Financial Distress Prediction across Firms

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 2, Pages: 646-651

J. Environ. Treat. Tech.

ISSN: 2309-1185

Journal web link: http://www.jett.dormaj.com

Financial Distress Prediction across Firms

Ali Akbar Rafatnia *, Suresh A/L Ramakrishnan , Dewi Fariha Binti Abdullah , Fazel

Mohammadi Nodeh , Mohammad Farajnezhad

Azman Hashim International Business School, UTM, Malaysia

Department of Management, Lahijan Branch, Islamic Azad University, Lahijan, Iran

Received: 18/11/2019 Accepted: 15/02/2020 Published: 20/05/2020

Abstract

One of the most important events in a firm’s life is financial distress, which can propel sectors into financial and sustainable growth

problems. Moreover, independent variables in the background of financial distress are accounting ratios, which are extracted from financial

statements and macroeconomic variables that are mostly beyond the control of a firm or sector. The current study analysed the information

related to a sample of 300 public Iranian companies, during the periods of 2000-2007 and 2009-2016. Logistic regression and decision trees

were applied to the prediction of financial distress. It was found that the profitability, liquidity, leverage, interest rate, cash flow, accruals,

and GDP were statistically significant in distinguishing distressed from non-distressed firms across sectors. The obtained results showed that

the predictive performance of a DT model was more successful than the other model.

Keywords: Prediction of Financial Distress, Accounting ratio, Decision Trees

Introduction ¹

The prediction of financial distress is absolutely vital for

Furthermore, the stakeholders of a firm are generally

concerned about the accuracy of financial distress predictions

during business activities. To increase the accuracy of financial

distress predictions, some studies have introduced different

statistical and artificial intelligence-based models. For example,

the multivariate model proposed in (6) was the initial study based

on the discriminant analysis approach. Literature contains some

artificial intelligence models that can be effectively used in this

regard, including Neural Networks (7), Decision Trees (8), and

Support Vector Machines (9). Furthermore, to increase the

accuracy of the financial distress predictions, not only the

financial statements information, but also other available data

such as macroeconomic factors and market information are taken

into account. To do an empirical study, the performance of a

sample of 300 listed Iranian companies, during the periods of

2000-2007 and 2009-2016, was considered. The whole

accounting information was extracted from financial reports. Two

prediction techniques of logistic regression and decision trees

were applied to the research. The reset of the paper is organized

as follows. The literature review and hypothesis are presented in

Section 2, the methodology in Section 3, and finally, the result of

the study and conclusions are presented in Section 4.

traders, creditors, and suppliers. To avoid any financial loss, they

need to assess the financial risk of a firm before they make any

decisions. Financial distress is not the same as bankruptcy. The

former occurs while the firm is not able to meet its financial

obligations due to a decrease in the firm’s operations and

excessive costs, while the latter is a very last state in which

corporations stop doing commercial enterprise due to financial

distress. The bankruptcy needs to be confirmed by a courtroom

determination; then, its assets are bought to pay and cover all

obligations of creditors (1). Thus, financial distress does not

necessarily lead to bankruptcy.

Based on a review of literature, some researchers implicitly

suppose that firms’ annual statements give an honest and genuine

view of the financial state of agencies; although, some annual

accounts are indeed unreliable (2). Some studies have stated that

despite the fact that the hyperlink between earnings management

and financial distress is clear, only few studies have covered such

indicators in financial distress prediction (3). Earnings

management can be interpreted beneficial, neutral, or pernicious

(4, 5). Providing private information on future economic

performance through management activities, the beneficial

earnings management improves the financial statements of the

company. This type of management can be interpreted as neutral

if earnings management can be economically efficient for

maximizing one’s own utility. Finally, earnings management is

recognized pernicious when it is about concealing and

misrepresenting financial reports of the company.

