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Open Access
Research article

Analyzing the Impact of COVID-19 on the Financial Failure Risk in Borsa İstanbul Manufacturing Companies

emre kaplanoğlu1*,
fatma moroğlu2
1
Assoc. Prof., Ege University, Bergama Technical and Business College, Accounting and Tax Application Program, Turkey
2
Lect., Ege University, Bergama Technical and Business College, Accounting and Tax Application Program, Turkey
Journal of Corporate Governance, Insurance, and Risk Management
|
Volume 8, Issue 2, 2021
|
Pages 83-113
Received: 10-08-2021,
Revised: 11-14-2021,
Accepted: 11-28-2021,
Available online: 12-28-2021
View Full Article|Download PDF

Abstract:

There is no agreed and precise definition of the concept of financial failure. This situation causes the studies of the concept to be associated with bankruptcies. Although not every company experiencing financial failure goes bankrupt, it can be noted that economic fluctuations that happen on a global scale cause many companies to face the risk of financial failure and even bankruptcy. Furthermore, the COVID-19 pandemic has also affected the economic policies of countries and thus affected the operations of companies. This study aims to analyze the financial failure risk of Borsa İstanbul (BIST) manufacturing companies before and after COVID-19. In the research, financial statements of BIST manufacturing industry companies published quarterly between the years 2019-2020 were used. Within the scope of the research, the quarterly financial statements of 146 BIST companies listed in the manufacturing industry for the years 2019-2020 were analyzed with the financial failure models of Altman (1968), S​p​r​i​n​g​a​t​e​ ​(​1​9​7​8​), Taffler (1983) and Zmijewski (1984).

Keywords: Borsa İstanbul, COVID-19, Financial failure, Manufacturing, Risk

1. Introduction

In recent years, the deepening of global trade and currency wars and the increase in protectionist concerns in international trade has adversely affected the economies of many developed and developing countries and businesses operating in these economies. In this context, states and businesses may experience fluctuations in the economic and financial context. While the results of these adverse effects can often be measured within specific methods, they are sometimes insufficient due to excessive uncertainty, which may increase risk. The global impact of the COVID-19 pandemic, which emerged in Wuhan, China, in December 2019 and spread worldwide in a short time, presents much uncertainty and, therefore, risk.

Pandemics have been occurring naturally since ancient times. A highly pathogenic and antigenically exceptional novel type A virus has spread easily to and from humans. Although the virus was not recognized until 1933, historical records describe pandemics dating back to Hippocrates. The first severe pandemic with vital historical records occurred in 1580 and was determined to have destroyed some Spanish cities. Ten pandemic outbreaks have been documented in the last 300 years (Kelley & Osterholm, 2008). However, it is controversial that the recent COVID-19 pandemic will cause radical changes in the current order. These changes also constitute a source of risk.

From a financial point of view, it is of great importance for businesses to keep their costs and expenses related to production, activities and financing under control, to closely monitor current and potential interest, exchange rate and liquidity risks, and to make their cash flows and profitability sustainable in order to avoid financial failure. It can be said that almost all sectors have been adversely affected by the COVID-19 process. Prominent sectors can be listed as follows: Airlines, hotels, restaurants, hospitality services, retail, especially manufacturers with complex supply chains, exporters heavily dependent on the Asian market first affected by the global pandemic, tourism-related businesses, transportation, cruises, ports, and the shipping sector, due to the decline in demand and commodity prices for oil, gas, mining, and metal industries. The problems companies faced during the COVID-19 period can be listed as follows: Consumer demand has decreased, and it is uncertain when it will increase and return to its former level. Companies' supply chain has been disrupted, and cash and working capital problems have emerged. Suppliers have had difficulty delivering critical components to manufacturers, and the production process has been delayed or stopped. The decline in consumer demand has caused a backlog in the stocks of companies, and it has become more and more challenging to clean their inventories. As a result, companies had difficulties collecting their receivables from their cash-strapped customers on time. There were also delays in supplier payments due to short-term cash flow constraints. Posted checks, which play a critical role in commercial life and are used as a method of financing receivables, caused serious collection problems due to the cash flow problems in this period. In addition, since companies guarantee post-dated checks, financing problems and their legal consequences arise due to the inability to pay post-dated checks (Deloitte, 2020). Therefore, the risk of financial failure increases due to these problems faced by companies during the COVID-19 process.This study aims to analyze the financial failure risk of companies listed in the Borsa İstanbul (BIST) manufacturing industry in 2019 and 2020 comparatively. The financial data of the companies for the years 2019 and 2020 was analyzed quarterly. Within the scope of the research, the quarterly financial statements of 146 BIST companies listed in the manufacturing industry sector for the years 2019-2020 were evaluated using Altman (1968), S​p​r​i​n​g​a​t​e​ ​(​1​9​7​8​), Taffler (1983) and Zmijewski (1984) financial failure models. The study consists of five main parts. In the introduction part of the study, the relationship between the risk of financial failure and the COVID-19 pandemic and the purpose of the study is explained. In the second part, the literature review on financial failure is presented, and in the third part, the scope and method of the study are given. In the fourth part, the analysis findings made according to the financial failure models of BIST manufacturing industry companies are presented. Finally, in the fifth part, the analysis findings are explained by comparing them with the literature and sharing suggestions for future studies.

2. Literature Review

Winakor & Smith (1935), one of the first financial failure modelling studies, examined 183 companies that went bankrupt between 1923 and 1931. Merwin (1942), on the other hand, examined the financial ratios of firms before bankruptcy to their financial failures over the data of a total of 900 firms that went bankrupt and continued their activities in the period 1926-1936. In both studies, net working capital ratio (net working capital/total assets) and current ratio were important variables in estimating financial failure.

Beaver (1966) conducted one of the most widely known studies among univariate financial failure models, including 79 successful and 79 unsuccessful companies listed in the USA between 1954 and 1964. Weibel (1973) studied 36 successful and 79 unsuccessful firms listed in Switzerland during the 1960-1971 period. They identified the most successful variables in estimating financial failure by using the financial data of 36 unsuccessful small-scale firms. Beaver (1966) found that the working capital ratio (working capital/total assets) and current ratio are among the most influential variables in predicting financial failure, while Weibel (1973) found that the current ratio is among the most influential variables in predicting financial failure.

The first multivariate financial failure model experiments were carried out by Tamari (1966) using the financial data for the period 1956-1960 and included 16 firms that filed for bankruptcy and 12 Israeli firms that went bankrupt. As a result of the study, it has been determined that the current ratio of the companies is one of the six variables that affect their financial failures. Among the financial failure models, the most widely known studies are Altman’s (1968; 1983; 1993) studies. Among these, Altman’s (1968) study has the feature of being the first financial failure study that does not contain personal judgments and is based entirely on statistical methods. In his study, Altman (1968) developed an estimation model based on the main variables affecting financial failure by using the data of 33 successful and 33 unsuccessful publicly traded manufacturing firms listed in the USA with multiple discriminant analyses. At the end of the study, five financial ratios, including the net working capital ratio, were selected among 22 financial ratios for the model called Z score. Altman (1983) developed the Z score model, which is a model that can also be used in non-public companies, and A​l​t​m​a​n​ ​&​n​b​s​p​;​(​1​9​9​3​) altered the Z score model, which can be used in both publicly traded companies and companies listed in industries other than manufacturing.

