Evaluation Of Clustering Algorithms Essay


Discuss about the Evaluation of Clustering Algorithms.



The following study incorporates a discussion on the needs of corporate for approaches other than the financial statements to predict the bankruptcy prediction. Use of financial statements has been a primary instrument to predict the bankruptcy probability but in recent years, financial statements are not considered as reliable source to estimate the bankruptcy risk due to several corporate scandals. According to the principles of accounting framework, financial statements are required to be presented by the companies to evaluate the financial performance and position for the use of stakeholders. However, in the present years, it has been noted that several companies prepare and present financial statements that provides misleading information and unfair result on financial performance. Accordingly, the source of financial statements could not be considered as reliable one to evaluate the company’s true financial position. Hence, the present assignment involves evaluation of reliable approaches to analyze the bankruptcy prediction for the business organizations.

Prediction of bankruptcy is an art to measure and predict the financial distress of the companies that consists of financial research as well as accounting research. In order to predict the probability of bankruptcy, it is essential to evaluate the value of creditors as well as investors since the factors involve the amounts due to be paid by the companies. Accordingly, financial statement has been a primary tool used to analyze the financial position of the companies together with the amount of loans, borrowings and liabilities as well as the duration of loan payments due (Sun et al. 2014). Moreover, it is important to provide true and material information in the financial statement about the financial transactions and other relevant information so that the correct financial position can be determined. On the contrary, several companies fail to comply the regulations of accounting principles and represent fraud and erroneous financial information that does not present true financial position (Yu et al. 2014). Altman et al. (2014) stated that due to several corporate scandals that involved large amount of conspiracy presented material misstatements and erroneous information in the financial statements hence; financial statements cannot be considered as a reliable source. In case of Lehman Brothers, the organizational collapse occurred due to erroneous and fraud valuation presented in the statement of financial position with respect to the valuation of assets, which was over $600 billion (Gambacorta and Mistrulli 2014).

One of the significant instruments has been financial ratio analysis to predict the probability of organizational bankruptcy that incorporates the ratio on financial leverage, liquidity as well as profitability (Kou, Peng and Wang 2014). However, the analysis of financial ratio component also depends on the information presented in the financial statement hence reflect unreliable result if the income statement or balance sheet is not correctly reported. Collapse of Bear Sterns is another investment bank, which failed during the year of financial crisis while the large amount loss and the financial statement did not reflect appropriate information of the illiquid assets. Accordingly, the prediction of bankruptcy probability cannot be reliably measured by analyzing financial statements of the company (Iturriaga and Sanz 2015).

Accordingly, there are certain approaches that can be used by the companies other than the financial statement analysis that provides reliable results to identify the corporate defaults and the financial status. The financial status of the company can be identified by option valuation method, which involves variability of the stock price and determines the volatility as well as fair pricing of the company’s shares in the current market scenario (Gambacorta and Mistrulli 2014). This approach is assists in identifying the correct pricing structure of the company’s stock and underlying assets by monitoring the potential risk associated with the underlying stock price, interest rates and dividend value. However, Steele (2014) contended that the approach of option value incorporates several assumptions and forecasts to determine the value of implied volatility and market risks which may not predict accurate theoretical vale of the shares at a given point of time.

Statistical techniques is an approach that predicts the financial distress of the company that associates with the matrix related to unique co- variance for each class of data by using the co- variance matrix (Abell?n and Mantas 2014). This approach includes assessment of financial distress by using “small sample size to evaluate the existing problems of the company related to the internal controls and valuation of resources. On the contrary, Lee and Choi (2013) stated that statistical approach is applied on the basis of inherent assumptions on linearity or independence which could not reflect the appropriate result for the bankruptcy prediction. Bankruptcy prediction is essential to measure the company’s ability to repay the liabilities and loans together with the evaluation of potential “risks return trade off from the investments”. Therefore, organizations are required to apply the most reliable and efficient approach to determine the financial distress free from all material misstatements and errors (Iturriaga and Sanz 2015). It has been observed that collapse of Lehman Brothers occurred due to fraud and erroneous financial information incorporated by the company, which was not identified by the independent auditors. As a result, money lenders, investors and other financial institutions could not evaluate the actual financial position as the organization presented overvalued assets and indebted on huge amount of loans hence filed for bankruptcy due to default in payments (Kou, Peng and Wang 2014).

