth in COVID-19 cases and the number of deaths hurt the stock returns whereas the lockdown policies had a positive and significant effect on the stock returns as the government prompt actions restored the confidence of Investors. The sector-wise analysis pointed out that the Property, Trade, Service, and Investment sectors were negatively impacted and the stocks of Consumer Goods and Mining sectors performed better. Herwany et al. [60] studied the impact of COVID-19 on different sectors and market returns of the Indonesian Stock Exchange. Event Study Method using market model is used to study 9 sectors for a period of 30 days before the event (January 20, 2020, to February 28, 2020) and a period from March 3, 2020, to April 15, 2020, during the event. Further, OLS Regression is applied, showing that COVID-19 has a significantly negative impact on market returns. Consumer Goods and Mining Sectors showed a temporary negative sentiment compared to other sectors that were negatively impacted. The work of [61] studied the impact of COVID-19 on the global economy. Various economic variables like- GDP, Stock Performance, Crude Oil, Gold, Silver, Natural Gas, and 20 years of Treasury Bills of the top 10 economies of the world were analyzed. The results showed that there was a moderate positive correlation between the variables.

The study of Ren et al. [62] focused on the Chinese stock market wherein the authors examined the effect of COVID-19 on local firms situated in the different provinces of the China. Based on the analysis carried out using the difference-in-difference model the authors noted an initial jerk in the stock performance affected locally by the degree of spread of the pandemic as the firms reported lower return compared to the benchmark. However, the authors highlighted the positive effect of the stringent control reflected in the greater resilience shown by the firms. Rahman et al. [63] investigated the stock market response to the COVID-19 in four South Asian Countries, Bangladesh, India, Pakistan, and Sri Lanka. The data relating to the daily spread of COVID-19 and daily stock returns were observed. The analysis was made using Dumitrescu and Hurlin panel Granger non-causality test and cross-validation was made using the pairwise Granger Causality Test. The study concluded that India’s stock market is more volatile and also has the highest gains in returns relative to the other South Asian Countries. The results also indicated a unidirectional causality from COVID-19 to stock market returns, which showed that COVID-19 has a dominant short-term influence on the market.

Behera et al. [64] studied the impact of immunization on the mortality rate and the performance of the Indian stock market. The data for the study was collected from MoHFW and BSE from February 2021 to July 2021. The study employed Exploratory Data Analysis to analyze the key feature of the dataset with the visual method and the statistical analysis to validate the relationship between vaccination with the stock market and the death rate. The analysis also has included the Machine Learning Regression Models like- Support Vector Regression, Random Forest Regression, and KNN Regression to make an objective prediction about the study. The results revealed that increasing the vaccination process has decreased the volatility and chaos in the security market and the death rate due to COVID-19. The study also highlighted the impact of policy recommendations taken by the Government of India to boost the confidence in the Financial Market and the Economy.

Naik et al. [65] analyzed the trading behavior of Institutional Investors, both foreign and domestic affects the market volatility. The study was made taking both Indian Equity and Debt market into consideration. The daily returns of Nifty 50, daily purchase and sale of FPI, from RBI and domestic institutional investors like Mutual Fund agencies from SEBI, and the daily confirmed case of COVID from January 2020 to July 2020 were taken for the study. EGARCH model was used to measure volatility and the Granger Causality test was also applied to understand how net sales by institutional investors in the Equity Market cause volatility in returns. The data when plotted in time series showed that in the wake of the pandemic, the Nifty index started declining and displays recovery at a slower pace with mild fluctuation post-March 2020. The study further revealed that FII’s trading behavior in the equity and debt market significantly influences market volatility whereas the DIIs do not have a significant role. It also concludes that the growth of COVID-19 is insignificant in influencing the market volatility, which may be due to corrective intervention by RBI and the government. Scherf et al. [66] studied how national stock exchanges worldwide responded to the news of national lockdown restriction. The study was made from January 2020 to May 2020 which was further split into 1st phase of lockdown from January 22, 2020, to March 27, 2020, and 2nd phase from March 28, 2020, to May 20, 2020. Multinational market panel analysis with event study was made on 42 OECD and BRICS nations. Infection data, Reuters data, and OXCGRT stringent Index were analyzed. The findings of the study were inconsistent with the Effective Market Hypothesis. The market reacted negatively in the 1st phase of restrictions and positively in the 2nd phase. The study concluded that the pandemic and national lockdowns had not significantly impacted the stock returns.

The second major section of the previous work showcased the effect of the pandemic on the firms’ financial performance vis-à-vis stock performance. In this regard, Shen et al. [67] followed a two stage approach. The authors utilized predictive models to forecast financial performance of selected Chinese companies across the sectors for the first quarter of 2020 based on their results during 2013–2019. Next, the authors used all performance values during the first quarter of each year from 2014 to 2020 under the moderating influence of investment growth and total revenue and applied the difference-in-difference approach to discern the firm level impact. The study reported a significant negative effect of the pandemic on the corporate performance and reduction in the sales and total revenue. The work of Golubeva [7] considered three levels such as overall firm level, financial aspect and country level for assessing the effect of the pandemic. For a deeper analysis the author further considered industrial sector, firm size, export and market demand parameters. The paper recognized the importance of country level interventions on assuring the firm performance amidst the disruption caused by COVID-19.

Hu and Zhang [68] observed an adverse effect of COVID-19 on overall firm performance such as return on assets in near and mid-term. The authors noted that the magnitude of effect decreases for the countries having sound healthcare system, good institutional governance and financial management. Chu et al. [69] worked on the real estate sector. The authors found that the firms with presence in wide geographic area with well diversified business operations could show resilience to the early impact of COVID-19 and sustain the return. The authors noted that higher leverage reduces the return irrespective of the geographic presence. Størdal et al. [70] conducted an event study of the performance of the forest product companies during the early phases of the pandemic. The authors noted medium term impact on the return and the systematic risk of the stocks at the marketplace after declaration of the pandemic while forestry subsector showed more vulnerability in comparison with the paper subsector. The work of Clampit et al. [71] made a comparative analysis of sales growth of 128 US manufacturing firms considering both pre and post COVID-19 phases. The study pointed out some interesting observations as obtained through ordinary least squares (OLS) regression approach. First, the firms with higher R&D focus showed better resilience. The authors advocated for better current asset management to keep optimum balance of cash and inventory. Secondly, the authors observed that the firms with higher operating risk had performed well during the hard time.

The study of Maemunah and Cuaca [72] added a new perspective by finding the positive impact of the business strategy, use of information technology and agility of the supply chains on the performance of the medical device manufacturing firms in Indonesia after the spread of coronavirus. In the Malaysian and Thai context, the enquiry of Srinok and Zandi [73] revealed that flexibility in formulation of the strategic decisions, effective utilization of organizational resources, especially slack resources and innovativeness have a significant positive influence on the firm performance while futuristic and proactive marketing efforts played a mediating role. Cho and Saki [39] concentrated on the textile and apparel industries in USA and noted a substantial adverse effect of COVID-19 on firm performance, much greater than the two prior notable dreadful events like terrorist attack on 9/11 and the sub-prime crisis in 2008. The authors also observed that the declaration of the special aids and relief have had a positive reinforcement in combating the aftershock of COVID-19. Kumar and Zbib [74] examined the role of managerial ability in mitigating the COVID disruption. The authors found that the ability of leadership of CEO helped the firms to ensure better return on equity (ROE) and stock market return. Further, CEO managerial ability enables maintaining beneficial liquidity level for showing resilience to catastrophic event like COVID-19. In another work, Ahmad et al. [75] considered a long horizon 2004 to 2020 for delving into the interrelationship of working capital management and organizational performance and the effect of the pandemic. The study on 577 units from the developing countries in Asia considering working capital policy, cash conversion cycle, net working capital (working capital management) and return on asset and Tobin’s Q (as indicators for firm performance) revealed that both working capital and return got affected by COVID-19 and working capital management has a causal bearing on return. Bose et al. [76] established an empirical model to find out the impact of pandemic on firm value while considering sustainability performance as a moderator. The authors pointed out that the firms from the countries having higher level of sustainability and orientation toward creation of the stakeholder value posit lesser vulnerability to the disruption.

2.1. Research gap

In this section we point out the identified gaps that are found from the review of the extant literature. Table 2 highlights a brief summary of the literature review.

Table 2.

Summary of the literature review.

Author (s)	Study area	Methodology
Golubeva [7]	Effect of the pandemic on three levels such as overall firm level, financial aspect and country level	Statistical analysis
Rao et al. [47]	Performance assessment of NIFTY 50 (India) stocks and 10 sectoral indices based on daily data vis-à-vis COVID-19 reported cases and deaths (Period: March 2020 to November 2020)	Panel regression
Mazur et al. [48]	Stock price volatility and revenue shock for S&P 500 stocks	Event study
Kumar and Kumara [49]	Performance of NIFTY 50 (India) before and after the pandemic (Period: January, April & June 2020)	Descriptive analysis and time series modeling
Sun et al. [50]	Investigation on investors’ sentiments and stock market reaction in terms of price movement and volatility in Chinese market (Period: 25th July 2019 to 31st March 2020)	Event study and panel regression
Agustin [51]	Performance of the Islamic stocks listed under the Indonesia Stock Exchange using daily stock prices (period: December 2019 to November 2020)	Panel regression
Bing [52]	Relationship between the retail investor’s behavior and their ability to forecast the market during the COVID-19 in Chinese stock market (Period: January 2019 to March 2020)	Bivariate Vector Auto Regression Model
Kusumahadi and Permana [53]	The impact of COVID-19 on the volatility of stock returns of 15 countries using daily data from January 2019 to June 2020	Generalised Autoregressive Conditional Heteroskedasticity Regression
Yong et al. [54]	Analyze the stock volatility and performance before and during the COVID-19 pandemic using the daily closing prices of indices for FTSE Malaysia KLCI Index and FTSE Straits Time Index. (period: July 2019 to August 2020 )	Time series analysis using GARCH models
Lee et al. [55]	The relationship between the COVID-19 outbreak, macroeconomic variables (exchange rate, interest rate, market returns), and hospitality industry returns in China (Period: January 13, 2020, to May 11, 2020)	SVAR framework
Bora and Basistha [56]	The impact of COVID-19 on volatility and returns earned at the daily stock prices listed on Nifty 50 and Sensex in India (Period: pre-COVID phase: September 3, 2019, to January 29, 2020 and COVID phase: January 30, 2020, to July 7, 2020)	Generalised Auto Regression Conditional Heteroscedasticity Model
Verma et al. [57]	Short-the term impact of the pandemic on NIFTY 50 in India using constant return, market, and adjusted market models	Event study
Mittal and Sharma [58]	Impact of COVID-19 on Indian Healthcare and Pharma stock returns using daily closing price of the stocks listed on BSE, India (Period: May 15, 2019 – to April 24, 2020).	Event study
Utomo and Hanggraeni [59]	impact of COVID-19 spread, mortalities, and the lockdown policies on the stock returns of 272 firms listed on the Indonesian Stock Exchange (March 2, 2020 to November 27, 2020)	Fixed Effects Panel Data Regression
Herwany et al. [60]	Impact of COVID-19 on different sectors and market returns of the Indonesian Stock Exchange (phase 1: January 20, 2020, to February 28, 2020; phase 2: March 3, 2020, to April 15, 2020)	Event Study Method and OLS Regression
Verma et al. [61]	Various economic variables like- GDP, Stock Performance, Crude Oil, Gold, Silver, Natural Gas, and 20 years of Treasury Bills of the top 10 economies	Statistical and time series analysis
Ren et al. [62]	Chinese stock market performance	Difference-in-difference model
Rahman et al. [63]	Stock market response to the COVID-19 in four South Asian Countries, Bangladesh, India, Pakistan, and Sri Lanka.	Dumitrescu and Hurlin panel Granger non-causality test and pairwise Granger Causality test
Behera et al. [64]	Impact of immunization on the mortality rate and the performance of the Indian stock market (February 2021 to July 2021).	Exploratory Data Analysis
Naik et al. [65]	Trading behavior of Institutional Investors, both foreign and domestic affects the market volatility (Indian Equity and Debt market) from January 2020 to July 2020	EGARCH model
Scherf et al. [66]	Effect of the news of national lockdown restriction on stock exchange	Event study
Shen et al. [67]	Forecasting of financial performance of selected Chinese companies across the sectors and discern the firm level impact.	difference-in-difference approach
Hu and Zhang [68]	Effect of COVID-19 on overall firm performance such as return on assets in near and mid-term.	Statistical analysis
Chu et al. [69]	Analysis of resilience of firm performance and sustain return across the geographical areas	Statistical analysis
Størdal et al. [70]	Performance of the forest product companies during the early phases of the pandemic.	Event study
Clampit et al. [71]	Comparative analysis of sales growth of 128 US manufacturing firms considering both pre and post COVID-19 phases.	Ordinary least squares (OLS) regression approach

In what follows are the gaps identified in the related work.

• –There is no evidence of comprehensive evaluation of the firm performance vis-à-vis COVID-19 that use multi-criteria based mathematical models. Past research have mostly used causal models and event studies to discern the impact of the pandemic. Hence, from methodological point of view there is a shortcoming related to development of MCDM based analytical framework
• –Multi-criteria based multi-period assessment of firm performance and aggregation of ranking is not seen.
• –The previous studies are predominantly focused on assessing stock market performance while fundamental performance measurement is seen in few cases. There is a gap in the literature that considers both market based and accounting measures.
• –The extant literature does not show any evidence of measuring the effect of the pandemic on firm capabilities in terms of dividend payment, sales and operational performance, financial stability and long-term growth prospect
• –There is a scantiness of work focusing on FMCG and CD sectors regarding the impact of the pandemic.

In this paper we fill the above-mentioned gaps in the literature by investigating the impact of COVID-19 on firm performance using MCDM model based comprehensive framework.

3. Research framework

The step by step brief description of the research design is pictorially depicted in Fig. 1. In what follows are the information regarding sample and study period.

Fig. 1.

