Reliability and Accuracy of Alternative Default Prediction Models: Evidence from Slovakia

Rybárová, Daniela; Majdúchová, Helena; Štetka, Peter; Luščíková, Darina

doi:10.3390/ijfs9040065

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Formula 1 Relevant Articles Accuracy tp+tn tp+ f p+tn+ f n [7,13,36,44,45,47,79,87,88,96,97,108,[111][112][113]116,117,[129][130][131][132][133] Error rate f p+ f n tp+ f p+tn+ f n [46,69,90,92,93,134] F-measure 2 * P * R P+R [56,74,79,135] Geometric mean √ tp * tn [50] 1 Note: tp-true positive, tn-true negative, fp-false positive, fn-false negative, P-precision, R-recall.…”

Section: Metricsmentioning

confidence: 99%

Predicting Construction Company Insolvent Failure: A Scientometric Analysis and Qualitative Review of Research Trends

Wang,

Li,

Skitmore

et al. 2024

Sustainability

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The construction industry is infamous for its high insolvent failure rate because construction projects require complex processes, heavy investment, and long durations. However, there is a lack of a comprehensive framework and a requirement for such a framework in predicting the financial distress of construction firms. This paper reviews relevant literature to summarize the existing knowledge, identify current problems, and point out future research directions needed in this area using a scientometric analysis approach. Based on a total of 93 journal articles relating to predicting construction company failure extracted from multiple databases, this study conducts a holistic review in terms of chronological trends, journal sources, active researchers, frequent keywords, and most cited documents. Qualitative analysis is also provided to explore the data collection and processing procedures, model selection and development process, and detailed performance evaluation metrics. Four research gaps and future directions for predicting construction company failure are presented: selecting a broader data sample, incorporating more heterogeneous variables, balancing model predictability and interpretability, and quantifying the causality and intercorrelation of variables. This study provides a big picture of existing research on predicting construction company insolvent failure and presents outcomes that can help researchers to comprehend relevant literature, directing research policy-makers and editorial boards to adopt the promising themes for further research and development.

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Section: Metricsmentioning

confidence: 99%

Predicting Construction Company Insolvent Failure: A Scientometric Analysis and Qualitative Review of Research Trends

Wang,

Li,

Skitmore

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…269-279), bankruptcy (Kubenka et al, 2021, pp. 167-185), financial difficulties (Svabova et al, 2020), default (Rybarova et al, 2021), credit risk assessment (Oreski & Oreski, 2018, pp. 59-73), and early warning systems (Valaskova et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries

Valášková

Gajdosikova

Belás

2023

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Research background: Effective monitoring of financial health is essential in the financial management of enterprises. Early studies to predict corporate bankruptcy were published at the beginning of the last century. The prediction models were developed with a significant delay even among the Visegrad group countries. Purpose of the article: The primary aim of this study is to create a model for predicting bankruptcy based on the financial information of 20,693 enterprises of all sectors that operated in the Visegrad group countries during the post-pandemic period (2020?2021) and identify significant predictors of bankruptcy. To reduce potential losses to shareholders, investors, and business partners brought on by the financial distress of enterprises, it is possible to use multiple discriminant analysis to build individual prediction models for each Visegrad group country and a complex model for the entire Visegrad group. Methods: A bankruptcy prediction model is developed using multiple discriminant analysis. Based on this model, prosperity is assessed using selected corporate financial indicators, which are assigned weights such that the difference between the average value calculated in the group of prosperous and non-prosperous enterprises is as large as possible. Findings & value added: The created models based on 6?14 financial indicators were developed using different predictor combinations and coefficients. For all Visegrad group countries, the best variable with the best discriminating power was the total indebtedness ratio, which was included in each developed model. These findings can be used also in other Central European countries where the economic development is similar to the analyzed countries. However, sufficient discriminant ability is required for the model to be used in practice, especially in the post-pandemic period, when the financial health and stability of enterprises is threatened by macroeconomic development and the performance and prediction ability of current bankruptcy prediction models may have decreased. Based on the results, the developed models have an overall discriminant ability greater than 88%, which may be relevant for academicians to conduct further empirical studies in this field.

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Reliability and Accuracy of Alternative Default Prediction Models: Evidence from Slovakia

Cited by 2 publications

References 23 publications

Predicting Construction Company Insolvent Failure: A Scientometric Analysis and Qualitative Review of Research Trends

Predicting Construction Company Insolvent Failure: A Scientometric Analysis and Qualitative Review of Research Trends

Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries

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