Accounting‐based variables as an early warning indicator of financial distress in crisis and non‐crisis periods

Powell, Robert; Dinh, Dung V.; Vu, Nam T.; Vo, Duc Hong

doi:10.1002/ijfe.2864

Cited by 2 publications

(1 citation statement)

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“…The traditional approaches to building prediction models are multivariate discriminant analysis (MDA) and logistic regression (LR), and these approaches are still in use nowadays (Maquieira, 2024;Powell, 2023). Later, other non-mathematical-statistical approaches were employed in this area.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network and decision tree-based modelling of non-prosperity of companies

Durica,

Frnda,

Svabova

2023

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Research background: Financial distress or non-prosperity prediction has been a widely discussed topic for several decades. Early detection of impending financial problems of the company is crucial for effective risk management and important for all entities involved in the company’s business activities. In this way, it is possible to take the actions in the management of the company and eliminate possible undesirable consequences of these problems. Purpose of the article: This article aims to innovate financial distress prediction through the creation of individual models and ensembles, combining machine learning techniques such as decision trees and neural networks. These models are developed using real data. Beyond serving as an autonomous and universal tool especially useful in the Slovak economic conditions, these models can also represent a benchmark for Central European economies confronting similar economic dynamics. Methods: The prediction models are created using a dataset consisting of more than 20 financial ratios of more than 19 thousand real companies. Partial models are created employing machine learning algorithms, namely decision trees and neural networks. Finally, all models are compared based on a wide range of selected performance metrics. During this process, we strictly use a data mining methodology CRISP-DM. Findings & value added: The research contributes to the evolution of financial prediction and reveals the effectiveness of ensemble modelling in predicting financial distress, achieving an overall predictive ability of nearly 90 percent. Beyond its Slovak origins, this study provides a framework for early financial distress prediction. Although the models are created for diverse industries within the Slovak economy, they could also be useful beyond national borders. Moreover, the CRISP-DM methodological framework enables its adaptability for companies in other countries.

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Section: Introductionmentioning

confidence: 99%

Artificial neural network and decision tree-based modelling of non-prosperity of companies

Durica,

Frnda,

Svabova

2023

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Leading Through the Storm and Confronting Challenges in the Energy Crisis Era

Rafique,

Baig

2024

Energy Crisis and Its Impact on Global Business

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The global energy challenge poses a significant challenge that requires skilled leadership to handle it in all its complexity. The chapter examines the characteristics of the energy crisis and investigates several leadership theories and models that might be used in crisis circumstances. An analysis is conducted on the attributes of crises and the dynamics of leadership to comprehend the distinct problems encountered by leaders during the energy crisis. Leaders facing crises confront a variety of complex problems, including difficult decision-making scenarios and the task of creating and inspiring teams in an uncertain climate. This study examines tactics for tackling these problems and suggests methods for improving the efficacy of crisis leadership. Furthermore, it explores the social difficulties that naturally arise in crisis leadership, highlighting the significance of team unity and drive. This chapter explores decision-making models specifically designed for crisis leadership, offering valuable insights into the cognitive processes required for making good decisions in times of crisis. The chapter finishes by delineating comprehensive techniques for proficient leadership in the energy crisis, emphasizing the crucial role leaders assume in guiding organizations and communities toward sustainable energy solutions. This chapter seeks to provide significant insights into the distinct difficulties and possibilities that leaders face amid the energy crisis, via a thorough analysis.

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Accounting‐based variables as an early warning indicator of financial distress in crisis and non‐crisis periods

Cited by 2 publications

References 70 publications

Artificial neural network and decision tree-based modelling of non-prosperity of companies

Artificial neural network and decision tree-based modelling of non-prosperity of companies

Leading Through the Storm and Confronting Challenges in the Energy Crisis Era

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