Research Framework

(SP: Stock performance; DPC: Dividend pay-out capability; SOP: Sales and operational performance; FS: Financial stability; ES: Economic sustainability).

3.1. Study period

The whole spectrum (for study) ranges from April 2013 to March 2021 (i.e., FY 2013–14 to FY 2020–21). We divide the work in two phases: phase 1: Pre COVID-19 (covering the period FY 2013–14 to FY 2019–20) and phase 2: Post COVID-19 (early phase, FY 2020–21)

3.2. Sample

The current work present a case study on the firms related to the FMCG and CD sectors in an emerging market like India. The sample units are selected based on their average market capitalization over the study period (excluding the COVID-19 phase). Accordingly, top 25 FMCG and top 5 CD companies are selected as the final sample. Hence, the final sample consists of 30 firms which satisfies the minimum requirement of the sample size as mentioned in [77], [78], [79], [80], [81]. Table 3 provides the list of firms under study

Table 3.

List of Alternatives (i.e., stocks) under comparison.

S/L	Alternatives	Category	S/L	Alternatives	Category
A1	Avanti Feeds Ltd.	FMCG	A16	I T C Ltd.	FMCG
A2	Bajaj Consumer Care Ltd.	FMCG	A17	Jyothy Labs Ltd.	FMCG
A3	Bombay Burmah Trdg. Corpn. Ltd.	FMCG	A18	K R B L Ltd.	FMCG
A4	Britannia Industries Ltd.	FMCG	A19	Marico Ltd.	FMCG
A5	C C L Products (India) Ltd.	FMCG	A20	Nestle India Ltd.	FMCG
A6	Colgate-Palmolive (India) Ltd.	FMCG	A21	Procter & Gamble Hygiene & Health Care Ltd.	FMCG
A7	Dabur India Ltd.	FMCG	A22	Radico Khaitan Ltd.	FMCG
A8	E I D-Parry (India) Ltd.	FMCG	A23	Tata Consumer Products Ltd.	FMCG
A9	Emami Ltd.	FMCG	A24	United Breweries Ltd.	FMCG
A10	Future Consumer Ltd.	FMCG	A25	Zydus Wellness Ltd.	FMCG
A11	Gillette India Ltd.	FMCG	A26	Rajesh Exports Ltd.	CD
A12	Godfrey Phillips India Ltd.	FMCG	A27	Symphony Ltd.	CD
A13	Godrej Consumer Products Ltd.	FMCG	A28	Titan Company Ltd.	CD
A14	Hatsun Agro Products Ltd.	FMCG	A29	Voltas Ltd.	CD
A15	Hindustan Unilever Ltd.	FMCG	A30	Whirlpool Of India Ltd.	CD

3.3. Data

The data for this work has been obtained from the valid secondary databases like CMIE Prowess IQ (version 1.96), company website and BSE websites. The supplementary data file (.xls) is given along with this paper.

4. Criteria description

In this section we discuss about the criteria used in this paper for comparing the sample units. As mentioned above the comparison of the firms are done from the five perspectives such as stock level (C1), DPC wise (C2), SOP level (C3) and on the basis of their FS (C4) and ES (C5).

4.1. Stock performance

There has been a number of research contributing toward finding out the criteria for assessing stock performance (for example, [82], [83], [84], [85], [86], [87]). To select the criteria for stock performance we consider the broad framework of the modern portfolio theory (MPT) started with the seminal work of Markowitz [88] and its subsequent developments along with investors’ sentiment in terms of expected utility [89] and prospects [90].

4.2. DPC

DPC of a particular company refers to a composite score based on its performance subject to the factors influencing the dividend payout. To derive the criteria/factors that influence the dividend payment we follow the extant literature in the stated field and consider the aspects like ownership pattern [91], [92], size of the organization [93], profitability [94], [95], growth prospect [96], liquidity [97], [98] and risk [95].

An effective IO structure helps to mitigate the agency cost problem and optimum holding and utilization of cash. Profitability provides a signal of earning prospect and supports payment of dividends while size has a positive impact on the profitability. In this paper we consider two criteria such as NPM and ROCE as indicators of profitability from the perspective of business operations and shareholders. The growth of the organization is a reflection of effectiveness and efficiency of its business and future prospect. To this end, we consider SG and MCEV as these show the acceptability of the company’s products by the customers and market. FCF is an indication of liquidity that enables the firms to overcome its burden and asset mobility. The debt position with respect to the earnings is of equal importance as a measure of near to middle term risk of the organization.

4.3. SOP

Sales and operation plays a major role in any organization and reflects the effectiveness and efficiency of the business operations while offsetting the risk. The effectiveness and efficiency of sales and operations of any organization get mirrored in its ability to increase the revenue (through sales), generate profits (from the business operations through optimum utilization of the resources and assets), ensure adequate liquidity and solvency (to meet short term and long term obligations) and short and long term capital (to continue the current operations), maintain the mobility of the assets and to tame down the risk (through control of the debt level). To this end we consider eight financial indicators to assess SOP of the firms in line with the discussions and findings recorded in the previous work [99], [100], [101], [102], [103], [104], [105], [106], [107], [108], [109], [110], [111], [112], [113], [114].

The criteria for assessing stock performance, DPC and SOP are described briefly in Table 4.

Table 4.

Criteria for comparing firm performance.

S/L	Criteria	Description	Effect direction	UOM	References
Aspect 1: Stock performance
C11	Average Stock Return (AROR)	AROR is derived by taking average of the monthly returns in a given financial year. The return for the $t\mathrm{th}$ month for the $i\mathrm{th}$ stock is defined as: $R_{it}=ln\left(\frac{P_t}{P_{t-1}}\right)$ ($P_t$ is the closing price for the $t\mathrm{th}$ month)	Max	Value	[82], [83], [84], [85], [86], [87]
C12	Return on Net Worth (RONW)	The return of equity (ROE) or RONW is calculated as $\text{RONW}=\frac{\text{Net Income}}{\text{Shareholder}{\mathrm{s}}^{\prime }\text{Equity}}$ A higher RONW is an indicator of the better utilization of the shareholder’s capital for generating income.	Max	%
C13	Earnings per Share (EPS)	EPS is defined as the net profit divided by the number of outstanding common shares. A higher value of EPS gives an indication of higher earnings with respect to the share prices.	Max	Rs.
C14	Price to Book Value (P/B)	The P/B ratio captures the market perception and is expressed in terms of the stock price divided by the book value per share.	Max	Times
C15	Turnover	Indicator of liquidity of the stocks and is measured by dividing the number of shares traded and average number of common shares outstanding in a given period.	Max	Rs. Million
C16	Shares Traded	The total number of shares of a specific company (i.e., equity stock) traded in a given period.	Max	Nos.
C17	Yield	The amount of cash flow to the investor against the invested capital. Higher is the yield, better is the growth potential of the firm.	Max	%
C18	Alpha	It is expressed as the average return of the stock/portfolio in excess of what is predicted by the CAPM. Hence, higher is the value better is the ability of the stock to beat the benchmark.	Max	Value
C19	Beta	An indicator to capture the systematic risk. The value is calculated as $R_{it}=\alpha +{\beta }_i{R}_{mt}+e_{it}$ Where, $R_{mt}$ is the market return at time t and $\alpha$ and ${\beta }_i$ are the intercept and slope respectively. Using the ordinary least square method, the beta value is calculated as ${\beta }_i=\frac{Co\mathrm{var}(R_{it},R_{mt})}{Var\left(R_{mt}\right)}$	Min	Value
Aspect 2: Dividend payout capability
C21	Institutional Ownership (IO)	% ownership by Non-promoters	Maximize	%	[91], [92]
C22	Size of the Firm (S)	Natural Log of total assets	Maximize	Value	[93]
C23	Net Profit Margin (NPM)	(Net Profit/Revenue)*100%	Maximize	%	[94], [95]
C24	Return on Capital Employed (ROCE)	(PBIT/Capital Employed)*100%	Maximize	%	[97], [98]
C25	Sales Growth (SG)	Natural Log of (Sales at t/Sales at (t $-$ 1))	Maximize	Value	[96]
C26	Market Cap/Enterprise Value (MCEV)	Market capitalization/Enterprise Value	Maximize	Times	[97], [98]
C27	Net Cash Flow (from operating activities) (NCF)	Net amount of money being generated from regular business operations	Maximize	Rs. Million	[97], [98]
C28	Leverage (L)	Debt/PBITDA	Minimize	Times	[95]
Aspect 3: Sales and operational performance
C31	Sales Growth (SG)	Natural Log of (Sales at t/Sales at (t $-$ 1))	Max	Value	[99], [100], [101], [102], [103], [104], [105], [106], [107], [108], [109]
C32	Return on Assets (ROA)	Net income/total asset	Max	%	[99], [100], [101], [102], [103], [104], [105], [106], [107], [108], [109]
C33	Return on Working Capital (RWC)	((Op. Profit-tax)(current asset/total asset))/working capital	Max	Times	[110]
C34	Operating Profit Margin (OPM)	ratio of the earnings before income and taxes (i.e., a firm’s net operating income) to sales	Max	%	[99], [100], [101], [102], [103], [104], [105], [106], [107], [108], [109]
C35	Asset Turnover Ratio (ATR)	Sales/total asset	Max	Times	[99], [100], [101], [102], [103], [104], [105], [106], [107], [108], [109]
C36	Net cash flow from operating activities (NCF)	Net cash from operating activities.	Max	Rs. Million	[99], [100], [101], [102], [103], [104], [105], [106], [107], [108], [109]
C37	Cash to current liability (CCL)	(cash $+$ cash equivalents $+$ marketable securities)/total current liabilities	Max	Times	[99], [100], [101], [102], [103], [104], [105], [106], [107], [108], [109]
C38	Net Working Capital Days (NWCD)	Days account receivable $+$ days inventory - days account payable	Max	Days	[111], [112], [113], [114]
C39	Leverage (L)	Debt/PBITDA	Min	Times	[99], [100], [101], [102], [103], [104], [105], [106], [107], [108], [109]

4.4. FS

According to the definition given by the World Bank [115], FS is “….about resilience of financial systems to stress…..A stable financial system is capable of efficiently allocating resources, assessing and managing financial risks… dissipates financial imbalances that arise endogenously or as a result of significant adverse and unforeseen events..”

From the investment point of view, often the investors are concerned about the long-term wellbeing of the organizations and their stability at the market place. In other words, a stable firm is one that shows solvency under stress and safeguard the bankruptcy risk [116]. Hence, in this study we use Altman’s Z score as the indicator for financial stability wherein higher is the score stable is the organization [117], [118], [119], [120]. The Z score is calculated as:

$$Z=1.2X_1+1.4X_2+3.3X_3+0.6X_4+1.0X_5$$ (1)

where,

$X_1$$=$ working capital/total assets

$X_2$$=$ retained earnings/total assets

$X_3$$=$ earnings before interest and taxes/total assets

$X_4$$=$ market value equity/book value of total liabilities

$X_5$$=$ sales/total assets, and

The Z value of 2.99 denotes the non-bankruptcy condition while below 1.81 indicates the bankruptcy. The zone between 1.81 and 2.99 is termed as the grey area [121].

Altman’s Z has been widely used in assessing the stability and predicting the bankruptcy risk of the firms. In what follows are some of the recent cases in the context of manufacturing organizations. For example, Swalih et al. [122] used Altman’s Z to evaluate the financial health of Indian automobile companies listed in the National Stock Exchange (NSE) and confirmed that stability of the firms against financial distress in short to medium term. For evaluating the performance of a group of socially responsible firms (during 2015–2019) listed in BSE India, the authors [123] utilized MCDM based approach wherein Altman’s Z was calculated to gauge the bankruptcy profile of the organizations. Sareen and Sharma [124] recognized the importance of noting the signs of financial distress at early stage of investment decision making. The authors calculated Altman’s Z score to predict the financial soundness vis-à-vis stock prices for Indian automobile firms under the influence of financial crisis and implementation of goods and services tax (GST). A continuous assessment of bankruptcy risk and prediction of future state of financial stability is of utmost importance for the firms under the rapid changes in the external business environment and uncertainty prevailing over the market especially in the country like India. Siekelova et al. [125] used Altman’s Z score based calculations (using a window of five years) to predict the financial health of 105 Romanian manufacturing firms. Manaseer and Al-Oshaibat [126] examined the validity of the Altman’s Z score based model for prediction of financial distress for a sample of insurance firms listed on Amman Stock Exchange (ASE) during 2011–2016.

4.5. ES

Over the years the area corporate sustainability has garnered extensive interest of the researchers. There have been many models that tried to put forth several dimensions to assess the corporate sustainability. In a nutshell, sustainability is aimed to achieve an inclusive development of the triple bottom lines such as people, planet and profit [127]. In this work from the point of view of investment decision making, financial sustainability or ES is considered. ES is obtained through organization’s focus on achieving long-term growth through financial solidity. In this regard, Tobin’s Q is considered by several researchers as the indicator for long-term growth and economic sustainability. For instance, Fu et al. [128] confirmed the positive causal association between a higher Tobin’s Q and better future operating performance through a study on US firms. Gharaibeh and Qader [129] treated Tobin’s Q as a measure of firm value and reported a positive association with market capitalization, profitability, business growth and solvency. Singh et al. [130] utilized Tobin’s Q as a measure of corporate performance. Hence, we consider Tobin’s Q as a measure of ES in this work wherein higher is the value of Q, better is the sustainability and log-term prospect of the organization. The Tobin’s Q value is calculated [131], [132] follows.

Tobin’s Q $=$ (Market value of equity + Book value of debts)/Book value of total assets

5. Methods

In this section we briefly discuss the methods used in the current work. The present paper utilizes a combined LOPCOW and EDAS framework for performance based ranking of the firms. To aggregate the year wise ranking results, we use a widely applied MCDM aggregation algorithm such as BC.