S​p​r​i​n​g​a​t​e​ ​(​1​9​7​8​) studied 40 manufacturing companies listed in Canada; Ohlson (1980) used 105 unsuccessful and 2058 successful companies listed in the USA; and Taffler (1982) examined 25 unsuccessful and 45 successful companies trading on the London Stock Exchange. They aimed to determine the financial ratios that affect financial failure by creating a model that could be used to predict financial failure. All three models have determined that the working capital ratio affects financial failure, which is included in the estimation models. In another study to develop a financial failure model, Zmijewski (1984) developed 12 models using data between 1972 and 1978 of 40 successful and 40 unsuccessful manufacturing companies whose shares were traded on the New York Stock Exchange (NYSE). It was determined that the current ratio is one of the determinants of financial failure in all models. Frydman et al. (1985) carried out financial failure modelling by using the data of 58 unsuccessful and 142 successful manufacturing companies listed in the USA during the 1971-1981 period, and 20 ratios including working capital ratio, current ratio and liquid assets ratio (cash stocks/total assets) were used. They determined that the financial ratio affects financial failure.

Odom & Sharda (1990) included 64 bankrupt and 65 non-bankrupt companies listed in the USA during the 1975-1982 periods in their study. Tirapat & Nittayagasetwat (1999) studied 55 unsuccessful, and 341 successful companies listed in Thailand in 1997; Jones & Hensher (2004) studied 24 unsuccessful and 62 successful IT and service companies listed in Australia in the period 1999-2003, whilst Salehi & Abedini (2009) used data from 30 successful and 30 unsuccessful companies listed in Iran between 1995-2007. As a result of all four studies, it has been found that the working capital ratio is one of the determinants of financial failure.

Ganesalingam & Kumar (2001) used the data of 42 successful and 29 unsuccessful companies listed in Australia between 1986 and 1998 to make financial failure predictions. As a result of the study, it has been determined that ten financial ratios, including the current ratio, acid-test ratio and cash ratio, can be used in estimating financial failure. In addition, Gruszczynski (2004), in his study on 200 companies listed in Poland between 1995-1997, and Keener (2013) in his study on 1203 retail companies listed in the USA between 2005 and 2013, found that the cash ratio had statistically significant effects on the financial failures of firms.

Chen et al. (2006) analyzed 89 unsuccessful and 940 successful companies listed in China between December 1999 and June 2003, and Ijaz et al. (2013) investigated the financial ratios affecting financial failure by using the financial data of 35 sugar companies in Pakistan between 2009 and 2010. As a result of both studies, it was determined that the current ratio has statistically significant effects on financial failure and can be used in financial failure predictions. Almansour (2015), on the other hand, examined the internal determinants of financial failure by using data from 11 successful and 11 unsuccessful companies listed in Jordan during the 2000-2003 period and found that the working capital ratio and current ratio had statistically significant effects on financial failure.

Tian & Yu (2017) used 29 financial ratios of 108 Japanese and 112 European companies listed in Japan and European countries in the 1998-2012 period. Different financial failure models for Japanese and European companies were developed, and the results of their models were compared to the results of Altman’s (1968) Z score model. As a result of the study, it was stated that retained earnings/total assets, leverage ratio and short-term liabilities/sales ratios were chosen for all models created for Japan, and equity/total debt ratio for European countries. In addition, it was noted that their model performed better than the Altman (1968) Z score model.

Fejér-Király et al. (2019) analyzed the determinants of financial failure by using data from 65 unsuccessful and 95 successful companies traded in the Bucharest Stock Exchange. As a result of the study, it was determined that the variable of net working capital was selected for the model, which was developed to predict financial failure 1 and 2 years before, and it had significant effects on financial failure. Li & Faff (2019) developed a model using the data of 421 unsuccessful and 441 successful companies between 1988 and 2011, and it was noted that the working capital ratio is one of the 11 variables that affect financial failure.

One of the first financial failure modelling studies in Turkey was carried out by Göktan in 1981. G​ö​k​t​a​n​ ​(​1​9​8​1​) predicted the financial failures of the companies 1, 2, 3 and 4 years before the failure, based on the data of 25 successful and 14 unsuccessful companies between 1976 and 1980. As a result of the study, nine financial ratios, including the current ratio, were included in the developed model. One of the first studies to make financial failure predictions with multidimensional statistical models in Turkey was carried out by Aktaş in 1993. Aktaş (1993) developed a financial failure prediction model based on data from 25 successful and 35 unsuccessful companies between 1980 and 1989. As a result of the study, it was determined that the current ratio, acid-test ratio and liquid assets ratio, and working capital management were among the determinants of financial failure. Ünsal (2001)  examined the financial ratios that can predict financial failure using data from 16 unsuccessful and 55 successful companies and stated that cash ratio, acid-test ratio and current ratio are among the variables that are determinants of financial failure. Aktaş et al. (2003) developed a financial failure prediction model using the data from 53 successful and 53 unsuccessful industrial, commercial and service companies between 1983 and 1997. As a result of the study, five financial ratios, including the acid-test ratio, were determined. It has also been determined that some influential variables can be used to predict financial failure. In the study of İçerli & Akkaya (2006), 40 unsuccessful and 40 successful companies operating in the 1990-2003 period were examined with financial ratios and the Z test. The cash, acid-test, and current ratios were significant predictors of financial failure.  Terzi (2011), on the other hand, aimed to develop a model to predict the financial failure risks of companies based on the data of 10 unsuccessful and 12 successful companies between 2009 and 2010. As a result of the study, six among nineteen financial ratios, including the net working capital ratio, were determined to predict failure effectively. Zeytinoğlu & Akarim (2013) developed a year-specific financial failure model for 2009, 2010 and 2011 with 115 companies. They found that the current ratio was dominant only in the 2009 model, and the net working capital ratio was the main factor of financial failure in all three models.