Literature Review

Financial ratios are vital for predicting financial distress

among firms and have been already used by some researchers (6,

0), (11). Every business has its own economic characteristics

based on its defined activities. In addition, choosing appropriate

Corresponding author: Ali Akbar Rafatnia, International Business School, UTM, Malaysia. Email: rafatnia90@gmail.com and

arali3@live.utm.my.

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 2, Pages: 646-651

ratios well matched with the financial condition of any market is

the key factor of the study. According to the results of few studies

with high accuracy among ratios, accounting and macro ratios are

used. Therefore, this study will attempt to answer the following

research question:

Research Question: What are the significant determinants of

accounting ratios and macroeconomic level variables of financial

distress prediction among the listed firms?

shareholders during activities (22). The ratio of working capital

to total assets is a significant factor that considers the liquidity in

the firm. If the firm experience operating losses consistently, it

will have a shrinkage in current liquidity in relation to total assets.

According to literature, insolvency of businesses is instigated by

the unsuitable practice of working capital measures, despite

optimistic revenues or profitability due to such practices. Thus, it

would be uncertain to just concentrate on profitability while

overlooking liquidity (23). In addition, in (24), it was found that

liquidity plays an important role in the determinants of the

financial distress prediction. Alternatively, liquidity is one of the

most significant indicators related to financial distress of firms.

.1. Independent Variables

.1.1. Accounting Ratios

The financial distress researchers generally focus on the

financial reports and market trends of the sectors during specific

periods. All factors are collected through reliable available

information.

2.1.1.4 Earnings Management

In the context of financial distress prediction, banks make

lending decisions based on the firms’ financial statements

disclosed (25-27). Financial information creates a loophole for

firms by managing their earnings to obtain loans with more

favorable terms. In this regard, the financial statements must

provide reliable financial information to the external and internal

stakeholders in accordance with International Financial

Reporting Standards (IFRS) in a way to be well compared with

others’ financial statements (28, 29). Collecting, preparing, and

publishing the financial information is the managers’

responsibility (30, 31). Earnings management as the process of a

business uses the generally-accepted accounting procedures for

the purpose of altering the earnings figures such as delayed

recognition of expenses, premature recognition of revenue, and

inventory methods such as last-in, first-out (Lifo) and first-in,

first-out (Fifo) (32, 33). Remember that the motivation to manage

earnings depends on the nature of the sectors. Various reasons

have been suggested in literature for variations in earnings

management activities including market development, the

structure of ownership, economic factors, initial public offerings,

and effective tax rates (34, 35). According to (36, 37), to avoid

reporting annual losses, firms use earnings management methods.

In addition, firms under financial distress are likely to take

different measures in order to decrease the concern of future

outcome events or any inherently uncertain conditions such as

window-dressing financial statements (38, 39). In this study, the

earnings management is discussed in terms of free cash flow of

the firm and accruals, which is effective on financial distress

prediction among different sectors.

.1.1.1 Profitability

A firm’s extreme survival is based on the profitability of its

business. In fact, the profitability ratios indicate how well a firm

has operated during the fiscal year. The static trade-off theory

indicates that profitable firms are likely to have a high tax burden

and low cost of bankruptcy (12). Moreover, the profitable firms

have more capability to tolerate being indebted since they may be

in a position to easily clear their debt on time. This indicator can

have a significant role in the bankruptcy investigation. The extant

studies on financial distress prediction found a significant

relationship between profitability and financial distress (13).

Their results suggested that financial distress intensities strongly

decrease the level of profitability for all prediction horizons

considered. In addition, the authors in (14) and (15) developed a

prediction model of financial distress in Iran by Bayesian

networks and genetic programming models, respectively. Their

findings showed that when a firm has a good profitability level,

creditors are sure that their interest’s expenses in the firm can be

achievable. Moreover, in the context of the Iran economy, the

authors in (14) found out that higher profitability makes higher

efficiency and better liquidity, hence lowering default risk.

Literature widely confirms the existence of a significant

relationship between profitability and financial distress prediction

(16).

