out according to the power of data mining and artificial intelligence in processing large databases and finding complex patterns and non-linear in that, a lot of research by using data mining has been done in various fields. Meanwhile, the financial decisions and vocation judgment in accounting and auditing because of the nature of the turbulent influencing variables have been suitable for using tools for discovery and data mining , so that this modeling by data mining technique in the world is as one of the most versatile items in the financial sphere . Therefore, this study aimed to determine the effectiveness of the Altmanmodel data mining to predict the bankruptcy companies listed on the Stock Exchange of Tehran in order to prevent capital losses through with timely alerts to investors, shareholders and companies. The research question in this case, "Which one of two models of data mining models to prediction bankruptcy Altman is more credible.
In case of the active companies, the observed assumption about poor financial management is disproved in both, the Z’-Score model as well as Z’’EM-Score. Return on equity of the monitored active enterprises represented a significant variable, which represents a critical element of successful business and avoidance of bankruptcy. In the Z’’EM-Score model there was intentionally left a significant explanatory variable ROE_bancrup, which specifically represents a competitive element in the industry and therefore it influences the overall rating. Based on the sequential elimination, it was decided to keep return on equity, which also significantly explained variability of the Z’’EM-Score. Within modelling, there were used data from own calculation of the Z’-Score and Z’’EM-Score per the set as a whole and at the same time there was carried out the comparison of results in calculations of the Z’-Score and Z’’EM-Score per individual enterprises. The outputs of the comparison are almost identical results within the conducted modelling.
The design of a credit risk measurement model in the monetary and banking system will play an important role in increasing the profitability of banking resources. This article attempts to use two models of Logit and Z Altman to determine and predict the credit risk of facilities provided to legal entities at a private bank in Iran. The variables studied in this research include qualitative variables (company life, financial credit document, experience of managers, type of company) and financial variables (working capital in total assets, book value of equity to book value of debt, total sales to total assets, accumulated profits to total assets, profit before interest and taxes on total assets). The results of this research show that the use of validation models, despite all the technical and statistical considerations, can accurately determine the credit status and credit risk of customers. Both models used more than 80% of the correct predictions, which are a significant figure in the real business environment. But in the Logit model with a slightly better difference than the Z-Altmanmodel, about 83% of its predictions were correct.
Statistical models applied. A detailed statistical analysis included the follow- ing: (i) correlation studies (Pearson and Spearman correlation coefficients), (ii) the Bland-Altman (1) difference plot for bias and agreement, including limits of agreement and confidence intervals, and (iii) the percent similarity model (4) for measuring agreement, including accuracy (percent similarity mean) and preci- sion (percent similarity standard deviation [SD]) and overall agreement (percent similarity CV). The Bland-Altmanmodel measures the difference between two methods (a ⫺ b). This model is represented by scatter plots of the difference between the methods on the vertical axis and the absolute value of the reference on the horizontal axis. The average absolute value is not used, as this evaluation is to determine whether the new method can replace the existing method and reported patient results are given as absolutes not averages between two meth- ods. The percent similarity model applies the formula (a ⫹ b)/2/a ⫻ 100, where a is the reference method and b is the new method. The percent similarity values between data pairs are then represented in a histogram format overlaid with a normal curve. The peak distance (mean percent similarity) from 100% shows the accuracy between the two methods, and the spread (SD) of the curve shows the precision between two methods. The overall agreement between the two meth- ods is then represented by a single unit, the percent similarity CV (SD/mean) which summarizes both accuracy and precision into one unit. A low percent similarity CV shows good agreement between methods. All statistical analysis was performed on log-transformed data, after converting AMPLICOR results in RNA copies/ml into IU/ml as described by Stevens et al. in 2005 (copies/ml ⫻ 0.51 ⫽ IU/ml) (5). For purposes of the statistical analyses, the COBAS AmpliPrep- AMPLICOR assay was considered the reference method against which the easyMAG-NucliSENS EasyQ HIV-1 v1.1 combination was evaluated.