5.1. LOPCOW method

The procedural steps [37] are described below briefly:

Step 1. Construction of the normalized decision matrix

Let, $X={\left[x_{ij}\right]}_{m\times n}$ be the decision-matrix ($m$ is the number of alternatives and $n$ is the number of criteria). The decision matrix is expressed as

$$X={\left[x_{ij}\right]}_{m\times n}=\left(\begin{array}{ccc}\hfill x_{11}\hfill & \hfill \dots \hfill & \hfill x_{1n}\hfill \\ \hfill ⋮\hfill & \hfill \ddots \hfill & \hfill ⋮\hfill \\ \hfill x_{m1}\hfill & \hfill \cdots \hfill & \hfill x_{mn}\hfill \end{array}\right)$$ (2)

where m and n are having usual meaning as given above Then by using the linear max–min type normalization scheme the elements of the normalized decision matrix $R={\left[r_{ij}\right]}_{m\times n}$ are calculated as

$$r_{ij}=\frac{x_{ij}-x_{min}^j}{x_{max}^j-x_{min}^j}(\text{when }j\in j^{+}\text{, desired direction: maximizing})$$ (3)

$$r_{ij}=\frac{x_{max}^j-x_{ij}}{x_{max}^j-x_{min}^j}(\text{when }j\in j^{-}\text{, desired direction: minimizing})$$ (4)

Step 2. Find the Percentage Value (PV) for each criterion

The PV for each criterion is given by

$$P_j=|ln\left(\frac{\sqrt{\frac{\sum _{i=1}^m{r_{ij}}^2}{m}}}{\sigma }\right).100|$$ (5)

$\sigma$ denotes the standard deviation. This step helps to reduce the gaps among the criteria weights.

Step 3. Calculation of the criteria weights

The weight for the $j\mathrm{th}$ criterion is given by

$$w_j=\frac{P_j}{\sum _{j=1}^n{P}_j}$$ (6)

where, ${\sum }_{j=1}^n{w}_j=1$ (i.e., sum of the weights of all criteria $=$ 1)

Table 5.

Decision matrix for stock performance (FY 2013–14).

Criteria	C11	C12	C13	C14	C15	C16	C17	C18	C19
Alternatives	($+$)	($+$)	($+$)	($+$)	($+$)	($+$)	($+$)	($+$)	($-$)
A1	0.1373	47.48	76.73	2.72	2.71	5295	1.25	1.65	0.67
A2	−0.0010	29.91	10.20	6.13	1.32	6043	2.99	0.32	0.63
A3	−0.0126	0.74	0.28	2.59	2.01	20 615	3.09	0.36	1.22
A4	0.0396	45.85	28.70	11.79	4.81	5737	1.01	0.43	0.35
A5	−0.1358	22.39	5.51	1.91	6.78	132 047	0.98	0.70	1.27
A6	0.0081	86.91	34.81	31.14	7.17	5216	1.97	0.45	0.23
A7	0.0225	38.84	3.86	16.46	4.61	25 714	0.89	0.49	0.30
A8	−0.0086	−0.22	−0.16	1.85	3.10	23 198	0.00	0.14	0.96
A9	−0.0269	37.12	13.95	10.63	5.41	12 374	1.92	0.59	0.78
A10	−0.0635	3.37	0.20	0.79	0.30	63 634	0.00	−0.30	0.27
A11	−0.0016	6.43	16.27	5.58	3.42	1720	0.76	0.35	0.42
A12	0.0128	16.56	175.58	2.86	1.06	330	1.25	0.53	0.62
A13	0.0074	19.40	16.49	9.59	26.56	31 579	0.59	0.73	0.38
A14	0.0888	49.30	7.01	16.37	1.28	4693	1.06	1.08	0.61
A15	0.0215	117.29	16.13	39.85	37.50	62 231	1.91	0.36	0.30
A16	0.0111	35.96	10.95	10.71	91.91	259 696	1.49	0.47	0.34
A17	0.0189	13.11	5.81	4.28	4.40	21 154	1.68	0.73	0.70
A18	0.0681	29.24	11.29	1.14	5.78	117 577	1.62	0.76	1.46
A19	−0.0010	29.51	9.07	6.85	1.63	7713	1.91	0.47	0.27
A20	0.0073	53.43	111.93	18.37	14.71	2945	0.72	0.46	0.16
A21	0.0196	33.87	80.24	10.21	9.43	2979	0.78	0.52	0.42
A22	0.0094	8.08	4.53	2.50	31.07	213 970	0.55	0.20	0.66
A23	0.0132	9.80	3.84	3.63	106.43	708 307	1.43	0.26	0.73
A24	0.0137	14.20	8.24	13.31	5.86	7120	0.09	0.79	0.91
A25	0.0113	31.14	23.19	5.97	1.71	3440	1.21	0.70	0.84
A26	−0.0265	9.03	7.69	1.01	11.00	124 420	1.12	0.40	1.18
A27	0.0595	47.04	26.31	10.46	10.50	14 358	1.15	1.88	0.76
A28	0.0019	33.73	8.53	9.23	80.35	304 394	0.80	0.62	0.74
A29	0.0631	11.07	5.15	3.33	44.23	272 910	0.99	0.17	1.49
A30	0.0040	18.03	9.46	4.03	2.37	10 267	0.00	0.71	1.02

5.2. EDAS method

The steps to carry out the comparative ranking using the EDAS method [27] is given below.

Step 1. Formation of the decision matrix

$$X={\left[x_{ij}\right]}_{m\times n}=\left(\begin{array}{ccc}\hfill x_{11}\hfill & \hfill \dots \hfill & \hfill x_{1n}\hfill \\ \hfill ⋮\hfill & \hfill \ddots \hfill & \hfill ⋮\hfill \\ \hfill x_{m1}\hfill & \hfill \cdots \hfill & \hfill x_{mn}\hfill \end{array}\right)$$

Table 6.

Normalized decision matrix for stock performance (FY 2013–14).

Alternatives	Criteria
	C11	C12	C13	C14	C15	C16	C17	C18	C19
A1	1.0000	0.4059	0.4375	0.0494	0.0227	0.0070	0.4045	0.8945	0.6165
A2	0.4937	0.2564	0.0590	0.1367	0.0096	0.0081	0.9676	0.2844	0.6466
A3	0.4513	0.0082	0.0025	0.0461	0.0161	0.0287	1.0000	0.3028	0.2030
A4	0.6423	0.3921	0.1642	0.2816	0.0425	0.0076	0.3269	0.3349	0.8571
A5	0.0000	0.1924	0.0323	0.0287	0.0611	0.1860	0.3172	0.4587	0.1654
A6	0.5269	0.7415	0.1990	0.7770	0.0647	0.0069	0.6375	0.3440	0.9474
A7	0.5797	0.3324	0.0229	0.4012	0.0406	0.0359	0.2880	0.3624	0.8947
A8	0.4657	0.0000	0.0000	0.0271	0.0264	0.0323	0.0000	0.2018	0.3985
A9	0.3988	0.3178	0.0803	0.2519	0.0481	0.0170	0.6214	0.4083	0.5338
A10	0.2648	0.0306	0.0020	0.0000	0.0000	0.0894	0.0000	0.0000	0.9173
A11	0.4916	0.0566	0.0935	0.1226	0.0294	0.0020	0.2460	0.2982	0.8045
A12	0.5441	0.1428	1.0000	0.0530	0.0072	0.0000	0.4045	0.3807	0.6541
A13	0.5244	0.1670	0.0947	0.2253	0.2474	0.0441	0.1909	0.4725	0.8346
A14	0.8225	0.4214	0.0408	0.3989	0.0092	0.0062	0.3430	0.6330	0.6617
A15	0.5762	1.0000	0.0927	1.0000	0.3505	0.0874	0.6181	0.3028	0.8947
A16	0.5378	0.3079	0.0632	0.2540	0.8632	0.3663	0.4822	0.3532	0.8647
A17	0.5666	0.1134	0.0340	0.0893	0.0386	0.0294	0.5437	0.4725	0.5940
A18	0.7465	0.2507	0.0652	0.0090	0.0516	0.1656	0.5243	0.4862	0.0226
A19	0.4935	0.2530	0.0525	0.1551	0.0125	0.0104	0.6181	0.3532	0.9173
A20	0.5239	0.4566	0.6378	0.4501	0.1358	0.0037	0.2330	0.3486	1.0000
A21	0.5692	0.2901	0.4575	0.2412	0.0860	0.0037	0.2524	0.3761	0.8045
A22	0.5316	0.0706	0.0267	0.0438	0.2899	0.3018	0.1780	0.2294	0.6241
A23	0.5456	0.0853	0.0228	0.0727	1.0000	1.0000	0.4628	0.2569	0.5714
A24	0.5475	0.1227	0.0478	0.3205	0.0524	0.0096	0.0291	0.5000	0.4361
A25	0.5386	0.2669	0.1329	0.1326	0.0133	0.0044	0.3916	0.4587	0.4887
A26	0.4004	0.0787	0.0447	0.0056	0.1008	0.1753	0.3625	0.3211	0.2331
A27	0.7152	0.4022	0.1506	0.2476	0.0961	0.0198	0.3722	1.0000	0.5489
A28	0.5043	0.2889	0.0494	0.2161	0.7543	0.4295	0.2589	0.4220	0.5639
A29	0.7284	0.0961	0.0302	0.0650	0.4139	0.3850	0.3204	0.2156	0.0000
A30	0.5120	0.1553	0.0547	0.0829	0.0195	0.0140	0.0000	0.4633	0.3534

Step 2. Find out the average solution

The average solution is found as

$$x_{j\left(avg\right)}=\frac{1}{m}\left(\sum _{i=1}^m{x}_{ij}\right);j=1,2,\dots ,n$$ (7)

Step 3. Calculation of PDA and NDA

The PDA and NDA are calculated as follows

PDA:

$$PD{A}_{ij}=\left(\begin{array}{c}\frac{Max(0,(x_{ij}-x_{j\left(avg\right)}))}{x_{j\left(avg\right)}};\forall j\in j^{+}(max\mathit{imizing})\hfill \\ \frac{Max(0,(x_{j\left(avg\right)}-x_{ij}))}{x_{j\left(avg\right)}};\forall j\in j^{-}(min\mathit{imizing})\hfill \end{array}\right\}$$ (8)

NDA:

$$ND{A}_{ij}=\left(\begin{array}{c}\frac{Max(0,(x_{j\left(avg\right)}-x_{ij}))}{x_{j\left(avg\right)}};\forall j\in j^{+}(max\mathit{imizing})\hfill \\ \frac{Max(0,(x_{ij}-x_{j\left(avg\right)}))}{x_{j\left(avg\right)}};\forall j\in j^{-}(min\mathit{imizing})\hfill \end{array}\right\}$$ (9)

Step 4. Find out the weighted sum of PDA (SP) and NDA values (SN) for all the alternatives

The weighted sums of PDA and NDA, termed as SP and SN are calculated as sum products and given below

$$S{P}_i=\sum _{j=1}^n{w}_{j}PD{A}_{ij}$$ (10)

$$S{N}_i=\sum _{j=1}^n{w}_{j}ND{A}_{ij}$$ (11)

Here, $w_j$ is the weight of the $j\mathrm{th}$ criterion.

Step 5. Find out the normalized weighted sum of PDA (NSP) and NDA values (NSN)

For weighted sum of PDAs:

$$NS{P}_i=\frac{S{P}_i}{\underset{i}{Max\left(S{P}_i\right)}}$$ (12)

For weighted sum of NDAs:

$$NS{N}_i=1-\frac{S{N}_i}{\underset{i}{Max\left(S{N}_i\right)}}$$ (13)

Step 6. Calculate the appraisal scores (AS) of the alternatives

The appraisal score of the $i\mathrm{th}$ alternative is computed as

$$\begin{array}{c}A{S}_i=\frac{1}{2}(NS{P}_i+NS{N}_i)\hfill \\ \text{Here, }0\le A{S}_i\le 1\hfill \end{array}$$ (14)

The alternatives are ranked as per their appraisal scores in descending order.

5.3. Borda Count (BC) method

BC is a widely used for aggregating the preference based rankings [133]. The procedural steps are given below as described in [134]

Step 1. Obtain the ranking of the alternatives based on preferences of the various decision makers or methods

Step 2. Find out the preference based point of each alternative equal to the number of alternatives succeeding the present alternative. Therefore, first ranked alternative shall receive (m $-$ 1) points, the second one shall get (m $-$ 2) points and so on.

Step 3. Find out the sum of the points obtained by each alternative

Step 4. Order the alternatives based on the total points in descending order.

5.4. Copeland Method (CM)

The CM starts after the BC as an extended and modified version. The procedural steps are described below as [134]

Step 1. Calculation of the win score of each alternative with respect to others

Step 2. Calculation of the loss score of each alternative equal to the score obtained by the alternatives in the first stage subtracted from majority wins’ score

Step 3. Find out the final score for each alternative equal to the difference between the win and loss scores.

Step 4. Order the alternatives based on their final scores in descending order.

6. Results

In this section the findings of the step by step data analysis using the combined LOPCOW-EDAS framework are highlighted. First, we present the results of the analysis of stock performance. As mentioned earlier, the decision matrices (FY 2013–14 to FY 2020–21) are given in the supplementary MS Excel file attached to this paper. As a sample, the decision matrix for evaluation of the stock performance is given in Table 5.

Now using the procedural steps of LOPCOW method (see expressions (3) to (6)) the criteria weights are calculated. The normalized decision matrix for stock performance for FY 2013–14 is given in Table 6.

