Predictions of Corporate Failures - Essay Example

Predictions of corporate failures Contents Contents 2 Introduction 3 Methodologies and assessment 4 Empirical evidences and assessment 7 Case 7 Case 2 8 Conclusion 10 References 11 Introduction The economic effect of corporate failure is heavy. This is especially stakeholders of public-held organizations. Before a corporate fails, the firm’s financial signs are regularly in distress. It is very important to finding a method to locate corporate financial distress as early as possible. It is a serious matter related to investors, auditors, creditors, and other stakeholders. The importance of this issue has resulted in a lot of research for the prediction of corporate failures or financial distress. Whatever maybe the size of the company and reputation of the company may be but every company has a serious threat of insolvency. Corporate failures gained higher rates in last two decades. There are lots of factors that lead businesses to fail. Those factors attributed by different economists are high interest rates, heavy debt burdens and recession-squeezed profits. There are lots of industry-specific factors such as government regulation and the nature of operations, can results into to a firm’s financial failures. Studies have found that small, private and newly opened companies with poor cash flow planning and ineffective controlling systems are more exposed to financial crisis than the large and well-established firms. It is very important to have robust and reliable models that predict corporate failure accurately and promptly. It is very important for the management to take either preventive or corrective measure to secure the interest of all the stakeholders. This study would go to discuss about different models for preventing corporate failures and the study would assess the strengths and weaknesses of those models. Financial satiability is very important for any corporate house. Corporate houses are having lots of stakeholders attached with it. It is also an important duty of Government and Financial regulating authorities to keep a close look on the financial health of firm. There are lots of models are there to take preventive actions but it is very important to chose the right one for the right problem. Every model has its own strength and weaknesses. Identification of the proper model for the specific crisis is very important things to follow for any corporate house to avoid corporate failures. Failure of one firm can cause for a serious disaster for all those stake holders related with that firm. Methodologies and assessment According to Beaver (1966), who used classification test to identify different financial ratios for corporate failure predictions. Author used 30 financial ratios and 79 pairs of companies for test purposes. The best measuring factor was the working debt and equity ratio. The model has correctly identified 90 percent of the firms one year ago before to failure. After that the second best influencing factor was the net income/total assets ratio, which had 88 percent success rate. After that the use of univariate models reduces and uses of multivariate models increased (Quinlan, 1979, pp. 168-201). Beaver first attempted to forecast corporate failure and his study is considered as a pioneer in this field. Beaver’s approach was that each ratio was evaluated in terms of how it could be used alone to predict failures without consideration of the other ratios. According to Altman (1968), to develop a multivariate statistical model to discriminate failure from non-failure firms is very important. Author used multivariate discriminate analysis (MDA) in that case author took a sample of 33 out of 66 firms in the two clusters each. Altman used five financial ratios in his MDA model were earnings before interest and tax to total assets, working capital to total assets, sales to total assets, market value of equity to book value of total liabilities and retained earnings to total assets. The model was very accurate in classification of 95% of the total sample correctly one year prior to failure (Zopounidis and Dimitras.1999, pp. 124-127). He tried to improve Beaver’s model by applying multivariate linear discriminate analysis (LDA). The method has certain limitations. Altman Misclassification of failed firms increased significantly as the prediction time increased (28% at –2 years, 52% at –3 years, 71% at –4 years). According to Ohlson (1980), who used a different log it model to guess corporate failure with a huge sample of 105 defaulted firms and 2,058 performing firms, he used nine financial ratios included in his model were the firm size, working capital/total assets, total liabilities/total assets, current liabilities/current assets, net income/total assets, a dummy variable implying whether the total value of assets were higher or lower than the total value of liabilities, funds from operation to total liabilities, one more artificial variable mentioning whether the net earning was negative i.e. loss for the past two years and changes in net income. He followed an impartial sampling procedure as ratio of failure and no failure was practical and realistic in nature (Ball and Brown, 1968, pp. 159–178). Ohlson However, the model did not perform as well