CREDIT ANALYSIS AND RATIO ANALYSIS OF MSB

QUESTION

Module:   Principles and Application of Credit Analysis – Report 40%

Student Number:    

 

Basis of Assessment

 

Mark

Weighting
%

Presentation and Style

Marker’s comment/s

 

10

Evidence of Reading/Application of Theory

Marker’s comment/s

 

20

Logical Development/Discussion of Approach to Credit Analysis and Mitigation of Risks

Marker’s comment/s

 

30

Demonstrating Understanding

Marker’s comment/s

 

30

Summary or Conclusion

Marker’s comment/s

 

10

 

 Students:  The mark for this assessment will be available via U-learn

 

 

 

General Comments: (Markers are required to complete this section)

Basis for Assessment (to be given to students)

Presentation and Style  (10)

 

Should be clearly written and easy to read. Any tables and figures should be explained. There should be an appropriate structure. The Harvard referencing system based upon the School website guideline should be adopted.

Inappropriate structure, unclear, referencing minimal or inaccurate                            4 or less

Appropriate structure, clear articulation, referencing acceptable                  5 to 6

Appropriate structure, clear articulation, correctly/fully referenced                             7 to 10

 

Evidence of Reading   (20)

 

Students must use referencing to demonstrate that they have read beyond the set textbooks for the module. There should be references to journal articles and specialist texts.

No referencing                                                                                                                           0

Reference to set texts only                                                                                                     1 to 14

Reference to range of books and a number of articles                                                     15 to 20

 

Logic of argument/ Discussion of topic  (30)

 

There should be a critical evaluative discussion of the assignment topic, not simply description. Good assignments will analyse the approaches to credit analysis and risk identification and develop a logical argument.

Wholly descriptive                                                                                                                     8 or less

Mainly descriptive but containing some discussion                                                           9 to 15

Roughly equal amounts of description and critical evaluation                                        16 to 20

Critically evaluative discussion with necessary description                               21 to 30

 

Demonstrating understanding  (30)

 

Students should see the relationships between the parts of the credit analysis and risk identification approaches, what each does and how they interrelate

Includes little awareness                                                                                                         8 or less

Includes some awareness                                                                                                        9 to 15

Includes a reasonable degree of awareness                                                                       16 to 20

Includes  a high degree of awareness                                                                                   21 to 30

 

Summary or Conclusion   (10)

 

Summarises the key points from the assignment and draws them together to demonstrate a good grasp of the literature and the formulation of an informed opinion.

Little reference to body of assignment                                                                                4 or less

Reference to main themes in text                                                                                         5 to 6

Clear drawing together of theory and applied examples                                                  7 to 10

SOLUTION

Discuss with examples how a UK bank might look to mitigate the risks of lending on a secured basis into the mid-cap UK corporate market?

Contents

Introduction. 1

Type of Finance sought by MSBs. 2

General Guidelines for Risk Mitigation. 3

Models and Methods. 4

Study of Financial Statements and Ratio Analysis. 4

Segmentation. 8

Survival Analysis. 9

Data Reduction and Credit Risk Estimation. 10

Credit Analysis and Markov Chain Model 11

Conclusion. 13

 

 

Introduction

Banks are not just mere lenders but channels of money creation and multiplication, when banks undertake appropriate lending decisions the money multiplies for the entire economy and the entire economy grows. Hence banks are channels of macro economic growth.

As per recent trends in UK, as evident from a study undertaken in 1999-2004 on credit analysis of UK companies, number of companies who showed a decrease in credit ratings significantly exceeded the number of companies which showed an increase in the same., as per the article, this was attributed to both factors, that of deterioration of credit quality of UK companies as well as to greater stringency in credit rating process by credit rating agencies.(Gonis&Taylor,2009)

As per article ‘Bank Lines of Credit in Corporate Finance: An Empirical Analysis’ entities with high cash flows tend to seek more loans and external financing as compared to entities with low cash flows. The latter depend more on internal funds for their liquidity requirements. the article also states that lending by banks helps in mitigating capital market frictions by ensuring availability of liquid funds. In the absence of external funds, entities would use their own available funds and that would keep them from allocating their own funds for better alternatives, thus leading to shortage of liquidity in the capital market.(Sufi,2007)

Bank loans are considered most appropriate for companies which want to include loan constituent in their capital structures so as to optimize their capital structures and attain maximum benefits out of their capital structures. For such companies having a loan constituent in their capital structure is better than having no loan constituent. In other words, such companies find the cost of using other sources of funds such as internal funds, equity funds and bond funds, more costly than the cost of loans.