For example, the calculation of the normalized values for $C_{11}$ and $C_{19}$ are shown below For the criterion $C_{11}$ the maximum and minimum values are given as

$$\begin{array}{c}{x}_{max}^1=max(x_{11},x_{21}....x_{m1})=0.137\hfill \\ x_{min}^1=min(x_{11},x_{21}....x_{m1})=-0.136\hfill \end{array}(\text{Here, m}=30)$$

Therefore, the difference is calculated as

$$x_{max}^1-x_{min}^1=0.137-(-0.136)=0.273$$

Similarly, for $C_{19}$ the values are

$$\begin{array}{c}{x}_{max}^9=max(x_{19},x_{29}....x_{m9})=1.490\hfill \\ x_{min}^9=min(x_{19},x_{29}....x_{m9})=0.160\hfill \\ x_{max}^9-x_{min}^9=1.490-0.160=1.330\hfill \end{array}$$

Now using the above-mentioned values, some of the calculations for finding out the normalized values are as given below

$$\begin{array}{c}{r}_{11}=\frac{x_{11}-x_{min}^1}{x_{max}^1-x_{min}^1}=\frac{0.137-(-0.136)}{0.273}=1.00\hfill \\ r_{131}=\frac{x_{131}-x_{min}^1}{x_{max}^1-x_{min}^1}=\frac{0.007-(-0.136)}{0.273}=0.524\hfill \\ r_{99}=\frac{x_{max}^9-x_{99}}{x_{max}^9-x_{min}^9}=\frac{1.490-0.780}{1.330}=0.534\hfill \end{array}$$

In this way, all other calculations are done to arrive at the normalized decision matrix for stock performance for FY 2013–14 (see Table 6).

Next, using the normalized decision matrix, the criteria weights are calculated using the expressions (5), (6). Examples of calculations are given below

$$P_1=|ln\left(\frac{\sqrt{\frac{\sum _{i=1}^m{r_{131}}^2}{30}}}{\sigma }\right).100|=ln\left(\frac{0.5668}{0.1707}\right)=120.03$$

$$w_1=\frac{P_1}{\sum _{j=1}^9{P}_j}=\frac{120.03}{466.196}=0.2575$$

$$w_9=\frac{P_9}{\sum _{j=1}^9{P}_j}=\frac{86.1713}{466.196}=0.1848$$

In this way, the criteria weights for stock performance for FY 2013–14 and all other FYs are calculated. Table 7 provides the summary of the criteria weight calculations and Table 8 shows the year wise criteria weights.

Table 7.

Year wise calculations for criteria weights (Stock performance).

FY	Values	C11	C12	C13	C14	C15	C16	C17	C18	C19
FY 2013–14	Mean square	0.3213	0.1108	0.0659	0.0921	0.0941	0.0558	0.2029	0.1932	0.4376
	SD	0.1707	0.2154	0.2191	0.2266	0.2640	0.2093	0.2462	0.1900	0.2794
	PV	120.0313	43.5296	15.8580	29.2398	15.0061	12.0794	60.3993	83.8811	86.1713
	Wj	0.2575	0.0934	0.0340	0.0627	0.0322	0.0259	0.1296	0.1799	0.1848
FY 2014–15	Mean square	0.5531	0.1515	0.1002	0.1048	0.0360	0.0384	0.1556	0.1293	0.5098
	SD	0.1885	0.2009	0.2571	0.2262	0.1832	0.1888	0.2340	0.1962	0.2827
	PV	137.2718	66.1469	20.8005	35.8097	3.4896	3.7943	52.2041	60.5688	92.6395
	Wj	0.2904	0.1399	0.0440	0.0758	0.0074	0.0080	0.1104	0.1281	0.1960
FY 2015–16	Mean square	0.3187	0.2082	0.0942	0.1714	0.0422	0.0450	0.1899	0.1198	0.5113
	SD	0.1845	0.2080	0.2228	0.2537	0.1869	0.1954	0.2545	0.1957	0.2808
	PV	111.8184	78.5812	32.0544	48.9709	9.4575	8.1926	53.8030	57.0345	93.4594
	Wj	0.2266	0.1593	0.0650	0.0993	0.0192	0.0166	0.1091	0.1156	0.1894
FY 2016–17	Mean square	0.2989	0.2633	0.0873	0.1659	0.1020	0.0668	0.1120	0.1693	0.5274
	SD	0.2030	0.2174	0.2254	0.2416	0.2624	0.2318	0.2358	0.2333	0.2449
	PV	99.0550	85.8899	27.0595	52.2429	19.6315	10.8815	35.0415	56.7218	108.6903
	Wj	0.2000	0.1734	0.0546	0.1055	0.0396	0.0220	0.0708	0.1145	0.2195
FY 2017–18	Mean square	0.2327	0.2559	0.1194	0.1396	0.0751	0.0768	0.0941	0.1693	0.5274
	SD	0.2127	0.1909	0.2619	0.2496	0.2292	0.2499	0.2246	0.2333	0.2449
	PV	81.9083	97.4805	27.7131	40.3727	17.8739	10.3230	31.1785	56.7218	108.6903
	Wj	0.1734	0.2064	0.0587	0.0855	0.0378	0.0219	0.0660	0.1201	0.2301
FY 2018–19	Mean square	0.6183	0.1791	0.0741	0.1186	0.0477	0.0500	0.0844	0.1074	0.5042
	SD	0.2105	0.1862	0.2076	0.2521	0.2078	0.2059	0.2010	0.2061	0.2364
	PV	131.8125	82.1021	27.1173	31.1715	5.0471	8.2205	36.8093	46.3480	109.9958
	Wj	0.2754	0.1715	0.0567	0.0651	0.0105	0.0172	0.0769	0.0968	0.2298
FY 2019–20	Mean square	0.5064	0.1717	0.0667	0.0905	0.0546	0.0415	0.1361	0.3928	0.4552
	SD	0.1982	0.2062	0.2019	0.2491	0.2126	0.1965	0.2645	0.2352	0.2631
	PV	127.8145	69.7626	24.6094	18.8861	9.5149	3.5664	33.2761	98.0092	94.1707
	Wj	0.2665	0.1455	0.0513	0.0394	0.0198	0.0074	0.0694	0.2044	0.1963
FY 2020–21	Mean square	0.2284	0.1974	0.1049	0.0876	0.0696	0.0597	0.0670	0.3862	0.4183
	SD	0.2358	0.1862	0.2205	0.2241	0.2309	0.2344	0.2173	0.2350	0.2893
	PV	70.6451	86.9628	38.4570	27.8116	13.3597	4.1427	17.4762	97.2287	80.4578
	Wj	0.1618	0.1992	0.0881	0.0637	0.0306	0.0095	0.0400	0.2227	0.1843

Table 8.

Year wise criteria weights (Stock performance).

Criteria	C11	C12	C13	C14	C15	C16	C17	C18	C19
2013–14	0.2575	0.0934	0.0340	0.0627	0.0322	0.0259	0.1296	0.1799	0.1848
2014–15	0.2904	0.1399	0.0440	0.0758	0.0074	0.0080	0.1104	0.1281	0.1960
2015–16	0.2266	0.1593	0.0650	0.0993	0.0192	0.0166	0.1091	0.1156	0.1894
2016–17	0.2000	0.1734	0.0546	0.1055	0.0396	0.0220	0.0708	0.1145	0.2195
2017–18	0.1734	0.2064	0.0587	0.0855	0.0378	0.0219	0.0660	0.1201	0.2301
2018–19	0.2754	0.1715	0.0567	0.0651	0.0105	0.0172	0.0769	0.0968	0.2298
2019–20	0.2665	0.1455	0.0513	0.0394	0.0198	0.0074	0.0694	0.2044	0.1963
2020–21	0.1618	0.1992	0.0881	0.0637	0.0306	0.0095	0.0400	0.2227	0.1843

Next, we move to rank the alternatives based on their performances using EDAS method. The procedural steps are defined by the expressions (7) to (14). For example,

$$x_{1\left(avg\right)}=\frac{1}{30}\left(\sum _{i=1}^{30}{x}_{ij}\right)=\frac{0.137+(-0.001)+(-0.013)+0.040+\cdots +0.004}{30}=0.01206$$

$$x_{9\left(avg\right)}=\frac{1}{30}\left(\sum _{i=1}^{30}{x}_{ij}\right)=\frac{0.670+0.630+1.220+0.350+\cdots +1.020}{30}=0.690$$

$$PD{A}_{11}=\left(\frac{Max(0,(x_{11}-x_{{}_1\left(avg\right)}))}{x_{{}_1\left(avg\right)}}\right\}=\frac{Max(0,(0.137-0.01206))}{0.01206}=\frac{0.12494}{0.01206}=10.360$$

$$ND{A}_{11}=\left(\frac{Max(0,\left(x_{{}_1\left(avg\right)-}{x}_{11}\right))}{x_{{}_1\left(avg\right)}}\right\}=\frac{Max(0,(0.01206-0.137))}{0.01206}=\frac{0.000}{0.01206}=0.000$$

$$PD{A}_{89}=\left(\frac{Max(0,\left(x_{{}_9\left(avg\right)-}{x}_{89}\right))}{x_{{}_9\left(avg\right)}}\right\}=\frac{Max(0,(0.690-0.960))}{0.690}=\frac{0.000}{0.01206}=0.000$$

It may be noted that criterion 9 is a non-beneficial (i.e., minimizing effect) one.

Proceeding further we calculate $S{P}_i$, $S{N}_i$, $NS{P}_i$, $NS{N}_i$ and $A{S}_i$ values which are recorded in Table 9.

Table 9.

Ranking based on stock performance (FY 2013–14).

Alternatives	SP	SN	NSP	NSN	AS	Rank
A1	3.1590	0.0949	1.0000	0.9725	0.9863	1
A2	0.2165	0.4501	0.0685	0.8697	0.4691	21
A3	0.2115	0.9504	0.0670	0.7248	0.3959	25
A4	0.7557	0.1091	0.2392	0.9684	0.6038	7
A5	0.0577	3.4534	0.0183	0.0000	0.0091	30
A6	0.5613	0.1654	0.1777	0.9521	0.5649	8
A7	0.4092	0.1261	0.1296	0.9635	0.5465	9
A8	0.0000	1.0022	0.0000	0.7098	0.3549	27
A9	0.1246	0.9150	0.0394	0.7350	0.3872	26
A10	0.1125	2.2291	0.0356	0.3545	0.1951	29
A11	0.0723	0.5644	0.0229	0.8366	0.4297	24
A12	0.2537	0.1521	0.0803	0.9560	0.5181	14
A13	0.1561	0.2236	0.0494	0.9352	0.4923	16
A14	1.9367	0.0911	0.6131	0.9736	0.7933	2
A15	0.9167	0.0836	0.2902	0.9758	0.6330	5
A16	0.3518	0.0711	0.1114	0.9794	0.5454	10
A17	0.2545	0.1570	0.0806	0.9545	0.5176	15
A18	1.3176	0.3032	0.4171	0.9122	0.6646	4
A19	0.1938	0.4003	0.0613	0.8841	0.4727	20
A20	0.4048	0.2166	0.1281	0.9373	0.5327	12
A21	0.3339	0.0984	0.1057	0.9715	0.5386	11
A22	0.0738	0.3836	0.0234	0.8889	0.4561	22
A23	0.4112	0.2367	0.1302	0.9314	0.5308	13
A24	0.1379	0.2955	0.0436	0.9144	0.4790	19
A25	0.0498	0.1325	0.0158	0.9616	0.4887	18
A26	0.0132	1.1689	0.0042	0.6615	0.3328	28
A27	1.4968	0.0559	0.4738	0.9838	0.7288	3
A28	0.2154	0.2935	0.0682	0.9150	0.4916	17
A29	1.1988	0.4856	0.3795	0.8594	0.6194	6
A30	0.0452	0.5327	0.0143	0.8458	0.4300	23

In the similar way we rank the alternatives based on their stock performance for other FYs (refer Appendix A). We then apply the steps of the BC method and the Copeland method to figure out the aggregated ranking of the alternatives from FY 2013–14 to FY 2019–20. Table 10, Table 11, Table 12 provides the summary of the year wise ranking of the stock performances and the aggregated ranking.

Table 10.

Summary of year wise ranking based on stock performance.

Alternatives	Rank								Rank_2020–21
	2013–14	2014–15	2015–16	2016–17	2017–18	2018–19	2019–20	Aggregate
A1	1	1	1	1	1	1	6	1	13
A2	21	6	6	8	9	4	21	8	23
A3	25	10	10	13	28	28	24	23	20
A4	7	4	23	6	6	5	11	4	5
A5	30	13	22	7	26	18	9	19	22
A6	8	5	2	10	10	19	18	6	4
A7	9	16	15	16	11	25	25	16	16
A8	27	27	25	25	29	11	28	29	21
A9	26	9	14	17	21	2	17	15	19
A10	29	19	28	18	12	13	1	18	30
A11	24	12	13	20	4	9	22	13	14
A12	14	30	29	30	30	30	10	30	29
A13	16	23	26	19	27	6	20	22	18
A14	2	21	24	5	8	15	14	10	7
A15	5	3	16	2	3	16	5	2	8
A16	10	15	18	15	19	23	4	14	15
A17	15	24	20	14	18	3	8	12	24
A18	4	11	27	4	17	7	2	7	26
A19	20	18	3	12	14	17	16	11	12
A20	12	7	5	11	7	21	3	5	1
A21	11	8	9	3	2	10	12	3	2
A22	22	29	21	24	13	27	7	26	3
A23	13	28	7	28	23	12	30	24	6
A24	19	25	8	29	25	29	26	28	25
A25	18	17	4	21	16	24	29	20	17
A26	28	20	30	26	20	14	13	27	28
A27	3	2	12	27	15	8	19	9	27
A28	17	22	11	23	5	26	15	17	9
A29	6	26	19	22	24	22	23	25	10
A30	23	14	17	9	22	20	27	21	11

Table 11.

Calculation of the aggregated ranking (BC method) for stock performance.