The 1-log loss in sensitivity is thus attributable mostly to a smaller sample input in RT-PCRs performed with DSS. FIG. 2. Serial 10-fold dilutions of the viral culture supernatant prepared in HAV RNA-negative human plasma were spotted and kept at room temperature for 24 h. Difference plots are shown. To assess a possible loss of HAV RNA induced by the blotting process, the viral loads of spotted and nonspotted dilutions were determined by quantitative RT-PCR. (a) After applying the 4.5 correction factor that accounts for the dilution of a blotted specimen, a nonsignificant bias toward lower viral loads obtained after blotting was shown by the Bland and Altmanmodel, indicating a mean difference of 0.34 log copies/ml (bias 95% CI ⫽ ⫺ 0.48 to ⫺ 0.19). (b) HAV viral loads were determined in 26 HAV RNA-positive sera and in matched DSS samples kept at room temperature for 24 h. Four DSS samples could not be quantified after blotting: two matched sera had viral loads of ⬍ 100 copies/ml, and two had 2.11 and 2.22 log copies/ml. Among the 22 quantifiable DSS, no significant bias toward lower viral loads obtained after blotting, as shown by the Bland and Altmanmodel, indicating a mean difference of 0.1 log copies/ml (bias 95% CI ⫽ ⫺ 0.48 to ⫺ 0.19).
Charles Moyer (1977) re-examined some critical aspects of Altman's model on the basis of: Altman model's success is notable during the first two years prior to bankruptcy but declines substantially for longer lead times. The parameters chosen by Altman are sensitive to either the time span used to develop the model or the firm size of Altman's original sample. Charles sample consisted of 27 bankrupt and 27 non-bankrupt firms during the period 1967 to 1975, and used linear MDA, direct and wilks methods. Empirical results suggested that X1, X2 and X3 (working capital/total assets, retained earnings/ total assets, EBIT/total assets) are significant variables in predicting bankruptcy prior to three years, X4(market value of equity/book value of total debt) is significant one year prior to bankruptcy and X5 (sales/total assets) does not have any significance at all
The paper examines Management efficiency in Banks Using the Altman Z-score for the period of recession, accompanied by the implementation of the Treasury single Account as well as the proliferation of ponzi schemes. Although previous studies have used the Altman Z-score for predicting bankruptcy and business failure two or more years before the actual failure, this study adopts the Altmanmodel as a veritable tool for measuring management efficiency in businesses with emphases on the banking sector of the economy using a comparative analysis before and during the recession. We found among other things, that various selected banks in Nigeria have had varying results in terms of their ability to remain efficient and viable during the period of recession. We therefore recommend that deposit money banks put in place strategic marketing plans to attract and secure deposits from the banking public.
Variabel independen dalam penelitian ini yaitu modelAltman, model Grover, dan model Zmijewski. Vriabel yang diteliti adalah yang diteliti adalah Working capital/total asset (WCTA), Retained Earnings/Total asset (RETA), Earnings before interest and taxes/total asset (EBITTA), Market value of equity/book value of total debt (MVEBVD), Sales/Total asset (SATA), Total liabilities/total asset (TLTA), Return On Asset (ROA), Current asset/current liabilities (CACL). Berikut adalah definisi pengukuran variabel- variabel tersebut:
Research is done in a simpler manner by Machek, who focused on a bankrupt only sample of firms from the Czech Republic. The study was focused on whether the models, among others the Altman’s z-score model for private firms (Z2), correctly predicted bankruptcies one to five years in advance. The results gave percentages of correct predictions of bankruptcies and compared these percentages with the results of other bankruptcy prediction models like the Kralicek Quick Tests (1991) and the Taffler model (1997). The results showed percentages of correct predictions between 37 and 45 % over the different years for the Altmanmodel. The predictive ability of Quick test and Taffler model in this study seemed limited, while the usefulness, in predicting financial distress in the Czech environment, of the Altman’s z-score was confirmed. Furthermore, the results showed that the z-score model does not significantly lose predictive ability over course of time. Thus it will be interesting to see if that will be the same for the sample used in this thesis, whether or not the predictive ability decreases as the years’ prior bankruptcy increase.