Ural et al. (2015) analyzed the financial failure risks of companies for one, two and three years before the failure, using the data of 24 food, beverage and tobacco companies in the 2005-2012 period. They used five financial ratios without any working capital variables for one year before failure, seven financial ratios including current ratio and acid-test ratio for two years before financial failure, and eight financial ratios including cash ratio and inventory ratio for three years before financial failure. They determined the current ratio as the significant variable of the financial failure prediction models. Toraman & Karaca (2016) used the data of 17 chemical companies between 2010 and 2013 and examined the effects of various financial ratios with the Altman (1968) Z score values. They found that the working capital ratio has a significant effect on the Z-score values of the companies. Akyüz et al. (2017) examined sixteen paper and paper products companies operating in 2015. Ertan & Ersan (2018) investigated financial failure using ratios of 175 successful and 33 unsuccessful manufacturing companies between 2000 and 2004. Karadeniz & Öcek (2019) examined the financial ratios affecting financial failure by using the financial data of 12 tourism companies operating in the period between 2012 and 2017. As a result of all three studies, it has been determined that cash ratio, acid-test ratio, and current ratio had statistically significant effects on financial failure and can be used in financial failure predictions. Karaca and Özen (2017) investigated financial failures in the tourism sector companies in Borsa İstanbul using the Altman model.

Arslantürk Çöllü et al. (2020) determined the financial ratios affecting financial failure by using 2016- 2018 data of textile, clothing and leather companies whose shares are listed in the BIST. They found that the current ratio, trade receivables ratio and inventory turnover are among the financial ratios that affect financial failure. In the study by Temelli & Tekin (2020), 241 companies in Borsa Istanbul between 2011 and 2019 were analyzed with the Springate model. It was found that 77.6% of the companies were financially successful in the analyzed period.

3. Scope, Data and Methodology

Manufacturing industry companies listed on BIST between 2019 and 2020 are within the scope of this research. Data and information of BIST manufacturing industry companies were obtained from the Public Disclosure Platform (KAP). As of July 2021, there are 180 companies operating in the BIST manufacturing industry (KAP, 2021). The 146 manufacturing industry companies analyzed in this study are given in Table 1.

Table 1. BIST Manufacturing Companies Analyzed Within the Scope of the Research

No.

Company

Code

1

Acıselsan Acıpayam Selüloz Sanayi Tic. A.Ş.

ACSEL

2

Adel Kalemcilik Tic.ve San. A.Ş.

ADEL

3

Afyon Çimento Sanayi T.A.Ş.

AFYON

4

Akçansa Çimento Sanayi ve Tic. A.Ş.

AKCNS

5

Akın Tekstil A.Ş.

ATEKS

6

Aksa Akrilik Kimya Sanayi A.Ş.

AKSA

7

Alarko Carrier Sanayi ve Tic. A.Ş.

ALCAR

8

Alcatel Lucent Teletaş Telekomünikasyon A.Ş.

ALKA

9

Alkim Kağıt Sanayi ve Tic.A.Ş.

ALKİM

10

Anadolu Efes Biracılık ve Malt Sanayii A.Ş.

AEFES

11

Arçelik A.Ş.

ARCLK

12

Arsan Tekstil Ticaret ve Sanayi A.Ş.

ARSAN

13

Anadodu Isuzu Otomotiv Sanayi ve Ticaret A.Ş.

ASUZU

14

A.V.O.D.Kurutulmuş Gıda ve Tarım Ürünleri San. Tic. A.Ş.

AVOD

15

Aygaz A.Ş.

AYGAZ

16

Bağfaş Bandırma Gübre Fabrikaları A.Ş.

BAGFS

17

Bak Ambalaj San.ve Tic. A.Ş.

BAKAB

18

Banvit Bandırma Vitaminli Yem San. A.Ş.

BANVT

19

Berkosan Yalıtım ve Tecrit Mad. Üretim ve Tic. A.Ş.

BRKSN

20

Bilici Yatırım Sanayi ve Tic. A.Ş.

BLCYT

21

Bantaş Bandırma Ambalaj San. Tic. A.Ş.

BNTAS

22

Batı Söke Söke Çimento San. T.A.Ş.

BSOKE

23

Batıçim Batı Anadolu Çimento San. A.Ş.

BTCIM

24

Birko Birleşik Koyunlular Mensucat Tic.ve San. A.Ş.

BRKO

25

Birlik Mensucat Tic.ve San. İşletmesi A.Ş.

BRMEN

26

Borusan Mannesmann Boru San.ve Tic. A.Ş.

BRSAN

27

Bossa Tic. Ve San İşletmeleri T.A.Ş.

BOSSA

28

Bosch Fren Sistemleri San.ve Tic. A.Ş.

BFREN

29

Brisa Bridgestone Sabancı Lastik San.ve Tic. A.Ş.

BRİSA

30

Burçelik Bursa Çelik Döküm Sanayi A.Ş.

BURCE

31

Burçelik Vana San.ve Tic. A.Ş.

BURVA

32

Bursa Çimento Fabrikası A.Ş.

BUCİM

33

Coca-Cola İçecek A.Ş.

CCOLA

34

Çelik Halat ve Tel San. A.Ş.

CELHA

35

Çemaş Döküm Sanayi A.Ş.

CEMAS

36

Çemtaş Çelik Makine Sanayi ve Tic. A.Ş.

CEMTS

37

Çimbeton Hazır beton ve Prefabrik Yapı Elemanları San. Ve Tic. A.Ş.

CMBTN

38

Çimentaş İzmir Çimento Fabrikası T.A.Ş.

CMENT

39

Çimsa Çimento Sanayi Tic. A.Ş.

CIMSA

40

Çuhadaroğlu Metal San. Ve Paz. A.Ş.

CUSAN

41

Dagi Giyim Sanayi ve Tic. A.Ş.

DAGİ

42

Dardanel Önentaş Gıda Sanayi A.Ş.

DARDL

43

Demisaş Döküm Emaye Mamulleri San. A.Ş.

DMSAS

44

Derimod Konf. Ayakkabı Deri San ve Tic. A.Ş.

DERİM

45

Desa Deri San.ve Tic. A.Ş.

DESA

46

Deva Holding A.Ş.

DEVA

47

Diriteks Diriliş Tekstil San.ve Tic. A.Ş.

DIRIT

48

Ditas Doğan Yedek Parça İmalat ve Teknik A.Ş.

DİTAS

49

Doğan Burda Dergi Yayıncılık ve Paz. A.Ş.

DOBUR

50

Doğtaş Kelebek Mobilya San ve Tic. A.Ş.

DGKLB

51

Doğusan Boru san ve Tic. A.Ş.

DOGUB

52

Döktaş Dökümcülük Tc.ve San. A.Ş.

DOKTA

53

Duran Doğan Basın ve Ambalaj San. A.Ş.

DURDO

54

DYO Boya Fabrikaları San.ve Tic. A.Ş.

DYOBY

55

Ege Endüstri ve Tic. A.Ş.

EGEEN

56

Ege Gübre Sanayi A.Ş.

EGGUB

57

Ege Profil Ticaret ve San. A.Ş.

EGPRO

58

Ege Seramik San.ve Tic. A.Ş.

EGESER

59

Ege Plast Ege Plastik Tic.ve San A.Ş.

EPLAS

60

Ekiz Kimya San.ve Tic. A.Ş.

EKİZ

61

Emek Elektrik End. A.Ş.