.1.1.2 Leverage

One of the main variables that explain financial distress is the

firm’s leverage that can pose a big risk to the firm due to its high

costs (17). This proxy demonstrates the risk and capital structure

of a firm. Leverage has been investigated by some researchers in

terms of its negative effects on the firm performance (18). For

example, in (19), a significant negative effect of leverage was

explored on the company’s risk in the non-financial sector. In

addition, the authors in (20) found out that through the use of the

agency argument, the benefits of leverage outweigh its cost. The

most commonly-used leverage ratio in financial distress

prediction is the debt ratio that is measured by dividing the total

debt over total assets. Furthermore, some studies have concluded

that the debt ratio is a significant factor in identifying the firms'

assets to meet the obligations (21).

2.1.1.5 Macroeconomic Variables

Macroeconomic factors affect the feasibility of a firm, and

these external factors are generally beyond the instant control of

sectors (40, 41). Moreover, the macroeconomic variables causing

financial distress are the interest rate, inflation, gross domestic

product, monetary policy, oil price, financial crisis, and debt

crisis. According to the authors in (42-47), macroeconomic

indicators affect financial distress prediction. Consistently, an

interest rate is the main macroeconomic indicator that affects the

corporate success or failure (48). Furthermore, in (49), the

interest rate is considered as an important variable that is effective

on the company’s flexibility and adaptability. It has been also

suggested that variation in inflation influences firms because of

the rising cost of production or it may generate higher prices that,

in turn, causes lower demand. Accordingly, GDP represents the

economic performance among sectors and any decline in GDP

causes the recession and other financial crises (50, 51). Financial

.1.1.3 Liquidity

A firm is able to pay off the obligations in a timely manner

and indicate its performance improvement when it holds a high

liquidity ratio. Therefore, the firms also can pay dividends to

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 2, Pages: 646-651

distress causes significant losses not only to the firms' activities,

but also to society as a whole. Thus, the high accuracy of

prediction models is very vital to stakeholders, management, and

employees as the models provide timely warnings. Generally,

macroeconomic conditions have good explanatory power for

financial distress prediction and are useful in modeling credit risk

between independent variables and dependent variable classes.

There are two main applications of logistic regression. First, this

method forecasts the category membership. The logistic

regression determines the likelihood of non-distressed over the

probability of distressed, and the results of the analysis are

presented in the form of a probability ratio. Second, this method

verifies the associations and strengths among the variables.

Coefficients of regression can be used to evaluate odds ratios for

all of the independent variables separately (55). Decision trees are

one of the most popular methods for classification and

forecasting. The main reason behind their recognition is their

easiness and transparency as well as interpretability. The

accuracy of classification or prediction in different applications is

a crucial factor. For instance, in credit scoring applications, the

model's ability to demonstrate the reasons is extremely critical as

it affects the decisions to predict credit scoring. In this regard, in

comparison with some statistical and data mining techniques, a

decision tree is able to depict rules of classification. Thus, this

technique is more attractive in order to classification applications

(56). The main difference between decision trees and linear and

logistic regression is that regarding the decision trees, models

commence by generating classification of observation into groups

and continue by obtaining a score for each group. However, in the

logistic regression methods, first, a score is generated; then, a

classification is done based on a discriminant rule. In order to

present the idea and estimate the probability of financial distress

of the firm, let us start by considering the following model of logit

regression; where the Logit of π is the probability of firm failure,

(52). In this regard, the current study will apply the three

important macroeconomic variables, i.e., the interest rate,

inflation, and gross domestic product to accurate prediction of

financial distress.

Table 1: Formulation of Independent Variables

Variables

Formulation

Accounting Variables

Profitability

Liquidity

EBIT /Total Asset

Working Capital / Total

Assets

Current Liabilities / Total

Equity

Leverage

Earning Management

Operating cash flow -- capital

expenditure

Free cash flow

Accrual

Net income-cash-dividends

Total Asset

Macroeconomic level

Central Bank of Iran

1 n

and X through X represent any of the independent variables

used to predict firm failure.

