Literature Review: Bankruptcy Prediction Models and Financial Ratios

This literature review explores various bankruptcy prediction models, starting with an overview of bankruptcy definitions and causes. It delves into prominent models such as the Beaver model, Altman Z-score model, Ohlson Logit Model, Hazard Model, and Hillegreist model, highlighting their methodologies and key variables. The review also examines the significance of financial ratios in predicting bankruptcy, including current ratio, cash ratio, profit margin ratio, return on capital employed, asset turnover ratio, debt-to-equity ratio, interest coverage ratio, turnover per employee ratio, and cash flow coverage ratio. The paper references key studies and provides a comprehensive understanding of the evolution and application of bankruptcy prediction models, offering valuable insights for students studying corporate finance and financial analysis.

Literature review for bankruptcy
predication models
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Literature review:
Bankruptcy –
According to Rocheteau et al., (2018), bankruptcy takes place when a business does not being
able to comply with its financial commitments rather simply being not able to pay off its
creditors tend to file for bankruptcy. In this context a petition is to be filed in the court of law to
pay off the organisational debts by disbursing the assets possessed by the company. In the US
scenario there are two ranging chapters like Chapter 7 and Chapter 11 governing the aspect of
bankruptcy.
Castelnuovo & Tran (2017), stated that Chapter 7 of the US Bankruptcy Act stands to settle the
matter by selling off the debt issues to the creditors. Filing for bankruptcy by the business under
Chapter 7 would put an end to the measures of the latter for its collection activities. Walters
(2017), pointed out that Chapter 11 of the US Bankruptcy regulation states that the organisation
in question would strive to restructure its debt measures to honour its debt obligations in future.
This particular legislation is quite significant in the corporate scenario as it gives a scope to the
business to continue in the market and take a lesson from the event to pay off the debts diligently
in the upcoming days (Atanasov & Black, 2016).
Causes of bankruptcy –
Bankruptcy in the business scenario are brought in for a variety of reasons such as financial
mismanagement or scenario way beyond the organisational control. Fracassi (2016), observed
that emergence of a greater level of competition, credit issues, management problems and
monetary issues like higher level of debts, capital loss, taxation problems and insufficient level
of cash flows leads to a bankruptcy scenario in business. Again there are impending business
aspects like a trustee being bankrupt, downgrading of credit ratings and foreclosures pave way to
bankruptcy for the company (Dang, et al., 2018). So the kinds of financial difficulties
experienced by the business are supposed to implicate a negative impact on its business
proceedings paving towards a bankruptcy scenario as happened in case of WorldCom and Enron.
Therefore the matter could be rightly tackled by the US business houses by means of an effective
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financial management mechanism through a series of financial planning and forecasting
activities.
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Bankruptcy prediction models –
The aspect of bankruptcy could be well understood by having an insight of a series of models
like the Beaver model, Altaman Z-score model, Ohlson Z-score model and the hazard model
amongst others. Beaver model incorporated in 1966 is considered as the very first bankruptcy
model studies widely taking consideration of the financial ratios in understanding the root cause
of such corporate failures (Beaver, et al., 2011). The model is based on a sample of 79 pairs of
failed and successful business entities within an industry of same dimension from 1954 to 1964.
Beaver worked out 30 financial ratios that are being segregated into five distinct groups on the
basis of single variables. Atanasov & Black (2016), observed that the Beaver model is crafted on
financial ratios to evaluate its strengths and weaknesses but the model is being criticised as the
sorts of variables are not correlated to each other.
Altman in 1968 came up with the Multivariate Discriminant Analysis (MDA) taking
consideration of 5 crucial financial metrics as a single score known as Altman Z-score model.
The financial ratios determine the aspect of profitability, liquidity, efficiency, activity and
leverage of the business organisations taking instance of 33 pairs of non-bankrupt and bankrupt
organisations of same dimension within the industry (Altman, 2000). Altman holds the fact that
each of the financial ratios has its own significance in the corporate scenario to work out the Z-
score to have an insight of the fact whether the organisation could fail or succeed in the given
circumstance. In the words of Fracassi (2016), a business having a lower score of 1.8 and below
runs a higher bankruptcy risk whereas a score equivalent to 3 and above indicates financial
soundness of the business concern.
Again inspired by Beaver and Altman, Ohlson in 1980 came out with his Ohlson Logit Model
accounting for 9 variable ratios to determine the aspect of profitability for 105 bankrupt
companies for a period ranging from 1970 to 1976 (Begley, et al., 1996). The logic model
initiated by Ohlson is distinct from MDA for having a statistical implication to it having a score
amidst 0 and 1 to showcase the element of default profitability. According to Walters (2017),
these factors strive to affect the profitability owing to failure of the businesses amidst its
corporate dimension, financial structure in terms of liquidity and expertise. Ohlson also induced
that scarcity of appropriate within the due time generate errors. The kinds of bankruptcy model
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inspired Zmijewski to come up with a bankruptcy theory in 1984 on the basis of accounting
figures with various set of independent variables (Zmijewski, 1984).