EMKEL

62

Eminiş Ambalaj San.ve Tic. A.Ş.

EMNIS

63

Erbosan Erciyas Boru Sanayii Tic. A.Ş.

ERBOS

64

Ereğli Demir Çelik Fabrikaları T.A.Ş.

EREGL

65

Ersu Meyve ve Gıda San. A.Ş.

ERSU

66

Federal -Mogul İzmit Piston ve Pim Üretim Tesisleri A.Ş.

FMZİP

67

Ford Otomotiv San .A.Ş.

FROTO

68

Formet Çelik Kapı San. Ve Tic. A.Ş.

FORMT

69

Frigo-Pak Gıda Maddeleri San. ve Tic. A.Ş.

FRİGO

70

Gediz Ambalaj San.ve Tic. A.Ş.

GEDZA

71

Gentaş Dekoratif Yüzeyler San.ve Tic. A.Ş.

GENTS

72

Gersan Elektrik Tic. Ve San. A.Ş.

GEREL

73

Goodyear Lastikleri T.A.Ş.

GOODY

74

Göltaş Göller Bölgesi Çimento Sanayi Tic. A.Ş.

GOLTS

75

Gübre Fabrikaları T.A.Ş.

GUBRF

76

Hateks Hatay Tekstil İşletmeleri A.Ş.

HATEK

77

Hektaş Tic. T.A.Ş.

HEKTS

78

Hürriyet Gazetecilik ve Matbaacılık A.Ş.

HURGZ

79

İhlas Ev Aletleri İmalat Sanayi ve Tic. A.Ş.

IHEVA

80

İhlas Gazetecilik A.Ş.

IHGZT

81

İskenderun Demir Çelik A.Ş.

ISDMR

82

İzmir Demir Çelik San. A.Ş.

IZDMC

83

Jantsa Jant Sanayi ve Tic. A.Ş.

JANTS

84

Kaplamin Ambalaj Sanayi ve Tic. A.Ş.

KAPLM

85

Kardemir Karabük Demir Çelik Sanayi Tic. A.Ş.

KARDMD

86

Karsan Otomotiv Sanayii ve Tic. A.Ş

KARSN

87

Karsu Tekstil Sanayii ve Tic. A.Ş

KRTEK

88

Kartonsan Karton San. Ve Tic. A.Ş.

KARTN

89

Katmerciler Araç Üstü Ekipman San. Ve Tic. A.Ş.

KATMR

90

Kent Gıda Maddeleri San. Ve Tic. A.Ş.

KENT

91

Kerevitaş Gıda San. Ve Tic.A.Ş.

KERVT

92

Klimasan Klima San. Ve Tic.A.Ş.

KLMSN

93

Konfrut Gıda San. Ve Tic.A.Ş.

KONFRT

94

Konya Çimento Sanayii A.Ş.

KONYA

95

Kordsa Teknik Tekstil A.Ş.

KORDS

96

Kristal Kola ve Meşrubat Sanayi Ticaret A.Ş.

KRSTL

97

Kütahya Porselen Sanayi A.Ş.

KUTPO

98

Lüks Kadife Ticaret ve Sanayii A.Ş.

LUKSK

99

Makina Takım Endüstrisi A.Ş.

MAKTK

100

Marshall Boya ve Vernik Sanayi A.Ş.

MRSHL

101

Menderes Tekstil Sanayi ve Ticaret A.Ş.

MNDRS

102

Mondi Olmuksan Kağıt ve Ambalaj Sanayi A.Ş.

OLMK

103

Mondi Tire Kutsan Kağıt ve Ambalaj Sanayi A.Ş.

TİRE

104

Niğbaş Niğde Beton Sanayi ve Ticaret A.Ş.

NİBAS

105

Nuh Çimento Sanayi A.Ş.

NUHCM

106

Otokar Otomotiv ve Savunma Sanayi A.Ş.

OTKAR

107

Oylum Sınai Yatırımlar A.Ş.

OYLUM

108

Özbal Çelik Boru Sanayi Tic. Ve Taahhüt A.Ş.

OZBAL

109

Parsan Makine Parçaları Sanayii A.Ş.

PARSN

110

Penguen Gıda Sanayi A.Ş.

PENGD

111

Petkim Petrokimya Holding A.Ş.

PETKM

112

Pınar Entegre Et ve Un Sanayii A.Ş.

PETUN

113

Pınar Su ve İçecek Sanayi ve Ticaret A.Ş.

PINSU

114

Pınar Süt Mamulleri Sanayii A.Ş.

PNSUT

115

Prizma Pres Matbaacılık Yayıncılık Sanayi ve Tic. A.Ş.

PRZMA

116

Royal Halı İplik Tekstil Mobilya Sanayi ve Tic. A.Ş.

ROYAL

117

RTA Laboratuvarları Biyolojik Ürünler İlaç ve Makine San. Tic. A.Ş.

RTLAB

118

Sanifoam Sünger Sanayi ve Ticaret A.Ş.

SANFM

119

Saray Matbaacılık Kağıtçılık Kırtasiyecilik Tic. Ve San. A.Ş.

SAMAT

120

Sarkuysan Elektrolikit Bakır Sanayi ve Tic.A.Ş.

SARKY

121

Say Yenilenebilir Enerji Ekipmanları Sanayi ve Tic. A.Ş.

SAYAS

122

Sasa Polyester Sanayi A.Ş.

SASA

123

Sekuro Plastik Ambalaj Sanayi A.Ş.

SEKUR

124

Selçuk Gıda Endüstri İhracat ve İthalat A.Ş.

SELGD

125

Silverline Endüstri ve Ticaret A.Ş.

SILVR

126

Söktaş Tekstil Sanayi ve Tic.A.Ş.

SKTAS

127

Sönmez Pamuklu Sanayii A.Ş.

SNPAM

128

Tat Gıda Sanayi A.Ş.

TATGD

129

Temapol Polimer Plastik ve İnşaat Sanayi Ticaret A.Ş.

TMPOL

130

Tofaş Türk Otomobil Fabrikası A.Ş.

TOASO

131

Tuğçelik Alüminyum ve Metal Mamulleri Sanayi ve Tic.A.Ş.

TUCLK

132

Tukaş Gıda Sanayi ve Tic.A.Ş.

TUKAS

133

Tümosan Motor ve Traktör Sanayi A.Ş.

TMSN

134

Tüpraş-Türkiye Petrol Rafinerileri A.Ş.

TUPRS

135

Türk Prysmian Kablo Sistemleri A.Ş.

PRKAB

136

Türk Traktör ve Ziraat Makineleri A.Ş.

TTRAK

137

Türk Tuborg Bira ve Malt Sanayii A.Ş.

TBORG

138

Ulusoy Elektrik İmalat Taahhüt ve Tic.A.Ş.

ULUSE

139

Ulusoy Un Sanayi ve Ticaret A.Ş.