variable is the time that a company is considered healthy. As soon as a company is not considered a healthy company anymore, it is dropped off the list of observations. The risk of a company to become bankrupt varies over time. A company’s health is based on the financial data and the age. The hazard model contains ten times more data compared to other bankruptcy prediction models as every year is observed as a single value. The sample Shumway used includes 300 bankrupt companies between 1962 and 1992, retrieved from the American and New York Stock Exchange. He discovered that the hazard model and Altman’s coefficients prove that companies are less likely to fail if they have higher earnings compared to assets, if large companies have less liabilities and if companies have high working capital. The hazard model allocates 70% of all bankrupt companies in the highest bankruptcy probability decile, whereas Altman’s discriminant analysis gives not an as exact percentage. Comparing the hazard model with Zmijewski’s model, both classify companies between 54% and 56% in the highest bankruptcy probability decile. Looking at the model based on market-driven variables, companies are classified 69% in the highest probability decile. Therefore, Shumway came to the conclusion, that forecasting bankruptcy is better when combining market-driven variables with two accounting ratios (Shumway, 2001).
To do that, several statistical techniques with a multifactorial focus have been used (Altman, 1968). There are two reasons to explain the use of traditional statistical techniques rather than advanced statistical techniques such as neural networks, decision trees and genetic programming. First, there is the aim to follow (Altman, 1968) approach and second because traditional statistical techniques have been proven to have very good performance in the context of the paper. Even if some studies show that advanced statistical techniques have better performance when dealing with predictive abilities, other studies have shown that the predicting capabilities of both approaches were sufficiently similar to make it difficult to distinguish between them (Abdou & Pointon, 2011).
76.923 when the models are re-estimated. The predictive accuracy of new model on estimation and holdout sample is found to be 98.46 and 87.179 respectively. There- fore, the new model is found to be a more robust model in comparison to Altman’s, Ohlson’s and Zmijewski’s models. The major finding of the study suggests the coef- ficients of the Altman ’ s (1968), Ohlson ’ s (1980) and Zmijewski ’ s (1984) models are sensitive to time periods and financial condition. The predictive accuracy of the models increases when more recent data are used in the estimation samples. The change in the financial environment leads to change in the relation between finan- cial distress and financial ratios. This also alters the comparative importance of the ratios to predict default. Hence, researchers should re-estimate the original models to get higher predictive accuracy. In case of Indian manufacturing companies, out of all competitive accounting based models, the new model outperforms regarding predictive accuracy, ROC, and long-range accuracy test.
Overall, it can be concluded that both the firm-specific factors and macroeconomics factors have its influence on Tobin’s Q of the Huawei separately . According to the Table 4.4 (Model Summary for Pooled Model 3) and Model 1 for firm-specific independent variables (refer Appendix B, Table B.3), the adjusted R- squared value shown implies that 70.1% of the variance in the Tobin’s Q of the Huawei can be explained. While the remaining of 29.9% of the adjusted R-Squared remain unknown and this implies that the variance in the Tobin’s Q of the Huawei are unable to be explained by the firm-specific factors (ROE). By referring to the Table C.3 in Appendix C, the Model 2 (macro-economics independent variables) can explain 30.5% of the variance in Tobin’s Q of the company whereas the remaining 69.5% implies that Model 2 is unable to explain by the macroeconomics factors. In conclusion, based on the values of adjusted R-squared obtained by Model 1 and Model 2, it can be concluded that the firm-specific factors have a greater impact in the Tobin’s Q of the Huawei as compared to the macroeconomic factors.
