Patrick BIMPONG Ishmael ARHIN Thomas hezkeal Khela NAN Edward DANSO Pious OPOKU Arthur BENEDICT Grace TETTEY

Assessing Predictive Power and Earnings Manipulations. Applied Study on Listed Consumer Goods and Service Companies in Ghana Using 3 Z-Score Models

This study uses data from Ghana’s public listed consumer goods and service companies for the period of 2014-2018 to test the predictive power of Altman’s (2000), Taffler’s (1983), and Beneish’s (1999) models in detecting bankruptcy and Earnings Manipulation. Prediction power (accuracy) was tested for two Z-Score models: Altman’s (2000) revised model and Taffler’s (1983) model. All two models were found to have significant predictive power. Altman’s revised model was found to be accurate for listed consumer firms in Ghana at a predictive power rate of 66%. Taffler’s (1983) Z-Score model was equally found to be accurate for prediction at a higher predictive power of 88%. The Taffler (1983) model has a higher predictive power compared with Altman’s (2000) revised model. The Beneish (1999) model also revealed that the financial statements of the industry were manipulated at a different degree. The study recommends that stakeholders would be better protected when the three models are deployed simultaneously as an important part of an Audit engagement. Also, Altman’s (2000), Taffler (1983), and Beneish’s (1999) model should be applied in predicting bankruptcy and financial statement fraud evaluation in the banking and mining sectors in Ghana taking into account the frequent collapse, mergers, and acquisitions that do occur.
Keywords
JEL Classification G32, G33
Full Article

1.Introduction

Corporate bankruptcy prediction is essential because the consequences of corporate bankruptcy result in heavy losses and affect the economy of a country. Enron's case is considered around the globe as one of the most famous bankruptcies. It is a major corporate accounting scandal that paved way to lots of regulations in the United States and other countries (Sulub, S. A. 2014). Financial bankruptcy is a term used in corporate finance to indicate that a firm cannot meet scheduled payments or cash flow expectations indicate that a firm will soon be unable to meet agreed payments plan (Brigham and Daves, 2004). The financial failure of any business may take the form of insolvency or bankruptcy. Insolvency means the company cannot meet its current obligations when it is due, which happens when the current liabilities exceed the current assets. On the other hand, bankruptcy may happen when the firm’s current liabilities exceed the fair value of its assets (Mohammed and Soon, 2012). According to Elloumis and Gueyie, (2001), financial bankruptcy is a situation whereby a company’s business worsens to the point where it unable to meet its financial obligations.Again, section 128 of South African’s Companies Act (2008), defines financial bankruptcy as a state of the firm that appears to be reasonably unlikely to offset all of its obligations as they become due and payable within the immediately ensuing six months, or that it is likely the company will become insolvent within the immediately ensuing six months. Also, Khaliq, et al, (2014) added that financial bankruptcy can be term as financial distress.

Corporate organizations consist of manufacturing and non-manufacturing companies that play an important role in the economic and social development of every country including Ghana. Managers, shareholders, employees, investors, and financial institutions are concerned about organizational financial health. The ability to predict company financial bankruptcy is particularly significant for stakeholders to take the necessary preventive measures. In addition, corporate ethics and governance although have provided a podium to avoid financial bankruptcy, however, an early prediction is essential for especially investors that intend to safeguard their financial investments (Mahama, 2015).

Several business failures have occurred; thus a failure prediction model is crucial to serve as a benchmark fororganizations. The prediction of organizational failure can enable companies to reduce bankruptcy costs, avoid failure, and help improve their financial stability. Therefore, financial health of any firm can be measured by its financial performance. Also, the ability to predict company bankruptcy is very important from both the social viewpoint and the investor’s viewpoint as it stands as the indicator that measures the misallocation of company resources (Glautier and Underdown, 2001).

Current studies have shown several corporate failures across the globe. The annual flow of corporate bankruptcy from the past decades had not stop growing and this drifts had become more obvious during the period of world financial crisis in 2008 (Sami, 2013). Specific reference to some eminent corporate failure can be made of General Motors (GM), Chrysler, American International Group Inc., Delta Airline Limited Xerox, AIG, Freddie, WorldCom, Lehman Brothers, and Enron Corp (Mclntyre and Ogg, 2008). Also, the Ghanaian banking sector had been experiencing financial distress which leadstopoor performance and failure of banks in 2019. Evidence of corporate failures includes the Ghana Co-operative Bank, Gateway Broadcasting Services, UT Bank, DKM financial, Bank for Housing and Construction, National Savings and Credit Bank and MensGold Ghana LTD (Appiah, 2011). An incident of corporate failure that is still renewed in the minds of Ghanaians is the collapse of UniBank Ghana Limited, Royal Bank Limited, Beige Bank Limited, Sovereign Bank Limited, and Construction Bank Limited due to liquidity and solvency challenges.

Deloitte (2008) indicated that there is a relationship between bankruptcy and fraud and there is a high probability that a firm at the brink of collapse would engage in financial statements fraud. This means that at the brink of bankruptcy; managers may be motivated to manipulate their financial statements to show the best financial performance to their capital providers. This creates a connection between the bankrupt firm and a fraudulent firm.

It is therefore desirable to find a method for detecting deteriorating financial conditions of Ghanaian companies for prudent measures to be put in place to ensure sustenance, growth, and business expansion. Therefore, the study aimed at testing the effectiveness or applicability of Altman’s (2000), Taffler’s (1983), and Beneish (1999) model on listed consumer goods and service companies in Ghana. As Altman and Taffler’s model (Z-score) can be used best on financial statements that are not manipulated, the Beneish model (M-score) is also used to determine whether the financial statement is manipulated. Therefore, for a firm’s successful analysis, there is the need to deploy the Beneish M-score model prior to the deployment of the Altman and Taffler’s Z-score model. To use Beneish M-score before Altman and Taffler’s Z-score model, the Beneish M-score model was first adopted to detect whether the financial statements were manipulated. Then the Altman (2000) and Taffler’s (1983) Z-score model was used to determine whether the sample firms are financially distressed.

The contribution of this study is to extend the application of Altmann (2000), Tattler (1983) and Beneish (1999) Model on listed consumer goods and service companies in Ghana which has not been previously carried out in the practice for companies’ failure prediction. The results of this study will further contribute to the literature on the applicability of Altman’s (2000) model and Taffler (1983) model on listed companies in Ghana, and help the general public and investors on the financial health of consumer goods and service companies listed on the Ghana Stock Exchange.

2. Theoretical Framework and Literature Review

The examination of corporate failure prediction can be categorized into three broad areas: First, developing a prediction models and it often provides general index which can be used to measure the possibility of failure, such as the study of Zeytunoglu and Akarim (2013); Altman (1968); Christidis and Gregory (2010); Beaver (1966). The Second field looks at the assessment of the validity and predictive accuracy of newly developed models such as the study of Wang and Campbell (2010); Kiyak and Labanauskaite (2012); Mamo (2011) and Soon et al., (2014). The third category deals with an applied investigation or studies which aim to tell the bankruptcy status of particular firms in a given country like the study of Mohammed and Soon (2012); Kenneth and Adeniyi, (2014).

This paper follows the path of the 2nd category where the Altman revised model, Taffler (1983) and Beneish (1999) model is tested on listed non-failed consumer goods and service companies in Ghana to determine the predictive accuracy and state of financial statement fraud.

2.1. Empirical Review

A number of studies have materialized to explicate corporate bankruptcy and the ability of predictive models in successfully predicting their occurrence. This section offers insight into the pieces of literature and models for forecasting business insolvency and manipulation of annual financial accounts.

The study of Patrick in 1932 is considered the earliest study in this field. The study was later extended by Beaver in 1966 and provided the first statistical model for business bankruptcy prediction. In his study Patrick used a model called "a univariate model", and 30 financial ratios were tested on 79 failed companies and 79 similar successful companies between 1954 and 1964. The study of Beaver (1966) also used 30 financial ratios among 79 failed and non-failed companies from 1954 to 1964. Beaver revealed that the most important ratio which can be used to expect bankruptcy is (Cash flow to total debt ratio) with a 78% success rate for five years before failing and 13% of the sample for one year before insolvency. Altman (1968) extended Beaver’s approach by using the Z-Score model. Altman (1968) study which used 66 failed and non-failed manufacturing companies among 22 ratios were classified into five groups namely; liquidity, activity, profitability, solvency ratios, and leverage. The Z-Score model overall correctly classified 95% of the total sample, one year prior to bankruptcy.

Moreover, Kidane (2004) surveyed the portability of Springate and Altman models in forecasting financial failure in IT and service firms of South Africa from 1999 to 2003, and the results showed that the two models were abortive in predicting failure and non-failure amongst South African service firms. Odipo, and Sitati, (2010), evaluated whether Altman’s model could be suitable in forecasting corporate failure in Kenya using a sample of 10 listed and delisted firms in the Nairobi Stock Exchange spanned from 1989 to 2008.The study revealed that the model could correctly predict failure in Kenya. Further studies were done by Kpodoh, (2009) to test the applicability of Altman’s Z score model using data set from the communication firms in Ghana. His results confirmed the ability of the Z score model in forecasting corporate failure. Alareeni and Branson (2013) conducted a study on 71 non failed and failed firms in Jordan to test the predictive accuracy of Altman’s model for the time span from 1989-2008 and the result showed that the model is effective and could predict the failure of industrial firms. However, for service firms, they found that the Altman models could not distinguish between non failed and failed companies. Therefore, Soon and Mohammed, (2012), used Altman’s financial bankruptcy prediction model and current ratio to evaluate the financial situation of 44 companies listed in the Malaysia Stock Exchange for 2008 and 2009.The study concluded that Altman’s model and current ratio are useful tools for investor to expect the financial failure of companies.

Naidoo and du Toit, (2007) used a two-stage approach to analyze the financial bankruptcy of listed companies. In the first stage, multi-state models were developed to expect the state of health of a company. In the second stage, a contemporary approach was used to produce underlying information independent of the first stage model, so as to enable management to make a more meaningful state of the company. The financial health of the sampled companies is accurately predicted by using these models. Moyer, (1977) verified the accuracy of Altman’s model on 27 non-failed and failed firms between1965-1975. These firms were paired on the basis of industry and assets size ranging from $15million to $1billion.The result of this study indicated that the forecasting accuracy on a genuinely post-dated sample of the firm collapse was 75% a year before bankruptcy, which conflicts with the 96%, proposed by Altman (1968). In re-estimating the Altman model parameters, Moyer used a new data set and the stepwise MDA approach.

Amoah-Gyarteng, (2014) employed the Altman’s modified Z-Score and Beneish Models to detects bankruptcy and financial statements fraud of AngloGold Asante for the year 2010-2012. The Beneish model revealed the company was engaged in financial statement fraud. However, Altman’s model was found effective in predicting the failure of AngloGold Asante.Maccarthy, (2017) used Altman Z-Score and Beneish model to evaluates financial statements fraud and bankruptcy of Enron corporation covering the period of 1996-2000. The study revealed that the financial statements for the study period were manipulated to give a good picture of the company’s performance.

Soon et al. (2014) used Altman’s financial bankruptcy model to predict the financial difficulties of 28 companies listed on trading services sector at the stock exchange of Malaysia for the period between 2003 and 2009, and this study concluded that Altman’s score can be used to differentiate between failure companies and the non-failure and that it is very useful for investors to predict financial failure of companies. Johansson and Kumbaro, (2011) used what is called "multiple discriminant analysis" on a sample of 45 American companies between 2007 and 2010 by applying Altman’s model, and the study concluded that these models could predict bankrupt firms for both one and the two-year period prior to bankruptcy.

Ohson (1980) used what is called "logit analysis " for 105 bankrupt firms and 2058 non-bankrupt firms for the period from 1970 to 1976 and the results of this study show that factors such as size, current liquidity, performance, and financial structure were important determinants of company’s bankruptcy. Low, Nor and Yatim (2001) used the logit model in Malaysia and the results concluded that the probability of financial bankruptcy is related to the ratio of current assets to current liabilities, the ratio of sales to current assets, and the percentage change in net income of a company. And also the study of XU, ZHAO, and BAO (2015) in China used the partial least-squares logistic regression model to estimate the early warnings of financial bankruptcy on quoted firms in the real estate sector, the results showed that the partial least-squares logistic model is accurate in detecting early warning signs of corporate failure due to its elimination of multicollinearity problem as paralleled to the logistic regression model. Premachandra, Watson, and Chen (2011) used what is called the "data envelopment analysis" (DEA) model as a tool for predicting corporate failure and success. The results concluded that the DEA model is relatively weak in predicting corporate failures.