ULUUN

140

Uşak Seramik Sanayi A.Ş.

USAK

141

Ülker Bisküvi Sanayi A.Ş.

ULKER

142

Vestel Beyaz Eşya Sanayi ve Tic. A.Ş.

VESBE

143

Vestel Elektronik Sanayi ve Ticaret A.Ş.

VESTL

144

Viking Kağıt ve Selüloz A.Ş.

VKING

145

Yataş Yatak ve Yorgan Sanayi ve Ticaret A.Ş.

YATAS

146

Yünsa Yünlü Sanayi ve Ticaret A.Ş.

YUNSA

Authors’ Compilation

While Mondi Olmuksan Kağıt ve Ambalaj Sanayi A.Ş was traded on the BIST with the OLMIP code, it started to be traded with the OLMK code on 01.07.2021 due to the company name change and was included in the analysis with the same code. Of the 34 manufacturing industry companies not included in the research, 11 were offered to the public (IPOs) in 2021, 3 in 2020 and 1 in 2019 and started to be traded on the BIST. They were not included in the analysis due to the lack of financial statements for the periods studied. Merko Gıda Sanayi ve Tic. A.Ş. (MERKO) was excluded from the analysis. Oyak Çimento (OYAKC) was excluded from the scope of the analysis since Oyak Cement group companies were merged under Mardin Çimento in 2019, and the name of Mardin Çimento was changed to Oyak Çimento since it does not have financial statements for the relevant year. Seventeen manufacturing companies that publish their financial reports every six months are also excluded from the analysis. The companies not included in the study are given in Table 2.

The research used financial statement data of BIST manufacturing companies published in quarterly periods between 2019 and 2020. The companies' financial statement data within the research scope for the relevant periods were taken from the KAP and Fintables databases. In addition, the closing prices of stocks are taken from investing.com.

Within the scope of the research, the quarterly financial statements of 146 BIST companies operating in the manufacturing industry sector for the years 2019-2020 were evaluated using Altman (1968), S​p​r​i​n​g​a​t​e​ ​(​1​9​7​8​), Taffler (1983) and Zmijewski (1984) financial failure models. The financial failure models and calculations used in the study are given in Table 3.

Table 2. BIST Manufacturing Companies Excluded from the Scope of the Research

No.

Company

Code

IPOs in 2021

1

BMS Çelik Hasır Sanayi ve Ticaret A.Ş.

BMS

2

Kalekim Kimyevi Maddeler San. ve Tic.A.Ş.

KALKIM

3

Işık Plastik Sanayi ve Dış Ticaret Pazarlama A.Ş.

ISKPL

4

Kervan Gıda Sanayi ve Ticaret A.Ş.

KRVGD

5

Kütahya Şeker Fabrikası A.Ş.

KTSKR

6

Meditera Tıbbi Malzeme Sanayi ve Ticaret A.Ş.

MEDTR

7

Mercan Kimya Sanayi ve Ticaret A.Ş.

MERCN

8

Qua Granite Hayal Yapı ve Ürünleri Sanayi Ticaret A.Ş.

QUAGR

9

Selva Gıda Sanayi A.Ş.

SELVA

10

Türk İlaç ve Serum Sanayi A.Ş.

TRILC

11

Boğaziçi Beton Sanayi Tic.A.Ş.

BOBET

IPOs in 2020

1

Fade Gıda Yatırım ve Sanayi Ticaret A.Ş.

FADE

2

Bayrak EBT Taban Sanayi ve Ticaret A.Ş.

BAYRK

3

Dinamik Isı Makine Yalıtım Malzemeleri Sanayi ve Ticaret A.Ş.

DNISI

IPOs in 2019

1

Yükselen Çelik A.Ş.

YKSLN

Companies that publish Financial Reports on a 6-monthly basis

1

Ayes Çelik Hasır ve Çit Sanayi A.Ş.

AYES

2

Balatacılar Balatacılık Sanayi ve Ticaret A.Ş.

BALAT

3

Baştaş Başkent Çimento Sanayi ve Ticaret A.Ş.

BASCM

4

İzmir Fırça Sanayi ve Ticaret A.Ş.

İZFAS

5

Mega Polietilen Köpük Sanayi ve Ticaret A.Ş.

MEGAP

6

Orma Orman Mahsulleri İntegre Sanayi ve Ticaret A.Ş.

ORMA

7

Özerden Plastik ve Sanayi Ticaret A.Ş.

OZRDN

8

Rodrigo Tekstil ve Sanayi Ticaret A.Ş.

RODRG

9

Politeknik Metal Sanayi ve Ticaret A.Ş.

POLTEK

10

Sodaş Sodyum Sanayii A.Ş.

SODSN

11

Taze Kuru Gıda Sanayi ve Ticaret A.Ş.

TKURU

12

Sumaş Suni Tahta ve Mobilya Sanayi A.Ş.

SUMAS

13

Vanet Gıda Sanayi İç ve Dış Ticaret A.Ş.

VANGD

14

Yibitaş Yozgat İşçi Birliği İnşaat Malzemeleri Ticaret ve San. A.Ş.

YİBİTAŞ

15

Yonga Mobilya Sanayi ve Ticaret A.Ş.

YONGA

16

Safkar Ege Soğutmacılık Klima Soğuk Hva Tes. İhracat İthalat San. A.Ş.

SAFKAR

17

Seyitler Kimya Sanayi A.Ş.

SEYKM

Authors’ Compilation
Table 3. Financial Failure Models in the Research

Financial Failure Model

Formula

Result

Altman Z = 0.012*X1 + 0.014*X2 + 0.033*X3 + 0.006*X4 + 0.999*X5

Z<1,81 then the company is classified as “not at risk of financial failure”

1,81 ≤ Z ≤ 2,99 then the company is classified as “uncertain”

Z> 2,99 then the company is classified as “not at risk of financial failure”

X1 = Working Capital / Total Assets

Altman (1968)

X2 = Retained Earnings / Total Assets

X3 = Net Profit Before Interest and Taxes (NPBIT) / Total Assets

X4 = Market Value / Total Liabilities

X5 = Sales / Total Assets

S​p​r​i​n​g​a​t​e​ ​(​1​9​7​8​)

Springatez = 1.03*X1 + 3.07*X2 + 0.66*X3 + 0.4*X4

X1 = Working Capital / Total Assets

X2 = Net Profit Before Interest and Taxes (NPBIT) / Total Assets X3 = Net Profit Before Taxes (NPAT) / Current Liabilities

X4 = Sales / Total Assets

Z < 0.862; then the company is classified as “at risk of financial failure”

Z > 0.862; then the company is classified as “not at risk of financial failure”

Taffler (1983)

TafflerZ = 3.20 + 12.18*X1 + 2.50*X2 – 10.68*X3 + 0.03*X4

X1 = Net Profit Before Taxes (NPAT) / Average Short Term Liabilities X2 = Current Assets / Total Liabilities