There is evidence that MSBs are very reliant on banks as the source of growth finance, as shown by the graph on this page. A CBI report found that “the reliance of most [mid-sized businesses] on bank lending […] is unsustainable” (Future Champions, CBI, October 2011).As per this source, banks in UK are finding it difficult to sustain the loan requirements of the mid sized businesses of the country.Most UK mid sized businesses have been significantly dependent on bank loans for their finance requirements. Most MSBs prefer bank lending option to asset based or bond based finance. Only around 10% of them opt for asset based finance, whereas only 5% opt for mezzanine or bond based finance. (http://discuss.bis.gov.uk,2011)

Type of Finance sought by MSBs

The graph shows that where external finance is required, MSBs are currently reliant on banks for finance, with bank overdraft, leasing & hire purchase and secured loan or mortgage the most commonly sought forms of finance in 2009 & 2010.

Source: BIS 2010 Finance Survey of Mid-Cap Businesses

General Guidelines for Risk Mitigation

Evaluation of the Loan Proposal: banks evaluate the loan proposals with respect to the applicants’ credibility, projected business returns or cash flows out of which loan repayments will be made, availability of collateral. Evaluation of the proposal also involves the Industry/sector analysis to which the applicant firm belongs. If the industry prospects are good then chances of the loan getting sanctioned increases. As per credit analysts Ganguin and Bellardello, “Credit Analysts should be able to identify the most important industry risk factors. Combined with an understanding of the supportiveness of a country’s laws, regulations and infrastructure, industry risks further mold the possible credit quality of the industry’s participants”.(Ganguin&Billardello,2005)

Banks should procure industry and country data on credit risks, such data can be procured from major rating agencies.

Banks also seek internal information regarding the firm, from its various disclosures and reports. Financial statements are the most important reports in this regard. While applying for loans, firms should provide all relevant information with proper documentation.

 

Matching Loan Requirement with Loan Type: Banks have various categories and subcategories of loans. Matching of loan category with the purpose for which loan is required is essential to derive maximum worth or minimum cost of money.

Monitoring Control and Risk Awareness: banks need to maintain a continuous vigil on the status of repayment of loan of debtors and take action whenever necessary. As such banks may ask borrowers to periodically submit relevant financial reports so as to periodically update their credit scores and determine further action related to loan repayments.

Hedging Credit Risks: banks can hedge their credit risks by purchasing credit derivative or credit insurance instruments.(Amerman,2008)

Differential Loan pricing: Banks may charge a higher interest rate from entities with low credit scores or they can differentiate the interest rates on the basis of credit scores of the applicants. (www.federalreserve.gov,2012)

Diverse Lending: Banks can mitigate risks associated with lending by lending to diverse firms rather than a single type of firms.(Markowitz,1952)

Models and Methods

Study of Financial Statements and Ratio Analysis

In order to assess credit worthiness of companies as well as to assign them credit scores, the most widely used quantitative method is review of the financial reports of the companies and analysis of certain financial ratios. The four basic financial reports are

The Balance Sheet

The Profit and Loss Statement

The Changes in Equity Statement

The Changes in Funds Flow Statement.

The Financial Statements provide useful information regarding the following:

Quantitative Information

  • Financial Structure
  • Solvency
  • Profitability and Cash Flow
  • Changes in cash flow

Qualitative Information

  • Availability and quality of audited financial statements.
  • Depth and skill of management.
  • Position of firm within the industry and future prospects.

Banks also collect additional qualitative information, such as:

  • Past data of debtors pertaining to internal credit
  • The firm identification numbers of debtors (this is provided by Govt. and is unique).
  • The rating of debtors wrt internal risks..
  • The kind of internal credit
  • The proportion of credit sanctioned.
  • Performance of debtors
  • The status of debt repayments
  • Industry codes of debtors

The financial ratios can be calculated from the data provided in the above four basic financial reports.

As per article ‘The Selection Of Financial Ratios As Independent Variables For Credit Risk Assessment’, the various types of credit scoring are

Type of Credit Scoring

Reference

   
Application scoring For assessing the creditworthiness of new applicants.

 

Behavioral scoring For assessing existing debtors.
Collection scoring Categorizing debtors as per different levels of insolvency.

 

Fraud detection Ranking applications wrt to probability of fraudulence.
   

 

Hence we can see from the above table that banks undertake credit scoring for various reasons and in order to be most efficient in their functions they employ several types of credit scoring criteria applicable to different groups of customers.