Again Shumway in 2001 introduced the Hazard Model to forecast on bankruptcy with relevance
of the market and financial data. According to Shumway (2001) most of the financial ratios used
for the Altman and Zhijewski model are not worthy to predict on bankruptcy. The aspect of
bankruptcy could be well understood by means of market size of the firm and return of the
stocks. So the model is strictly based on financial ratios and market oriented variables which are
appropriate, simplistic and unswerving in nature (Atanasov & Black, 2016). The assumptions are
being tested on approximate 300 organisations from 1962 to 1992, a gap of 30 years.
Lately Hillegreist in 2004 took relevance of the Black-Scholes-Merton pricing option model to
forecast the risk of bankruptcy. This particular model categorised the resources as ‘call option’
and the financial obligations as ‘strike price’ paving way towards bankruptcy in case the
organisational assets are lower than its liabilities (Castelnuovo & Tran, 2017). It might also
happen in a scenario when there is a probability that the call option would run on the verge of
expiry for being worthless.
Financial ratios and prediction of bankruptcy –
According to (Fracassi, 2016), financial ratios are quite significant as it tends to come up with
the facts and figures of the financial performance of the business organisations and it is true in
the US scenario as well. The American companies tend to comply with the US GAAP
requirement in terms of various financial statements like the Income Statement, Balance Sheet,
Cash Flow Statement and Equity Statement to portray the financials rightly and understand the
position of the specified company. In this regard the financial statements come up with a number
of financial ratios which are quite indicative to understand the aspect of bankruptcy in the
business scenario such as follows:
1. Current ratio – It is derived by dividing the current assets by current liabilities to have an
insight of the liquidity aspect of business in honouring its short-term obligations that are
supposed to arise within a year (Walters, 2017).
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2. Cash ratio – It stands for a strict outlook to determine the capability of the organisation in
honouring its debts through cash and cash equivalent resources (Castelnuovo & Tran,
2017). Cash ratio is derived by dividing the cash and marketable securities of the firm
with its current liabilities.
3. Profit margin ratio – Profit margin ratio is worked out by dividing the net profit of the
company by its sales revenue and tends to have a profitability aspect determining the
worth of revenue earned for each dollar of sales after expending the sorts of costs.
4. Return on capital employed – ROCE is a profitability ratio indicating the organisational
efficiency to have its profitability off the capital employed for the purpose (Castelnuovo
& Tran, 2017). It is derived by dividing net operating profit by the difference of total
assets and current liabilities.
5. Asset turnover ratio – Asset turnover ratio determines the worth of the organisational
revenue with respect to the worth of its resources and derived by dividing the sales
revenue by the average total assets of the firm (Walters, 2017).
6. Debt to equity ratio – Debt-to-equity (D/E) ratio indicates the financial leverage of the
company by dividing the total organisational liabilities by the shareholders’ equity of the
firm (Rocheteau, et al., 2018).
7. Interest coverage ratio – It evaluates the capability of the organisation to pay off the
interests against the debts raised for the purpose of business and calculated by dividing
the operating profit by the interest expenses of the organisation.
8. Turnover per employee ratio – It is also known as revenue per employee determining the
level of revenue generated by each of the organisational staff on an average basis (Dang,
et al., 2018). It is derived by dividing the total revenue of the business by the average
number of staff serving the firm.
9. Cash flow coverage ratio – It determines the capability of the operating cash flow of the
organisation to honour its financial commitments. It is calculated by dividing the
operating cash flow of the organisation by its sales.
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References
Altman, E., 2000. Predicting financial distress of companies: revisiting the Z-score and ZETA
models. Stern School of Business, 4(12), pp. 9-12.
Atanasov, V. & Black, B., 2016. Shock-based causal inference in corporate finance and
accounting research. Critical Finance Review, 5(2), pp. 207-304.
Beaver, W., Correia, M. & McNichols, M., 2011. Financial statement analysis and the prediction
of financial distress. Foundations and Trends® in Accounting, 5(2), pp. 99-173.
Begley, J., Ming, J. & Watts, S., 1996. Bankruptcy classification errors in the 1980s: An
empirical analysis of Altman's and Ohlson's models. Review of accounting Studies, 1(4), pp. 267-
284..
Castelnuovo, E. & Tran, T., 2017. Google it Up! A Google trends-based uncertainty index for
the United States and Australia. Economics Letters, Volume 161, pp. 149-153.
Dang, C., Li, Z. & Yang, C., 2018. Measuring firm size in empirical corporate finance. Journal
of Banking & Finance, 86(2), pp. 159-176.
Fracassi, C., 2016. Corporate finance policies and social networks. Management Science, 63(8),
pp. 2420-2438.
Rocheteau, G., Wright, R. & Zhang, C., 2018. Corporate finance and monetary policy. American
Economic Review, 108(4-5), pp. 1147-86.
Shumway, T., 2001. Forecasting bankruptcy more accurately: A simple hazard model. The
journal of business, 74(1), pp. 101-124.
Walters, A., 2017. United States’ bankruptcy jurisdiction over foreign entities: exorbitant or
congruent?. Journal of Corporate Law Studies, 17(2), pp. 367-404.
Zmijewski, M., 1984. Methodological issues related to the estimation of financial distress
prediction models. Journal of Accounting research, 12(2), pp. 59-82.
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