Gepp, A. and Kumar, (2015) used decision tree software called Classification and Regression Trees (CART) and the conclusions provided empirical evidence to support the use of survival analysis and decision tree techniques in financial bankruptcy warning systems that are very useful to most institutions in the financial market. Charitou et al. (2004) examined the incremental information content of operating cash flows in predicting organization bankruptcy by using logit analysis and neural networks on 51 matched pairs of failed and non-failed of United Kingdom companies from 1988 to 1997. They developed a parsimonious model with three financial ratios namely; financial leverage, profitability, and operating cash flow that resulted in an overall classification accuracy of 83%.

There are some studies that are conducted on mining companies that also used various financial bankruptcy predicting models. Such as Zlatanovic et al. (2016) that used the Altman Z-score model to study on a sample of two mining companies in Serbia. The conclusions of this study indicated that one of the two mines companies was in a state of financial bankruptcy. In addition, Saden and Prihatiningtias (2015) focused on 18 different mining companies listed on the Indonesian Stock Exchange, where some mining companies appeared to show signs of financial bankruptcy.

Some studies were conducted in South Africa that used several financial bankruptcy predictive models. For instance, Hlahla (2010) conducted a study on a sample of 28 companies listed on the Johannesburg Securities Exchange (JSE). The companies were grouped into failed and non-failed companies by using means of multiple discriminant analysis following normality tests. Three variables namely; cash to debt, working capital, and times interest earned to turnover was found to be significant. The model classified about 75% of failed and non-failed companies in the original and cross-validation procedures.

2.2.1. Theoretical Framework-Altman’s (2000) Revised Model

This model was developed by Professor Edward Altman in the year 2000. The original Altman Z-score was later modified to overcome its shortcoming.

The Altman Z-score model can now be used for both manufacturing and non-manufacturing, private companies and for those listed on the emerging markets. The model, for some reason, appears to create a lot of mixed emotions; some of these emotions are in favour of it while others are against it. The study of Grice and Ingram (2001) indicated that the accuracy of the Altman Z-score model is significantly lower in recent periods than reported in Altman’s study. Most criticisms against this model focus on its over-reliance on accounting data; focus on failure rather than sustainability of the business; inadequate recognition of cash-flow as a relevant component; lack of consideration on non-financial ratios; the need for industry-specific or geography-specific model types and the danger of flexible interpretation or manipulation of financial results resulting in “window dressing” or inappropriate favourable report of financial position (Wilkinson, 2009). The first shortcoming of the Altman Z-score model necessitated for industry-specific or geography-specific model types. Specific industries have different characteristics; hence it would not be suitable to apply a general model for all these industries. This model assumes that financial ratios are taken from public financial information and that will be accurate. According to Panneerselvam, (2008). Firms in financial bankruptcy manipulate their financial statements to show good performance. Therefore, errors in these secondary data will influence the level of accuracy of the outcomes and will not be suitable for the present purpose. The interpretation of the Z-score as presented by Professor Altman’s theory indicates that overall Score more than 2.9 represents a zone of creditworthiness or financial soundness. However, a score below 1.23 is classified as an insolvency or liquidation zone (Failed zone). Finally, the gap between 1.23 and 2.9 is the Zone of Ignorance or uncertainty.

The working capital is ascertained by subtracting current liabilities from the current asset. This matrix of X1 is used to estimates the net liquid asset as a ratio of the total book value of identifiable assets. In the ideal situation, continuous operating losses can lead to a deterioration of current assets with respect to total Assets.

The matrix X2 measures the firm-level leverage and it embodies the reinvest profit into the asset. The logic behind X2 is that the accumulated profit of a firm is subject or prone to falsifications because of the reorganization and disbursement of dividends.

 

The relation (X3) examines the efficient utilization of assets in creating of worth. A lower ratio is an indication of inefficiency in the utilization of the company's assets. The ratio, therefore, produces the cash available for creditors settlement, Government and shareholder’s payments.

The book value of equity is calculated by adding the book value of ordinary and preference shares whereas the book value of total debts is estimated as either the addition of current and non-current debt or the total of long-term debts. The variable X4 is the reversal of the equity ratio.

This is the ratio that defines the activity of sales and assets. This ratio is used to assess the ability of asset in generating profit or earnings. Though the impact of X5 was underscored by Altman (2000) however, it’s inclusion will enhance the predictive ability of the Model.

2.2.2.Taffler (1983) Z-Score (Model 2)

Professor Taffler in 1983 suggested in his studies that failure models should reflect certain key variables of corporate solvency and performance such as profitability, working capital adequacy, financial risk, and liquidity. He thus formulated his Z-score as:

Where:

The weight X1, X2, X3, and X4 in the model are the explanatory variables employed to estimates the explained variable (Z- Value) in the model. X1 represents a measure of profitability, X2 is a measure of working capital position, X3, on the other hand, is a measure of financial risk and finally, X4 denotes the number of credit intervals. The benchmark for Taffler’s model is subjected to Negative (-) and positive (+). A negative (-) score means the company has a financial profile similar to the previously failed business. While a positive (+) score indicates the company is safe from insolvency risk.

2.2.3. Beneish 1999 M-Score (Model 3)

Recent collapses of prominent businesses such as WorldCom, Lehman Brothers, and Enron Corp and many others despite their good looking financial statements provides justification that most financial statements published by organisations are prone to manipulations and fraud. Therefore, in other to guarantee shareholders, creditors, and bankers protections in their evaluations, required a balancing scientific tool to provides check and balances to the Z-Score Models. With this in mind, Professor Beneish developed a model called (M-score) in 1999. The model gained recognition and is widely used to spot areas of possible manipulation on the firm’s financial statements by practitioners (accountants, auditors, and particularly the SEC). The overarching aim of the model is to bring forth companies that engages in financial statements fraud.

The model was developed base on eight variable estimated from the financial statements with an intercept. This study employed the same principles to obtained the eight variables from the firm’s financial statements and used to determine the M-score of this companies.When an M-score is greater than -2.22 indicates that the firm’s financial statements may have been manipulated (Warshavsky, 2012). Hence, when the score that is obtained from the computation of the eight variables from understudy firm’s financials is greater than the cut-off point of negative 2.22, then it concludes that the financial statements were manipulated. M-score model is a probability model, and such cannot provide 100% justification (MacCarthy, 2017). Beneish concluded that it is possible to determine 76% manipulators accurately and 17.5% incorrectly is considered as non-manipulator. According to Beneish et al.,(1999), the indices have varying rationales as described below.

where:

Day Sales Receivable Index (DRI)

This index measures the ratio of A/C receivable stability with the variations in revenue. The bench mark is set between 1.031 and 1.465. A score below or equal 1.031 is an indication of fraud free financial statements. However, a score of 1.465 and above represent a manipulated financial statement. When this does not show a fair consistent trend then it suggests that either the majority of revenue is on credit terms rather than cash or the company has difficulty in the collection of receivables (MacCarthy, 2017). A rising DRI may be the perfect legal activity of the firm extending more credit to customers and the firms that overstated revenue. Therefore, a sharp rise in the DRI score provides signals to auditors that, the financial statements of the firms are manipulated or terms of credit have changed. Empirically described as:

Gross Margin Index (GMI)

According to Harrington, (2005), the GMI score of 1.041 or lower suggests gross profit of the current period is not manipulated however a score of 1.193 is an indication that gross profit of the firm is manipulated. Financial Analyst orated that earning quality is considered a very important aspect for assessing the firm’s financial fitness and therefore, can create an avenue for earnings manipulations especially when performance isdowngraded (MacCarthy, 2017).The numerical representation is shown below.

Asset Quality Index (AQI)

The AQI is calculated as the percentage of total assets of the current year (CY) to the preceding year (PY). According to Pustylnick (2009) cited by MacCarthy, (2017), a ratio greater than 1.0 is a signal of overheads and intangible assets capitalization. Harrington 2005, suggested that growth in AQI suggests additional expenses have been capitalized to avoid writing-off to the comprehensive income statement in order to preserve profit. This is the mathematical representation;

(2.2.3.4)

Sales Growth Index (SGI)

SGI is calculated by dividing sales or revenue for the current year (CY) by sales or revenue of the preceding year (PY). Benchmark value of 1.134 or below forecast non-manipulation and a value above 1.607 predicts the possibility of sales or revenue manipulations. Harrington (2005) noted that, firms withhigher growth rate find themselves highly motivated to commit fraud when the trends reverse. Below is the mathematical representation;

(2.2.3.5)

Depreciation Index (DEPI)

DEPI is calculated as the ratio of the depreciation expense against the firm’s value of PPE in the current year against that of the preceding year. DEPI ratio of 1.001 or lower is an indication of DEPI manipulations. However, a score above 1.077 indicates the value of the assets has been revalued or the useful life of the assets has been adjusted upward (Beneish, 1999). The ratio is described as follows:

(2.2.3.6)

Sales, General, and Administrative Expenses Index (SGAI)

SGAI is the ratio of sales, general and administrative expenses for the current year over the preceding year. When a score of 1.001 or below is obtained, it indicates that SGAI has not been manipulated. According to Lev, and Thiagarajan. (1993), a disproportional increase in SGAI is considered as an indicator of a negative signal about the firm’s upcoming prospects. A positive relation gives an indication of possible manipulations.

(2.2.3.7)

Leverage Index (LEVI)

LEVI can be used to measure the firm’s ratio in terms of total debt to total assets for the current year is divided over the preceding year’s ratio. When a LEVI is greater than 1 it indicates there is an increase in leverage position in the firm and that the firm has taken more debt to operate or to run the business for the period under review. Empirically;

(2.2.3.8)

Total Accruals to Total Assets Index (TATAI)

TATAI is the ratio of change in working capital other than cash and less depreciation. The increase in TATAI may indicate that goodwill and amortization numbers in the financial statements of the company have been tampered with. When a mean score is 0.018, it indicates there are non-financial manipulations in respect of TATAI while as a mean score of 0.031 and above is an indicator that the financial data have been tampered with. Mathematically presented as:

(2.2.3.9)

3. Methodology

3.1. Sample and Research Method

The study adopted Altman (2000), Taffler (1983) and Beneish (1999) style of predicting corporate failure and detecting financial statement fraud using 17 listed consumer goods and service companies in Ghana. The study evaluates the effectiveness of Altman, Taffler, and Beneish M-Score model in predicting failure and detecting earnings manipulation in a survey setting. The study uses numerical investigation on the dataset extracted from the financial position (Balance sheet), and Comprehensive income statement (profit and loss account) of the sample firms. The financial statements were taken from the website of the companies, Ghana Stock Exchange(GSE) and Annual Report Ghana. The time spinning from 2014 to 2018 was considered as the covered period for the study and long enough to detect any financial or insolvency risk. The selection of the consumer goods sector was purposively considered by the authors due to the current instability and inefficiency in the sector. The analytical tools adopted for this study include; excel for the computations of variables, Z-Scores, M-score and Eviews version ten for descriptive and correlation analysis. Altman (2000) Z-Score and Taffler (1983) Z-Score model were applied for the detection and establishment of the financial soundness of the firms under review. And the Beneish M-Score model was employed to investigates the possibility of earnings Manipulations for the understudy years.

3.2. Hypotheses

All the empirical theories and studies being highlighted in the literature such as Sulub S.A (2014), Soon et. al, (2014), Soon and Mohammed (2012), Gyimah, P. and Boachie (2018), Naidoo and du Toit, (2007), Alareeni and Branson (2013) and Maccarthy (2017) -have proved that Altman Z-Score model can successfully predict corporate failure particularly in the presence of error-free financial statements. According to Zavgren, (1985), MDA models are suitable to a certain extent in predicting company bankruptcy. As a result, a more suitable approach based on less or no assumption should be considered apparent; other methods should rank alongside the above statistical tools, which motivated the inclusion of Beneish (1999) and Taffler (1983) model. According to Macarthy 2017, Gyarteng 2014, and Beneish 1999 which concluded that financial ratios taken from public financial information will not be accurate considering the fact that firms with financial distress manipulate their financials to show healthier performance as in the case of Enron Corporation. Consequently, manipulations in these financial data will affect the level of accuracy of the outcomes and will not be appropriate for the failure prediction (Panneerselvam, 2008). Giving the background, the hypotheses of the study can be specified as follows:

H1:Altman's (2000) model can accurately predict the bankruptcy status of the listed consumer goods and service companies in Ghana.