Alternatives	Rank based number							Borda count	Aggregated rank
	2013–14	2014–15	2015–16	2016–17	2017–18	2018–19	2019–20
A1	29	29	29	29	29	29	24	198	1
A2	9	24	24	22	21	26	9	135	8
A3	5	20	20	17	2	2	6	72	23
A4	23	26	7	24	24	25	19	148	4
A5	0	17	8	23	4	12	21	85	19
A6	22	25	28	20	20	11	12	138	6
A7	21	14	15	14	19	5	5	93	16
A8	3	3	5	5	1	19	2	38	29
A9	4	21	16	13	9	28	13	104	15
A10	1	11	2	12	18	17	29	90	18
A11	6	18	17	10	26	21	8	106	13
A12	16	0	1	0	0	0	20	37	30
A13	14	7	4	11	3	24	10	73	22
A14	28	9	6	25	22	15	16	121	10
A15	25	27	14	28	27	14	25	160	2
A16	20	15	12	15	11	7	26	106	14
A17	15	6	10	16	12	27	22	108	12
A18	26	19	3	26	13	23	28	138	7
A19	10	12	27	18	16	13	14	110	11
A20	18	23	25	19	23	9	27	144	5
A21	19	22	21	27	28	20	18	155	3
A22	8	1	9	6	17	3	23	67	26
A23	17	2	23	2	7	18	0	69	24
A24	11	5	22	1	5	1	4	49	28
A25	12	13	26	9	14	6	1	81	20
A26	2	10	0	4	10	16	17	59	27
A27	27	28	18	3	15	22	11	124	9
A28	13	8	19	7	25	4	15	91	17
A29	24	4	11	8	6	8	7	68	25
A30	7	16	13	21	8	10	3	78	21
							Sum	3045

Table 12.

Calculation of the aggregated ranking (Copeland method) for stock performance.

Alternatives	Wins	Losses	Final score	Final rank		Alternatives	Wins	Losses	Final score	Final rank
A1	198	2847	−2649	1		A16	106	2939	−2833	14
A2	135	2910	−2775	8		A17	108	2937	−2829	12
A3	72	2973	−2901	23		A18	138	2907	−2769	7
A4	148	2897	−2749	4		A19	110	2935	−2825	11
A5	85	2960	−2875	19		A20	144	2901	−2757	5
A6	138	2907	−2769	6		A21	155	2890	−2735	3
A7	93	2952	−2859	16		A22	67	2978	−2911	26
A8	38	3007	−2969	29		A23	69	2976	−2907	24
A9	104	2941	−2837	15		A24	49	2996	−2947	28
A10	90	2955	−2865	18		A25	81	2964	−2883	20
A11	106	2939	−2833	13		A26	59	2986	−2927	27
A12	37	3008	−2971	30		A27	124	2921	−2797	9
A13	73	2972	−2899	22		A28	91	2954	−2863	17
A14	121	2924	−2803	10		A29	68	2977	−2909	25
A15	160	2885	−2725	2		A30	78	2967	−2889	21

Next, we move to find out the DPC of the alternatives year wise and rank them. Proceeding in the same way as described above, the comparative rankings of the alternatives are derived. Table 13 exhibits the year wise criteria weights and Table 14 provides the summary of the year wise ranking of the alternatives as per their DPC.

Table 13.

Year wise criteria weights (for finding out DPC).

Criteria	C21	C22	C23	C24	C25	C26	C27	C28
2013–14	0.0597	0.1226	0.0771	0.0615	0.1831	0.1532	0.1489	0.1940
2014–15	0.0986	0.1891	0.2256	0.1537	0.1479	0.1272	0.0394	0.0186
2015–16	0.0596	0.1148	0.1515	0.0984	0.1533	0.1779	0.0165	0.2279
2016–17	0.0749	0.1216	0.1643	0.1411	0.1908	0.0074	0.0241	0.2759
2017–18	0.0601	0.1060	0.1818	0.1124	0.1553	0.0019	0.0990	0.2835
2018–19	0.0564	0.0952	0.1225	0.0765	0.2130	0.2299	0.0199	0.1867
2019–20	0.0751	0.1072	0.1788	0.0840	0.0919	0.2119	0.0157	0.2355
2020–21	0.0459	0.0624	0.1604	0.0734	0.1653	0.1774	0.1359	0.1792

Table 14.

Year wise ranking of the Alternatives (based on DPC).

Alternatives	Rank								Rank_2020–21
	2013–14	2014–15	2015–16	2016–17	2017–18	2018–19	2019–20	Aggregate
A1	3	8	2	7	2	26	9	3	21
A2	12	7	11	6	11	10	8	4	8
A3	27	29	30	30	30	25	29	30	27
A4	10	14	7	9	14	15	13	9	14
A5	22	17	21	18	12	27	10	19	13
A6	6	4	10	10	19	19	11	8	7
A7	8	11	17	19	18	12	14	13	11
A8	9	1	9	27	28	29	28	21	28
A9	13	5	12	14	21	22	21	15	15
A10	29	30	27	29	5	13	1	23	5
A11	24	22	23	17	23	16	25	26	19
A12	16	25	26	12	25	30	7	25	17
A13	11	15	19	20	17	9	16	14	16
A14	20	26	5	11	26	20	24	22	25
A15	2	3	4	4	3	5	5	2	2
A16	1	2	3	2	1	6	3	1	1
A17	19	19	20	21	27	23	27	27	24
A18	14	20	16	25	22	3	15	16	12
A19	15	9	13	16	9	11	18	11	9
A20	5	13	29	5	10	8	6	6	3
A21	7	12	14	8	15	2	12	5	10
A22	21	27	1	23	6	1	19	12	26
A23	17	21	22	26	20	24	4	24	29
A24	23	24	6	24	8	17	26	20	20
A25	18	6	8	3	4	14	30	10	30
A26	30	28	25	28	29	4	20	29	4
A27	4	10	18	1	13	28	2	7	6
A28	26	16	28	15	7	7	22	17	22
A29	28	23	24	22	24	18	23	28	18
A30	25	18	15	13	16	21	17	18	23

In the similar fashion we derive the criteria weights and appraisal scores for ranking the alternatives based on their SOP (see Table 15, Table 16)

Table 15.

Year wise criteria weights for ranking based on SOP.

Criteria	C31	C32	C33	C34	C35	C36	C37	C38	C39
2013–14	0.1780	0.1026	0.1024	0.0531	0.1157	0.1449	0.0195	0.0952	0.1886
2014–15	0.1229	0.1877	0.2405	0.1116	0.1506	0.0327	0.0213	0.1171	0.0154
2015–16	0.1446	0.1232	0.2618	0.0521	0.1073	0.0155	0.0046	0.0759	0.2150
2016–17	0.1832	0.1548	0.0397	0.0728	0.1445	0.0231	0.0137	0.1033	0.2649
2017–18	0.1272	0.1094	0.2021	0.0536	0.1064	0.0811	0.0100	0.0778	0.2323
2018–19	0.2527	0.1224	0.0683	0.0675	0.1079	0.0236	0.0400	0.0959	0.2215
2019–20	0.0852	0.1181	0.2156	0.1292	0.1010	0.0145	0.0392	0.0791	0.2182
2020–21	0.1709	0.1126	0.0970	0.1591	0.0592	0.1404	0.0236	0.0521	0.1852

Table 16.

Year wise ranking of the Alternatives (based on SOP).

Alternatives	Rank								Rank_2020–21
	2013–14	2014–15	2015–16	2016–17	2017–18	2018–19	2019–20	Aggregate
A1	3	2	5	5	5	18	9	3	17
A2	10	9	11	10	6	13	14	7	16
A3	26	27	28	27	27	22	28	30	20
A4	7	4	8	11	9	15	1	4	27
A5	13	17	6	13	19	17	8	14	13
A6	24	30	29	3	30	19	29	27	11
A7	11	7	10	22	8	14	17	10	15
A8	28	24	17	30	26	29	27	29	8
A9	14	10	27	21	28	25	16	21	19
A10	29	29	30	29	20	12	6	26	3
A11	21	18	21	14	4	16	18	18	21
A12	8	12	18	2	17	30	2	11	12
A13	22	1	2	25	29	8	21	17	23
A14	17	26	15	7	25	20	24	19	25
A15	2	5	3	9	3	6	10	2	4
A16	1	3	12	4	7	5	5	1	1
A17	18	28	25	24	2	27	23	24	28
A18	9	13	13	15	16	1	11	8	9
A19	15	15	14	16	10	10	12	13	10
A20	6	20	26	12	15	7	4	12	2
A21	5	11	16	17	1	4	13	6	7
A22	16	25	9	20	12	2	15	15	24
A23	19	22	23	23	24	23	7	22	29
A24	12	8	1	19	11	11	19	9	14
A25	20	14	22	8	22	26	30	23	30
A26	30	19	4	28	21	9	26	20	5
A27	4	6	7	1	14	28	3	5	6
A28	23	16	24	6	13	3	20	16	18
A29	27	23	19	26	23	21	25	28	22
A30	25	21	20	18	18	24	22	25	26

We now use the expression (1) to calculate the Altman’s Z scores for the alternatives year wise to find out their financial stability (see Table 17). It may be noted that higher is the value of Z, better is the financial stability of the company or alternative. Hence, we sort the Z values in descending manner and find out the comparative ranking of the alternatives for each FY. After that we apply the ranking aggregation methods such as BC and Copeland. Table 18 provides the summary of the comparative positions of the alternatives in terms of their FS.

Table 17.

Year wise Altman’s Z scores of the alternatives.

	Altman’s Z score
	2013–14	2014–15	2015–16	2016–17	2017–18	2018–19	2019–20	2020–21
A1	17.88	22.187	22.999	17.097	16.038	21.038	18.425	17.452
A2	19.31	19.307	16.790	15.105	13.495	12.147	13.715	14.884
A3	16.82	10.115	10.810	10.744	9.590	8.362	6.680	3.742
A4	48.77	54.687	52.529	41.133	34.999	20.501	15.783	14.809
A5	19.08	18.936	18.626	21.288	10.012	7.025	7.217	7.433
A6	14.73	13.273	11.082	10.238	8.796	8.761	7.007	7.664
A7	33.94	30.356	28.420	24.857	37.463	35.578	37.229	27.739
A8	2.37	2.510	2.656	3.543	2.680	2.982	2.619	4.937
A9	25.26	38.578	13.772	18.004	21.014	24.585	19.295	22.209
A10	2.90	1.007	1.501	1.656	1.896	1.908	1.325	−0.333
A11	25.61	23.632	21.951	22.361	22.074	25.463	24.364	18.607
A12	7.18	7.212	8.494	9.624	8.218	5.864	4.836	4.577
A13	23.14	22.919	17.583	16.117	14.743	14.089	14.838	14.867
A14	20.23	16.594	15.515	12.374	8.869	10.120	8.478	7.638
A15	38.36	47.578	43.363	37.001	36.199	33.355	17.639	18.058
A16	17.93	26.567	24.622	20.984	19.483	20.747	18.265	17.315
A17	4.30	4.651	4.749	4.630	5.585	5.065	5.714	5.568
A18	3.77	3.703	3.883	4.217	4.260	3.858	4.975	5.925
A19	25.96	43.630	46.471	41.240	32.358	33.082	29.498	29.429
A20	37.41	33.123	30.986	28.683	25.655	22.452	20.851	20.391
A21	55.39	40.558	54.699	44.360	45.699	40.957	42.255	32.928
A22	7.87	7.788	6.131	7.503	9.161	11.770	9.878	11.011
A23	17.56	42.597	64.968	61.394	72.446	33.943	19.343	18.154
A24	13.92	15.047	13.681	14.226	15.170	14.481	14.284	12.719
A25	76.08	197.495	122.599	120.874	3.797	3.818	60.676	64.858
A26	3.85	3.671	2.821	2.954	2.732	2.956	3.659	3.077
A27	43.73	38.618	37.833	28.487	28.075	27.399	25.423	24.376
A28	41.29	26.878	30.213	29.847	23.464	19.753	15.487	13.021
A29	9.54	9.453	8.047	7.501	7.217	6.769	7.042	6.002
A30	15.95	14.256	11.173	11.332	10.407	9.251	7.805	8.075

Table 18.

Comparative year wise ranking of the Alternatives based on FS.

Alternatives	Rank								Rank_2020–21
	2013–14	2014–15	2015–16	2016–17	2017–18	2018–19	2019–20	Final
A1	17	15	12	15	13	10	10	14	11
A2	14	16	16	17	16	16	17	16	13
A3	19	22	22	21	19	21	24	21	28
A4	3	2	4	5	5	12	13	5	15
A5	15	17	14	12	18	22	21	17	22
A6	21	21	21	22	22	20	23	22	20
A7	8	10	10	10	3	2	3	7	4
A8	30	29	29	28	29	28	29	29	26
A9	11	8	18	14	11	8	9	12	6
A10	29	30	30	30	30	30	30	30	30
A11	10	13	13	11	10	7	6	11	8
A12	25	25	23	23	23	24	27	25	27
A13	12	14	15	16	15	15	15	15	14
A14	13	18	17	19	21	18	19	19	21
A15	6	3	6	6	4	4	12	4	10
A16	16	12	11	13	12	11	11	13	12
A17	26	26	26	26	25	25	25	26	25
A18	28	27	27	27	26	26	26	27	24
A19	9	4	5	4	6	5	4	2	3
A20	7	9	8	8	8	9	7	8	7
A21	2	6	3	3	2	1	2	1	2
A22	24	24	25	24	20	17	18	23	18
A23	18	5	2	2	1	3	8	3	9
A24	22	19	19	18	14	14	16	18	17
A25	1	1	1	1	27	27	1	9	1
A26	27	28	28	29	28	29	28	28	29
A27	4	7	7	9	7	6	5	6	5
A28	5	11	9	7	9	13	14	10	16
A29	23	23	24	25	24	23	22	24	23
A30	20	20	20	20	17	19	20	20	19

We move forward to calculate the Tobin’s Q values for the alternatives year wise which is seen in Table 19. It is suggested to have a higher Q value for an organization (i.e., alternative) to have ES with growth potential. Hence, the alternatives are ranked in terms of their Q values wherein higher is the Q value, preferred is the alternative. The results are summarized in Table 20.

Table 19.

Year wise Tobin’s Q scores of the Alternatives.

