Financial Statement Analysis for Bankruptcy Prediction

Question: Discuss about the Financial Statement Analysis for Bankruptcy Prediction. Answer: Introduction The topic selected for the study is discussing on whether corporate require other approaches than financial statement analysis for bankruptcy prediction in any form (Jones 2016). Addition to that, it is understood that Financial Statement Analysis has been considered as one of the major instrument that is used for predicting the probability of bankruptcy in business firms. Hence, it is noted that due some of the significant corporate scandals, financial statements are not considered as one of the reliable way for estimating the default or bankruptcy risk in a firm. This study has been conducted for bringing out the relevant journal articles that links with the selected topic and discuss on the usefulness of other approaches apart from financial statement analysis for predicting the probability of bankruptcy of firms as a whole (Geng, Bose and Chen 2015). Main objectives of the report The main objective of the report is to suggest other approaches apart from financial statement analysis for predicting the bankruptcy of the firms. Summary of finings from the research undertaken research This research report has been undertaken for finding out the approaches other than financial statement analysis so that it can predict the bankruptcy risks that are faced by business firms. Secondary sources of data are used for conducting the research such as review of academic journals that links with the current topic of discussion. This research reviews six academic journals that give an overview of explanation as to which approaches to be used for predicting the bankruptcy of the firms (Jones, Johnstone and Wilson 2015). Discussing the approaches that should be used by Corporate for bankruptcy prediction As rightly put forward by Zhou (2013), Bankruptcy Prediction is the art used for predicting the bankruptcy as well as various measures undertaken due to financial distress of Corporate. This area of research is for creditors and investors who have evaluated the likelihood that a firm may go bankrupt. Addition to that, bankruptcy prediction mainly takes into consideration the use of various statistical tools that are made available and include deepening appreciation of several pitfalls in the previous analysis (Karas and ReÃ… ¾?kov 2014). In the recent accounting research area, Corporate should use survival methods in case they face any corporate scandals from their financial statement accounts that lead to the condition of bankruptcy (Liang, Tsai and Wu 2015). In other words, option valuation approaches help in understanding the stock price variability for development purpose. Business Corporate should use structural models as it will detect the default events that took place in the firm when their assets reaches sufficiently lower level in comparison with the liabilities. Neutral methods models as well as other sophisticated models are used for testing the bankruptcy prediction so that Corporate can minimize their risks as far as possible. It is necessary for the Business Corporate in using the modern methods as it will help most of the business information companies as they cam surpass the annual accounts content as well as consider the current events such as bad press, payment incidents, bad payment experiences fr om the creditors and judgments (Lin et al. 2014). As per the latest research, Prediction majorly compares various approaches such as modeling technique and individuals as it ascertain whether any one of the technique is superior to the other counterparts. Zhou (2013) provides an excellent discussion of the literature that takes into consideration the empirical evaluation of various models from the existing literature. The mentioned models range from the univariate models of Beaver and continue with the recent techniques that involves option valuation approaches. It is noted by the researcher that models are based on market data like option valuation approach that majorly outperforms than the earlier models as it relies more on accounting numbers (Sun et al. 2014). Review of Journal Articles The first journal article is taken from Expert Systems with Applications and titled as Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring (Abelln and Mantas 2014). This article is useful as it ensembles the schemes for complex clarifiers as it is applied. From the previous works, it highlights about the datasets on bankruptcy prediction as well as credit scoring. The proposal used will be a simpler as well as brings improvement in the complex procedures. According to previous studies, it is understood that classifiers for bankruptcy forecast as well as recognition scoring are obtainable in the current scenario. The best consequences are even obtained by making use of Random Subspace methods. In addition to that, Bagging Scheme is used for the methods for comparison purpose. It is essential for using the collection system on weak as well as unbalanced classifiers for producing assortment after combining the factors associated. Co mparison can be improved by using Bagging scheme for various decision trees as it encourages diversification for the grouping of classifiers. Therefore, an experiment is conducted that highlights the Bagging Scheme on decision trees that yield best consequences for bankruptcy calculation as well as credit scoring at the same time (Geng, Bose and Chen 2015). The second journal article is taken from Journal of International Financial Management and Accounting and titled as Financial Distress Prediction in an International context: A review and Empirical analysis of Altmans Z-Score Model (Altman et al. 2016). From this article, it is noted that categorization presentation of Z-Score Model are used for predicting the insolvency as well as other types of firm distress. They have a common goal for examining the models usefulness to all the relevant parties in case of banks as they operating internationally and require assessing the failure risk firms. It is analyzed that use of Z-Score Model help firms in bringing modifications to the original model. The article majorly offers various comprehensive international analyses for the future research purpose. There are some evidence noted where Z-Score Model of bankruptcy forecast can be outperformed by competing market-based or hazard model. It is