H2: Taffler’s (1983) model can accurately predict the bankruptcy status of the listed consumer goods and service companies in Ghana.

H3: The annual financial statements published by the sample firms are likely to exhibit signs of manipulation.

 4.Empirical Results

4.1. Descriptive Statistics

Table, -1 provides a summary of the descriptive statistics of the explanatory variables for all the Models employed for this study. This report the average indicators of variables computed from the annual financial statements for the sample firms. The working capital/total assets (X1), retained earnings/total assets (X2), earnings before interest and taxes/total assets (X3), market value of equity/book value of total debt (X4) and sales/total assets (X5) reveals an average of -0.078, 0.158, 0.202, 1.354 and 1.999 respectively for Altman computations. These results suggest a poor performance in working capital management during the period under study. However, operating profit/current liability (x1), current asset/total liabilities (x2), current liabilities/total asset (x3), and credit interval (x4) reported an average of 80%, 92%, 29%, and 53% respectively as indicated by Taffler’s Computations. The results suggest a good performance in working capital positions and profitability ratio with a lower financial risk ratio. Finally, the Beneish model recorded the highest variable mean score value of 1.790 with a maximum of 43.601 and this score is attributed to Sales, General, and administrative expenses index (SGAI). This indicates a high probability of SGAI manipulation since it is greater than the benchmark figure of 1.041.

Tables- 2, 3, and 4,provide summary of the descriptive statistics of the explained variables (Z-Scores and M-Score).  The tables report the average of Z-Scores and M-Scores computed from the annual financial statements for the period under review. From Table 2, it can be observed that the Z-score reported banks chronicled a maximum value of 12.817 in 2014 and a minimum value of -0.295 in 2018. Except for the year 2014, the mean Z-score recorded the least value of 3.098 in 2018 and this observation could possibly mean the firmswere financially not healthy in the year 2018 as compared to the remaining years under review. The results presented in table 3 recorded a maximum Z-score of 17.088 in 2018 and a minimum of -2.102 in 2017. However, the mean z- score recorded the least value 0.146 in 2014 followed by 2017 then 2015, and 2016, and the highest mean (1.899) was recorded in 2018.This result may indicate a sign of financial distressed among the firms in the year 2014 according to Taffler’s Z- scores and the observation is contrary to that of Altman model (see table 2 above). Finally, Beneish M-score (Table 4), registered a maximum M-Score of 6.443 in 2014 and a minimum of -7.911 in 2018. However, the mean M-Score recorded a peak value of -1.457 in 2016 and the lowest value of -2.262 in 2015. This result illustrates a clear indication of higher earnings manipulations in 2016 among the sample firms.

Table 1. Descriptive statistics of the independent variables for all Models

ALTMANMODEL 2014-2018
STATISTICS X1 X2 X3 X4 X5
 Mean (0.078) 0.158 0.202 1.354 1.999
 Median 0.077 0.176 0.052 0.890 0.989
 Maximum 0.823 1.683 1.259 6.122 12.126
 Minimum (5.859) (0.680) (0.324) (0.104) 0.054
 Std. Dev. 0.883 0.351 0.304 1.318 2.228
TAFFLER MODEL (2) 2014-2018
STATISTICS X1 X2 X3 X4
 Mean 0.800 0.923 0.299 0.536
 Median 0.220 0.767 0.275 (0.073)
 Maximum 6.673 5.547 0.818 103.394
 Minimum (1.023) 0.032 0.046 (14.536)
 Std. Dev. 1.405 0.833 0.192 11.727
BENEISH MODEL (3) -2018
STATISTICS DSRI GMI AQI SGI DEPI SGAI TATAI LEVI
 Mean 1.259 1.048 1.062 1.006 1.441 1.790 0.022 0.512
 Median 1.040 0.998 1.008 1.019 1.023 0.990 0.040 0.528
 Maximum 12.543 5.840 4.104 1.916 10.804 43.601 0.689 1.116
Minimum 0.045 (4.790) 0.318 0.009 0.002 0.009 (1.228) 0.039
 Std. Dev. 1.408 1.304 0.474 0.325 1.800 4.912 0.284 0.192

Source: Financial Reports (2014 – 2018)

Table 2. Descriptive statistics (Altman Z-Scores)

  MEAN MEDIAN MINIMUM MAXIMUM Std.Dev.
2014 3.458 2.272 0.322 12.817 3.237
2015 3.394 3.156 0.910 8.979 2.374
2016 3.198 2.851 0.857 8.119 1.728
2017 3.190 2.964 0.218 9.535 2.183
2018 3.098 3.058 (0.295) 7.448 2.012

Table 3. Descriptive Statistics (Taffler Z-Scores)

  MEAN MEDIAN MINIMUM MAXIMUM Std.Dev.
2014 0.146 0.166 (1.310) 1.967 0.708
2015 0.460 0.287 (2.077) 2.015 0.950
2016 0.476 0.467 (1.723) 2.310 0.996
2017 0.431 0.434 (2.102) 2.098 0.881
2018 1.899 0.602 (0.008) 17.088 4.053

Table 4. Descriptive Statistics (Beneish M-Score).

  MEAN MEDIAN MINIMUM MAXIMUM Std.Dev.
2014 (2.251) (3.217) (5.708) 6.443 3.055
2015 (2.262) (2.712) (6.732) 2.620 2.120
2016 (1.457) (1.575) (3.987) 1.512 1.329
2017 (2.037) (2.061) (3.828) (0.372) 1.013
2018 (2.021) (1.375) (7.911) 1.421 2.180

Source: Financial Reports (2014 – 2018)

4.2 Correlation Matrix Analysis

4.2.1. Correlation of the Independent Variables to the Z-Scores

The correlation of the independent variables to the Z-Scores basically explain what variables or ratios are the main drivers of the Z-score. Therefore, knowing the main drivers of the Z-scores, management can enhance those ratios or variables to affect the performance of the firm. Table, - 5 (Altman Model) shown a strong correlation between asset turnover ratio (X5) and Z- Score, suggesting that, a high asset turnover ratio was a major driver for the Z-Score determination. Except for working capital/Total asset (x1) and market value of equity/total debt (X4) which showed a negative correlation with the Z-Score, the remaining ratios x2 and x3 indicated a weak positive correlation with the Z-Score.

Table, -6 (Taffler’s model) revealed a strong positive correlation between credit interval (x4) and the Z-Score indicating that credit interval is the major determinants of business survival. However, with the exception of the financial risk ratio (x3), the remaining ratios (profitability and working capital position) indicated a weak positive correlation with the Z-Score.

Table 5. Correlation matrix of independent variables to the Z-scores (Altman-Model)

  X1 X2 X3 X4 X5 ZSCORE
X1 1 0.173 -0.273 0.16 -0.285 -0.051
X2 0.173 1 0.16 0.028 -0.046 0.204
X3 -0.273 0.16 1 -0.239 0.122 0.413
X4 0.16 0.028 -0.239 1 -0.259 -0.059
X5 -0.285 -0.046 0.122 -0.259 1 0.866
Z-SCORE -0.051 0.204 0.413 -0.059 0.866 1

Source: Financial Reports (2014 – 2018)

Table 6. Correlation matrix of independent variables to the Z-scores (Taffler-Model 2)

  X1 X2 X3 X4 Z-SCORE
X1 1 -0.11 -0.139 -0.036 0.325
X2 -0.11 1 0.006 0.418 0.399
X3 -0.139 0.006 1 -0.072 -0.101
X4 -0.036 0.418 -0.072 1 0.932
Z-SCORE 0.325 0.399 -0.101 0.932 1

Source: Financial Reports (2014 – 2018)

Table, - 7 presents the correlation results of the independent variables to the M-Score computed using Beneish Model. The results indicated a significant positive correlation between total accruals to total asset index (TATAI), days Sales receivable index (DSRI), Gross margin index (GMI), Asset quality index (AQI), and Sales growth index (SGI) to M-Score. However, Sales, General and Administration index (SGAI), leverage index (LEVI), and depreciation index (DEPI) showed a significant negative correlation to the M-Score.

The result suggests TATAI as the major driver for earnings Manipulations among the sample firms. In general, we observed that all the independent variables of the models are substantially correlated with the dependent variables (Z and M scores), and this guarantees the suitability and reliability of Altman (2000), Taffler (1983) and Beneish (1999) models, meaning the explanatory variables are important factors for determining the Z and M Scores. 

Table 7. Correlation matrix of the independent variables to M-scores (Beneish Model)

DSRI GMI AQI SGI DEPI SGAI LEVI TATAI M-SCORE
DSRI 1 0.039 0.136 -0.079 -0.064 0.477 0.111 0.057 0.48
GMI 0.039 1 0.339 -0.185 -0.052 0.005 -0.079 0.004 0.366
AQI 0.136 0.339 1 -0.247 0.026 -0.055 -0.09 0.148 0.384
SGI -0.079 -0.185 -0.247 1 -0.004 -0.072 -0.024 0.215 0.177
DEPI -0.064 -0.052 0.026 -0.004 1 0.039 0.16 -0.157 -0.078
SGAI 0.477 0.005 -0.055 -0.072 0.039 1 0.059 0.034 -0.101
LEVI 0.111 -0.079 -0.09 -0.024 0.16 0.059 1 -0.472 -0.314
TATAI 0.057 0.004 0.148 0.215 -0.157 0.034 -0.472 1 0.717
M-SCORE 0.48 0.366 0.384 0.177 -0.078 -0.101 -0.314 0.717 1

4.2.2 Correlation of Z-scores (Altman and Taffler) to Beneish M-Score

The rationale of knowing the correlation between Z-Scores and Beneish M-Score will go a long way to assist scholars and management in decision making. To scholars, it will give reasons for the need to use both models in corporate failure prediction research. Management, shareholders, auditors, and investors on the other hand will appreciate the importance of using Altman and Taffler’s model to assess the performance of corporate entities. As reported in Table, - 8 below, it can be observed that there is a significant positive correlation between Beneish M-Score and the Z-Scores models employed for this study (Altman 2000 and Taffler 1983 model). Note, the fact there is such a positive correlation between the Z-Scores and the M-Score serves as a reasonabilitycheck, as the upwards trend in M-Score indicates the possibility of earning manipulations. Hence, a positive correlation suggests that the firms under review manipulate their financial statements to vehicle a good performance. Therefore, as the banks manipulate their earnings, Altman and Taffler’s Z-Scores improve from distress to safe zone.

Table 8. Correlation matrix of the Z-scores (Altman and Taffler) to Beneish M-Score

  BENEISH M-SCORE ALTMAN Z-SCORE TAFFLER Z-SCORE
BENEISH M-SCORE 1 0.009 0.231
ALTMAN Z-SCORE 0.009 1 0.118
TAFFLER Z-SCORE 0.231 0.118 1

Source: Financial Reports (2014 – 2018)

4.3 Z-Models and Scores Analysis

Examining the annual financial statements using Altman and Taffler Z-Scores provides justifications for appreciating the outcomes of business operations and understands how well the firm has performed. In this regard, Altman (2000) and Taffler (1983) Z-Score models were employed to examine the bankruptcy status of 17 listed consumer Goods and service sectors in Ghana.