Banks in order to take most appropriate credit analysis decisions can make use of several quantitative techniques and models, few major ones are mentioned as below:

Model Application Examples
Accounting variables based models To solve the classification problems Linear discriminant analysis, logistic

regression, multivariate adaptive regression splines, classification and regression tree, case based reasoning,

artificial neural networks), linear probability and multivariate conditional probability

models, the recursive partitioning algorithm, multi-criteria decision-making (MCDM)

Market and Macro Economic  Information Based Models To estimate upturns and downturns in the economy and their impact on loan money.  

 

Major Financial Ratios used in Credit Analysis are as follows:

Financial Ratio Significance
Current ratio Liquidity
Total debt to equity Financial Structure
Sales to total assets Activity
Working capital to total assets Liquidity
Total liabilities to total assets Financial Structure
Net profit to total assets Profitability
Equity to total assets Financial Structure
Net Profit to Equity Profitability
Quick Ratio Liquidity
Net Profit Margin Profitability
Inappropriate balance to total assets Other
Working Capital to Sales Other
Gross Profitability Profitability
Current assets to total assets Other
Cash to total assets Other
EBIT to total assets Other, characterize the efficiency of using assets
EBIT to sales Other, indicates the proportion of

sales that is earned as EBIT. The higher this ratio, the better the performance of a company.

Cash to current liabilities Liquidity, indicates the proportion of current liabilities that a company is able to refund by cash immediately
Sales to long term assets Activity, indicates the efficiency of

using long term assets. It is the ability of a company to get more income using less long term assets

Long term debt to equity Financial structure, indicates the relation between these partitions in balance-sheet. Sound financial structures are characterized by high equity and less long term debts.

 

As per the findings of the article in a particular situation (this situation should be assessed for application in UK) “All profitability ratios of default companies are negative one year before bankruptcy. Also the working capital is negative of these companies. The average net profit margin of not default companies is 8.7%. The debt ratio indicates that average debt of

not default companies does not exceed 0.5 of total assets. The analysis highlighted the differences between current ratio and quick ratio. The liquidity is about 3 times higher in group of not default companies”.

 

As per the above article, the author undertook a study of credit worthiness of certain firms.The author found the following regarding defaulting companies:

  • The defaulting companies had negative profitability ratios one year before bankruptcy.
  • They had negative working capital
  • Non defaulting companies had an average net profit margin of 8.7%.
  • The average debt of non default companies did not exceed 0.5 of total assets.
  • Non default companies had three times high liquidity.

(Boguslauskas, Mileris and Adlytė,2011).

Segmentation

Given in article ‘Does segmentation always improve model performance in credit scoring?’-segmentation can be defined as ‘‘the process of identifying homogeneous populations with respect to their predictive relationships’’.

From the above definition we understand that segmentation is categorizing of loan seeking companies into groups having similar or same characteristics. The categorization is done based on certain variables. There is similarity within group members but discernible dissimilarities between the groups.

As per the article, the process of segmentation involves identifying separate populations. Thereafter scoring model is developed by separate treatment of each of these identified populations. This is because the each population is unique with respect to certain characteristics or variables. In the present times segmentation in banking sector is very much in vogue. The factors that are discernible and significant for the purpose of segmentation can be categorized into five broad groups, these are as follows:

  • Marketing Related factors
  • Customer or Customer group related factors
  • Data related factors (for example availability or non availability of certain data)
  • Business process related factors
  • Model or Method related factors (i.e. models or methods most appropriate for data)

The distinct presence of the above mentioned factors can call for developing more than one score cards for credit scoring of the debtors.

Segmentation method is used significantly by UK banks. Segmentation enables banks to develop appropriate methods and strategies for the different customer groups.

In the article it is also mentioned that banks should select segmentation methods/models judiciously. Too much segmentation or too less segmentation can lead to lower performance levels of the banks. Too less segmentation can make the bank miss out on important characteristics that should have been taken into account formulating appropriate strategies and taking appropriate decisions. Too much segmentation can create unnecessary data and complexities and divert the focus of the bank from the matters of relevance.. The methods and appropriateness of segmentation methods are explained in the article. (Bijak & Thomas, 2012)

Survival Analysis

 

In article ‘Mixture cure models in credit scoring: If and when borrowers default’, it is stated that concepts related to a particular type of analysis called Survival Analysis which found original use in medicine, can be applied in credit analysis as well.With the help of this analysis it is possible to construct probabilities regarding entities which can default and when they can default, over a certain time period.As per the article, the way banks all over the world  regulate their capital, has been of recent, much influenced by the regulations provided in the Bassel II Accord. The new regulations laid down in the accord allow banks to:

  • Apply their own rating systems to estimate credit risks and use these estimates.
  • To assess their own requirements pertaining to maintaining a certain level of capital , as a fallback for covering risks of loans.

