A Review of the Research on Financial Performance and Its Determinants

Tudose, Mihaela Brîndușa; Avasilcăi, Silvia

doi:10.1007/978-3-030-44711-3_17

Cited by 5 publications

(2 citation statements)

References 55 publications

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“…The choice for this research direction was justified from two points of view. First, we took the fact that, in the research on corporate performance, the traditional system of performance measurement indicators (focused on profit and return on assets/capital) is mainly used into account (Geng et al 2021;Tudose and Avasilcai 2020). In this context, the research methodology focuses on the analysis of financial ratios (Batchimeg 2017;Luo et al 2017;Egbunike and Okerekeoti 2018;Xu and Wang 2018;Kassi et al 2019), which does not take all the costs of a business (such as the costs of capitals) into account and that allows for distorting or hiding of the real performance (Novyarni and Ningsih 2020).…”

Section: Introductionmentioning

confidence: 99%

Performance Management for Growth: A Framework Based on EVA

Tudose

Rusu

Avasilcăi

2021

JRFM

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Some of the constructs in the field of performance management are intuitive or not empirically validated. This study provides a data-driven framework for measuring and improving the performance through synchronized strategies. The ultimate goal was to provide support for increasing business performance. Empirical research materializes in an exploratory case study and a statistical analysis with econometric models. The case study revealed that a company can improve its performance, even in periods of growth, being characterized by consistent investments. The statistical analysis, performed on a restricted sample of companies, confirmed the results that were provided by the case study. The measurement of performance was made by capitalizing on financial and non-financial data precisely to intensify the interest for corporate sustainability. The obtained results, contrary to previous research that showed that economic value added (EVA) is negatively influenced by the increase in invested capital, open up new research perspectives to find out whether, at the industry level, performance appraisal that is based on EVA stimulates the development of a business’s economic capital. The research has a double utility: scientific (by providing an overview of the state of the art in the field of performance management) and practical (by providing a reference model for measuring and monitoring performance).

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Section: Introductionmentioning

confidence: 99%

Performance Management for Growth: A Framework Based on EVA

Tudose

Rusu

Avasilcăi

2021

JRFM

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“…We utilized a comprehensive set of financial ratios for prediction, acknowledging their widespread applicability and establishing a new precedent in financial analysis. Financial ratios offer a diverse set of financial metrics that provide a concise yet powerful snapshot of a company's financial health and resilience [44]. This research aims to address these gaps by focusing on the bankruptcy risk analysis of healthcare industries in the United States, with particular emphasis on the economically tumultuous year of 2022.…”

Section: Objectivementioning

confidence: 99%

Risk Analysis of Bankruptcy in the U.S. Healthcare Industries Based on Financial Ratios: A Machine Learning Analysis

Gholampoor,

Asadi

2024

JTAER

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The prediction of bankruptcy risk poses a formidable challenge in the fields of economics and finance, particularly within the healthcare industry, where it carries significant economic implications. The burgeoning field of healthcare electronic commerce, continuously evolving through technological advancements and changing regulations, introduces additional layers of complexity. We collected financial data from 1265 U.S. healthcare industries to predict bankruptcy based on 40 financial ratios using multi-class classification machine learning models across various industry subsectors and market capitalizations. The exceptionally high post-tuning accuracy rates, exceeding 90%, along with high-performance metrics solidified the robustness and exceptional predictive capability of the gradient boosting model in bankruptcy prediction. The results also demonstrate the power and sensitivity of financial ratios in predicting bankruptcy based on financial ratios. The Altman models highlight the return on investment (ROI) as the most important parameter for predicting bankruptcy risk in healthcare industries. The Ohlson model identifies return on assets (ROA) as an important ratio specifically for predicting bankruptcy risk within industry subsectors. Furthermore, it underscores the significance of both ROA and the enterprise value to earnings before interest and taxes (EV/EBIT) ratios as important parameters for predicting bankruptcy based on market capitalization. Recognizing these ratios enables proactive decision making that enhances resilience. Our findings contribute to informed risk management strategies, allowing for better management of healthcare industries in crises like those experienced in 2022 and even on a global scale.

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How well do the companies responsible for facilitating the digital transition perform? An analysis on Romanian ICT sector

Tudose,

Clipa,

Clipa

2024

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PurposeThis study proposes an analysis of the performance of companies that have assumed the responsibility of facilitating the digitalization of economic activities. Because of their potential to accelerate digitization, these companies have been financially supported. The monitoring of the performances recorded by these companies, including the evaluation of the impact of different determining factors, meets both the needs of the financiers (concerned with the evaluation of the efficiency of the use of nonreimbursable financing) and the needs of continuous improvement of the activities of the companies in the field.Design/methodology/approachThe study assesses performance dynamics and the impact of its determinants. The model allows achieving a simplified vision of performance and its determinants, supporting decision-makers in the management process. The construction of an estimation model based on the multiple regression method was considered. Robustness tests were performed on the results, using parametric and nonparametric tests.FindingsThe results of the analysis at the level of the extended sample indicated that, during the analyzed period, the economic and commercial performances decreased, and significant influences in this respect include the financing structure, sales dynamics and volume of receivables. The analysis at the level of the restricted sample confirmed these interdependencies and provided additional evidence of the impact of other determinants.Research limitations/implicationsThe study contributes both to performance research and to the assessment of the prospects for accelerating digitalization in support of economic activities. Since the empirical research was carried out on a sample of Romanian companies that provide services in information technology, which accessed nonreimbursable financing, the representativeness of the results is limited to this sector. For the analyzed sample, the study provides support for improving performance.Practical implicationsThe results of the study prove to be useful from a microeconomic and macroeconomic perspective as well, as they provide evidence on the performance of companies that have implemented information and communication technology (ICT) projects and on the efficiency of the use of non-reimbursable funding dedicated to business support.Originality/valueThe study fills the literature gap regarding the performance of companies that have developed ICT projects and received grant funding for the implementation of these projects. The literature review indicated that there are few studies conducted on these companies, which did not include Romanian companies.

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A Review of the Research on Financial Performance and Its Determinants

Cited by 5 publications

References 55 publications

Performance Management for Growth: A Framework Based on EVA

Performance Management for Growth: A Framework Based on EVA

Risk Analysis of Bankruptcy in the U.S. Healthcare Industries Based on Financial Ratios: A Machine Learning Analysis

How well do the companies responsible for facilitating the digital transition perform? An analysis on Romanian ICT sector

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