Table 9. Results of Z-Score using Altman's (2000) Model

COMPANY CODE 2014 2015 2016 2017 2018 AVERAGE
Z-SCORE Z-SCORE Z-SCORE Z-SCORE Z-SCORE Z-SCORE
PZ 2.224 3.966 3.633 3.721 3.559 3.421
CMLT 5.805 3.781 4.085 4.622 2.73 4.205
ALW 4.487 2.852 1.451 3.513 4.244 3.309
BOPP 1.683 3.247 2.709 2.964 2.315 2.584
CPC 2.272 1.459 1.212 0.218 0.638 1.160
FMLK 3.112 3.702 4.347 4.006 3.273 3.688
GGBL 8.751 8.893 8.119 9.535 7.234 8.506
SWL 0.322 1.447 2.761 1.06 -0.295 1.059
MMH.GH 0.815 1.023 2.882 1.817 3.008 1.909
HORD 1.978 1.634 2.579 1.567 2.114 1.975
MLC 1.133 2.048 2.929 2.983 3.271 2.473
DIGICUT 0.652 0.91 0.857 1.163 1.02 0.921
PBC 1.719 2.278 2.317 2.113 3.156 2.317
SAMBA 2.361 3.406 2.242 2.102 1.623 2.347
ACI 3.526 3.156 3.826 4.402 4.267 3.835
UNIL 12.817 8.979 5.561 5.784 7.448 8.118
AYRTON 5.121 4.919 2.851 2.664 3.058 3.722

Table 10. Results of Z-Score using Taffler (1983) Z-Score Model

COMPANY CODE 2014 2015 2016 2017 2018 AVERAGE
  Z-SCORE Z-SCORE Z-SCORE Z-SCORE Z-SCORE Z-SCORE
PZ 0.369 0.933 0.945 0.818 0.756 0.764
CMLT 0.306 0.248 2.021 0.086 2.108 0.954
ALW (1.089) (2.077) (1.723) (2.102) 1.420 (1.114)
BOPP 0.234 0.890 0.472 0.664 0.661 0.584
CPC (1.310) 0.458 0.467 0.379 0.602 0.119
FMLK 0.563 0.700 0.818 0.722 0.330 0.627
GGBL 1.967 1.987 1.822 2.098 3.173 2.209
SWL (0.306) (0.290) 0.486 0.131 0.048 0.014
MMH.GH 0.166 0.064 2.310 1.002 3.585 1.425
HORD 0.202 1.139 (0.880) 1.596 17.088 3.829
MLC 0.063 0.209 0.653 0.647 0.514 0.417
DIGICUT 0.089 0.106 (0.453) (0.006) 0.023 (0.048)
PBC 0.138 (0.244) 0.405 0.396 0.576 0.254
SAMBA (0.163) 2.015 0.071 (0.448) (0.008) 0.293
ACI 0.274 0.287 0.084 0.589 0.396 0.326
UNIL 0.833 0.084 0.177 0.317 0.328 0.348
AYRTON 0.150 1.308 0.413 0.434 0.680 0.597

Source: Financial Reports (2014 – 2018)

Table 11. Firms correctly classified as Safe on a year-on-year Z-score (Altman Model)

  2014 2015 2016 2017 2018
CODE Z-SCORE CODE Z-SCORE CODE Z-SCORE CODE Z-SCORE CODE Z-SCORE
CMLT 5.805 PZ 3.966 PZ 3.633 PZ 3.721 PZ 3.559
ALW 4.487 CMLT 3.781 CMLT 4.085 CMLT 4.622 ALW 4.244
FMLK 3.112 BOPP 3.247 FMLK 4.347 ALW 3.513 FMLK 3.273
GGBL 8.751 FMLK 3.702 GGBL 8.119 BOPP 2.964 GGBL 7.234
ACI 3.526 GGBL 8.893 MLC 2.929 FMLK 4.006 MMH.GH 3.008
UNIL 12.817 SAMBA 3.406 ACI 3.826 GGBL 9.535 MLC 3.271
AYRTON 5.121 ACI 3.156 UNIL 5.561 MLC 2.983 ACI 3.835
UNIL 8.979 ACI 4.402 UNIL 8.118
AYRTON 4.919 UNIL 5.784 AYRTON 3.722
No. of firms 7 9 7 9 9
% 41% 53% 41% 53% 53%

Source: Financial Reports (2014 – 2018)

Table 12. Firms classified into Grey zone on a year-on-year Z-score (Altman Model).

  2014 2015 2016 2017 2018
CODE Z-SCORE CODE Z-SCORE CODE Z-SCORE CODE Z-SCORE CODE Z-SCORE
PZ 2.224 ALW 2.852 ALW 1.451 MMH 1.817 CMLT 2.73
BOPP 1.683 CPC 1.459 BOPP 2.709 HORD 1.567 BOPP 2.315
CPC 2.272 SWL 2.761 SWL 2.761 PBC 2.113 HORD 2.114
HORD 1.978 MMH 2.882 MMH 2.882 SAMBA 2.102 SAMBA 1.623
PBC 1.719 HORD 2.579 HORD 2.579 AYRTON 2.664  
SAMBA 2.361 MLC 2.048 PBC 2.317  
  PBC 2.278 SAMBA 2.242
  AYRTON 2.851
No. of firms   6   7   8   5   4
% 35% 41% 47% 29% 24%

Source: Financial Reports (2014 – 2018)

Table 13. Firms correctly classified as Safe using Average Z-Scores (Altman model)

  COMPANY CODE AVERAGE Z-SCORE
PZ 3.421
CMLT 4.205
ALW 3.309
FMLK 3.688
GGBL 8.506
ACI 3.835
UNIL 8.118
AYRTON 3.722
No. of firms 8
 % 47%

Table 14. Firms classified into Grey zone using average z-Scores (Altman Model)

  COMPANY CODE AVERAGE Z-SCORE
BOPP 2.584
MMH.GH 1.909
HORD 1.975
MLC 2.473
PBC 2.317
SAMBA 2.347
No. of firms 6
 % 35%

Source: Financial Reports (2014 – 2018)

Table 15. Non-Failed Firms classified as failed on year-on-year Z-Score (Altman model) (Type II Error)

  2014 2015 2016 2017 2018
CODE Z-SCORE CODE Z-SCORE CODE Z-SCORE CODE Z-SCORE CODE Z-SCORE
SWL 0.322 MMH 1.023 CPC 1.212 CPC 0.218 CPC 0.638
MMH.GH 0.815 DIGICUT 0.91 DIGICUT 0.857 SWL 1.06 SWL -0.295
MLC 1.133         DIGICUT 1.163 DIGICUT 1.02
DIGICUT 0.652                
No. of firms 4 2 2 3 3
% 24% 12% 12% 18% 18%

Source: Financial Reports (2014 – 2018)

Table 16. Non-Failed Firms classified as failed using average Z-Score (Altman model).

  COMPANY CODE AVERAGE Z-SCORE
CPC 1.160
DIGICUT 0.921
SWL 1.059
No. of firms 3
 % 18%

Source: Financial Reports (2014 – 2018)

Table 17. Firms correctly classified as Safe on year-on-year Z-Score. (Taffler model)

  2014 2015 2016 2017 2018
CODE Z-SCORE CODE Z-SCORE CODE Z-SCORE CODE Z-SCORE CODE Z-SCORE
PZ 0.369 PZ 0.933 PZ 0.945 PZ 0.818 PZ 0.756
CMLT 0.306 CMLT 0.248 CMLT 2.021 CMLT 0.086 CMLT 2.108
BOPP 0.234 BOPP 0.890 BOPP 0.472 BOPP 0.664 ALW 1.420
FMLK 0.563 CPC 0.458 CPC 0.467 CPC 0.379 BOPP 0.661
GGBL 1.967 FMLK 0.700 FMLK 0.818 FMLK 0.722 CPC 0.602
MMH.GH 0.166 GGBL 1.987 GGBL 1.822 GGBL 2.098 FMLK 0.330
HORD 0.202 MMH.GH 0.064 SWL 0.486 SWL 0.131 GGBL 3.173
MLC 0.063 HORD 1.139 MMH 2.310 MMH.GH 1.002 SWL 0.048
DIGICUT 0.089 MLC 0.209 MLC 0.653 HORD 1.596 MMH 3.585
PBC 0.138 DIGICUT 0.106 PBC 0.405 MLC 0.647 HORD 17.088
ACI 0.274 SAMBA 2.015 SAMBA 0.071 ACI 0.589 MLC 0.514
UNIL 0.833 ACI 0.287 ACI 0.084 UNIL 0.317 DIGICUT 0.023
AYRTON 0.150 UNIL 0.084 UNIL 0.177 AYRTON 0.434 PBC 0.576
  AYRTON 1.308 AYRTON 0.413 PBC 0.396 ACI 0.396
  UNIL 0.328
AYRTON 0.680
No. of firms   13   14   14   14   16
% 76% 82% 82% 82% 94%

Source: Financial Reports (2014 – 2018)

Table 18. Non-Failed Firms classified as failed on the year-on-year score (Taffler)

  2014 2015 2016 2017 2018
CODE Z-SCORE CODE Z-SCORE CODE Z-SCORE CODE Z-SCORE CODE Z-SCORE
ALW (1.089) PBC (0.244) ALW (1.723) ALW (2.102) SAMBA (0.008)
SWL (0.306) SLW (0.290) HORD (0.880) DIGICUT (0.006)    
BOPP (0.163) ALW (2.077) BOPP   SAMBA (0.448)    
No. of firms 3 3 3 3 1
% 18% 18% 18% 18% 6%

Source: Financial Reports (2014 – 2018)

Table 19. Firms correctly classified using Average Z-Scores (Taffler model)

  COMPANY CODE AVERAGE Z-SCORE
PZ 0.764
CMLT 0.954
BOPP 0.584
CPC 0.119
FMLK 0.627
GGBL 2.209
SWL 0.014
MMH.GH 1.425
HORD 4.181
MLC 0.417
PBC 0.254
SAMBA 0.293
ACI 0.326
UNIL 0.348
AYRTON 0.597
No. of firms 15
 % 88%

Source: Financial Reports (2014 – 2018)

Table 20. Non-Failed Firms classified as failed by Taffler’s model using average Z-Score

  COMPANY CODE AVERAGE Z-SCORE
ALW (1.114)
DIGICUT (0.048)
   
No. of firms 2
 % 12%

Source: Financial Reports (2014 – 2018)

4.4 M-Score Model Analysis

Investigating the accuracy of annual financial statements used for computing the Z-Score provides explanations for appreciating and assessing whether earnings were manipulated. To achieved these, the Beneish M-Score model was employed to establish whether the annual statements were manipulated and the output is presented below in Tables 21, 22, and 23.

Table 21. Results of M-Score (Beneish 1999).

COMPANY CODE 2014 2015 2016 2017 2018 AVERAGE
M-SCORE M-SCORE M-SCORE M-SCORE M-SCORE M-SCORE
PZ (3.611) (2.712) (1.575) (2.591) (3.084) (2.715)
CMLT (2.709) (2.842) (2.326) (2.621) (1.964) (2.492)
ALW (3.373) (3.322) (3.987) (3.252) (5.485) (3.884)
BOPP 6.443 (6.732) (1.287) (1.901) (7.911) (2.278)
CPC (5.708) 2.620 0.793 (2.205) 0.293 (0.841)
FMLK (3.217) 0.034 (1.906) (0.867) (1.209) (1.433)
GGBL 2.783 (2.195) (1.961) (2.061) (0.972) (0.881)
SWL (2.954) (3.024) 1.512 (3.828) (1.371) (1.933)
MMH.GH (2.445) (3.061) (2.402) (2.461) (3.193) (2.712)
HORD (3.978) (0.001) (0.411) (0.513) 1.421 (0.696)
MLC (1.625) (0.889) (2.918) (2.500) (0.068) (1.600)
DIGICUT (4.856) (4.717) (0.519) (1.607) (2.467) (2.833)
PBC (4.206) (3.122) (2.331) (3.437) (3.124) (3.244)
SAMBA (4.040) (0.695) (1.183) (1.815) (1.209) (1.788)
ACI (1.894) (2.456) (1.399) (1.977) (1.375) (1.820)
UNIL (3.993) (3.960) (2.106) (0.372) (1.044) (2.295)
AYRTON 1.119 (1.386) (0.768) (0.627) (1.597) (0.652)

Table 22. Assessments of signs of manipulation on year-on-year M-score (Note, ** indicates a sign of possible manipulation)

COMPANY CODE 2014 2015 2016 2017 2018
  M-SCORE M-SCORE M-SCORE M-SCORE M-SCORE
PZ (3.611) (2.712) (1.575)** (2.591) (3.084)
CMLT (2.709) (2.842) (2.326) (2.621) (1.964)**
ALW (3.373) (3.322) (3.987) (3.252) (5.485)
BOPP 6.443** (6.732) (1.287)** (1.901)** (7.911)
CPC (5.708) 2.620** 0.793** (2.205)** 0.293**
FMLK (3.217) 0.034** (1.906)** (0.867)** (1.209)**
GGBL 2.783** (2.195)** (1.961)** (2.061)** (0.972)**
SWL (2.954) (3.024) 1.512** (3.828) (1.371)**
MMH.GH (2.445) (3.061) (2.402) (2.461) (3.193)
HORD (3.978) (0.001)** (0.411)** (0.513)** 1.421**
MLC (1.625)** (0.889)** (2.918) (2.500) (0.068)**
DIGICUT (4.856) (4.717) (0.519)** (1.607)** (2.467)
PBC (4.206) (3.122) (2.331) (3.437) (3.124)
SAMBA (4.040) (0.695)** (1.183)** (1.815)** (1.209)**
ACI (1.894)** (2.456) (1.399)** (1.977)** (1.375)**
UNIL (3.993) (3.960) (2.106)** (0.372)** (1.044)**
AYRTON 1.119** (1.386)** (0.768)** (0.627)** (1.597)**
No. of firms 5 7 12 10 11
% 29% 41% 71% 59% 65%

Table 23. Assessments of signs of manipulation based on Average M-scores.