as MDA, which suggested that previous researchers might have overstated the discriminatory power of their models. According to Zmijewski (1984), he did it into different way. He used the probate model on six sets of data where the ratio of failure/non-failure varied from 1:1 to 1:20 different from Contrary to the common 1:1 failure/non-failure formula. The results showed that the choice-based sample bias reduced as the failure/non-failure ratio went for the probability of population. The result of the study showed huge biases. The results of two cases did not indicate prominent changes in terms overall classifications and predictions (Barber, and Lyon, 1997, pp. 341–372). The Logit model does not have guesses for a prior probabilities and distribution of indicators. According to Kumar and Ganesalingam (2001), Seventy-one companies predicting financial distress among a selection of major Australian companies were taken into consideration for analysis to determine various facts about that organization. Long-term stability is most important thing. Among this one fact is common each company becoming bankrupt and those companies has been classified into distinct groups based upon their financial ratios. According to the author this kind of group would help investors to diversify its portfolio in terms of risk and return trade off (Bernard, and Thomas, 1989, pp. 1-36). According to Edminister (1972), MDA statistical technique is designed to discriminate among loss and non loss borrowers. This analysis has used MDA model with seven different financial ratios. Accuracy of Edministers model was 93%, and model error was 7%. It is very clear from the research also that model’s predictive power depends on the approach of ratios calculation. This technique was done in consideration with industry average (Jegadeesh, and Titman, 1993, pp. 65-91). According to Lugovskaja (2009), MDA model resulted with finding that six variables were important for bankruptcy prediction: cash/current liabilities, current liabilities/total assets, current assets/current liabilities, (cash + short term debtors)/current liabilities, ROA and cash/ total assets. The second MDA model suggests financial ratios and non-financial variables both are very important. Model with only financial ratios had classification accuracy for sampling is 76.2%, Model with non financial ratios having accuracy rate of 77% which is higher than the only financial ratios (Kothari, Sabino and Zach, 2005, pp. 129-161). According to Zenzerović (2009), he took stratified sample size and industry of 55 stabile and 55 unstable companies. According to author definition of unstable companies includes such companies which went insolvency procedures. Author used MDA in which 5 variables were very important discriminators. The total model accuracy was 95.3%. The testing was totally done on the basis of estimated samples (Kraft, Leone, and Wasley, 2006. pp. 297-339). Every methodology like DA, MDA and LA are not limitations free. All these methods over the years are used extensively for predicting corporate failures. Many modern methods are there but those above written methods are very basics and extensively used by researchers. Empirical evidences and assessment In this section different methodologies based on empirical evidences are discussed and assessed. Case 1 Empirical evidences related to UK corporate failure can be good empirical case. It is one of the major global economic markets (Beaver, 1967, pp. 71-111). The London Stock Exchange practises lots of share transaction on a huge volume and competes very well with other stock exchanges like US stock exchange Tokyo stock exchange and European stock markets. That is why, it provides suitable environment for using statistical methods for the prediction of corporate failures. The main objective of this Empirical study is to develop and test of insolvency prediction models for UK public industrial firms. The study consists of 51 failed and non failed public industrial firms from UK. First of all three financial variables i.e. financial leverage, operating cash flow and profitability those three variables were also used for the development of alternative prediction models (Shumway, 1997, pp. 327-340). A predicting test was done to check the models developed with the two methods. Log it model came out with great prediction outcomes regarding the UK‘s corporate failures. Predictions had good accuracy levels even before one and two years. This also shows that operating cash flows are very important for predicting models (Deakin, 1972, pp. 167-179). This adds significant complexity to the process. As the complex procedure and sample size this model can be less accurate for the purpose. This model is very useful in case of modelling of populations. This model is different than the model given by Altman. This model can be divided into two parts nested Logit model and blocked Logit model. This model is dependent on variable and this model can be used in case of discrete function. In that case the model is over dependent on set of discrete numbers (Shumway, and Warther, 1999, pp. 2361-2389). This method cannot be used in continuous variable. Often large number of sample is not good for analysis. It can make the study more and more complex. This