A bibliometric study on intelligent techniques of bankruptcy prediction for corporate firms

Yin Shi, Xiaoni Li
<span title="2019-12-18">2019</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ifjemtdanzevfbtz3nv7p24qjm" style="color: black;">Heliyon</a> </i> &nbsp;
Bibliometric analysis is an effective method to carry out quantitative study of academic output to address the research trends on a given area of investigation through analysing existing documents. This paper aims to explore the application of intelligent techniques in bankruptcy predictions so as to assess its progress and describe the research trend through bibliometric analysis over the last five decades. The results indicate that, although there is a significant increase in publication
more &raquo; ... r since the 2008 financial crisis, the collaboration among authors is weak, especially at the international dimension. Also, the findings provide a comprehensive view of interdisciplinary research on bankruptcy modelling in finance, business management and computer science fields. The authors sought to contribute to the theoretical development of bankruptcy prediction modeling by bringing new knowledge and key insights. Artificial intelligent techniques are now serving as important alternatives to statistical methods and demonstrate very promising results. This paper has both theoretical and practical implications. First, it provides insights for scholars into the theoretical evolution and intellectual structure for conducting future research in this field. Second, it sheds light on identifying under-explored machine learning techniques applied in bankruptcy prediction which can be crucial in management and decision-making for corporate firm managers and policy makers.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.heliyon.2019.e02997">doi:10.1016/j.heliyon.2019.e02997</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31890956">pmid:31890956</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6928309/">pmcid:PMC6928309</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kxevwo2fnbbijejm72fwlxgxxq">fatcat:kxevwo2fnbbijejm72fwlxgxxq</a> </span>
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