Models to predict companies’ bankruptcy levels have been formulated but the z-score
model is used for advanced system failure warnings. Challenges concerned with companies
running bankrupt have been rampant hence causing failure to many business enterprises.
Through the z-score model, companies should be able to capitalize on their weak points and take
enough precautions to avoid the incident. The Z-score model uses statistical tools to determine
the independent variables and the objective function score described from the multivalent
discriminant analysis.
The Z-score Machine Learning Technique
The results from the Z-score model are used to link up current and future financial
situation. The model has revealed that numerous features could reveal bankrupt institutions
without any dogma. It provided complete results for a period predicted to be five years before
bankrupt onset. The z-score is more convenient compared to financial analysis. Feature selection
in the problem of bankruptcy prediction will be discussed in detail (Alkhatib and Ahmad 89).
Some of the features mostly used by the science-based model to predict company
bankruptcy is accounting and market variables. The model uses the entire information on the
company accounted profit to be able to predict on what terms the accounted finance will be
depleted within the coming years (Altman et al. 123). The marketed variables are also another
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feature to be considered when evaluating and predicting the profitable company progress. The
model also analyzes this variable and checks on the future implication if they limit or magnify
the marketed variables. The feature on the market based information was used to acknowledge
current pricing, which would be used in return to predict the future company loss (Bauer and
Vineet 438). The model opens up the potholes in a company enabling it to come up with a plan.
Firm-characteristics is another feature which can be used to predict bankruptcy within an
organization. Firm characteristics implies on the number of business’ segments a company poses
and the total financial impacts they represent to the company yearly (Grice and Robert 58).
Analysis revealed that abundance in business segments the more reliable the company is
regarding financial capability (Li 38). From this, the idea of why the most prominent firm has a
lower percentage of bankruptcy was confirmed. Another feature which can be of great use is the
company's interest in the well-being of his employees. The success of any organization depends
mostly on the employees since they do most of the workforce as well as management. For these
reasons company shows ensures that their employees’ welfare is well catered for regarding
salaries, vacations, and good working conditions (Siddiqui 78). This in return has boosted
companies finance since employees work intensively thereafter.
Conclusion
In summary, the scientific models have revealed various features which can trigger
bankruptcy if they are not taken into consideration. They have also enabled companies to focus
on both current and future plans thus strengthening and improving its existing span. On the other
hand, the employee's well -being has been well catered for thus improving their working
condition and in turn positively impacting company finance. These features are not only crucial
to existing companies but can also be used by investors to establish new companies. Application
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of the various bankruptcy prediction models offers the much needed outline and correction
model that may be employed in identifying and allowing financial corporations to improve their
financial credibility and ranking among the consumers through improved ethical practice.
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Work Cited
Alkhatib, Khalid, and Ahmad Eqab Al Bzour. "Predicting corporate bankruptcy of Jordanian
listed companies: Using Altman and Kida models." International Journal of Business and
Management 6.3 (2011): 208.
Altman, Edward I., et al. "Distressed firm and bankruptcy prediction in an international context:
A review and empirical analysis of Altman's Z-score model." Available at SSRN
2536340 (2014).
Bauer, Julian, and Vineet Agarwal. "Are hazard models superior to traditional bankruptcy
prediction approaches? A comprehensive test." Journal of Banking & Finance 40 (2014):
432-442.
Grice, John Stephen, and Robert W. Ingram. "Tests of the generalizability of Altman's
bankruptcy prediction model." Journal of Business Research 54.1 (2001): 53-61.
Li, June. "Prediction of corporate bankruptcy from 2008 through 2011." Journal of Accounting
and Finance 12.1 (2012): 31-41.
Siddiqui, Sanobar Anjum. "Business bankruptcy prediction models: A significant study of the
Altman’s Z-score model." Available at SSRN 2128475 (2012).