The impact of Data structure on classification ability of financial failure prediction model

Abstract

The creation of prediction models to reveal the threat of financial difficulties of the companies is realized by the application of various multivariate statistical methods. From a global perspective, prediction models serve to classify a company into a group of prosperous or non-prosperous companies, or to quantify the probability of financial difficulties in the company. In many countries around the world, real financial data about the companies are used in developing these prediction models. In Slovakia, standard data from the financial statements and annual reports of Slovak companies are used for the creation of the company’s failure model. Since in this case there are generally large data files, it is necessary to pre-process the data by the selected methods before the prediction model is constructed. A database of the companies needs to be prepared for the subsequent application of statistical methods, and it is also highly appropriate to focus globally on the detection of potential extreme and remote observations. Therefore, the article will focus on quantifying the impact of the data structure detected, for example, the occurrence of extreme and remote observations in the data set, on the resulting overall classification of the prediction ability of the models created.

Financial Distress Phenomenon of Non-financial Companies in Indonesia

“…Kemudian para peneliti mengembangkan kembali kajian mengenai financial distress dengan berbagai metode pada berbagai negara. Pada kajian financial distress di berbagai negara, berbagai metode telah dikembangkan untuk menganalisis kondisi financial distress antara lain menggunakan model logit (Mselmi et al, 2017;), artificial neural networks (Mselmi et al, 2017;Choi et al, 2018;Barboza et al, 2017), support vector machine (Mselmi et al, 2017;Choi et al, 2018), partial least square (Mselmi et al, 2017;), model hybrid (Mselmi et al, 2017), model deep learning (Mai et al, 2018;Ogachi dkk., 2020), discriminant analysis (Pham Vo Ninh et al, 2018;Svabova & Michalkova, 2020;Agrawal & Maheshwari, 2019), distance-to-default (DD) models (Pham Vo Ninh et al, 2018), maximum weighted count of errors and correct result (Choi et al, 2018), commercial version 4.5 (Choi et al, 2018), naïve baves (Choi et al, 2018), logistic regression (Choi et al, 2018;Svabova & Michalkova, 2020;Agrawal & Maheshwari, 2019;Shrivastava et al, 2018;Barboza et al, 2017), k-nearest neighbor (Choi et al, 2018;), multi-period logit model (Charalambakis & Garrett, 2019), multiple binary regression logistic (Yazdanfar & Öhman, 2020), CART binominal tree method (Svabova & Michalkova, 2020), dan decisions trees (Klepac & Hampel, 2017).…”