COMPANY CODE AVERAGE M-SCORE ZONE OF DISCRIMINATION
PZ (2.715) Non- Manipulation
CMLT (2.492) Non- Manipulation
ALW (3.884) Non- Manipulation
BOPP (2.278) Non- Manipulation
CPC (0.841) Manipulation
FMLK (1.433) Manipulation
GGBL (0.881) Manipulation
SWL (1.933) Manipulation
MMH.GH (2.712) Non- Manipulation
HORD (0.696) Manipulation
MLC (1.600) Manipulation
DIGICUT (2.833) Non- Manipulation
PBC (3.244) Non- Manipulation
SAMBA (1.788) Manipulation
ACI (1.820) Manipulation
UNIL (2.295) Non- Manipulation
AYRTON (0.652)  Manipulation

Source: Financial Reports (2014 – 2018)

4.5. Discussion of Results

Tables, - 9, and 10 show the results of Z-Scores computed from the secondary data collected from 2014 to 2018. Altman Z-Score computation reported that on average 47% of the firms showed an impressive Z-Score performance of being financially sound. In the case of a year-on-year score, the result showed a Z-Score performance of 41% of the firms classified as safe from distressed for 2014 and 2016 while recorded 53% in the remaining years. Also, the result classified 35%, 41%, 47%, 29%, and 24% not financially distressed but in the zone of distress or Grey Zone in the year 2014 through 2018 respectively. Note, it is important to report that the model misclassified (Type II error) 24%, 12%, 12%, 18%, and 18% of the firms in the year 2014 to 2018 respectively. In general, the model reported a predictive power of 66% on average. These findings accept the hypotheses (H1) which states that Altman's (2000) model can Accurately predict the bankruptcy status of the listed consumer goods and service companies in Ghana and this is consistent with the findings of Sulub S.A (2014), Soon et. al, (2014), Soon and Mohammed,(2012), and Gyimah, P. and Boachie, (2018), which concluded that Altman’s model can successfully predict corporates failure.

Table, -10 (Taffler model) revealed that on average 88% of the firms showed outstanding Z-Score performance of being safe from bankruptcy. Using a year-on-year score to determine distress revealed an impressive Z-Score performance of 82% of the firms classified as safe from distressed through 2015 to 2017 except 2014, and 2018 which recorded 76% and 94% respectively as healthy. However, the model constantly misclassified three non-failed firms as failed for the period 2014 to 2017 representing (18%) error rate except 2018 which recorded one (6%) non-failed as failed. In general, the model does extremely well in predicting the success of the firms with a predictive power of 83% for consumer goods and service companies in Ghana. This finding supports hypotheses (H2) which states that Taffler’s model can Accurately predict the bankruptcy status of the listed consumer goods and service companies in Ghana and this conclusion is consistent with the findings of Taffler (1983).

Tables, - 21, 22, and 23 reports the result and assessments of m-score calculated from the financial data of 17 listed firms starting from 2014 to 2018. A carefully look at Tables, - 21 and 22 indicated that financial statements of two firms (Aryton drugs manufacturing company and Guinness Ghana Breweries limited) showed signs of possible manipulations as far back 2014 to 2018 as their M-Score figure is above the standard score for non- manipulated earning figures of negative 2.22. Five firms on the other hand (SAMBA, ACI, HORD, FMLK, and CPC) showed signs of manipulation for a four-years score whereas MLC and BOPP reported three years of manipulations. The remaining firms only reported one or two-year manipulation sign except for MMH.GH and ALW who were found to be free from financial statement fraud in all the five-year study period. The Five-year Average M-Score in table, - 23 revealed 53 % of the firms engaging in earnings manipulation as their average M-Score lies above the benchmark figure of negative 2.22. Therefore, a detailed overview of the results in table, - 21 as confirmed by Tables, 22 and 23 revealed that financial statement fraud was found to be common among the sample firms. This result is similar to McCarthy, J, (2017),who reported that, the financial statements for the five years studied were manipulated by the management of Enron corporation to hide the true picture of the company’s distress status, hence hypotheses (H3) is accepted. Contrary to these findings is Amoa-Gyarteng, K., (2014),who analysed listed firms in Ghana for early warning signs of bankruptcy and financial statement fraud with the Beneish model. His findings revealed that the companies were not engaging in financial statement fraud.

4.6. Assessments of Classification Power of Altman and Taffler Z-Score Model

The classification accuracy of Altman (2000) and Taffler’s (1983) Z-Score models was evaluated using a sample of 17 firms from the consumer goods sector. The z-scores are obtained for both models using five years’ annual financial data. The accuracy is calculated by dividing the number of firms correctly classified by the total number of firms in the sample (Predictive Power= TCA ÷ NO). The tenacity of Altman's (1968) study was to develop a model that could predict a corporate future in the light of failed, non-failed and zone of ignorance. However, the question of the accuracy determination method was not dealt with due to his failure to authenticate his model. Therefore, in other to confirm the model in dissimilar countries and circumstances, we consider the accuracy calculation imperative. To meritoriously evaluate the predictive ability between the two models, it is statistically appropriate to include the greyzone count area thus zone which cannot be regarded as failed or non-failed.

 Table- 24 presents the results of the calculation of both Taffler (1983), and Altman (2000) predictive power. The Altman model was found to be accurate with an average predictive power of 66% for the study period of five years. In the case of using the overall average Z- scores of each firm in the study sample, the model showed a classification power of 65%. In the case of Taffler (1983), the model does equally well for predicting the firms with an average predicting accuracy of 83%. However, using the overall average Z- scores of each firm, the model does improve its classification accuracy to 88%. In general, it can be concluded that Taffler (1983) model has a high predictive power than that of Altman's (2000) model in the consumer goods industry with a statistical difference of 17% (83%-66%).

Table 24. Calculations of Classification Power of Model 1&2 including Grey-zone count

YEAR ALTMAN (2000) MODEL Taffler (1983) model
  NO. OF FIRMS CLASSIFIED AS SAFE NO. OF FIRMS CLASSIFIED INTO GREY ZONE PREDICTIVE POWER (%) NO. OF FIRMS CLASSIFIED AS SAFE PREDICTIVE POWER (%)
2014 7 6 10/17=59% 13 13/17=76%
2015 9 7 12.5/17=74% 14 14/17=82%
2016 7 8 11/17=65% 14 14/17=82%
2017 9 5 11.5/17=68% 14 14/17=82%
2018 9 4 11/17=65% 16 16/17=94%
Average:     66%   83%
USING AVERAGE Z-SCORES 8 6 11/17=65% 15 88%

Source: Financial Reports (2014 – 2018)

5. Conclusion and Recommendations

In this paper, we investigated the predictive power of applying Altman’s (2000) revised model and Taffler’s (1983) model to 17 listed non-failed consumer goods and service companies in Ghana. These firms were purposively sampled for the analysis. The study also investigated financial statement fraud by applying Beneish’s (1999) M-Score model on the data set extracted from the annual financial data of the sample firms. Professor Edward Altman’s (2000) Revised model was found to be accurate for listed Consumer goods and service companies in Ghana at a predictive power range from 65% to 66% on average. Professor Richard J. Taffler (1983) model was equally found to be accurate for the listed consumer goods and service sector in Ghana at a high predictive power ranges from 83% to 88% on average. Professor Messod Beneish (1999) model also revealed that financial statements fraud was common among the sample firms, some for all the five years and others show signs of manipulations for four and three years. Generally, the findings confirmed the assumptions under the hypotheses H1, H2, and H3.

This study adds to the literature of finance and accounting particularly on failure and insolvency prediction from the viewpoint of an emerging economy. The study is however restricted to the extent that, it relies on only seventeen (17) listed consumer goods and service firms in Ghana. Also, Taffler and Altman’s models were developed based on UK and US-GAAP whilst the data for the study were based on IAS and IFRS standards. Furthermore, considering the relatively high firms classified into the grey zone using the Altman model with 17% type II error rate documented, criticizers or critics, of this paper, may argue that the investigation was biased considering the sample size used for the analysis and other relevant limitations of the model. Giving the above reproaches, it would be appropriate to consider the following for further studies: How applicable is Altman’s (2000), Taffler (1983) and Beneish’s (1999) model in predicting bankruptcy and detecting financial statement fraud in the banking and mining sectors in Ghana taking into account the frequent collapse, mergers, and acquisitions in the aforementioned sectors?Furthermore, at what degree can corporate failure be predicted in Ghana using a new set of models? Finally, a detailed experimental study that would test the existing models and estimate a new model based on the characteristics of Ghanaian firms, which will be appropriate for predicting bankruptcy in Ghana.  

Appendix

Appendix A. Description of Companies Used For The Studies

COMPANY SYMBOL SECTOR
ALUWORKS ALW CONSUMER GOODS
BENSO OIL PALM PLANTATION BOPP CONSUMER GOODS
COCOA PROCESSING COMPANY CPC CONSUMER GOODS
FAN MILK FML CONSUMER GOODS
GUINNESS GHANA BREWERIES GGBL CONSUMER GOODS
HORDS HORDS CONSUMER GOODS
PRODUCE BUYING COMPANY PBC CONSUMER GOODS
PZ CUSSONS GHANA PZC CONSUMER GOODS
SAMBA FOODS SAMBA CONSUMER GOODS
UNILEVER GHANA UNIL CONSUMER GOODS
DIGICUT PRODUCTION AND ADVERTISING DIGICUT CONSUMER SERVICES
MECHANICAL LLOYD COMPANY MLC CONSUMER SERVICES
MERIDIAN-MARSHALLS HOLDINGS MMH.GH CONSUMER SERVICES
SAM WOODE SWL CONSUMER SERVICES
CAMELOT GHANA CMLT CONSUMER GOODS
Ayrton Drug AYRTON CONSUMER GOODS
African Champion Industries ACI CONSUMER GOODS