X3 = Short Term Liabilities / Total Assets

X4 = (Current Assets – Stoklar - Short Term Liabilities) / (Sales - Net Profit Before Taxes + Amortization)

Z < 0,3 then the company is classified as “at risk of financial failure”,

Z > 0,3 then the company is classified as “not at risk of financial failure”

Zmijewski (1984)

Zmijewski = -4.336 - 4.513*X1 + 5.769*X2 + 0.004*X3

X1 = Net Income / Total Assets = ROA

X2 = Total Liabilities / Total Assets = Leverage

X3 = Current Assets / Current Liabilities = Current Ratio = Liquidity

Z > 0 then the company is classified as “at risk of financial failure”,

Z < 0 then the company is classified as “not at risk of financial failure”

Authors’ Compilation

Abbreviations are used in the tables while giving the financial status of the companies. RoFF (Risk of Financial Failure) was used for companies with a risk of financial failure, NRoFF (No Risk of Financial Failure) for companies with no risk of financial failure, and UN (Uncertain) for companies with an uncertain risk of financial failure.

4. Findings

The Altman Z values of the BIST manufacturing companies within the scope of the research, calculated between 2019 and 2020, are given in Table 4.

Table 4. Altman Z Values of Manufacturing Companies for 2019-2020

No.

Code

Altman Z

1

ACSEL

4,83

5,52

4,50

9,03

5,63

20,10

6,46

13,08

2

ADEL

1,40

1,02

1,03

1,07

1,09

1,15

1,72

1,49

3

AFYON

0,13

-0,07

0,09

4,15

0,18

5,48

0,15

5,89

4

AKCNS

0,92

0,96

1,00

1,75

1,16

1,92

1,61

2,04

Authors’ Compilation

The companies' determination within the research scope according to the Altman Z values in Table 4 for the years 2019-2020 according to the threshold values (Table 3) is given in Table 5. Again, RoFF (Risk of Financial Failure) was used for companies with a risk of financial failure, NRoFF (No Risk of Financial Failure) for companies with no risk of financial failure, and UN (Uncertain) for companies with an uncertain risk of financial failure.

Table 5. Financial Status According to Altman Z Values of Manufacturing Companies for 2019-2020

No.

Code

Altman Z-Financial Status

2019/3

2020/3

2019/6

2020/6

2019/9

2020/9

2019/12

2020/12

1

ACSEL

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NroFF

2

ADEL

RoFF

RoFF

RoFF

RoFF

RoFF

RoFF

RoFF

RoFF

3

AFYON

RoFF

RoFF

RoFF

NRoFF

RoFF

NRoFF

RoFF

NroFF

4

AKCNS

RoFF

RoFF

RoFF

RoFF

RoFF

UC

RoFF

UC

5

ATEKS

RoFF

RoFF

RoFF

RoFF

RoFF

UC

RoFF

UC

Authors’ Compilation

When Table 5 is examined, it is seen that in the period of 2019/3, when the number of financially unsuccessful firms was the highest, 120 of 146 firms failed Altman Z scores. According to the Altman Z-score, it is seen that the period with the highest success belongs to 2020/12 where 51 out of 146 companies are successful. The companies that have been successful in each quarter with the Altman Z Score calculated for the period 2019-2020 are: ACSEL, BFREN, CEMTS, EGEEN, FMZIP, KONYA, KRSTL, NIBAS, PRZMA, RTLAB, ULUSE. Companies that failed in terms of Altman Z Score in all periods are: ADEL, AKSA, AEFES, ARCLK, BAGFS, BSOKE, BTCIM, BRKO, BRMEN, BRSAN, BOSSA, BRISA, BURCE, CCOLA, CELHA, CIMSA, DAGI, DARDNL, DERİM, DESA, DIRIT, DITAS, DGKLB, DOKTA, DURDO, DYOBY, EKİZ, EMKEL, GOLTS, HATEK, HEKTS, IZDMC, KARDMD, KARSN, KRTEK, KATMR, KLMSN, KORDS, MNDRS, PETKM, PINSU, PNSUT, ROYAL, SANFM, SASA, SEKUR, SILVR, SKTAS, TMPOL, TOASO, TUCLK, TUPRS, ULUUN, USAK, ULKER, VESTL, VKING, YUNSA.

.According to the formula in Table 3, the Springate model’s values of the BIST manufacturing companies within the scope of the research for the years 2019-2020 are given in Table 6.

Table 6. Springate Values of BIST Manufacturing Companies for 2019-2020

No.

Code

Springate

1

ACSEL

0,86

0,85

1,67

1,16

0,91

1,09

0,96

1,38

2

ADEL

0,83

0,68

0,61

0,66

0,62

0,65

0,50

0,42

3

AFYON

-0,31

-0,36

-0,30

0,05

-0,26

0,10

-0,35

0,06

4

AKCNS

0,16

0,17

0,25

0,37

0,29

0,48

0,35

0,43

Authors’ Compilation

The determination of the companies within the scope of the research according to the Springate values in Table 6 for the years 2019-2020 according to the threshold values (Table 3) is given in Table 7. RoFF (Risk of Financial Failure) was used for companies with a risk of financial failure and NRoFF (No Risk of Financial Failure) for companies with no risk of financial failure.

Table 7. Financial Status According to Springate Values of Manufacturing Companies for 2019-2020

No.

Code

Springate-Financial Status

2019/3

2020/3

2019/6

2020/6

2019/9

2020/9

2019/12

2020/12

1

ACSEL

NRoFF

RoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

2

ADEL

RoFF

RoFF

RoFF

RoFF

RoFF

RoFF

RoFF

RoFF

3

AFYON

RoFF

RoFF

RoFF

RoFF

RoFF

RoFF

RoFF

RoFF

4

AKCNS

RoFF

RoFF

RoFF

RoFF

RoFF

RoFF

RoFF

RoFF

Authors’ Compilation

When Table 7 is examined, the period of 2020/12 is seen as the period with the highest success, and the Springate scores show a total of 38 companies being successful. The least successful periods were experienced in 2019/3 and 2019/6. Only 20 companies scored successfully within those periods. In Table 7, companies that are financially successful in all quarters in terms of Springate scores as of 2019-2020 and quarterly periods are as follows: ALCAR, ALKIM, CEMTS, KUTPO, ULUSE. The companies that failed in all periods are ADEL, AFYON, AKCNS, ATEKS, AKSA, AEFES, ARSAN, ASUZU, AVOD, AYGAZ, BAGFS, BAKAB, BTCIM, BRKO, BRMEN BRSAN, BOSSA, BRISA, BURCE, CEMAS, CMBTN, CMENT, CIMSA, CUSAN, DAGI, DARDL, DERİM, DIRIT, DGKLB, DOGUB, DOKTA, DURDO, DYOBY, EGGUB, EGPRO, EKİZ, EMKEL, EREGL, ERSU, FORMT, GEREL, GUBRF, IZDMC, KAPLM, KARDMD, KARSN, KENT, KLMSN, KORDS, KRSTL, LUKSK, MRSHL, MNDRS, OLMK, TIRE, NIBAS, NUHCM, OYLUM, OZBAL, PARSN, PENGD, PETKM, PETUN, PINSU, PNSUT, PRZMA, ROYAL, SANFM, SAMAT, SARKY, TOASO, TUCLK, TUKAS, TMSN, TUPRS, PRKAB, ULUUN, USAK, VESBE, VESTL, VKING and YUNSA.