Interest rate

GDP

1 1 2 2 3 3 k k

Logit(π) =β0 + β X + β X + β X +…+ β X

Per capita

푃 =

푒+1⁻⁽^훼^ꢀ^훽^ꢁ^푥^ꢁ^ꢀ^훽²^푥²^ꢀ^훽^푛^푥^푛⁾

Inflation

Consumer Price Index (CPI)

As the decision tree (DT) is a non-parametric and preliminary

procedure, it can learn from samples by process of

generalization (Figure 1). Commonly, DT refers to binary trees

that comprise a set of subdivisions (paths from roots to leaf

nodes), leaf nodes (objects classes), and nodes (decision rules),

which categorize objects based on their characteristics (57).

.2 Dependent Variable

Dependent variable of financial distress is also a dummy

variable, and if the value of the financial distress variables gets 1

in a year, it is considered that the firm was unable to settle the

debt on that given year, or it has gone into financial distress on

that year (Central Bank of Iran, 2014). The criterion for

classifying firms into their financial conditions such as normal or

distressed firms is based on the listed firm that is specially treated

(ST) by the Teheran Stock Exchange. According to the Iran

Business Law, Article 141, if the accumulated losses of a firm are

more than 50% of stockholder equity, the firm is considered as a

distressed firm (53).

Methodology

Technological advancements and a rise in the availability and

power of computerization in the 1990s enabled researchers of

financial distress prediction to resort to a more extensive range of

methods. Today, financial distress prediction models are utilizing

both statistical analyses and artificial intelligence to upgrade the

decision support tools and to enhance the decision-making

processes (54). Logistic regression and decision trees are applied

to the current study. The Logistic regression studies the outcome

of several independent variables to predict the relationship

Figure1: Structure of Decision Trees

Empirical Results

This study examined the significant financial distress

determinants of Iranian firms listed on the Tehran Stock

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 2, Pages: 646-651

Exchange (TSE) across non-financial sectors. This study executes

a sector-wise analysis of 300 non-financial Iranian firms across

sectors. The selected firms were chosen from a dataset for periods

of 2000-2007 and 2009-2016 across sectors. In addition, the

current study extracted the financial information from the firms

based on the Commercial Law, Act 141 of Tehran Stock

Exchange, which classifies firms as distressed if the retained

losses are more than 50% of their capital. As discussed, the

following equation provides the association between the

accounting and macroeconomic factors using logistic regression

analysis.

profitability and liquidity are negatively associated with financial

distress. In addition, leverage ratio and free cash flow are

positively associated with financial distress. Moreover, accrual

variable is found statistically significant and finally, interest rate,

inflation rate, and GDP are persistently maintained significant to

the probability of financial distress across sectors. In general, the

significant relationship between accounting and macroeconomic

factors and the probability of financial distress across sectors

show that firms among sectors tend to affect the probability of

financial distress by its determinants.

Table 5: Classification of decision tree model of overall

FD = βo + β

PROFit + β

(INT) + β

LEVit + β

(GDP)+ €

LIQ it + β

FCF it + β

ACRit

independent variables

(INF) + β

Predicted

Overall

Non

Distress

Percentage

Table 2: Model summary of accounting and macroeconomic level

determinants based on logistic regression

141

87.4

50.1

85.1

status Distress

Predicted

Non

Distress

Overall

Percentage

Distress

Percentage

Non

Distress

90.4

Status

Table 5 shows the classification table of the practical results

using the decision tree model. It is observed that the classification

shows the overall analysis of firm and macroeconomic level

determinants; it has correctly classified with 85.1% accuracy.

Distress

145

60.1

83.3

Overall Percentage

The model effectively grouped 83.3% of determinants of

financial distress across firms, which is higher than half, while the

examination was directed without the independent factors that are

utilized in the model. Table 3 exhibits log likelihood ratio, Cox &

Snell R Square and Nagelkerke R Square.