Alternatives	Tobin’s Q values
	2013–14	2014–15	2015–16	2016–17	2017–18	2018–19	2019–20	2020–21
A1	14.44	12.842	10.043	7.401	5.110	5.006	4.392	3.624
A2	3.80	3.908	3.739	3.688	3.881	3.101	2.788	2.634
A3	13.65	10.561	10.698	9.852	9.733	9.385	8.773	5.543
A4	37.58	29.892	25.145	19.776	15.927	12.697	12.715	13.783
A5	10.22	9.312	8.741	7.801	5.863	4.907	4.366	4.109
A6	15.72	13.452	11.501	10.014	9.141	8.902	8.159	7.695
A7	26.24	22.406	18.763	16.058	16.719	17.260	15.854	12.988
A8	2.85	2.807	3.087	3.194	2.910	2.943	2.823	2.641
A9	14.25	13.470	8.477	8.728	8.189	8.150	8.586	8.994
A10	0.47	0.665	0.732	0.568	0.573	0.599	0.713	0.749
A11	15.92	14.023	11.806	17.162	14.281	13.988	12.373	12.210
A12	3.26	3.096	2.959	2.895	2.638	2.355	2.121	1.896
A13	15.77	14.436	11.976	11.144	10.347	10.050	9.027	7.883
A14	23.08	18.760	17.554	13.229	10.192	9.130	7.855	6.892
A15	42.83	41.273	39.093	33.825	31.920	30.040	8.828	8.870
A16	8.30	7.294	6.766	5.972	5.314	4.955	5.168	4.984
A17	3.43	4.749	4.265	4.007	3.920	4.122	3.773	3.771
A18	2.51	2.299	2.275	2.085	1.896	1.637	1.608	1.457
A19	16.66	18.789	17.338	16.402	13.734	15.850	14.847	14.724
A20	30.45	28.849	26.005	24.233	21.677	24.769	22.761	22.393
A21	30.06	24.028	21.471	39.102	31.765	28.339	24.639	28.162
A22	6.46	6.324	5.144	5.559	5.647	5.714	4.789	4.617
A23	14.29	15.877	15.924	14.480	13.966	5.900	5.388	5.265
A24	11.45	11.456	10.155	9.873	9.260	8.177	7.838	7.650
A25	21.16	20.255	16.919	13.660	2.343	2.404	2.508	2.526
A26	2.34	1.976	1.745	1.667	1.697	1.611	1.691	2.342
A27	19.30	16.011	15.352	10.339	8.369	7.626	7.564	6.675
A28	30.72	23.953	23.167	20.753	16.691	14.512	12.292	10.100
A29	7.99	6.955	5.987	5.248	4.903	4.554	4.290	3.790
A30	11.48	9.774	7.739	6.972	6.028	5.154	4.644	4.656

Table 20.

Comparative year wise ranking of the Alternatives based on Tobin’s Q scores.

Alternatives	Rank								Rank_2020–21
	2013–14	2014–15	2015–16	2016–17	2017–18	2018–19	2019–20	Aggregate
A1	14	16	17	19	21	19	20	18	23
A2	24	25	25	25	24	24	25	25	25
A3	17	18	15	16	12	10	10	14	15
A4	2	2	3	5	6	8	5	4	4
A5	20	20	18	18	18	21	21	20	20
A6	13	15	14	14	14	12	12	13	11
A7	6	6	6	8	4	4	3	6	5
A8	27	27	26	26	25	25	24	26	24
A9	16	14	19	17	16	14	11	15	8
A10	30	30	30	30	30	30	30	30	30
A11	11	13	13	6	7	7	6	8	6
A12	26	26	27	27	26	27	27	27	28
A13	12	12	12	12	10	9	8	10	10
A14	7	9	7	11	11	11	13	9	13
A15	1	1	1	2	1	1	9	1	9
A16	21	21	21	21	20	20	17	21	17
A17	25	24	24	24	23	23	23	24	22
A18	28	28	28	28	28	28	29	28	29
A19	10	8	8	7	9	5	4	7	3
A20	4	3	2	3	3	3	2	2	2
A21	5	4	5	1	2	2	1	3	1
A22	23	23	23	22	19	17	18	22	19
A23	15	11	10	9	8	16	16	11	16
A24	19	17	16	15	13	13	14	16	12
A25	8	7	9	10	27	26	26	17	26
A26	29	29	29	29	29	29	28	29	27
A27	9	10	11	13	15	15	15	12	14
A28	3	5	4	4	5	6	7	5	7
A29	22	22	22	23	22	22	22	23	21
A30	18	19	20	20	17	18	19	19	18

Next, we attempt to discern the effect of COVID-19 on the performance of the alternatives. Table 21, Table 22, Table 23, Table 24, Table 25, Table 26 show the comparative analysis of the rankings for both periods, i.e., pre COVID-19 (FY 13–14 to FY 19–20) and post COVID 19 (FY 20–21) for stock performance, DPC, SOP, FS and ES respectively.

Table 21.

COVID-19 impact on ranking of Alternatives based on stock performance.

Alternatives	Category	Pre-Covid	Post-Covid	Difference		Alternatives	Category	Pre-Covid	Post-Covid	Difference
A1	FMCG	1	13	−12		A7	FMCG	16	16	0
A15	FMCG	2	8	−6		A28	CD	17	9	8
A21	FMCG	3	2	1		A10	FMCG	18	30	−12
A4	FMCG	4	5	−1		A5	FMCG	19	22	−3
A20	FMCG	5	1	4		A25	FMCG	20	17	3
A6	FMCG	6	4	2		A30	CD	21	11	10
A18	FMCG	7	26	−19		A13	FMCG	22	18	4
A2	FMCG	8	23	−15		A3	FMCG	23	20	3
A27	CD	9	27	−18		A23	FMCG	24	6	18
A14	FMCG	10	7	3		A29	CD	25	10	15
A19	FMCG	11	12	−1		A22	FMCG	26	3	23
A17	FMCG	12	24	−12		A26	CD	27	28	−1
A11	FMCG	13	14	−1		A24	FMCG	28	25	3
A16	FMCG	14	15	−1		A8	FMCG	29	21	8
A9	FMCG	15	19	−4		A12	FMCG	30	29	1

Pre-Covid: Aggregated ranking based on stock performance during FY 2013–14 to FY 2019–20

Post-Covid: Stock performance during FY 2020–21.

Table 22.

COVID-19 impact on ranking of Alternatives based on DPC.

Alternatives	Category	Pre-COVID	Post-COVID	Difference		Alternatives	Category	Pre-COVID	Post-COVID	Difference
A16	FMCG	1	1	0		A18	FMCG	16	12	4
A15	FMCG	2	2	0		A28	CD	17	22	−5
A1	FMCG	3	21	−18		A30	CD	18	23	−5
A2	FMCG	4	8	−4		A5	FMCG	19	13	6
A21	FMCG	5	10	−5		A24	FMCG	20	20	0
A20	FMCG	6	3	3		A8	FMCG	21	28	−7
A27	CD	7	6	1		A14	FMCG	22	25	−3
A6	FMCG	8	7	1		A10	FMCG	23	5	18
A4	FMCG	9	14	−5		A23	FMCG	24	29	−5
A25	FMCG	10	30	−20		A12	FMCG	25	17	8
A19	FMCG	11	9	2		A11	FMCG	26	19	7
A22	FMCG	12	26	−14		A17	FMCG	27	24	3
A7	FMCG	13	11	2		A29	CD	28	18	10
A13	FMCG	14	16	−2		A26	CD	29	4	25
A9	FMCG	15	15	0		A3	FMCG	30	27	3

Table 23.

COVID-19 impact on ranking of Alternatives based on SOP.

Alternatives	Category	Pre-COVID	Post-COVID	Difference		Alternatives	Category	Pre-COVID	Post-COVID	Difference
A16	FMCG	1	1	0		A28	CD	16	18	−2
A15	FMCG	2	4	−2		A13	FMCG	17	23	−6
A1	FMCG	3	17	−14		A11	FMCG	18	21	−3
A4	FMCG	4	27	−23		A14	FMCG	19	25	−6
A27	CD	5	6	−1		A26	CD	20	5	15
A21	FMCG	6	7	−1		A9	FMCG	21	19	2
A2	FMCG	7	16	−9		A23	FMCG	22	29	−7
A18	FMCG	8	9	−1		A25	FMCG	23	30	−7
A24	FMCG	9	14	−5		A17	FMCG	24	28	−4
A7	FMCG	10	15	−5		A30	CD	25	26	−1
A12	FMCG	11	12	−1		A10	FMCG	26	3	23
A20	FMCG	12	2	10		A6	FMCG	27	11	16
A19	FMCG	13	10	3		A29	CD	28	22	6
A5	FMCG	14	13	1		A8	FMCG	29	8	21
A22	FMCG	15	24	−9		A3	FMCG	30	20	10

Table 24.

COVID-19 impact on ranking of Alternatives based on FS.

Company	Category	Pre-COVID	Post-COVID	Difference		Company	Category	Pre-COVID	Post-COVID	Difference
A21	FMCG	1	2	−1		A2	FMCG	16	13	3
A19	FMCG	2	3	−1		A5	FMCG	17	22	−5
A23	FMCG	3	9	−6		A24	FMCG	18	17	1
A15	FMCG	4	10	−6		A14	FMCG	19	21	−2
A4	FMCG	5	15	−10		A30	CD	20	19	1
A27	CD	6	5	1		A3	FMCG	21	28	−7
A7	FMCG	7	4	3		A6	FMCG	22	20	2
A20	FMCG	8	7	1		A22	FMCG	23	18	5
A25	FMCG	9	1	8		A29	CD	24	23	1
A28	CD	10	16	−6		A12	FMCG	25	27	−2
A11	FMCG	11	8	3		A17	FMCG	26	25	1
A9	FMCG	12	6	6		A18	FMCG	27	24	3
A16	FMCG	13	12	1		A26	CD	28	29	−1
A1	FMCG	14	11	3		A8	FMCG	29	26	3
A13	FMCG	15	14	1		A10	FMCG	30	30	0

Table 25.

COVID-19 impact on ranking of Alternatives based on ES.

Company	Category	Pre-COVID	Post-COVID	Difference		Company	Category	Pre-COVID	Post-COVID	Difference
A15	FMCG	1	9	−8		A24	FMCG	16	12	4
A20	FMCG	2	2	0		A25	FMCG	17	26	−9
A21	FMCG	3	1	2		A1	FMCG	18	23	−5
A4	FMCG	4	4	0		A30	CD	19	18	1
A28	CD	5	7	−2		A5	FMCG	20	20	0
A7	FMCG	6	5	1		A16	FMCG	21	17	4
A19	FMCG	7	3	4		A22	FMCG	22	19	3
A11	FMCG	8	6	2		A29	CD	23	21	2
A14	FMCG	9	13	−4		A17	FMCG	24	22	2
A13	FMCG	10	10	0		A2	FMCG	25	25	0
A23	FMCG	11	16	−5		A8	FMCG	26	24	2
A27	CD	12	14	−2		A12	FMCG	27	28	−1
A6	FMCG	13	11	2		A18	FMCG	28	29	−1
A3	FMCG	14	15	−1		A26	CD	29	27	2
A9	FMCG	15	8	7		A10	FMCG	30	30	0

We also check for statistically significant association between pre and post COVID 19 performance related to stock performance, DPC, SOP, FS and ES using Spearman’s rank correlation test (see Table 28)

Table 28.

Rank correlation (EDAS and COPRAS) for post COVID 19 period.

Aspect	Correlation		EDAS
SP	COPRAS	Spearman’s rho	.999⁎⁎
		Sig. (2-tailed)	0.000
	Correlation		EDAS
DPC	COPRAS	Spearman’s rho	0.927⁎⁎
		Sig. (2-tailed)	0.000
	Correlation		EDAS
SOP	COPRAS	Spearman’s rho	0.966⁎⁎
		Sig. (2-tailed)	0.000

^**Correlation is significant at the 0.01 level (2-tailed).

Table 26.

Rank correlation test: Before and after COVID-19.

Aspect	Correlation		Rank_Post Covid
SP	Rank_ Pre Covid	Spearman’s rho	.380⁎
		Sig. (2-tailed)	0.038
	Correlation		Rank_Post Covid
DPC	Rank_ Pre Covid	Spearman’s rho	.472⁎⁎
		Sig. (2-tailed)	0.008
	Correlation		Rank_Post Covid
SOP	Rank_ Pre Covid	Spearman’s rho	.367⁎
		Sig. (2-tailed)	0.046
	Correlation		Rank_Post Covid
FS	Rank_ Pre Covid	Spearman’s rho	.892⁎⁎
		Sig. (2-tailed)	0.000
	Correlation		Rank_Post Covid
ES	Rank_ Pre Covid	Spearman’s rho	.920⁎⁎
		Sig. (2-tailed)	0.000

^*Correlation is significant at the 0.05 level (2-tailed).

^**Correlation is significant at the 0.01 level (2-tailed).

6.1. Validation of the MCDM result

The analysis using MCDM methods are based on the selection of the alternatives and criteria, the relative priorities of the criteria and their influences on the performance values of the alternatives and many other assumptions on given conditions [135]. Hence, it is important to check the reliability of the results before drawing any conclusions. To this end the extant literature shows umpteen evidences of comparative analysis of result by the given MCDM model with that obtained by applying other established algorithms (for instance, [136], [137], [138], [139]). In the present paper we compare our result with another popular model such as the Complex Proportional Assessment (COPRAS) method [140]. Table 21, Table 22 show the results of the comparison of EDAS and COPRAS methods for both pre COVID-19 (i.e., aggregated performance during FY 2013–14 to FY 2019–20) and post COVID-19 (i.e., FY 2020–21) periods and confirm the reasonable reliability of our result (see Table 27).

Table 27.

Rank correlation (EDAS and COPRAS) for pre COVID 19 period.

Aspect	Correlation		EDAS
SP	COPRAS	Spearman’s rho	.992⁎⁎
		Sig. (2-tailed)	0.000
	Correlation		EDAS
DPC	COPRAS	Spearman’s rho	0.972⁎⁎
		Sig. (2-tailed)	0.000
	Correlation		EDAS
SOP	COPRAS	Spearman’s rho	0.987⁎⁎
		Sig. (2-tailed)	0.000

^**Correlation is significant at the 0.01 level (2-tailed).

