Intelligent hybrid model for financial crisis prediction using machine learning techniques
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Intelligent hybrid model for financial crisis prediction using machine learning techniques J. Uthayakumar1 · Noura Metawa2,3 · K. Shankar4 · S. K. Lakshmanaprabu5 Received: 1 November 2018 / Revised: 25 November 2018 / Accepted: 3 December 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018
Abstract Financial crisis prediction (FCP) plays a vital role in the economic phenomenon. The precise prediction of the number and possibility of failing firms acts as an index of the growth and strength of a nation’s economy. Traditionally, several methods have been presented for effective FCP. On the other hand, the classification performance and prediction accuracy and data legality is not good enough for practical applications. In addition, many of the developed methods perform well for some of the particular dataset but not adaptable to different dataset. Hence, there is a requirement to develop an efficient prediction model for better classification performance and adaptable to diverse dataset. This paper presents a cluster based classification model, comprises of two stages: improved K-means clustering and a fitness-scaling chaotic genetic ant colony algorithm (FSCGACA) based classification model. In the first stage, an improved K-means algorithm is devised to eliminate the wrongly clustered data. Then, a rule-based model is selected to design to fit the given dataset. At the end, FSCGACA is employed for seeking the optimal parameters of the rulebased model. The proposed algorithm is employed to a collection of three benchmark dataset which include qualitative bankruptcy dataset, Weislaw dataset and Polish dataset. A detailed statistical analysis of the dataset is also given. The results analysis ensured that the presented FCP model is superior to other classification model based on the different measures and also found to be more appropriate for diverse dataset. Keywords FCP · K-means algorithm · Genetic algorithm · Ant colony optimization
* Noura Metawa [email protected] Extended author information available on the last page of the article
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1 Introduction In the past decade, the raise of financial crisis of companies in every part of the world, the companies are paying much attention to the area of financial crisis prediction (FCP) (Ala’raj and Abbod 2016). For a company or financial institution, it is very essential to model a reliable and earlier prediction model to forecast the potential risk of the company’s status of financial failure in advance. FCP commonly produces a binary classification model which has been solved in a rational way (Martin et al. 2012). The outcome from the classification model can be categorized into two types: one which represent the failure status of a company and the other one represent the non-failure status of a company. The input to the classification model is always the statistical ratios obtained from the financial statements in the actual organizations. Till now, more number of classification models has been develop
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