Conclusion

Financial distress prediction is the inevitable phenomenon

that has been a hot topic of corporate finance literature. The

consequences of financial distress affect negatively the social and

economic states of any country in general. Therefore, appropriate

prediction tools are needed to be proposed. Furthermore, to keep

track of firm business activities, minimize the risk of failure, and

make an effective decision, the prediction models can help

managers analyse important trends of the market. In addition,

financial distress models assist the creditors to assess the risk of

the firm in order to issue a new loan, and they may warn the

auditor of the firm to monitor the performance of the financial

activities. Accordingly, the results of this study showed that a DT

model outperforms the other models in terms of predicting

financial distress.

Table 3: Classification of overall independent variables

Nagelkerke R

2Log likelihood

Cox & Snell R Square

Square

196.345a

0.317

0.445

Table 4: Estimation results of logit analysis of independent

variables

Wald

Ind. Variable

S.E.

Sig

PROF

LEV

LIQ

-2.118 0.709 10.516 0.031

21.051 3.257 15.314 0.040

9.324 3.751 11.623 0.045

25.310 0.415 5.312 0.130

14.346 1.254 5.271 0.203

0.684 0.852 2.321 0.052

2.361 3.523 14.501 0.058

6.052 8.321 21.301 0.054

References

Poston KM, Harmon K, Gramlich JD. A test of financial

ratios as predictors of turnaround versus failure among

financially distressed firms. Journal of Applied Business

Research (JABR). 1994;10(1):41-56.

FCF

ACR

GDP

INF

2. Balcaen S, Manigart S, Buyze J, Ooghe H. Firm exit after

distress: differentiating between bankruptcy, voluntary

liquidation and M&A. Small Business Economics.

012;39(4):949-75.

INT

3. Beaver WH, Correia M, McNichols MF. Do differences in

financial reporting attributes impair the predictive ability of

financial ratios for bankruptcy? Review of Accounting

Studies. 2012;17(4):969-1010.

Table 4 exhibits the overall analysis based on firm and

macroeconomic level determinants. The independent variables

are profitability (PROF), leverage ratio (LEV), liquidity (LIQ),

free cash flow (FCF), accrual (ACR), inflation (INF), interest rate

Mora A. Joshua Ronen, Varda Yaari. Earnings management:

emerging insights in theory, practice, and research. Journal of

Management & Governance. 2010;14(1):87.

(

INT), and gross domestic product (GDP). Based on Table 4, the

Ronen J, Yaari V. Earnings management: Springer; 2008.

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 2, Pages: 646-651

Altman EI. Financial ratios, discriminant analysis and the

prediction of corporate bankruptcy. The journal of finance.

24. Lin F, Liang D, Chen E. Financial ratio selection for business

crisis prediction. Expert Systems with Applications.

2011;38(12):15094-102.

25. Tabrizi ES. Examine the Relationship Between Capital

Structure, Free Cash and Operational Risks. International

Academic Journal of Accounting and Financial Management.

2016;3(3):24-31.

968;23(4):589-609.

Charalambous C, Charitou A, Kaourou F. Comparative

analysis of artificial neural network models: Application in

bankruptcy prediction. Annals of operations research.

000;99(1-4):403-25.

Gepp A, Kumar K, Bhattacharya S. Business failure

prediction using decision trees. Journal of forecasting.

26. Lazzem S, Jilani F. The impact of leverage on accrual-based

earnings management: The case of listed French firms.

Research in International Business and Finance.

2018;44:350-8.

27. Nodeh FM, Anuar MA, Ramakrishnan S, Rafatnia AA,

Nodeh AM. Mediating Risk Taking on Relationship between

Board Structure Determinants and Banks Financial

Performance. Asian Social Science. 2015;11(23):96.

010;29(6):536-55.

Shin K-S, Lee TS, Kim H-j. An application of support vector

machines in bankruptcy prediction model. Expert systems

with applications. 2005;28(1):127-35.

0. Beaver WH. Financial ratios as predictors of failure. Journal

of accounting research. 1966:71-111.