According to the formula in Table 3, the Taffler model’s values of the BIST manufacturing companies within the scope of the research for the years 2019-2020 are given in Table 8.

Table 8. Taffler Model’s Values of BIST Manufacturing Companies for 2019-2020

No.

Code

Taffler

2019/3

2020/3

2019/6

2020/6

2019/9

2020/9

2019/12

2020/12

1

ACSEL

9,41

13,79

12,59

18,67

9,47

18,67

10,22

23,28

2

ADEL

0,76

-0,24

-0,67

-0,64

-0,62

-0,80

0,46

-1,28

3

AFYON

-1,77

-2,50

-1,82

2,05

-1,59

2,56

-2,23

2,50

4

AKCNS

0,23

0,35

0,41

1,96

1,07

3,00

2,18

2,47

Authors’ Compilation

The companies' determination within the research scope according to the Taffler values in Table 8 for the years 2019-2020 according to the threshold values (Table 3) is given in Table 9. RoFF (Risk of Financial Failure) was used for companies with a risk of financial failure and NRoFF (No Risk of Financial Failure) for companies with no risk of financial failure.

Table 9. Financial Status According to Taffler Model’s Values of Manufacturing Companies for 2019-2020

No.

Code

Taffler-Financial Status

2019/3

2020/3

2019/6

2020/6

2019/9

2020/9

2019/12

2020/12

1

ACSEL

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

2

ADEL

NRoFF

RoFF

RoFF

RoFF

RoFF

RoFF

NRoFF

RoFF

3

AFYON

RoFF

RoFF

RoFF

NRoFF

RoFF

NRoFF

RoFF

NRoFF

4

AKCNS

RoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

Authors’ Compilation

When Table 9 is examined, a total of 112 companies showed success in the period of 2020/12, when the number of companies with the highest Taffler score was successful. It is seen that the Taffler score shows a total of 54 companies that failed in the 2019/12 period when the success was the lowest. Companies with financially successful Taffler score in all periods include: ACSEL, ATEKS, ALCAR, ALKA, ALKİM, AEFES, ARCLK, ARSAN, AYGAZ, BAKAB, BLCYT, BNTAS, BRSAN,  BFREN, BUCİM, CCOLA, CEMTS, CMENT, CUSAN, DAGİ , DMSAS, DEVA, EGEEN,  EGESER, EPLAS, ERBOS, EREGL, ERSU, FMZIP, FRIG, GEDZA, GENTS, HATEK, HEKTS, HURGZ, IHEVA, IHGZT, ISDMR, JANTS, KARTN, KENT, KERVTYA, KLMSN, RSTLKON , KUTPO, LUKSK, MAKTK, NUHCM, OTKAR, OYLUM, PARSN, PETKM, PETUN, PNSUT, PRZMA, RTLAB, SANFM, SEKUR, SELGD, SNPAM, TATGD, ULUSE, YATAS. Companies with unsuccessful Taffler scores in all periods include: ASUZU, BRMEN, CELHA, DGKLB, DYOBY, EKİZ, EMKEL, GUBRF, IZDMC, KAPLM, MRSHL, TIRE, SILVR, SKTAS, TUPRS, ULUUN, VESTL, VKING.

According to the formula in Table 3, the Zmijewski model’s values of the BIST manufacturing companies within the scope of the research for the years 2019-2020 are given in Table 10.

Table 10. Zmijewski Values of BIST Manufacturing Companies for 2019-2020

No.

Code

Zmij

ewski

2019/3

2020/3

2019/6

2020/6

2019/9

2020/9

2019/12

2020/12

1

ACSEL

-3,49

-3,56

-4,07

-3,78

-4,14

-4,00

-4,29

-4,20

2

ADEL

-0,94

-0,42

-0,46

-0,29

-0,44

-0,03

-0,81

0,10

3

AFYON

-0,34

0,11

-0,11

-2,53

0,10

-2,68

0,33

-2,68

4

AKCNS

-1,21

-1,08

-1,34

-1,30

-1,55

-1,61

-1,72

-1,64

5

ATEKS

-2,57

-2,30

-2,57

-2,33

-2,42

-2,19

-2,36

-2,71

Authors’ Compilation

The companies' determination within the research scope according to the Zmijewski values  in Table 10 for the years 2019-2020 according to the threshold values (Table 3) is given in Table 11. RoFF (Risk of Financial Failure) was used for companies with a risk of financial failure and NRoFF (No Risk of Financial Failure) for companies with no risk of financial failure.

Table 11. Financial Status According to Zmijewski Model’s Values of Manufacturing Companies for 2019- 2020

No.

Code

Zmijewski-Financial Status

2019/3

2020/3

2019/6

2020/6

2019/9

2020/9

2019/12

2020/12

1

ACSEL

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

2

ADEL

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

RoFF

3

AFYON

NRoFF

RoFF

NRoFF

NRoFF

RoFF

NRoFF

RoFF

NRoFF

4

AKCNS

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

5

ATEKS

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

6

AKSA

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

Authors’ Compilation

According to Table 11, the period with the highest number of successful companies with a Zmijewski score is 2020/12 with a total of 123 companies. The period with the highest number of unsuccessful companies is 2020/3. The manufacturing companies that failed in all periods are: CCOLA, CELHA, DERİM, DGKLB, DOKTA, IZDMC, KATMR, MNDRS, PINSU, ROYAL, SKTAS, TUPRS, VKING.

Those with successful financial scores in all periods include: ACSEL, AKCNS, ATEKS, AKSA, ALCAR, ALKA, ALKİM, AEFES, ARCLK, ARSAN, ASUZU, AVOD, AYGAZ, BAKAB, BANVT, BRKSN, BLCYT, BNTAS, BRKO, BRSAN, BFREN, BURCE , BURVA, BUCİM, CEMAS, CEMTS, CMENT, CIMSA, CUSAN, DAGI, DMSAS, DEVA, DOBUR, DOGUB, EGEEN, EGGUB, EGPRO, EGESER, EPLAS, ERBOS, EREGL, ERSU, FMZIP, FROTO, FORMT, FRIG, GED , GENTS, GEREL, GOODY, GOLTS, HATEK, HEKTS, HURGZ, IHEVA, IHGZT, ISDMR, JANTS, KAPLM, KARDMD, KARTN, KENT, KERVT, KONFRT, KONYA, KORDS, KRSTL, KUTPO, LUKSK, MAKTK, TIRE, NIBAS, NUHCM, OYLUM, PARSN, PETKM, YATAS PETUN, PNSUT, PRZMA, RTLAB, SARKY, SAYDAS, SARKY, SAYDAS , TATGD, TMPOL, TOASO, TUCLK, TUKAS, TMSN, PRKAB, TBORG, ULUSE, USAK, ULKER, VESBE.