6.2. Sensitivity analysis

For any MCDM based comparative ranking, stability of the outcome is of paramount importance. The stability of results get disturbed by the external changes in the underlying conditions. The changes in the criteria weights is one such major issue [141]. To check the stability of the result we perform the sensitivity analysis using the scheme as demonstrated in [35]. We generate the experimental cases by reducing the weight of the most prioritized criterion by 2% at each stage and subsequently, add to the other criteria proportionately. Table 28 showcases one sample experiment for doing the sensitivity analysis for the stock performance for FY 2019–20. The result of the sensitivity analysis is pictorially shown in Fig. 2. It is seen that the result is considerably stable (see Table 29).

Fig. 2.

Result of sensitivity analysis (Stock performance for FY 2019–20).

Table 29.

Experimental cases for sensitivity analysis (Stock performance for FY 2019–20).

Cases	C1	C2	C3	C4	C5	C6	C7	C8	C9	Sum
Original	0.2665	0.1455	0.0513	0.0394	0.0198	0.0074	0.0694	0.2044	0.1963	1.0000
Exp 1	0.2532	0.1471	0.0530	0.0410	0.0215	0.0091	0.0710	0.2060	0.1980	1.0000
Exp 2	0.2405	0.1487	0.0546	0.0426	0.0231	0.0107	0.0726	0.2076	0.1996	1.0000
Exp 3	0.2285	0.1502	0.0561	0.0441	0.0246	0.0122	0.0741	0.2091	0.2011	1.0000
Exp 4	0.2171	0.1516	0.0575	0.0456	0.0260	0.0136	0.0756	0.2105	0.2025	1.0000
Exp 5	0.2062	0.1530	0.0588	0.0469	0.0274	0.0150	0.0769	0.2119	0.2039	1.0000
Exp 6	0.1959	0.1543	0.0601	0.0482	0.0287	0.0163	0.0782	0.2132	0.2052	1.0000
Exp 7	0.1861	0.1555	0.0614	0.0494	0.0299	0.0175	0.0794	0.2144	0.2064	1.0000
Exp 8	0.1768	0.1567	0.0625	0.0506	0.0311	0.0186	0.0806	0.2156	0.2076	1.0000
Exp 9	0.1680	0.1578	0.0636	0.0517	0.0322	0.0198	0.0817	0.2167	0.2087	1.0000
Exp 10	0.1596	0.1588	0.0647	0.0527	0.0332	0.0208	0.0827	0.2177	0.2097	1.0000

In the similar way we have carried the sensitivity analysis of all results and notice no significant variations. Hence, we contend that the results obtained by using our model is stable in nature.

7. Discussions

The present study shows some interesting observations. It is evident from Table 8 that for stock performance comparison, return and risk get more weightage than others which supports the modern portfolio theory started with the work of Markowitz [88]. Further, it may be noted that the distribution of the weights shows reasonable evenness despite substantial variations in the performance values of the alternatives (including the presence of some negative values), which justifies the use of the LOPCOW method for calculating the criteria weights. Table 10 shows that the stock’s performance maintains considerably steady positions until the spread of COVID-19. The FMCG firms dominate the top half (i.e., first 15 positions) with only one representative (A27, Symphony Ltd.) from the CD segment. We observe that mostly the large-cap multinational, multiproduct FMCG firms like Hindustan Unilever Ltd., Procter & Gamble Hygiene & Health Care Ltd., Nestle India Ltd. etc. dominate the top positions. However, the relative positions vary notably during the early post-pandemic period (FY 20–21). It is further noticed (see Table 23) that the stocks which held top positions prior to COVID-19 suffer more afterward than the bottom performers. The results of the rank correlation test (see Table 28) show that pre and post-COVID-19 rankings for stock performance hold a weak statistically significant correlation with the sig value close to 0.05. This finding is a reflection of the past work (for example, [47], [48], [49]).

Moving to the comparative analysis of DPC, we observe that leverage ($C_{28}$) and profitability ($C_{23}$) assume a higher weightage than others (see Table 13). For the performance-based ranking considering DPC (see Table 14, Table 22), we find the same pattern as the stock performance. However, we find a slightly better correlation between pre and post-COVID-19 ranking with strong statistical significance (see Table 26). In the case of SOP, we find that sales growth ($C_{31}$) and leverage ($C_{39}$) carry higher weightage (Table 15). Further from the comparative ranking, it is also seen that (ref Table 15, Table 23) there has been a significant variation in the ranking. The correlation test (see Table 26) also suggests the same.

Immediately after the spread of COVID-19 in the country, the Government took several countermeasures, including a complete lockdown for a prolonged period. With the fear of an unknown future coupled with the shutdown of trades and businesses, the stock market showed abnormal behaviors [142] alongside SOP and dividend payments by the firms. In that sense, the variations are not uncommon. We notice different outcomes while assessing the financial stabilities (Altman’s Z), economic sustainability, and growth prospects (Tobin’s Q). We find that there was no effect of COVID-19 on the comparative rankings, as may be seen in Table 24, Table 25, Table 26 (very high values for correlation coefficients with strong statistical significance). On the aggregation of the performance-based rankings (considering stock performance, DPC, SOP, FS, and ES), we find that the firms like Hindustan Unilever Ltd., Procter & Gamble Hygiene & Health Care Ltd., Britannia Industries Ltd., Nestle India Ltd., Avanti Feeds Ltd., Symphony Ltd., Marico Ltd., ITC Ltd., Dabur India Ltd., and Bajaj Consumer Care Ltd. have performed well during the pre-COVID 19 period. Nevertheless, their performance in the stock market, DPC, and SOP have been affected notably by the ‘black swan’ event COVID-19. However, the firms could maintain their FS and ES.

From the technical point of view, the comparative analysis of EDAS and COPRAS based rankings (see Table 28) assures the reliability of the results used in this paper. We notice very high correlation with strong evidences of statistical significance. We observe that the model provides considerably stable results as the changes in the influencing conditions like criteria weights do not disturb the overall ranking much. The results of the sensitivity analysis reflects the stability of the model. In the present work the size of the decision matrices are reasonably large. Therefore, we contend that our model can be applied to various real-life complex issues. It is noticed that LOPCOW can work fine with the presence of negative values in the decision matrix which supports its superiority. Besides many advantages of our model, there are some limitations. In the present work we have not considered any subjective information. Therefore, the efficacy of LOPCOW in the presence of subjective bias has not been tested. The model (LOPCOW-EDAS) may be extended and tested by using fuzzy and rough set based models as demonstrated in various recently published work (for instance, [143], [144], [145], [146], [147]). Secondly, while aggregating the year wise rankings the BC model does not consider priority of any single year. It may be a limitation as the most recent performance is sometime given more priority over the others.

Our work supports the views of contemporary work. However, it provides a multi-perspective objective evaluation of the firm performance. Further, our work reveals a mixed impact of COVID-19. The pandemic affects the stock market reactions and the operational performance and reduce DPC, but the financial health and long-term growth prospect remain unaltered. Hence, we contend that the early effect of a catastrophic event like COVID-19 might affect the performance of a fundamentally stable organization, reflecting financial health and long-term growth prospect that may be given priority when selecting securities for investment during turbulent times.

The present analysis sheds light on important policy implications. The extant literature has been increasingly contributed with scholarly works that utilize the predictive models for forecasting the effect of COVID-19 on stock market performance and/or statistical time series based analysis to discern the causal effect on market performance. The present study is a distinguished from the past work in this regard. It provides a robust analytical framework for multi-period and multi-perspective evaluations of the performance based on market indicators and accounting measures considering the both top line and bottom line performance. Our reliable and stable yet easy to use MCDM model shall enable the policy makers to investigate the inclusive growth of the organizations and shall be able to bring to surface the possible areas of vulnerabilities. In effect, the organizations shall plan for resilience planning and management of disruption risks. From the investment decision making point of view, the present work also add value to the consultants and common investors in the market as it provides a holistic view to analyze the investment prospect of the stocks.

8. Conclusion and future scope

In the current work we have attempted to put forth a multi-perspective evaluation of the performance of the selected FMCG and CD firms listed in BSE, India. The underlying intention is to descry the effect of the recent pandemic on the performance-based ranking of the firms. We have selected multiple features to carry out the comparative analysis of the firms regarding their stock performance, capabilities to pay dividends, operational performance, financial solidity, economic sustainability and long term growth prospect. A total of 30 firms (i.e., alternatives) have been considered out of which 25 are from the FMCG sector and rest 5 belong to the CD sector. A period of seven consecutive FYs (FY 13–14 to FY 19–20) has been considered for comparing the performance of alternatives in the pre-pandemic phase while the early effect of COVID-19 has been considered (FY 20–21). To compare the alternatives, a combined MCDM framework of LOPCOW and EDAS has been utilized wherein for aggregation of MCDM rankings popular methods like Borda count and Copeland methods have been applied. We have noticed that the relative positions (while assessing stock performance, DPC and SOP) vary notably during the early post-pandemic period (FY 20–21). It is further noticed that the alternatives which held top positions prior to COVID-19 suffer more afterward than the bottom performers. However, there has not been any major effect of COVID-19 on firms’ financial health and long-term growth prospect. The results show substantial stability as revealed through the sensitivity analysis. The comparison with other MCDM models reflect on the reliability of the results provided by our model.

The present work is one of its kind that fills the gap in the literature by providing a comprehensive multi-dimensional comparative assessment of FMCG and CD firms in Indian context. However, we do find some of the future scope of research. Firstly, the present paper only considers FMCG and CD sectors which may be further extended by including other sectors to have a deeper inspection of the effect of pandemic on sector wise performance. Secondly, the current work is based on a limited number of features or ratios (i.e., proxy variables) which may further be expanded. Thirdly, we have not considered some of the other essential dimensions like innovativeness, CSR performance, corporate governance, marketing performance and human capital management which may be interesting to be worked upon. Fourthly, behavioral finance aspects are not addressed in this paper. Fifth, a future work may be carried out using panel regression based causal analysis to enfold the impact of DPC, SOP, FS and ES on the stock performance vis-à-vis COVID-19. Sixth, from the technical point of view, the LOPCOW model may be further tested for its consistency aspect while dealing with subjective opinions. In this regard, the extensions using fuzzy and rough numbers may be developed. We plan to extend the framework (LOPCOW-EDAS) with intuitionistic fuzzy, picture fuzzy, neutrosophic fuzzy, spherical fuzzy, q rung orthopair fuzzy, fermatean fuzzy and other variants along with grey and rough numbers. Seventh, we also plan to use LOPCOW method in conjunction with the other weighting methods like entropy, SECA, CRITIC, PIPRECIA, LBWA, FUCOM among others.

Nevertheless, we are hopeful that the current paper shall meet the expectations of the readers and invoke further work in the directions laid down. Our reliable and stable yet easy to use MCDM model shall enable the policy makers to investigate the inclusive growth of the organizations and shall be able to bring to surface the possible areas of vulnerabilities. In effect, the organizations shall plan for resilience planning and management of disruption risks. The policymakers and top managers of the firms shall also use the present framework as a supportive tool for comprehensive assessment of the sustainability performance of the firms.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A.

See Table A.1, Table A.2, Table A.3, Table A.4, Table A.5, Table A.6, Table A.7

Table A.1.

Ranking of DMUs based on stock performance (FY 2014–15).

DMU	SP	SN	NSP	NSN	AS	Rank
A1	1.1209	0.0586	1.0000	0.9648	0.9824	1
A2	0.4295	0.0381	0.3832	0.9771	0.6802	6
A3	0.5778	0.4758	0.5155	0.7146	0.6150	10
A4	0.5028	0.0472	0.4486	0.9717	0.7101	4
A5	0.4590	0.3761	0.4095	0.7744	0.5919	13
A6	0.5403	0.1164	0.4821	0.9302	0.7061	5
A7	0.1820	0.1406	0.1624	0.9157	0.5390	16
A8	0.0467	0.5793	0.0417	0.6525	0.3471	27
A9	0.3128	0.0225	0.2791	0.9865	0.6328	9
A10	0.3535	0.4653	0.3154	0.7209	0.5181	19
A11	0.3314	0.1579	0.2956	0.9053	0.6005	12
A12	0.1954	1.6669	0.1743	0.0000	0.0872	30
A13	0.0661	0.2984	0.0590	0.8210	0.4400	23
A14	0.1622	0.3983	0.1447	0.7610	0.4529	21
A15	0.7915	0.1030	0.7061	0.9382	0.8222	3
A16	0.4683	0.4299	0.4178	0.7421	0.5800	15
A17	0.0224	0.3197	0.0199	0.8082	0.4141	24
A18	0.4424	0.2830	0.3947	0.8302	0.6125	11
A19	0.1493	0.1028	0.1332	0.9383	0.5358	18
A20	0.4619	0.1530	0.4121	0.9082	0.6601	7
A21	0.4056	0.0811	0.3619	0.9513	0.6566	8
A22	0.0413	0.9322	0.0368	0.4408	0.2388	29
A23	0.1163	0.6850	0.1038	0.5891	0.3464	28
A24	0.0413	0.4043	0.0369	0.7575	0.3972	25
A25	0.1511	0.0990	0.1348	0.9406	0.5377	17
A26	0.1478	0.3158	0.1319	0.8106	0.4712	20
A27	0.8410	0.1346	0.7503	0.9192	0.8348	2
A28	0.0044	0.1825	0.0039	0.8905	0.4472	22
A29	0.0251	0.4310	0.0224	0.7414	0.3819	26
A30	0.3532	0.2450	0.3151	0.8530	0.5840	14

Table A.2.

Ranking of DMUs based on stock performance (FY 2015–16).