1. Kumar PR, Ravi V. Bankruptcy prediction in banks and firms

via statistical and intelligent techniques–A review. European

journal of operational research. 2007;180(1):1-28.

28. Ball R, Foster G. Corporate financial reporting: A

methodological review of empirical research. Journal of

accounting Research. 1982:161-234.

2. Fumani M, Moghadam A. The Effect of Capital Structure on

Firm Value, The Rate of Return on Equity and Earnings Per

Share of Listed Companies in Tehran Stock Exchange.

Journal of Finance and Accounting. 2015;6(15).

3. Duan J-C, Sun J, Wang T. Multiperiod corporate default

prediction—A forward intensity approach. Journal of

Econometrics. 2012;170(1):191-209.

4. Aghaie A, Saeedi A, editors. Using bayesian networks for

bankruptcy prediction: Empirical evidence from iranian

companies. 2009 International Conference on Information

Management and Engineering; 2009: IEEE.

29. Hoque M, Bhandari SB, Iyer R. Predicting business failure

using cash flow statement based measures. Managerial

Finance. 2013.

30. Macve RH. Fair value vs conservatism? Aspects of the

history of accounting, auditing, business and finance from

ancient Mesopotamia to modern China. The British

Accounting Review. 2015;47(2):124-41.

31. Cohen A, Sayag G. The effectiveness of internal auditing: an

empirical examination of its determinants in Israeli

organisations.

Australian

Accounting

Review.

2010;20(3):296-307.

5. Etemadi H, Rostamy AAA, Dehkordi HF. A genetic

programming model for bankruptcy prediction: Empirical

evidence from Iran. Expert Systems with Applications.

32. Burgstahler D, Dichev I. Earnings management to avoid

earnings decreases and losses. Journal of accounting and

economics. 1997;24(1):99-126.

33. Lin JW, Li JF, Yang JS. The effect of audit committee

performance on earnings quality. Managerial Auditing

Journal. 2006.

009;36(2):3199-207.

6. Bharath ST, Shumway T. Forecasting default with the Merton

distance to default model. The Review of Financial Studies.

008;21(3):1339-69.

34. Doukakis LC. The effect of mandatory IFRS adoption on real

and accrual-based earnings management activities. Journal of

Accounting and Public Policy. 2014;33(6):551-72.

35. Enomoto M, Kimura F, Yamaguchi T. Accrual-based and real

earnings management: An international comparison for

investor protection. Journal of Contemporary Accounting &

Economics. 2015;11(3):183-98.

7. Brealey RA, Myers SC. Financing and risk management:

McGraw Hill Professional; 2003.

8. Nodeh FM, Anuar MA, Ramakrishnan S, Raftnia AA. The

effect of board structure on banks financial performance by

moderating firm size. Mediterranean Journal of Social

Sciences. 2016;7(1):258-.

9. Hsu L-T, Jang S. The determinant of the hospitality industry's

unsystematic risk: A comparison between hotel and restaurant

firms. International Journal of Hospitality & Tourism

Administration. 2008;9(2):105-27.

36. Feng M, Li C, McVay SE, Skaife H. Does ineffective internal

control over financial reporting affect a firm's operations?

Evidence from firms' inventory management. The

Accounting Review. 2015;90(2):529-57.

0. Opler T, Titman S. The determinants of leveraged buyout

activity: Free cash flow vs. financial distress costs. The

Journal of Finance. 1993;48(5):1985-99.

1. Abdullah NAH, Zainudin N, Ahmad AH, Rus RM. Predictors

of financially distressed small and medium-sized enterprises:

a case of Malaysia. International Proceedings of Economics

Development and Research. 2014;76:108.

2. DeAngelo H, DeAngelo L, Wruck KH. Asset liquidity, debt

covenants, and managerial discretion in financial distress::

the collapse of LA Gear. Journal of financial economics.

37. Zang AY. Evidence on the trade-off between real activities

manipulation and accrual-based earnings management. The

accounting review. 2012;87(2):675-703.