In order to fulfill the requirements of the accord banks need to focus on both calibration and discrimination aspects of models and not only the latter. Before the Accord, models of credit scoring were developed mainly to accurately rank borrowers with respect to risk.After the introduction of the accord credit scoring models are being  developed so as to also bring out the default probability of the debtors and its accuracy, which is also called calibration of risks.

(Tong,Mues &Thomas, 2012)

 

From the above paragraph, we can understand that the Method of Survival Analysis can enable banks to:

  • Determine their regulatory capital
  • Determine minimum capital requirements to cover the risk associated with lending
  • Rank as well as Calibrate risks.

The method enables banks to predict that which entities are likely to default and when they are likely to default.

 

As per the article multiple scorecards, that is separate scorecard for each segment may not yield better results as compared to single score card method for the entire population of debtors, only when distinct factors are identified segmentation method and multiple score cards can yield better results as compared to single score card system. Hence the main challenge is to discern distinguishable and significant characteristics amongst the population , as per the author “ For a suite of scorecards, it is difficult to choose cut-offs that are independent, good and robust at the same time”.

 

The above paragraph from the article puts forth the significance of segmentation method and techniques and when segmentation can be useful. Banks can develop different scorecards for different segments of debtors in order to assign them credit scores most efficiently. The only issue in segmentation is the identification of distinct and significant characteristics. If these characteristics can be identified and set of distinct score cards can be developed, then the bank can benefit from multi score card system instead of single score card system resulting into fewer refusals of loan applications and better understandability of each segment.

 

Data Reduction and Credit Risk Estimation

The article ‘Data Reduction Influence on the Accuracy of Credit Risk Estimation Models’ is about:

  • Using discriminant analysis, logistic regression and artificial neural networks for analyzing data on debtors.
  • Developing credit risk estimation models based on the data so obtained.
  • Applying the models to categorize debtors into creditworthy and non creditworthy groups.
  • Predicting the future loan servicing behaviors of the debtors.

Application of factor analysis to assess impact of input data reduction upon classification accuracy. As per authors, ‘.As the results of the factor analysis indicated, 15 variables extracted 89.37% of variance from initial variables. That reduced the classification accuracy of models from 2% to 14.6%. Also 6 new variables extracted 63.92% of variance from initial

variables. That reduced the classification accuracy of models from 11% to 15.7%.’ .(Mileris & Boguslauskas, 2010)

 

The above paragraph from the article  describes how relevant data can be selected to develop appropriate credit risk models. Working with enormous and irrelevant amount of data can result into lower performance levels. This can be applied by UK  banks also, after making suitable modifications if required.

The methods applied for data analysis were quantitative and included discriminant analysis, logistic regression and artificial neural networks. By using this method the proposed credit applicants could be classified into creditworthy and non credit worthy groups, their financial characteristics could also be known.

Credit Analysis and Markov Chain Model

As per article ‘How to gauge credit risk: an investigation based on data envelopment analysis and the Markov chain mode’ (ca 4), the concepts and findings put forth in the article can be applied by all types of financial firms. These can be applied to develop effective and proactive tool that can enable the firms to identify defaulters and the actions to be taken against them, at a sufficiently early stage.

Components of Data Envelopment Analysis Method (DEA Method)

  • Chain of analytical methods comprising factor analysis, regression analysis and discriminant analysis.
  • Measurement of  financial efficacy of debtors in a relative manner
  • Observing outputs and inputs of firms
  • Comparison of the outputs and inputs
  • Identification of best practices based on above

The procedure used in the article is as follows:

  • Application of Data Envelopment Analysis (DEA) and Markov Chain Model (MCM) to estimate the risks associated with debts.
  • At the very initial step, the financial data is sorted with reference to dimensions and ratios by applying factor analysis.
  • Next the credibility scores of debtors are worked out by the application of DEA.
  • The credibility scores so obtained are checked by the application of regression analysis and discriminant analysis.
  • Next the validated credibility scores are applied to Markov Chain Model.
  • Lastly, transition matrices are constructed

Advantages of the Method

  • The transition process related to the financial efficiency of debtors can be obtained
  • Simple and easy to use
  • Enables better and detailed monitoring of credit risks.
  • Enables better lending decisions.
  • Enables compliance with the Bassel Capital Accord
  • Uses ‘ex-post’ information as compared to other methods like logistic regression analysis and neural networks which use ‘ex- ante’ information.
  • Construction of a single financial credibility score based on the overall performance of the debtors.
  • Provides a multidimensional measure of financial efficacy.
  • Can be applied to estimate credit risks of different types of firms.