Appendix B. Data Presentation of Altman Z-Score Computations

CODE YEARS X1 X2 X3 X4 X5
PZ 2014 0.13341 0.47911 0.12334 1.14984 0.85891
  2015 (0.06319) 0.45429 0.79818 0.89485 0.77832
  2016 (0.04558) 0.44100 0.71575 0.88765 0.70224
  2017 (0.05791) 0.46527 0.75278 0.81946 0.69222
  2018 (0.06987) 0.52635 0.65352 0.93048 0.74831
CMLT 2014 0.07280 (0.45938) 0.02365 0.45841 5.88789
  2015 0.08100 (0.63646) 0.03528 0.51214 3.94563
  2016 0.04348 (0.61415) 0.37607 0.62605 3.15113
  2017 0.09697 (0.61934) (0.00873) 0.59889 4.86217
  2018 (5.85940) (0.68026) 0.41849 0.68220 5.93557
ALW 2014 (0.17647) (0.19767) (0.03416) 0.74357 4.58379
  2015 (0.20949) 0.42668 0.10420 0.53082 2.09889
  2016 (0.15693) 0.63886 0.09638 0.89902 0.34665
  2017 (0.28686) (0.44137) (0.12236) 0.36102 4.32873
  2018 0.80267 0.11078 0.07692 5.91079 0.85550
BOPP 2014 0.22555 0.20127 0.03530 0.60451 0.98913
  2015 0.05805 0.23476 0.53603 0.70757 1.05012
  2016 (0.00679) 0.25007 0.35103 0.71023 1.11798
  2017 0.02322 0.21189 0.43059 0.70926 1.13782
  2018 (1.22734) 0.20744 0.42736 0.61816 1.43757
CPC 2014 (0.42364) (0.40678) 0.11334 0.09317 2.53509
  2015 0.29015 (0.23465) 0.02761 0.91901 0.98019
  2016 0.25248 (0.27204) 0.04014 0.84680 0.78312
  2017 0.23952 (0.28931) (0.03262) 0.80714 0.05395
  2018 0.64473 (0.27720) (0.01753) 0.88600 0.09277
FMLK 2014 (0.19520) 0.57315 0.17535 1.88895 1.43239
  2015 0.41637 0.51480 0.30982 1.28042 1.47240
  2016 0.08532 0.67076 0.35728 2.46699 1.57674
  2017 0.11104 0.70533 0.21265 2.82631 1.48600
  2018 0.38183 0.68803 0.04924 2.55453 1.19334
GGBL 2014 0.09489 0.10617 0.74508 0.49369 6.08779
  2015 0.05747 0.14083 0.74911 0.55934 6.18741
  2016 0.07694 0.13201 0.72233 0.55595 5.48980
  2017 0.07945 0.18979 0.88706 0.71510 6.27912
  2018 (4.59374) 0.19135 1.25862 0.65065 6.20293
SWL 2014 (0.13637) 0.12219 (0.32433) 0.11715 1.27460
  2015 (0.03352) 0.01982 (0.04764) 3.44549 0.15505
  2016 0.20922 0.01133 (0.01397) 6.12190 0.07405
  2017 (0.13173) 0.09791 0.07774 (0.10383) 0.87543
  2018 (1.37060) 0.08914 (0.02804) 0.11182 0.65317
MMH.GH 2014 0.07710 (0.23864) 0.02020 1.14679 0.41833
  2015 0.00829 0.24624 0.00077 0.89012 0.43306
  2016 0.03137 0.24798 0.57745 1.01415 0.43404
  2017 0.07536 0.22222 0.23021 1.05778 0.41718
  2018 0.02428 0.17593 0.67280 0.79676 0.42229
HORD 2014 (0.24719) 0.10365 0.32924 0.26207 0.93866
  2015 0.39889 0.03519 0.00871 1.70226 0.57739
  2016 0.42637 0.14199 0.02928 3.29794 0.67867
  2017 (1.35036) 0.17814 0.03655 3.72844 0.70695
  2018 (0.12762) 0.02966 0.00792 2.89227 0.94278
MLC 2014 0.25392 0.12096 0.05054 0.74335 0.37992
  2015 0.16388 0.24949 0.13701 1.49927 0.66626
  2016 (0.04480) 0.22432 0.42126 1.58062 0.80312
  2017 (0.01796) 0.16898 0.61795 0.99977 0.51837
  2018 0.43657 0.12944 0.55046 0.89324 0.76874
DIGICUT 2014 (0.06028) (0.27416) 0.00419 0.86865 0.55102
  2015 (0.13928) 0.28287 0.00056 0.64530 0.49876
  2016 (0.13984) 0.29182 0.00040 0.71161 0.41108
  2017 0.07536 0.22222 0.00016 1.05778 0.47657
  2018 0.12077 0.17593 0.00121 0.79676 0.44725
PBC 2014 (0.38888) 0.13556 (0.00283) 0.16573 1.82619
  2015 (0.23691) 0.20330 0.15506 0.26283 1.68830
  2016 0.41445 0.19803 0.14079 0.25306 1.31162
  2017 0.13064 0.25417 0.13947 0.34651 1.22861
  2018 0.82254 0.40421 0.34456 0.68423 0.87015
SAMBA 2014 0.08895 (0.02719) (0.06544) 4.89041 0.47012
  2015 0.25627 0.10395 0.16764 5.08902 0.47792
  2016 0.32827 0.03913 (0.04697) 4.42818 0.25972
  2017 0.20077 (0.04542) (0.10930) 4.78461 0.32684
  2018 0.20534 (0.07119) (0.01984) 2.87018 0.39253
ACI 2014 0.13008 (0.01918) 0.02031 1.10670 2.92663
  2015 0.14194 0.05611 0.01174 1.31869 2.42141
  2016 0.13918 0.03404 (0.01018) 1.29671 3.19100
  2017 0.17766 0.01986 0.05192 1.26208 3.57406
  2018 0.16848 0.01849 0.03563 1.25090 3.50133
UNIL 2014 (0.13421) 0.42657 0.02870 0.85915 12.12634
  2015 (0.23510) 0.57725 (0.00841) 1.68138 7.99408
  2016 (0.15344) 0.56384 0.02687 1.58342 4.45389
  2017 0.41063 0.47755 (0.01212) 1.08507 4.67648
  2018 0.38126 0.48133 0.01239 1.10831 6.27609
AYRTON 2014 (0.22866) 1.68335 0.02601 0.33612 3.64460
  2015 0.15299 0.21810 1.16862 0.72755 0.69719
  2016 0.29223 0.34010 0.11896 1.34560 1.42193
  2017 0.33884 0.38465 0.10874 1.41722 1.16520
  2018 0.55059 0.45004 0.15522 1.73476 1.07412

Appendix C. Data Presentation of Taffler Z-Score Computations

CODE YEARS X1 X2 X3 X4
PZ 2014 0.41608 0.92412 0.29644 (0.15804)
  2015 1.75036 0.74433 0.45601 (1.08534)
  2016 1.51165 0.80775 0.47349 (0.28704)
  2017 1.52804 0.79097 0.49264 (1.14868)
  2018 1.41491 0.75677 0.46188 (1.09806)
CMLT 2014 0.21951 0.26329 0.10773 0.85115
  2015 0.36351 0.28026 0.09706 0.00750
  2016 3.53947 0.26506 0.10625 0.57451
  2017 (0.07368) 0.33068 0.11844 0.37815
  2018 3.50567 0.31071 0.11937 1.17838
ALW 2014 (0.12408) 0.30148 0.27533 (6.94701)
  2015 0.27932 0.26002 0.37306 (14.53634)
  2016 0.37279 0.21440 0.25852 (12.46713)
  2017 (0.44155) 0.14715 0.27712 (12.10503)
  2018 1.07896 5.54708 0.07129 0.71320
BOPP 2014 0.08009 0.90305 0.44072 (0.02979)
  2015 1.34734 0.87245 0.39784 (0.05700)
  2016 0.77734 0.89758 0.45158 (0.10625)
  2017 0.89527 0.94287 0.48096 (0.12425)
  2018 0.88650 0.91848 0.48208 (0.09298)
CPC 2014 0.20210 0.15464 0.56083 (9.61420)
  2015 0.09680 1.11400 0.28527 1.30779
  2016 0.13240 1.06551 0.30314 1.27320
  2017 (0.10112) 1.06836 0.32263 1.47510
  2018 (0.05554) 1.21618 0.31565 2.60324
FMLK 2014 0.58976 1.46510 0.29732 0.04121
  2015 0.76260 1.62056 0.40627 0.07204
  2016 1.38900 1.26045 0.25722 (0.79939)
  2017 0.93594 1.48625 0.22720 (0.04848)
  2018 0.19305 1.41005 0.25504 (0.01149)
GGBL 2014 3.52711 0.48582 0.21124 (0.02477)
  2015 3.65406 0.46350 0.20501 (0.28960)
  2016 3.32633 0.48343 0.21716 (0.26691)
  2017 3.84234 0.51439 0.23087 (0.29388)
  2018 5.88050 0.48278 0.21403 (0.27918)
SWL 2014 (0.57644) 0.49019 0.56265 (1.03504)
  2015 (0.52830) 0.22330 0.09018 (0.34671)
  2016 (0.30301) 1.84381 0.04612 2.49143
  2017 0.18872 0.21605 0.41190 (0.44248)
  2018 (0.08724) 0.37294 0.32144 (0.12071)
MMH.GH 2014 0.19422 0.34210 0.10399 0.00070
  2015 0.00798 0.29202 0.09597 0.02562
  2016 4.25646 0.33471 0.13567 (0.08407)
  2017 1.78454 0.34640 0.12900 (0.07302)
  2018 6.67262 0.27112 0.10083 (0.02777)
HORD 2014 1.02402 0.32777 0.32152 (2.75279)
  2015 0.03181 1.46347 0.27378 5.51472
  2016 0.19941 2.42837 0.14684 (8.30115)
  2017 0.27263 2.90208 0.13407 6.56427
  2018 0.03279 3.72611 0.24160 103.39375
MLC 2014 0.10335 1.00512 0.48903 (1.31330)
  2015 0.37018 1.20426 0.37012 (1.31433)
  2016 1.19592 1.09254 0.35225 (1.16180)
  2017 1.38646 0.89939 0.44570 (1.78112)
  2018 1.13776 0.84748 0.48381 (1.78730)
DIGICUT 2014 0.03506 0.06396 0.11946 0.25132
  2015 0.00507 0.04721 0.11025 0.48373
  2016 0.00249 0.03218 0.15964 (3.04255)
  2017 0.00126 0.34640 0.12900 (0.46843)
  2018 0.01199 0.27112 0.10083 (0.22694)
PBC 2014 (0.00345) 0.76742 0.81816 (0.67052)
  2015 0.20242 0.90219 0.76603 (3.78866)
  2016 0.18225 0.91378 0.77247 0.31312
  2017 0.19509 1.01966 0.71490 0.19524
  2018 0.59829 1.34992 0.57591 (0.12515)
SAMBA 2014 (0.69140) 1.28424 0.09464 0.12368
  2015 3.07411 1.88240 0.05453 0.82238
  2016 (0.40058) 1.55579 0.11726 0.37576
  2017 (1.02344) 0.70276 0.10679 (0.10146)
  2018 (0.14404) 0.79472 0.13777 (0.37252)
ACI 2014 0.23021 0.45988 0.08822 0.47469
  2015 0.20833 0.45977 0.05635 0.66416
  2016 (0.12229) 0.51076 0.08321 0.41992
  2017 0.76681 0.55506 0.06771 0.61515
  2018 0.41298 0.57340 0.08627 0.54497
UNIL 2014 0.05801 0.67022 0.49471 3.91311
  2015 (0.02550) 0.25375 0.32974 0.03336
  2016 0.07707 0.50434 0.34866 0.04685
  2017 (0.02644) 1.81204 0.45842 0.07949
  2018 0.02802 1.73574 0.44202 0.04806
AYRTON 2014 0.03475 0.69448 0.74843 (0.58170)
  2015 2.01886 1.26429 0.57885 (0.19109)
  2016 0.27904 1.68546 0.42633 (0.18899)
  2017 0.26616 1.80664 0.40856 (0.09374)
  2018 0.69183 2.11930 0.22436 (0.01867)