As described above, an Altman’s model is complemented by a mathematical model procedure of continuous best mean-square approximation of Altman sets of polynomial degree, obtained by the method of mean inte- grated squared approximation, and also the model introduces a procedure for calculating values of membership functions of fuzzy sets that allows us to specify which of the subsets is clearer or not clearly specified. Selected optimal degree of the polynomial provides on the one hand a sufficient minimum of the objective function and on the other hand, the monotonicity of the polynomial. A priori selection of optimal parameters of Newton’s op- timization algorithm yields: parameter regularization and iterative step setting. We proved a corollary of the theorem on the convergence of Newton’s method, which was a generalization of the approximate numerical Newton method for solving systems of nonlinear equations in normed linear spaces  to search for the opti- mum class of strongly convex functions by a special choice of the iteration parameters in each iteration step.
Assessing the financial stability of a company is always a prime concern for accounting executives, business analysts, prospective investors etc. because there are enormous corollaries for both internal and external customers in case of financial distress of a company. Bankruptcy seems to be a big concern for them as they are very much interested in figuring out the ins and outs of investment and to keep track about the near future and sustainability of the firm they are eyeing to invest. Bankruptcy is not something which immediately happen rather it is followed by a series of events. So, to be bankrupt, a firm must be in financial distress in first place. Financial distress is actually a term of corporate finance and is primarily used to give “early warning” to companies prior to default and, hence, it may be very meaningful for those who wish to protect their stakes in company. Four generic terms stated in literature by Altman and Hotchkiss (2006) to define financial distress are failure, default, insolvency and bankruptcy Failure happens to be when the rate of return realized on capital invested is considerably low than that on the same investments. Failure happens when the rate of return realized on capital invested is considerably low than the rate prevailing on the similar investments. And when average return is not adequate to cover cost of capital, firm is said to be financially distressed. Default is another term akin to financial distress. It exists between debtor and creditor when interest or principal amount is not paid within prescribed time. A more technical term mentioned in this regard is insolvency. It is a situation in which firm is unable to maintain its liquidity and to fulfill its promises, hence, ranked as financially distressed. And the last term associated with distress is bankruptcy which is transpired when the net worth of firm‟s assets is not more than its total liabilities. It is a set going process borne by a company for its inability to pay the claims.
calculated at - 215 and 176 N (Figure 4). Thus, FIR predicted by the FIR-WC model may be 215 N lower or 176 N higher than FIR measured by laboratory test. The average percentage differences for FIR prediction using the FIR-WC model and laboratory test was 2.5%.
We set out to explore and analyse the relationship between financial health, as measured by the Altman (1968) Z-Score, and firm performance, as measured by Return on Equity (ROE) ratios, of manufacturing companies listed in Japan’s TSE. We found that there was a statistically significant and positive relationship between Return on Equity (ROE) and Altman Z-Scores in the market. Furthermore, in our descriptive analysis, we observed generally moderate-to-healthy mean and median Z-Scores in the market and similar mean and median Z- Score trends over the period. These relationships may be construed as a positive assurance for stakeholders in this market, such as investors.
When applying the Beneish model, a score of greater than -2.22 (i.e., less of a negative) is an indication that the company‟s financial statements may have been manipulated (Warshavsky, 2012). Applying this standard to the case of Enron, the modeling (Table 3) shows the financial statements appeared to have been manipulated as back as 1998 when an M-Score of -2.426 was made. This is contrary to the results of the work of Warshavsky (2012) and those of the Z- Score in this study. Again, using metrics developed by Beneish, Catanach & Rhoades-Catanach (2003) find a high probability of earnings manipulation in Enron‟s financial statements for several years preceding its bankruptcy. The individual indices used in the model showed mixed results. The indices for the year (1998) were 0.786, 0.936, 1.064, 1.542, 0.847, 0.772, -0.052 and 1.009 for DSRI, GMI, AQI, SGI, DEPI, SGAI, TATA and LEVI respectively. Of the eight indices, only three (AQI, SGI and LEVI) had values above 1.0 in 1998 thus agreeing with the results of the M-Score.