model assumes that the dependent and independent variables are linear in nature. This assumption may not be right in every case. This method has both advantages and disadvantages. Using this model for the proper case is very important (Kraft, Leone, and Wasley, 2006, pp. 297-339). Improper implementation of the model will result failure in predictions. Empirical studies are the best judges for the effectiveness of any particular method. This study has clearly shown different positives and negatives of the method. This empirical study has shown a suitable usage of Log it model for the purpose of predicting the failure of UK public holding companies (Ruud, Frederikslust and Ang, 2008, pp. 24-27). Case 2 In between 2000-2005 lots of Croatian companies were defaulting. Very less numbers of companies were able to solve the problems (Hair, Black, Babin, and Anderson, 2010, pp. 132-157). As the different companies fails to predicts their failures they were defaulting in a high speed. Companies were starting their restructuring process too late to gain anything. The main objective of this Empirical study is to come up with a restructuring model to avoid bankruptcy. This model would be developed by using publicly available information. For this model identification of bankrupted companies are needed those can be collected from different websites (Lugovskaja, 2009, pp. 301-313). The model was done for a fix period of time. 78 companies were taken into consideration for the study. It is very important to select different financial ratios which would explain different probabilities related to bankruptcy. All important ratios related with liquidity, profitability, financial structure, activity, and cash flow are taken into consideration. From the financial statements 15 financial ratios were taken for this model of research. Discriminate analysis i.e. DA method is being used (Bernard, and Thomas, 1990, pp. 305-310). Use of DA has shown that this model is having lots of limitations as two very important assumptions are not right. Equality and Data normality were missing. This model has used EBITA, leverage and current liability can be used for prediction purpose of bankruptcy (Crosson and Needles, 2010, pp. 123-125). This model is having lower accurate predicting capacity (Kraft, Leone and Wasley, 2006, pp. 297-239). This model is older than Log it model. This method is having multiple dependent variables it is an advantage of the method. This method is having lower error rate. This method is very simple to analyse. There is lots of limitation with these methods (Bhargava, Dubelaar, and Scott, 1998, pp. 105-117). This analysis is based on certain assumptions. Assumptions are very important to give these analysis right results. This method is extremely sensitive in nature. This technique has lack of a variety in terms of measuring continuous variables and samples. This technique is not suitable for all kind of analysis. This method can give strong guessing power to managers (Sudi, 2003, pp. 123-126). This technique is very much popular with banking industry (Thomas, and Zhang, 2002, pp. 163-187). There are lots of other methods are there. This method is having its own benefit to attract management for applying this method. This method can play a very important role in predicting the corporate failures (Sloan, 1996, pp. 289-315). No method is free from limitations this methods is also having some above discussed limitations. Empirical study is the best way to show the effectiveness of any methods. This empirical study shows the advantages and disadvantages of this method. This method can be divided into three categories (Mashruwala, Rajgopal and Shevlin, 2006, pp. 1-2). Here in this case DA was use very suitably for predicting bankruptcy of the Croatian companies. Conclusion Any business is very venerable in nature. The business world is ever changing. Change is the only constant thing in this world. There is no scope of complacency at any level. This world is full of risks and rough roads. Prediction of corporate failure is very important to analyse the modern situation of any company. Motoring is very much important for prediction of corporate failures. Company able to predicts its failure early can take preventive measures. Company cannot take any preventive measures and can be declared as bankrupt if it recognizes the problem very late. This kind of predictions can be very fruitful for all the stakeholders related with the company. There are lots of investments and hope is related with the companies. Insolvency of the company can cause huge economical disaster. Lots of direct and indirect effects would create a very adverse situation for the economy. This kind of corporate prediction measures are very important for any company. There are lots of methods are present. Choosing the right method for right purpose is also very important. Every method is having its advantages and limitations. Corporate prediction would give clear picture to its manager about the financial health of the company. Management uses that prediction as a guideline for taking proper steps. Often management act according to those predictions. It is very important for any management to take or adjust any decisions. 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