Appendix D. Data Presentation of Beneish M-Score Computation

CODE YEARS DSRI GMI AQI SGI DEPI SGAI LEVI TATAI
PZ 2014 0.5935 1.0826 0.9347 0.0090 1.0368 4.1618 0.4652 0.1013
  2015 1.1261 1.0167 0.9951 0.9509 0.7302 1.1852 0.5277 (0.0861)
  2016 0.9751 0.9524 1.2477 1.0026 9.5775 0.9336 0.5298 (0.0644)
  2017 1.1067 1.0171 1.0000 0.9854 1.0014 1.0094 0.5496 (0.0749)
  2018 0.9139 0.9702 0.8465 0.9424 0.1498 1.0170 0.5180 (0.0967)
CMLT 2014 0.7996 0.8072 0.9310 0.9422 0.9776 0.4277 0.6857 (0.0132)
  2015 0.9084 0.9544 0.9472 0.7761 1.1550 1.2407 0.6613 (0.0257)
  2016 1.2766 1.1050 0.9602 1.0192 1.0232 0.7673 0.6150 (0.0696)
  2017 0.8212 0.7367 1.1325 1.0602 1.0964 0.9896 0.6254 (0.0170)
  2018 1.0768 1.1379 1.0195 0.8743 0.8712 1.3583 0.5945 0.0899
ALW 2014 0.6460 0.9005 1.0492 1.3243 0.8935 1.2649 0.5735 (0.1935)
  2015 1.1361 0.8010 0.9818 1.0237 1.7255 0.7213 0.6532 (0.2395)
  2016 0.4794 0.9902 0.6640 0.8831 0.9368 1.3240 0.5266 (0.1869)
  2017 0.8458 0.1625 0.9591 1.2159 0.7653 0.6129 0.7347 (0.1048)
  2018 1.1524 (4.7900) 0.6755 0.7398 0.8958 1.6769 0.5060 0.0517
BOPP 2014 12.5432 0.5387 0.7828 1.2158 0.0027 16.2423 0.6232 0.2255
  2015 3.7080 1.1379 0.9671 0.9000 2.7975 43.6006 0.5856 0.0579
  2016 0.9070 0.9608 1.0254 1.0677 10.8039 0.9811 0.5847 (0.0082)
  2017 1.3948 0.8926 1.0131 1.0525 0.7778 0.9605 0.5850 0.0222
  2018 1.1254 1.1425 1.0082 1.0212 0.8482 1.0257 0.6180 (1.2282)
CPC 2014 1.0145 0.0782 0.6853 0.6048 1.2588 2.0842 0.9148 (0.4587)
  2015 1.4472 5.0781 4.1037 0.7659 0.8967 0.6524 0.5211 0.2749
  2016 1.0957 5.8399 0.9939 0.4792 0.9909 1.9446 0.5415 0.2372
  2017 1.0972 (1.1861) 1.0247 1.1064 0.9805 0.7757 0.5534 0.2250
  2018 1.2656 (1.9087) 1.0908 1.9158 0.9840 0.4169 0.5302 0.6324
FMLK 2014 0.9651 0.8319 1.3067 1.2772 1.1884 0.3040 0.3461 (0.2869)
  2015 0.6934 1.0868 1.3964 1.7770 1.0392 0.8573 0.4385 0.3599
  2016 1.2587 1.0112 0.5145 1.2251 0.5073 0.8890 0.2884 0.0279
  2017 2.1683 0.9173 1.0647 1.1541 1.2438 1.2451 0.2613 0.0408
  2018 0.9829 0.8165 1.0193 0.8734 1.2425 1.8461 0.2813 0.2931
GGBL 2014 5.1655 2.4777 3.5156 0.0435 1.3713 0.8787 0.6695 0.0675
  2015 0.8532 0.9992 1.0076 1.1421 0.6880 0.8805 0.6413 0.0404
  2016 1.1260 1.0238 1.0079 0.9796 1.0197 0.9788 0.6427 0.0603
  2017 0.8333 1.0184 0.9966 1.1581 0.7596 0.9939 0.5831 0.0669
  2018 1.0400 1.0094 1.0018 1.0176 1.3040 1.0287 0.6058 0.2759
SWL 2014 2.5725 0.3766 0.6015 0.7031 0.3719 1.0819 0.8951 (0.2380)
  2015 0.4444 0.5205 2.0348 0.7758 1.2858 1.1246 0.2249 (0.0562)
  2016 1.6459 5.7908 1.0210 0.8672 0.4492 2.0796 0.1404 0.2021
  2017 0.3294 1.2827 0.3179 1.4257 1.2414 0.6833 1.1159 (0.2199)
  2018 1.0831 0.6399 1.1948 0.8654 1.0019 0.7566 0.8994 0.2542
MMH.GH 2014 0.8414 0.8614 1.0945 0.9871 1.1616 0.2965 0.4658 (0.0187)
  2015 0.8404 1.0108 0.9308 0.8873 1.0476 1.1419 0.5291 (0.0955)
  2016 0.9329 1.3188 1.0592 1.0802 1.0487 0.6454 0.4965 (0.0759)
  2017 1.1810 0.9575 0.9249 1.0045 0.9479 1.7432 0.4860 (0.0285)
  2018 0.8130 0.8725 1.1585 0.9334 1.1115 2.3451 0.5566 (0.0864)
HORD 2014 0.7785 1.0296 0.5723 1.2342 1.1033 1.6693 0.7924 (0.2800)
  2015 1.4040 0.8625 1.3342 1.1389 1.0363 1.2819 0.3701 0.3760
  2016 0.7585 0.9980 1.0181 1.1421 1.6170 0.9065 0.2327 0.3891
  2017 0.8658 0.8769 1.0456 1.0771 1.0652 0.8853 0.2115 0.3811
  2018 1.4838 1.0095 1.0786 1.0841 1.0089 0.9272 0.2852 0.6618
MLC 2014 0.5979 0.9313 1.0067 0.7977 3.0173 1.2402 0.5736 0.2370
  2015 1.3073 0.9202 0.8630 1.5286 0.9128 0.6061 0.4001 0.1454
  2016 0.8077 0.6014 0.9002 1.0881 1.1641 0.9199 0.3875 (0.0687)
  2017 1.2499 1.2727 1.0801 0.6780 1.0364 1.6657 0.5001 (0.0411)
  2018 0.8741 1.0050 0.9875 1.4336 1.0004 0.6732 0.5282 0.4131
DIGICUT 2014 1.1813 0.3766 0.9634 0.7031 0.9038 0.4163 0.5351 (0.4649)
  2015 0.9611 0.5205 1.0248 0.7758 1.0079 1.3058 0.6078 (0.3922)
  2016 1.1621 5.7908 1.0020 0.8672 0.0082 0.8039 0.5842 (0.1401)
  2017 0.8320 1.2827 1.0083 1.4257 1.1307 1.2281 0.4860 0.0752
  2018 0.8769 0.6399 0.9970 0.8654 0.9273 2.5295 0.5566 0.1206
PBC 2014 1.0967 0.8447 1.0085 1.2691 8.6413 0.5777 0.8578 (0.6356)
  2015 1.2456 1.2592 1.0411 1.2638 0.2197 0.9519 0.7919 (0.2657)
  2016 1.4153 1.0670 0.9792 0.9568 6.7943 1.0500 0.7980 (0.2020)
  2017 1.0037 0.9948 1.0333 1.1601 0.8836 0.8272 0.7427 (0.2595)
  2018 2.0700 1.0003 1.0584 1.0979 1.3965 1.1016 0.5937 (0.4063)
SAMBA 2014 0.0447 (0.0027) 1.0470 0.0900 2.0783 0.0094 0.1698 0.0161
  2015 0.9780 1.6799 1.0393 1.2622 1.1301 0.5702 0.1642 0.1783
  2016 1.4595 0.4536 1.0667 0.7344 0.8104 1.9856 0.1842 0.2773
  2017 0.6043 1.0929 0.8776 1.1540 0.9490 1.0474 0.1729 0.1358
  2018 0.5373 1.3669 0.8672 1.3376 0.0018 0.0249 0.2584 0.2052
ACI 2014 1.1270 1.0317 1.0677 0.8762 0.9484 1.0457 0.4747 0.0808
  2015 0.5413 1.0597 1.0490 0.8345 0.9322 1.5527 0.4313 0.0981
  2016 1.4465 1.0630 0.8567 1.1029 1.1561 0.8766 0.4354 0.0812
  2017 0.6284 1.0264 0.9903 1.0995 1.0417 0.9251 0.4421 0.1166
  2018 1.4853 0.9692 1.0145 0.9829 0.9725 1.0474 0.4443 0.1098
UNIL 2014 0.4011 0.6206 0.9446 1.0474 1.1520 0.9077 0.5379 (0.2064)
  2015 1.0879 1.5577 1.0047 0.4704 0.6633 2.4529 0.3729 (0.2781)
  2016 2.1365 1.9919 1.0767 0.5655 1.2805 1.8521 0.3871 (0.1977)
  2017 1.1349 0.5852 1.1031 1.2024 1.6577 0.7755 0.4796 0.3625
  2018 0.5420 0.6394 0.9373 1.3239 0.6089 0.6955 0.4743 0.3431
AYRTON 2014 0.7464 0.7495 1.0492 1.4492 0.8072 0.8410 0.0386 0.6891
  2015 1.7627 1.3928 0.9737 0.6115 2.0096 2.1954 0.5789 0.1056
  2016 0.5055 1.0885 0.9817 1.8170 1.0396 0.5092 0.4263 0.2399
  2017 1.3439 1.1378 1.0542 0.9031 1.1007 1.1534 0.4137 0.2889
  2018 1.3108 1.0721 1.0228 1.0343 4.0280 0.9199 0.3657 (0.0107)
CODE YEARS 0.5935 1.0826 0.9347 0.0090 1.0368 4.1618 0.4652 0.1013

Appendix E.

