首页|Data on Artificial Intelligence Described by a Researcher at University of Marib or (Private Firm Valuation Using Multiples: Can Artificial Intelligence Algorith ms Learn Better Peer Groups?)
Data on Artificial Intelligence Described by a Researcher at University of Marib or (Private Firm Valuation Using Multiples: Can Artificial Intelligence Algorith ms Learn Better Peer Groups?)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on artificial intelligence have bee n presented. According to news reporting from Maribor, Slovenia, by NewsRx journ alists, research stated, “Forming optimal peer groups is a crucial step in multi plier valuation.” Our news correspondents obtained a quote from the research from University of Ma ribor: “Among others, the traditional regression methodology requires the defini tion of the optimal set of peer selection criteria and the optimal size of the p eer group a priori. Since there exists no universally applicable set of closed a nd complementary rules on selection criteria due to the complexity and the diver se nature of firms, this research exclusively examines unlisted companies, rende ring direct comparisons with existing studies impractical. To address this, we d eveloped a bespoke benchmark model through rigorous regression analysis. Our aim was to juxtapose its outcomes with our unique approach, enriching the understan ding of unlisted company transaction dynamics. To stretch the performance of the linear regression method to the maximum, various datasets on selection criteria (full as well as F- and NCA-optimized) were employed. Using a sample of over 20 ,000 private firm transactions, model performance was evaluated employing multip lier prediction error measures (emphasizing bias and accuracy) as well as predic tion superiority directly.”
University of MariborMariborSloveniaEuropeAlgorithmsArtificial IntelligenceEmerging TechnologiesMachine Le arning
NETL
NSTL