38. Bhagat S, Bolton B. Corporate governance and firm

performance. Journal of corporate finance. 2008;14(3):257-

73.

39. Nekhili M, Amar IFB, Chtioui T, Lakhal F. Free cash flow

and earnings management: The moderating role of

governance and ownership. Journal of Applied Business

Research (JABR). 2016;32(1):255-68.

40. Inekwe JN, Jin Y, Valenzuela MR. The effects of financial

distress: Evidence from US GDP growth. Economic

Modelling. 2018;72:8-21.

002;64(1):3-34.

3. Samiloglu F, Akgün Aİ. The relationship between working

capital management and profitability: Evidence from Turkey.

Business and Economics Research Journal. 2016;7(2):1.

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 2, Pages: 646-651

1. Bachmann R, Elstner S, Sims ER. Uncertainty and economic

activity: Evidence from business survey data. American

Economic Journal: Macroeconomics. 2013;5(2):217-49.

2. Bhattacharjee A, Han J. Financial distress of Chinese firms:

Microeconomic, macroeconomic and institutional influences.

China Economic Review. 2014;30:244-62.

3. French J. Macroeconomic forces and arbitrage pricing theory.

Journal of Comparative Asian Development. 2017;16(1):1-

4. Brown SJ, Otsuki T. Macroeconomic factors and the Japanese

equity markets: The CAPMD project: Salomon Bros. Center

for the Study of Financial Institutions, Graduate …; 1989.

5. Tinoco MH, Wilson N. Financial distress and bankruptcy

prediction among listed companies using accounting, market

and macroeconomic variables. International Review of

Financial Analysis. 2013;30:394-419.

6. Farajnezhad M, Ziaei SM, Choo LG, Karimiyan A. Credit

Channel of Monetary Policy Transmission Mechanism in

BRICS. Journal of Economics and Sustainabe Development.

016;7(4).

7. M Farajnezhad SALR, Mani Shehni Karam Zadeh. Analyses

the Effect of Monetary Policy Transmission on the Inequality

in OECD Countries. Journal of Environmental Treatment

Techniques;special issue of “environment management and

economics. 2020;8(2):589-96.

8. Farajnezhad M, Ramakrishnan SAL. Effectiveness of Credit

Channel of Monetary Policy Transmission Mechanism on

Commercial Banks in Malaysia. International Jouranal of

Recent Technology and Engineering (IJRTE). 2019;8(1C2).

9. Rose AK. An alternative approach to the American demand

for money. Journal of Money, Credit and Banking.

985;17(4):439-55.

0. Mensah YM. An examination of the stationarity of

multivariate bankruptcy prediction models:

methodological study. Journal of accounting research.

984:380-95.

1. Chiaramonte L, Casu B. Capital and liquidity ratios and

financial distress. Evidence from. 2016.

2. Becchetti L, Sierra J. Bankruptcy risk and productive

efficiency in manufacturing firms. Journal of banking &

finance. 2003;27(11):2099-120.

3. Rafiei FM, Manzari S, Bostanian S. Financial health

prediction models using artificial neural networks, genetic

algorithm and multivariate discriminant analysis: Iranian

evidence.

Expert

Systems

with

Applications.

011;38(8):10210-7.

4. Tinoco MH, Holmes P, Wilson N. Polytomous response

financial distress models: The role of accounting, market and

macroeconomic variables. International Review of Financial

Analysis. 2018;59:276-89.

5. Demyanyk Y, Hasan I. Financial crises and bank failures: A

review of prediction methods. Omega. 2010;38(5):315-24.

6. Amani FA, Fadlalla AM. Data mining applications in

accounting: A review of the literature and organizing

framework. International Journal of Accounting Information

Systems. 2017;24:32-58.

7. Dimitras A, Slowinski R, Susmaga R, Zopounidis C.

Business failure prediction using rough sets. European

Journal of Operational Research. 1999;114(2):263-80.