DMU	SP	SN	NSP	NSN	AS	Rank
A1	0.5641	−22.6655	0.9401	35.8557	18.3979	1
A2	0.3985	−2.6801	0.6642	5.1216	2.8929	6
A3	0.0185	−2.1256	0.0309	4.2688	2.1498	10
A4	−3.4328	0.0601	−5.7216	0.9076	−2.4070	23
A5	−1.5749	0.2904	−2.6249	0.5535	−1.0357	22
A6	0.4334	−14.8377	0.7224	23.8178	12.2701	2
A7	0.1869	−0.7294	0.3115	2.1216	1.2166	15
A8	−3.8578	0.6503	−6.4300	0.0000	−3.2150	25
A9	0.0950	−0.9512	0.1583	2.4628	1.3106	14
A10	−9.1410	0.4514	−15.2357	0.3058	−7.4649	28
A11	0.2165	−1.1240	0.3609	2.7286	1.5447	13
A12	−17.7054	0.2173	−29.5106	0.6658	−14.4224	29
A13	−4.9520	0.1243	−8.2538	0.8088	−3.7225	26
A14	−4.3524	0.0763	−7.2543	0.8827	−3.1858	24
A15	0.6000	0.0128	1.0000	0.9803	0.9902	16
A16	0.1452	0.1316	0.2420	0.7976	0.5198	18
A17	−1.3666	0.1877	−2.2777	0.7114	−0.7832	20
A18	−4.9813	0.1781	−8.3025	0.7262	−3.7882	27
A19	0.1654	−7.6695	0.2756	12.7943	6.5350	3
A20	0.4816	−2.7954	0.8026	5.2988	3.0507	5
A21	0.4667	−2.0232	0.7779	4.1113	2.4446	9
A22	−1.3979	0.4353	−2.3300	0.3306	−0.9997	21
A23	0.0900	−2.8334	0.1499	5.3572	2.7536	7
A24	0.0047	−2.8222	0.0078	5.3400	2.6739	8
A25	0.0959	−4.9050	0.1599	8.5431	4.3515	4
A26	−19.7157	0.2052	−32.8612	0.6845	−16.0884	30
A27	0.4608	−1.0957	0.7680	2.6850	1.7265	12
A28	0.0273	−2.0762	0.0456	4.1928	2.1192	11
A29	−0.0537	0.4565	−0.0895	0.2980	0.1043	19
A30	0.0000	−0.2240	0.0000	1.3445	0.6722	17

Table A.3.

Ranking of DMUs based on stock performance (FY 2016–17).

DMU	SP	SN	NSP	NSN	AS	Rank
A1	0.8401	0.0933	1.0000	0.8925	0.9462	1
A2	0.4489	0.2591	0.5343	0.7012	0.6178	8
A3	0.6848	0.6568	0.8151	0.2427	0.5289	13
A4	0.3506	0.0289	0.4173	0.9666	0.6920	6
A5	0.5310	0.2711	0.6321	0.6874	0.6598	7
A6	0.3419	0.1599	0.4070	0.8156	0.6113	10
A7	0.1699	0.1767	0.2022	0.7963	0.4992	16
A8	0.1197	0.4613	0.1425	0.4681	0.3053	25
A9	0.1343	0.1446	0.1599	0.8333	0.4966	17
A10	0.2797	0.3772	0.3329	0.5651	0.4490	18
A11	0.2492	0.4018	0.2966	0.5367	0.4167	20
A12	0.0012	0.6632	0.0014	0.2353	0.1184	30
A13	0.0452	0.1455	0.0538	0.8322	0.4430	19
A14	0.4958	0.1493	0.5902	0.8278	0.7090	5
A15	0.7785	0.2139	0.9266	0.7534	0.8400	2
A16	0.5175	0.5132	0.6160	0.4082	0.5121	15
A17	0.1664	0.1363	0.1981	0.8428	0.5205	14
A18	0.6101	0.1699	0.7262	0.8041	0.7651	4
A19	0.1876	0.0857	0.2233	0.9012	0.5622	12
A20	0.3818	0.2084	0.4545	0.7597	0.6071	11
A21	0.6339	0.0693	0.7545	0.9201	0.8373	3
A22	0.1538	0.4381	0.1831	0.4948	0.3390	24
A23	0.0852	0.5756	0.1015	0.3363	0.2189	28
A24	0.0094	0.6506	0.0112	0.2499	0.1305	29
A25	0.0898	0.2377	0.1069	0.7259	0.4164	21
A26	0.0951	0.4724	0.1132	0.4552	0.2842	26
A27	0.3685	0.8673	0.4386	0.0000	0.2193	27
A28	0.0934	0.2672	0.1111	0.6919	0.4015	23
A29	0.2400	0.3954	0.2856	0.5441	0.4148	22
A30	0.4092	0.2229	0.4871	0.7429	0.6150	9

Table A.4.

Ranking of DMUs based on stock performance (FY 2017–18).

Company	SP	SN	NSP	NSN	AS	Rank
A1	1.3214	0.0716	1.0000	0.9159	0.9580	1
A2	0.3733	0.1311	0.2825	0.8459	0.5642	9
A3	0.1423	0.8188	0.1077	0.0377	0.0727	28
A4	0.4160	0.0510	0.3148	0.9401	0.6274	6
A5	0.2099	0.6034	0.1589	0.2908	0.2248	26
A6	0.3285	0.2670	0.2486	0.6862	0.4674	10
A7	0.1305	0.1542	0.0987	0.8188	0.4588	11
A8	0.0000	0.8168	0.0000	0.0401	0.0200	29
A9	0.0909	0.3504	0.0688	0.5882	0.3285	21
A10	0.4467	0.3695	0.3380	0.5657	0.4519	12
A11	0.6885	0.1398	0.5211	0.8357	0.6784	4
A12	0.0083	0.8509	0.0062	0.0000	0.0031	30
A13	0.0350	0.5946	0.0265	0.3012	0.1638	27
A14	0.3698	0.1229	0.2799	0.8556	0.5677	8
A15	0.8893	0.0576	0.6730	0.9323	0.8026	3
A16	0.3762	0.4341	0.2847	0.4898	0.3873	19
A17	0.1466	0.2612	0.1109	0.6930	0.4019	18
A18	0.2382	0.3199	0.1802	0.6241	0.4021	17
A19	0.1275	0.2033	0.0965	0.7610	0.4287	14
A20	0.4798	0.1502	0.3631	0.8234	0.5933	7
A21	1.0560	0.0555	0.7991	0.9348	0.8669	2
A22	0.5070	0.4288	0.3837	0.4961	0.4399	13
A23	0.2580	0.5089	0.1952	0.4019	0.2986	23
A24	0.0098	0.3789	0.0074	0.5547	0.2811	25
A25	0.1357	0.2451	0.1027	0.7119	0.4073	16
A26	0.0997	0.3009	0.0755	0.6463	0.3609	20
A27	0.2501	0.2952	0.1893	0.6531	0.4212	15
A28	0.6114	0.1583	0.4627	0.8140	0.6383	5
A29	0.1248	0.4387	0.0945	0.4844	0.2895	24
A30	0.0153	0.3081	0.0116	0.6379	0.3247	22

Table A.5.

Ranking of DMUs based on stock performance (FY 2018–19).

Company	SP	SN	NSP	NSN	AS	Rank
A1	0.3933	−3.6593	0.7315	6.0334	3.3825	1
A2	0.5376	−0.6155	1.0000	1.8466	1.4233	4
A3	−0.4035	0.7270	−0.7505	0.0000	−0.3753	28
A4	0.2612	−0.7997	0.4859	2.1001	1.2930	5
A5	−0.2418	0.1720	−0.4497	0.7634	0.1568	18
A6	−0.3626	0.0867	−0.6744	0.8807	0.1032	19
A7	−0.5681	0.0908	−1.0567	0.8751	−0.0908	25
A8	0.1178	0.1472	0.2190	0.7975	0.5083	11
A9	0.0533	−1.8688	0.0992	3.5706	1.8349	2
A10	0.0407	0.2045	0.0757	0.7188	0.3972	13
A11	0.0840	0.0384	0.1562	0.9472	0.5517	9
A12	−1.0454	0.4272	−1.9445	0.4123	−0.7661	30
A13	0.1762	−0.7333	0.3278	2.0087	1.1683	6
A14	−0.0572	0.1286	−0.1064	0.8232	0.3584	15
A15	−0.1495	0.0268	−0.2781	0.9632	0.3425	16
A16	−0.4097	0.1804	−0.7620	0.7519	−0.0050	23
A17	0.0773	−1.3356	0.1437	2.8371	1.4904	3
A18	0.1606	−0.1794	0.2987	1.2468	0.7727	7
A19	−0.2443	0.0683	−0.4544	0.9061	0.2259	17
A20	−0.3604	0.0986	−0.6703	0.8644	0.0970	21
A21	0.0519	0.0450	0.0966	0.9381	0.5174	10
A22	−0.6942	0.2993	−1.2912	0.5883	−0.3514	27
A23	0.0443	0.1721	0.0823	0.7632	0.4228	12
A24	−0.9020	0.2398	−1.6778	0.6701	−0.5038	29
A25	−0.4241	0.2702	−0.7889	0.6283	−0.0803	24
A26	0.0702	0.2459	0.1306	0.6617	0.3962	14
A27	0.0714	−0.1016	0.1328	1.1398	0.6363	8
A28	−0.7000	0.0980	−1.3020	0.8652	−0.2184	26
A29	−0.2904	0.3265	−0.5401	0.5509	0.0054	22
A30	−0.2647	0.2219	−0.4924	0.6948	0.1012	20

Table A.6.

Ranking of DMUs based on stock performance (FY 2019–20).

Company	SP	SN	NSP	NSN	AS	Rank
A1	0.4676	0.1640	1.0000	0.6526	0.8263	6
A2	0.1106	0.2647	0.2365	0.4394	0.3379	21
A3	0.1618	0.3348	0.3460	0.2910	0.3185	24
A4	0.1861	0.0516	0.3980	0.8906	0.6443	11
A5	0.2211	0.0394	0.4729	0.9166	0.6947	9
A6	0.0393	0.0832	0.0840	0.8238	0.4539	18
A7	−0.2226	0.0842	−0.4760	0.8216	0.1728	25
A8	0.0000	0.4722	0.0000	0.0000	0.0000	28
A9	0.1351	0.1526	0.2890	0.6768	0.4829	17
A10	0.0475	−0.6215	0.1017	2.3162	1.2089	1
A11	0.0602	0.2252	0.1287	0.5230	0.3259	22
A12	0.3928	0.2508	0.8400	0.4688	0.6544	10
A13	−0.0173	0.0575	−0.0369	0.8783	0.4207	20
A14	0.2444	0.1673	0.5226	0.6456	0.5841	14
A15	0.3583	0.0287	0.7662	0.9392	0.8527	5
A16	0.4193	0.0717	0.8967	0.8481	0.8724	4
A17	0.2339	0.0452	0.5003	0.9043	0.7023	8
A18	0.1289	−0.3211	0.2757	1.6801	0.9779	2
A19	0.0816	0.0865	0.1746	0.8168	0.4957	16
A20	0.3744	0.0072	0.8007	0.9848	0.8927	3
A21	0.2369	0.1033	0.5067	0.7812	0.6440	12
A22	0.3715	0.1587	0.7946	0.6639	0.7293	7
A23	−0.4858	0.2818	−1.0388	0.4031	−0.3179	30
A24	0.0000	0.3105	0.0000	0.3423	0.1712	26
A25	−0.2089	0.3967	−0.4468	0.1599	−0.1434	29
A26	0.3320	0.2362	0.7099	0.4997	0.6048	13
A27	0.0864	0.1471	0.1848	0.6884	0.4366	19
A28	0.0967	0.0734	0.2069	0.8446	0.5257	15
A29	0.0105	0.1772	0.0225	0.6248	0.3236	23
A30	−0.2182	0.1152	−0.4667	0.7560	0.1446	27

Table A.7.

Ranking of DMUs based on stock performance (FY 2020–21).

Company	SP	SN	NSP	NSN	AS	Rank
A1	0.4064	0.2928	0.2011	0.8747	0.5379	13
A2	0.2964	0.8341	0.1466	0.6431	0.3949	23
A3	0.2747	0.5375	0.1359	0.7700	0.4529	20
A4	0.8457	0.0303	0.4185	0.9870	0.7028	5
A5	0.0407	0.4739	0.0202	0.7972	0.4087	22
A6	0.8969	0.0861	0.4438	0.9632	0.7035	4
A7	0.2829	0.1868	0.1400	0.9201	0.5300	16
A8	0.2206	0.5321	0.1092	0.7723	0.4407	21
A9	0.3837	0.6429	0.1899	0.7249	0.4574	19
A10	0.1071	2.3367	0.0530	0.0000	0.0265	30
A11	0.4406	0.3543	0.2180	0.8484	0.5332	14
A12	0.0934	1.2299	0.0462	0.4737	0.2599	29
A13	0.1271	0.2837	0.0629	0.8786	0.4707	18
A14	0.7113	0.1044	0.3520	0.9553	0.6536	7
A15	0.6095	0.1484	0.3016	0.9365	0.6190	8
A16	0.5776	0.5219	0.2858	0.7767	0.5312	15
A17	0.0364	0.6727	0.0180	0.7121	0.3651	24
A18	0.0000	0.6831	0.0000	0.7077	0.3538	26
A19	0.2971	0.0974	0.1470	0.9583	0.5527	12
A20	2.0209	0.1470	1.0000	0.9371	0.9685	1
A21	1.3416	0.1153	0.6638	0.9507	0.8073	2
A22	1.1092	0.2475	0.5489	0.8941	0.7215	3
A23	1.0554	0.3192	0.5222	0.8634	0.6928	6
A24	0.0000	0.6433	0.0000	0.7247	0.3624	25
A25	0.4883	0.5367	0.2416	0.7703	0.5060	17
A26	0.1062	1.0777	0.0525	0.5388	0.2957	28
A27	0.0555	0.7627	0.0275	0.6736	0.3505	27
A28	0.6476	0.2281	0.3204	0.9024	0.6114	9
A29	0.5870	0.2307	0.2905	0.9013	0.5959	10
A30	0.5493	0.2522	0.2718	0.8921	0.5819	11

Appendix B. Supplementary data

The following is the Supplementary material related to this article.

MMC S1

Dataset.

Data availability

Data will be made available on request.