analyzed that Z-Score model works well in most of the countries as the predication accuracy is 0.75 as well as classification of correctness can be enhanced above 0.90 by making use of country-specific judgment that incorporates supplementary variables. The third article is taken from Journal of Banking Finance and titled as Are Hazard models superior to traditional bankruptcy prediction approaches? (Bauer and Agarwal 2014) This article takes into consideration the recent year hazard by making use of both market as well as accounting information that has become state of the art in predicting the firm bankruptcies. Addition to that, a comprehensive test will help in judging the level of performance by comparing it with the conventional accounting based approach or the conditional claims advance. Therefore, use of mixed regime competitive loan market by taking various costs for clarification purpose. This can be done by viewing at the economic benefit when the presentation can be judged with risk return weighted possessions (Geng, Bose and Chen 2015). The forth journal article is taken from Expert Systems with Applications and titled as An improved boosting based on feature selection for corporate bankruptcy prediction (Wang, Ma and Yang 2014). This article highlights the fact that there is no overall best technique that can be used for predicting the corporate bankruptcy. Addition to that, new and enhanced Boosting known as FS-Boosting has been planned for predicting the corporate bankruptcy. Selected datasets will help in demonstrating the effectiveness as well as feasibility of FS-Boosting. It depends on the experimental results that reveal the fact that FS-Boosting can be used as an alternative technique. From the current financial crisis and European debt crisis, it is noted that Corporate Bankruptcy Prediction had become one of the vital issue especially for the financial institutions. Most of the statistical and intelligent method is used for predicting the commercial bankruptcy. The article suggests use of FS-Boosting as i t gets better performance as base learners that will give more correctness and assortment. It is used for testing purpose by taking two real world bankruptcy datasets as it demonstrates the effectiveness as well as feasibility of FS-Boosting. Therefore, the consequences signify the FS-Boosting can be effectively used as an alternative technique for business bankruptcy prediction. The fifth journal article is taken from Knowledge-based systems and titled as Performance of corporate bankruptcy prediction models on imbalanced dataset: The effect of sampling methods (Zhou 2013). This article explains that Corporate Bankruptcy Prediction is essential for the creditors as well as investors. Researcher are of the opinion that there can be improvement in level of performance by using prediction models after developing as well as optimizing the quantitative methods. It will clearly highlight the consequence of sample methods that are used on the presentation of quantitative bankruptcy models on real excessive dataset. It is necessary for making the contrast of model presentation by testing on random balancing sample set as well as real imbalanced sample that is conducted. Therefore, the experiments suggest ways that correct sample methods help in increasing prediction model that is majorly reliant upon number of bankruptcies in the preparation sample set. The sixth article is taken from Knowledge-Based Systems and titled as Predicting financial distress and corporate failure: A review from the state of the art definitions, modeling, sampling and featuring approaches (Sun et al. 2014). In this article, it is explained that bankruptcy prediction plays major role at the time of decision-making in various areas such as accounting, finance and engineering. Literature on Financial Distress Prediction reviews from various unique aspects where it introduces the sampling approaches. It is used by qualitative selection as well as combination of qualitative and quantitative selection for future analysis purpose (Geng, Bose and Chen 2015). Conclusion From the above analysis, it is easy to predict the fact that financial statement analysis got through some loopholes in the recent times that make the approach not feasible for predicting the bankruptcy risks of a firm. It was understood from the data present that earlier financial statement analysis was considered as proper instrument that used to be predicting the probability of bankruptcy. After the emergence of several corporate scandals, it was not essential for finding out other approaches that best suit for predicting the bankruptcy in a business firm. The above study had reviewed six academic journal articles that help in identifying several approaches that can be used for predicting the bankruptcy risks in a business firm. References Abelln, 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., 2016. Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman's Z?Score Model. Journal of International Financial Management Accounting. Bauer, J. and Agarwal, V., 2014. Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test. Journal of Banking Finance, 40, pp.432-442. Geng, R., Bose, I. and Chen, X., 2015. Prediction of financial distress: An empirical study of listed Chinese companies using data mining. European Journal of Operational Research, 241(1), pp.236-247. Jones, F.L., 2016. Current techniques in bankruptcy prediction. Journal of accounting Literature, 6(1), pp.131-164. Jones, S., Johnstone, D. and Wilson, R., 2015. An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes. Journal of Banking Finance, 56, pp.72-85. Karas, M. and ReÃ… ¾?kov, M., 2014. A parametric or nonparametric approach for creating a new bankruptcy prediction model: The Evidence from the Czech Republic. International Journal of Mathematical Models and Methods in Applied Sciences, 8(1), pp.214-223. Liang, D., Tsai, C.F. and Wu, H.T., 2015. The effect of feature selection on financial distress prediction. Knowledge-Based Systems, 73, pp.289-297. Lin, F., Liang, D., Yeh, C.C. and Huang, J.C., 2014. Novel feature selection methods to financial distress prediction. Expert Systems with Applications, 41(5), pp.2472-2483. 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. Zhou, L., 2013. Performance of corporate bankruptcy prediction models on imbalanced dataset: The effect of sampling methods. Knowledge-Based Systems, 41, pp.16-25.