Comparative information on the financial status of the BIST manufacturing companies for the periods 2019/12 and 2020/12 for financial failure models within the scope of the research is given in Table 12.

Table 12. Evaluation of Financial Failure Models Used in the Research for the Periods of 2019/12 and 2020/12

No.

Code

201

9/12

202

0/12

Altman

Springate

Taffler

Zmijewski

Altman

Springate

Taffler

Zmijewski

1

ACSEL

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

NRoFF

2

ADEL

RoFF

RoFF

NRoFF

NRoFF

RoFF

RoFF

RoFF

RoFF

3

AFYON

RoFF

RoFF

RoFF

RoFF

NRoFF

RoFF

RoFF

NRoFF

4

AKCNS

RoFF

RoFF

NRoFF

NRoFF

UN

RoFF

NRoFF

NRoFF

5

ATEKS

RoFF

RoFF

NRoFF

NRoFF

UN

RoFF

NRoFF

NRoFF

6

AKSA

RoFF

RoFF

NRoFF

NRoFF

RoFF

RoFF

NRoFF

NRoFF

Authors’ Compilation

According to the data in Table 12, it is seen that the BIST manufacturing companies with successful Altman Z, Springate, Taffler, Zmijewski scores in the quarterly periods of 2019 and 2020  are: ACSEL, ALCAR, ALKA,  BFREN,  EGEEN,  FMZIP,  IHEVA,  ULUSE. BIST companies with unsuccessful scores were: BSOKE, CELHA, DERİM, DIRIT, DGKLB, EMKEL, IZDMC, PINSU, SILVR, SKTAS, TUPRS, VKING.

5. Conclusion

This study aimed to analyze the financial failure risk of BIST manufacturing companies in the COVID-19 period by comparing 2019 and 2020 with four different financial failure models. Analysis was carried out with the most used Altman (1968), S​p​r​i​n​g​a​t​e​ ​(​1​9​7​8​), Taffler (1983) and Zmijewski (1984) financial failure models in the literature, with quarterly data from 146 companies.

The analysis results show that while the risk of financial failure decreased in all four models, the number of financially successful companies increased when the 2019 and 2020 quarterly data were compared. This can be interpreted as meaning that the BIST manufacturing industry companies are less affected by the COVID-19 process when comparing 2019 and 2020. While the risk of financial failure decreased in the quarter of 2019, the number of financially successful companies increased. This increase is also valid for the quarters of 2020. While the number of financially failed companies  in the first quarter of 2019 and 2020 decreased in Altman and Zmijewski models, there was no change in the number of financially successful or unsuccessful companies in Springate and Tafller  models. The difference between these models in the first quarter is not found in the following quarters. In the Zmijewski and Taffler models, financially successful companies by quarter are higher than in the Altman and Springate models.

On the other hand, in Altman and Springate models, the number of companies at risk of financial failure is considerably higher than in Zmijewski and Taffler models. This may be due to the fact that the variables in the models are similar to each other. According to the Springate model, the number of companies at risk of financial failure is higher than the number of companies calculated according to the Altman model. It can be explained by the Altman model using market value, total debt, and retained earnings as variables. As a result of the calculations made according to the four financial failure models, some companies are at risk of financial failure in all quarters. Companies at risk of financial failure in all quarters in both the Altman and Springate models are; ADEL, AKSA, AEFES, BAGFS, BTCIM, BRKO, BRMEN, BRSAN, BOSSA, BRISA, BURCE, CELHA, CIMSA, DAGI, DARDNL, DERİM, DIRIT, DGKLB, DOKTA, DURDO, DYOBY, EKİZ, EMKEL, IZDMC, KAR KARSN, KLMSN, KORDS, MNDRS, PINSU, PNSUT, ROYAL, SANFM, TOASO, TUCLK, TUPRS, ULUUN, USAK, VESTL, VKING, YUNSA. Companies at risk of financial failure in all quarters in the Altman, Springate, and Taffler models include CELHA, DERİM, DGKLB, DOKTA, IZDMC, MNDRS, PINSU, TUPRS, VKING.

As a result of these two (Altman and Springate) or three models (Altman, Springate and Taffler), a detailed study can be made about the financial ratios of companies with financial failure risk in all quarters. Therefore, a more robust model can be proposed for the risk of financial failure. While this will benefit companies in shaping their financial policies, it will enable them to reach more reliable results in their decision making. In the literature, financially unsuccessful and financially successful companies are examined, while financially unsuccessful companies are bankrupt. In this study, one notes that companies that have not gone bankrupt may also risk financial failure. For this reason, companies with and without the risk of financial failure can be identified with sector-specific financial ratios, and even different thresholds can be found.

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Kaplanoğlu, E. & Moroğlu, F. (2021). Analyzing the Impact of COVID-19 on the Financial Failure Risk in Borsa İstanbul Manufacturing Companies. J. Corp. Gov. Insur. Risk Manag., 8(2), 83-113. https://doi.org/10.51410/jcgirm.8.2.7
E. Kaplanoğlu and F. Moroğlu, "Analyzing the Impact of COVID-19 on the Financial Failure Risk in Borsa İstanbul Manufacturing Companies," J. Corp. Gov. Insur. Risk Manag., vol. 8, no. 2, pp. 83-113, 2021. https://doi.org/10.51410/jcgirm.8.2.7
@research-article{Kaplanoğlu2021AnalyzingTI,
title={Analyzing the Impact of COVID-19 on the Financial Failure Risk in Borsa İstanbul Manufacturing Companies},
author={Emre KaplanoğLu and Fatma MoroğLu},
journal={Journal of Corporate Governance, Insurance, and Risk Management},
year={2021},
page={83-113},
doi={https://doi.org/10.51410/jcgirm.8.2.7}
}
Emre KaplanoğLu, et al. "Analyzing the Impact of COVID-19 on the Financial Failure Risk in Borsa İstanbul Manufacturing Companies." Journal of Corporate Governance, Insurance, and Risk Management, v 8, pp 83-113. doi: https://doi.org/10.51410/jcgirm.8.2.7
Emre KaplanoğLu and Fatma MoroğLu. "Analyzing the Impact of COVID-19 on the Financial Failure Risk in Borsa İstanbul Manufacturing Companies." Journal of Corporate Governance, Insurance, and Risk Management, 8, (2021): 83-113. doi: https://doi.org/10.51410/jcgirm.8.2.7
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