Results of Z-Score Using Altman's (2000) Model
COMPANY CODE 2014 2015 2016 2017 2018 AVERAGE
  Z-SCORE Z-SCORE Z-SCORE Z-SCORE Z-SCORE Z-SCORE
PZ 2.224 3.966 3.633 3.721 3.559 3.421
CMLT 5.805 3.781 4.085 4.622 2.73 4.205
ALW 4.487 2.852 1.451 3.513 4.244 3.309
BOPP 1.683 3.247 2.709 2.964 2.315 2.584
CPC 2.272 1.459 1.212 0.218 0.638 1.160
FMLK 3.112 3.702 4.347 4.006 3.273 3.688
GGBL 8.751 8.893 8.119 9.535 7.234 8.506
SWL 0.322 1.447 2.761 1.06 -0.295 1.059
MMH.GH 0.815 1.023 2.882 1.817 3.008 1.909
HORD 1.978 1.634 2.579 1.567 2.114 1.975
MLC 1.133 2.048 2.929 2.983 3.271 2.473
DIGICUT 0.652 0.91 0.857 1.163 1.02 0.921
PBC 1.719 2.278 2.317 2.113 3.156 2.317
SAMBA 2.361 3.406 2.242 2.102 1.623 2.347
ACI 3.526 3.156 3.826 4.402 4.267 3.835
UNIL 12.817 8.979 5.561 5.784 7.448 8.118
AYRTON 5.121 4.919 2.851 2.664 3.058 3.722
Results of Z-Score using Taffler (1983) Z-Score Model.
COMPANY CODE 2014 2015 2016 2017 2018 AVERAGE
  Z-SCORE Z-SCORE Z-SCORE Z-SCORE Z-SCORE Z-SCORE
PZ 0.369 0.933 0.945 0.818 0.756 0.764
CMLT 0.306 0.248 2.021 0.086 2.108 0.954
ALW (1.089) (2.077) (1.723) (2.102) 1.420 (1.114)
BOPP 0.234 0.890 0.472 0.664 0.661 0.584
CPC (1.310) 0.458 0.467 0.379 0.602 0.119
FMLK 0.563 0.700 0.818 0.722 0.330 0.627
GGBL 1.967 1.987 1.822 2.098 3.173 2.209
SWL (0.306) (0.290) 0.486 0.131 0.048 0.014
MMH.GH 0.166 0.064 2.310 1.002 3.585 1.425
HORD 0.202 1.139 (0.880) 1.596 17.088 3.829
MLC 0.063 0.209 0.653 0.647 0.514 0.417
DIGICUT 0.089 0.106 (0.453) (0.006) 0.023 (0.048)
PBC 0.138 (0.244) 0.405 0.396 0.576 0.254
SAMBA (0.163) 2.015 0.071 (0.448) (0.008) 0.293
ACI 0.274 0.287 0.084 0.589 0.396 0.326
UNIL 0.833 0.084 0.177 0.317 0.328 0.348
AYRTON 0.150 1.308 0.413 0.434 0.680 0.597
Results of M-Score (Beneish 1999)
COMPANY CODE 2014 2015 2016 2017 2018 AVERAGE
  M-SCORE M-SCORE M-SCORE M-SCORE M-SCORE M-SCORE
PZ (3.611) (2.712) (1.575) (2.591) (3.084) (2.715)
CMLT (2.709) (2.842) (2.326) (2.621) (1.964) (2.492)
ALW (3.373) (3.322) (3.987) (3.252) (5.485) (3.884)
BOPP 6.443 (6.732) (1.287) (1.901) (7.911) (2.278)
CPC (5.708) 2.620 0.793 (2.205) 0.293 (0.841)
FMLK (3.217) 0.034 (1.906) (0.867) (1.209) (1.433)
GGBL 2.783 (2.195) (1.961) (2.061) (0.972) (0.881)
SWL (2.954) (3.024) 1.512 (3.828) (1.371) (1.933)
MMH.GH (2.445) (3.061) (2.402) (2.461) (3.193) (2.712)
HORD (3.978) (0.001) (0.411) (0.513) 1.421 (0.696)
MLC (1.625) (0.889) (2.918) (2.500) (0.068) (1.600)
DIGICUT (4.856) (4.717) (0.519) (1.607) (2.467) (2.833)
PBC (4.206) (3.122) (2.331) (3.437) (3.124) (3.244)
SAMBA (4.040) (0.695) (1.183) (1.815) (1.209) (1.788)
ACI (1.894) (2.456) (1.399) (1.977) (1.375) (1.820)
UNIL (3.993) (3.960) (2.106) (0.372) (1.044) (2.295)
AYRTON 1.119 (1.386) (0.768) (0.627) (1.597) (0.652)
References
  1. Alareeni, B. and Branson, J., 2013. Predicting Listed Companies' Failure in Jordan Using Altman Models: A Case Study. International Journal of Business and Management8(1), p.113-126.
  2. Altman, E. I., 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), pp.589-609. doi:10.1111/j.1540-6261.1968.tb00843.x
  3. Altman, E. I., 2013. Predicting financial distress of companies: revisiting the Z-score and ZETA® models. In Bell, A., Brooks, C. and Marcel Prokopczuk, M. (Ed.) Handbook of research methods and applications in empirical finance. Cheltenham, United Kingdom: Edward Elgar Publishing. doi: 10.4337/9780857936080.00027
  4. Amoa-Gyarteng, K., 2014. Analyzing a listed firm in Ghana for early warning signs of bankruptcy and financial statement fraud: An empirical investigation of AngloGold Ashanti. European Journal of Business and Management, 6(5), pp.10-17.
  5. Annual Report Ghana, 2019. Financials for companies listed on the Ghana Stock Exchange. [online] Available at: http://annualreportsghana.com/Services/Reports.aspx [Accessed on 2 January 2020].
  6. Appiah, K. O., 2011. Corporate failure prediction: some empirical evidence from listed firms in Ghana. China-USA Business Review10(1), pp. 32-41.
  7. Beaver, W. H., 1966. Financial ratios as predictors of failure. Journal of Accounting Research, 4, pp.71-111.
  8. Beneish, M. D., 1999. The detection of earnings manipulation. Financial Analysts Journal55(5), pp.24-36.
  9. Brigham, E. F. and Daves, P. R., 2014. Intermediate financial management. Boston, USA: Cengage Learning.
  10. Charitou, A., Neophytou, E. and Charalambous, C., 2004. Predicting corporate failure: empirical evidence for the UK. European Accounting Review, 13(3), pp.465-497.
  11. Christidis, A. and Gregory, A., 2010. Some new models for financial distress prediction in the UK. Xfi-Centre for Finance and Investment Discussion Paper No. 10. doi:10.2139/ssrn.1687166
  12. Deloitte, L. L. P., 2008. Ten things about financial statement fraud [online] Available at: https://assets.corporatecompliance.org/Portals/1/Users/169/29/60329/10%20Things%20about%20financial%20statement%20fraud.pdf [Accessed on 11 January 2019].
  13. Elloumi, F. and Gueyié, J. P., 2001. Financial distress and corporate governance: an empirical analysis. Corporate Governance, 1(1), pp.15-23.
  14. Gepp, A. and Kumar, K., 2015. Predicting financial distress: a comparison of survival analysis and decision tree techniques. Procedia Computer Science, 54, pp.396-404.
  15. Ghana Stock Exchange, 2019. Ghana Stock Exchange (GSE) - Listed Companies. [online] Available at:  https://www.african-markets.com/en/stock-markets/gse/listed-companies [Accessed on 10 January 2020].
  16. Glautier, M. W. E. and Underdown, B., 2001. Accounting theory and practice. London: Pearson Education.
  17. Grice, J. S. and Ingram, R. W., 2001. Tests of the generalizability of Altman's bankruptcy prediction model. Journal of Business Research, 54(1), pp.53-61.
  18. Gyimah, P. and Boachie, W. K., 2018. Portability of multiple discriminant analysis prediction model of listed firms: an emerging market perspective. Research Journal of Accounting and Finance9(6), pp.94-99.
  19. Harrington, C., 2005. Formulas for detection. Analysis ratios for detecting financial statement fraud. Association of Certified Fraud Examiners, Fraud Magazine [online] Available at: https://www.fraud-magazine.com/article.aspx?id=4294967726 [Accessed 11 January 2019].
  20. Hlahla, B.F., 2010. Assessing corporate financial distress in South Africa. Master of Management in Finance and Investment thesis, Wits Business School, University of the Witwatersrand, Johannesburg [online] Available at: http://wiredspace.wits.ac.za/handle/10539/10761 [Accessed 11 January 2019].
  21. Johansson, T. and Kumbaro, J., 2011. Predicting Corporate Default-An Assessment of the Z-score model on the US market 2007-2010. [online] Available at: https://lup.lub.lu.se/student-papers/search/publication/2061526  [Accessed 11 January 2019].
  22. Kenneth, U. O. and Adeniyi, A. M., 2014. Prediction of Bank Failure Using Camel and Market Information: Comparative Appraisal of Some Selected Banks in Nigeria. Research Journal of Finance and Accounting, 5(3), pp.1-17.
  23. Khaliq, A., Altarturi, B. H. M., Thaker, H. M. T., Harun, M. Y. and Nahar, N., 2014. Identifying Financial distress firms: a case study of Malaysia’s government linked companies (GLC). International Journal of Economics, Finance and Management, 3(4), pp.141-150.
  24. Kidane, H. W., 2004. Predicting financial distress in IT and services companies in South Africa. Doctoral dissertation, University of the Free State, Bloemfontein, South Africa.
  25. Kiyak, D. and Labanauskaitė, D., 2012. Assessment of the practical application of corporate bankruptcy prediction models. Economics and Management17(3), pp.895-905.
  26. Kpodoh, B., 2009. Bankruptcy and Financial Distress Prediction in the Mobile Telecom Industry: The Case MTN-Ghana, Millicom-Ghana and Ghana Telecom. Master’s degree Thesis, School of Management Blekinge Institute of Technology, Karlskrona, Sweden.
  27. Lev, B. and Thiagarajan, S. R., 1993. Fundamental information analysis. Journal of Accounting research, 31(2), pp.190-215.
  28. Low, S. W., Nor, F. M. and Yatim, P., 2001. Predicting corporate financial distress using the logit model: The case of Malaysia. Asian Academy of Management Journal6(1), pp.49-61.
  29. MacCarthy, J., 2017. Using Altman Z-score and Beneish M-score models to detect financial fraud and corporate failure: A case study of Enron Corporation. International Journal of Finance and Accounting6(6), pp.159-166.
  30. Mahama, M., 2015. Detecting corporate fraud and financial distress using the Altman and Beneish models. International Journal of Economics, Commerce and Management3(1), pp.1-18.
  31. Mamo, A. Q., 2011. Applicability of Altman (1968) model in predicting financial distress of commercial banks in Kenya. Master’s degree Thesis, School of Business, University of Nairobi, Nairobi, Kenya.
  32. Mclntyre, D. A. and Ogg, C., 2008. The 24/7 Wall St. bankruptcy odds watch. [online] Available at:  http://247wallst.com/2008/06/09/the-247-wal-st/ [Accessed 11 January 2019].
  33. Mohammed, A. A. E. and Kim-Soon, N., 2012. Using Altman's model and current ratio to assess the financial status of companies quoted in the Malaysian stock exchange. International Journal of Scientific and Research Publications2(7), pp.1-11.
  34. Moyer, R. C., 1977. Forecasting financial failure: a re-examination. Financial Management6(1), p.11.
  35. Naidoo, S. R. and Du Toit, G. S., 2007. A predictive model of the states of financial health in South African businesses. Southern African Business Review11(3), pp.33-55.
  36. Odipo, M. K. and Sitati, A., 2010. Evaluation of applicability of Altman’s revised model in prediction of financial distress: a case of companies quoted in the Nairobi Stock Exchange. [online] Available at:  http://erepository.uonbi.ac.ke/bitstream/handle/11295/9904/aibuma2011-submission236%20-%20EVALUATION%20OF%20APPLICABILITY%20OF%20ALTMAN'S%20REVISED%20MODEL%20IN%20PREDICTION%20OF%20FINANCIAL%20DISTRESS.pdf?sequence=1 [Accessed 11 January 2019].
  37. Ohlson, J. A., 1980. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), pp.109-131.
  38. Panneerselvam, R., 2008. Research methodology. New Delhi: Prentice-Hall of India Private Limited.
  39. Patrick, P., 1932. A comparison of ratios of successful industrial enterprises with those of failed firms. Certified Public Accountant2, pp.598-605.
  40. Premachandra, I. M., Chen, Y. and Watson, J., 2011. DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment. Omega39(6), pp.620-626.
  41. Pustylnick, I., 2009. Combined algorithm for detection of manipulation in financial statements. [online] Available at:  https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1422693 [Accessed 11 January 2019].
  42. Saden, N. S. and Prihatiningtias, Y. W., 2015. Financial distress prediction of mining companies listed in Indonesian Stock Exchange: An analysis using Altman Z-score model. Jurnal Ilmiah Mahasiswa. 4(1), pp.1-15.
  43. Sami, B. J., 2013. Financial Distress and Bankruptcy costs, in H. Dincer, H., Hacioglu, U., (Ed.) Global Strategies for Banking and Finance, pp.369–379. United States: IGI Global.
  44. Sulub, S. A., 2014. Testing the predictive power of Altman’s revised Z’-model: The case of 10 multinational companies. Research Journal of Finance and Accounting5(21), pp.174-184.
  45. Taffler, R. J., 1983. The assessment of company solvency and performance using a statistical model. Accounting and Business Research13(52), pp.295-308.
  46. Ul Hassan, E., Zainuddin, Z. and Nordin, S., 2017. A review of financial distress prediction models: logistic regression and multivariate discriminant analysis. Indian-Pacific Journal of Accounting and Finance1(3), pp.13-23.
  47. Wang, Y. and Campbell, M., 2010. Do Bankruptcy Models Really Have Predictive Ability? Evidence using China Publicly Listed Companies. International Management Review6(2), pp.77-82.
  48. Warshavsky, M., 2012. Analyzing earnings quality as a financial forensic tool. Financial Valuation and Litigation Expert Journal39(16), pp.16-20.
  49. Wilkinson, B., 2009. Predicting the risk of corporate failure for Australian listed companies: A fresh approach using probability-based tri-dimensional modeling. Doctor of Business Administration thesis, Graduate School of Business, University of Wollongong, North Wollongong, Australia.
  50. Xu, K., Zhao, Q. and Bao, X., 2015. Study on Early Warning of Enterprise Financial Distress—Based on Partial Least-squares Logistic Regression. Acta Oeconomica65(2), pp.3-16.
  51. Zeytınoglu, E. and Akarim, Y. D., 2013. Financial Failure Prediction Using Financial Ratios: An Empirical Application on the Istanbul Stock Exchange. Journal of Applied Finance and Banking, 3(3), pp.107–116.
  52. Zlatanović, D., Bugarin, M., Milisavljević, V. and Zlatanović, V., 2016. Forecasting the financial distress of mining companies: Tool for testing the key performance indicators. Mining and Metallurgy Engineering Bor, 1, pp.73-80.

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© 2020 The Authors. Published by Sprint Investify. ISSN 2359-7712. This article is licensed under a Creative Commons Attribution 4.0 International License. Creative Commons License
Corresponding Author
Patrick Bimpong, Zhongnan University of Economics and Law-China, School of Accounting, 182 Nanhu Avenue, Wuhan 430073, P.R. China
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Author(s)

Patrick BIMPONG
Zhongnan University of Economics and Law, China

Ishmael ARHIN
Dokuz Eylul University, Turkey

Thomas hezkeal Khela NAN
Zhongnan University of Economics and Law, China

Edward DANSO
University of Education, Ghana

Pious OPOKU
Zhongnan University of Economics and Law, China

Arthur BENEDICT
Zhongnan University of Economics and Law, China

Grace TETTEY
University for Development Studies, Ghana
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