 

The article states that though the study was conducted in Taiwan, the method can be applicable universally. The article also mentions that most of the Taiwanese firms failed to score sufficiently with respect to credit scoring. Besides classification this method addresses the change process of the financial efficiency of firms. With the help of this method banks can regularly scrutinize their credit risks in a detailed manner. ( Lua, Lee & Zou,2012)

Conclusion

Banks deal with a large number of customers and apply a mix of quantitative and qualitative measures for assessing credit worthiness of its clients. They have to select models, methods, policies and rates such as to enable continuous money multiplication, they must not lend to possible defaulters and at the same time they must also not stifle promising ventures and projects by withholding loans from deserving clients. Banks assign credit scores to the applicants based on their data and analysis regarding country, industry, financial ratios, past repayment records. .Banks use credit scores to establish priorities. This the banks may do to maximize their own efficiencies and define and maintain their focus as they do not have unlimited funds for lending. The credit scores are not fixed but are periodically updated Here it should also be mentioned that the decision to sanction loans may also be determined by the banks capacity and demand for loan. If the demand for loan is in significant excess in comparison to the banks capacity, then the bank may decide to contract its lending activities and apply stringent procedures and methods.. This is being increasingly realized in contemporary times in the UK, demand for loans are far exceeding the capacity of banks to lend and hence the Government is planning to introduce other external financing alternatives for the mid cap firms.

 

 

References

Ganguin, B, Bilardello, J., 2005. Fundamentals of Corporate Credit Analysis.  New York : McGraw-Hill Professional.

Amerman, D.R., 2008.AIG’s Dangerous Collapse- A Credit Derivatives Risk Primer. [Online] Available at: http://www.financialsensearchive.com/fsu/editorials/amerman/2008/0917.html

[Accessed 26 April 2012].

Federal Reserve Board, U.S.A, 2012. Agencies Issue Proposed Rules on Risk-Based Pricing Notices [Online] Available at: http://www.federalreserve.gov/newsevents/press/bcreg/20080508a.htm  [Accessed 26 April 2012].

Markowitz, H.M., 1952. “Portfolio Selection”. The Journal of Finance 7 (1): 77–91. Abstract only. Available through: http://www.jstor.org/discover/10.2307/2975974?uid=3738256&uid=2&uid=4&sid=21100745731141   [Accessed 26 April, 2012].

Ricardas Mileris, Vytautas Boguslauskas, 2010. Data Reduction Influence on the Accuracy of Credit Risk Estimation Models. Economics of Engineering Decisions [e-journal] 21(1). Available through Business Source Complete database [Accessed 9 April 2012].

 

Amir Sufi, 2007. Bank Lines of Credit in Corporate Finance:An Empirical Analysis. The Review of Financial Studies [e-journal] 22(3). Available through Business Source Complete database [Accessed 9 April 2012].

 

Su-Lien Lua,,Kuo-Jung Lee and Ming-Lun Zou,2012. How to gauge credit risk: an

investigation based on data envelopment analysis and the Markov chain model. Applied Financial Economics [e-journal] 22. Available through Business Source Complete database [Accessed 8 April 2012].

 

Katarzyna Bijak , Lyn C. Thomas,2012. Does segmentation always improve model performance in credit scoring? Expert Systems with Applications [e-journal] 39.Available through Science Direct database [Accessed 8 April 2012].

 

Edward N.C. Tong , Christophe Mues, Lyn C. Thomas,2012. Mixture cure models in credit scoring: If and when borrowers default. European Journal of Operational Research [e-journal] 218. Available through Science Direct database [Accessed 8 April 2012].

 

Vytautas Boguslauskas, Ričardas Mileris, Rūta Adlytė,2011. The Selection Of Financial Ratios As Independent Variables For Credit Risk Assessment. Economics And Management

[e-journal] 16. Available through Business Source Complete database [Accessed 9 April 2012].

 

Eleimon Gonis and Peter Taylor,2009. Changing credit rating standards in the UK: empirical evidence from 1999 to 2004..Applied Financial Economics [e-journal] 19. Available through Econlit database [Accessed 9 April 2012].

 

http://discuss.bis.gov.uk,2011. Supporting Access to Finance for Investment and Growth[Online] Available at: http://discuss.bis.gov.uk/midsizereview/investment-for-growth/ [Accessed 7 April 2012].

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