...Sahil Narang; Rudra P. Pradhan Purpose The study examines 498 initial public offerings (IPOs) listed during 2007–2022 from an emerging IPO market. Design/methodology/approach The study proposes a novel framework that utilizes the integration of machine learning (ML) outputs with multi-criteria...

..., beginning with an extensive literature review on fraud identification, assessment, and prevention strategies. A theoretical framework is constructed to support the proposed SG and BG measures. Machine learning-based data analysis, using Artificial Neural Networks, is employed to dynamically assess...

... financing and information regulation. This study also highlights areas for future empirical research on the role of AI in the financial industry. © Emerald Publishing Limited 2024 Emerald Publishing Limited Licensed re-use rights only Firm growth Artificial intelligence Machine learning Data...

... in IPO data. This study introduces a machine learning method, known as random forest, to address issues that linear regression may struggle to resolve. Design/methodology/approach The study’s sample comprises 352 fixed-priced IPOs from the year 2004 until 2021. A unique aspect of this research is its...

... This paper contributes to the existing literature on the application of machine learning in the banking industry. To the author's knowledge, this is the first study that predicts the level of customer deposits using banking specific and macroeconomic variables. Banks have a critical role to play...