Therefore, other than financial statements analysis, approach of intelligent technique can be used by the corporates to measure the true and transparent financial position of the business. Intelligent technique incorporates elements of decisions trees to determine the significant area, which solves the problems of classification (Wang, Ma and Yang 2014). Decision tree is an analysis, which consists of developed algorithms and reflects accurate results from identified samples. The system is used by using several applications in service of finance to evaluate the level of bankruptcy based on the organizational actual performance. Use of intelligent technique provides improved and accurate information on the level of company’s finances based on its capacity, which detects the company’s efficiency to generate the profitability and cash flow to maintain the business liquidity (Serrano-Cinca and Guti?Rrez-Nieto 2013). Several organizations use independent variable, which is strongly associated with the dependent variable based on a specific criterion so that the correct level of financial distress is obtained. In addition, this approach use the index based on gain- ratio which is a method used to measure the financial attributes in different segments of the business organizations hence the obtained result considers less impact from the segmentation downside. While analyzing the sample size in different segments, various trials are considered and in each of the trial segment, new decision tree is been assembled which provides accurate result of bankruptcy prediction of the companies (Delen, Kuzey and Uyar 2013). Therefore, the companies are required to consider intelligent approach to measure the bankruptcy prediction other than the analysis of financial statements which can be manipulated and reflect misstated information on financial performance and position.


In view of the above discussion, it can be said the prediction of financial position is essential for the purpose of investment, lending loans and advances as well as business deals in credit policy. It has been observed that financial statements do not reflect true and reliable information on company’s liquidity position due to corporate scandals and material misstatements. Accordingly, several other methods and techniques have been considered by the corporate that determines accurate level of financial distress of the companies. One such method is option value approach, which evaluates the fair value of company’s shares by considering interest rate, dividend amount or rate of inflation. However, intelligent technique is mostly applied by the corporate that incorporates decision trees to determine the level of financial distress and underlying price within the organization. This approach provides accurate result for bankruptcy prediction since it involves independent variables strongly connected with the dependent variables. The intelligent technique helps in evaluating the accurate financial level in different segments since it consists of several trials and each trial incorporates the construction of new decision tree analysis. Therefore, companies are recommended to implement other approaches other than financial statement analysis to determine the bankruptcy prediction.

Reference List

Abell?n, J. and Mantas, C.J., 2014. Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring. Expert Systems with Applications, 41(8), pp.3825-3830.

Altman, E.I., Iwanicz-Drozdowska, M., Laitinen, E.K. and Suvas, A., 2014. Distressed Firm and Bankruptcy Prediction in an International Context: A Review and Empirical Analysis of Altman's Z-Score Model.

Delen, D., Kuzey, C. and Uyar, A., 2013. Measuring firm performance using financial ratios: A decision tree approach. Expert Systems with Applications, 40(10), pp.3970-3983.

Gambacorta, L. and Mistrulli, P.E., 2014. Bank heterogeneity and interest rate setting: what lessons have we learned since Lehman Brothers?. Journal of Money, Credit and Banking, 46(4), pp.753-778.

Iturriaga, F.J.L. and Sanz, I.P., 2015. Bankruptcy visualization and prediction using neural networks: A study of US commercial banks. Expert Systems with applications, 42(6), pp.2857-2869.

Kou, G., Peng, Y. and Wang, G., 2014. Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences, 275, pp.1-12.

Lee, S. and Choi, W.S., 2013.A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis. Expert Systems with Applications, 40(8), pp.2941-2946.

Serrano-Cinca, C. and Guti?Rrez-Nieto, B., 2013.Partial least square discriminant analysis for bankruptcy prediction. Decision Support Systems, 54(3), pp.1245-1255.

Steele, S., 2014. The collapse of Lehman brothers and derivative disputes: The relevance of bankruptcy cultures to roles for courts and attitudes of judges. Law Context: A Socio-Legal J., 30, p.51.

Sun, J., Li, H., Huang, Q.H. and He, K.Y., 2014.Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, pp.41-56.

Wang, G., Ma, J. and Yang, S., 2014. An improved boosting based on feature selection for corporate bankruptcy prediction. Expert Systems with Applications, 41(5), pp.2353-2361.

Yu, Q., Miche, Y., S?verin, E. and Lendasse, A., 2014.Bankruptcy prediction using extreme learning machine and financial expertise. Neurocomputing, 128, pp.296-302.

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