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Understanding the indicative factors of university/college closings
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作者 larissa adamiec Deborah Cernauskas Andrew Kumiega 《Journal of Management Analytics》 EI 2022年第3期330-350,共21页
Higher education has been in a financially precarious position for many years –facing either a total transformation or elimination. Tuition increases and fewercollege-age students from shifting demographics are prima... Higher education has been in a financially precarious position for many years –facing either a total transformation or elimination. Tuition increases and fewercollege-age students from shifting demographics are primary reasons for thefinancial distress. Alternative financial stability models have assumed linearvariable relationships and improperly calculate the probability of default.Stakeholders have historically relied upon models such as those developed byEdmit and the Department of Education which are inadequate at separatingfinancially sound from unsound universities. We used an Automated MachineLearning approach utilizing multiple models to explain the relationship betweenmetrics and the probability of default/closure allowing for more informedmanagerial decisions. This research, although applied to the homogeneousgroup of small liberal arts universities, can be applied to online and stateuniversities and will allow the opportunity to take preventive steps to mitigatethe likelihood of closing due to financial distress. 展开更多
关键词 bankruptcy prediction STATISTICS decision analysis machine learning forecasting applications random forest
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