Application of machine learning techniques to predict entrepreneurial firm valuation

Zhang, Ruling; Tian, Zhilong; McCarthy, Killian J.; Wang, Xiao; Zhang, Kun

doi:10.1002/for.2912

Cited by 5 publications

(2 citation statements)

References 64 publications

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“…Machine learning, specifically deep neural network learning, is acknowledged as a powerful resource. Its strong abilities in analyzing data and recognizing patterns render it a valuable asset across diverse domains pertinent to talent acquisition (Zhang et al, 2023). Machine learning (ML) belongs to the realm of artificial intelligence, empowering computers to independently think and learn.…”

Section: Machine Learningmentioning

confidence: 99%

Revolutionizing Talent Acquisition in Indonesia's E-Commerce Industry: The Transformative Impact of AI and Machine Learning

Rayyan,

Sharifah,

Kuswati

2024

jhsss

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This research explores the impact of Artificial Intelligence (AI) and Machine Learning (ML) on talent acquisition within Indonesia's growing e-commerce sector. While acknowledging the transformative potential of these technologies, the study uncovers significant challenges in their integration into existing talent acquisition processes. These challenges include issues related to data quality, model accuracy, and system adaptability. The study emphasizes the difficulty of acquiring talent with expertise in AI and ML, given the increasing demand for skilled workers in the e-commerce boom. It explores strategies employed by companies to address this talent gap, such as upskilling existing staff and seeking external expertise. The research provides a nuanced understanding of AI and ML applications in the Indonesian e-commerce landscape, highlighting both benefits and obstacles. The insights derived from the study aim to offer actionable guidance for e-commerce firms, HR professionals, and researchers navigating the evolving landscape of AI, ML, and talent acquisition in Indonesia's digital marketplace.

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Section: Machine Learningmentioning

confidence: 99%

Revolutionizing Talent Acquisition in Indonesia's E-Commerce Industry: The Transformative Impact of AI and Machine Learning

Rayyan,

Sharifah,

Kuswati

2024

jhsss

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“…In recent years, machine learning‐based models have shown remarkable success in forecasting. Due to their capacity to uncover embedded trends in financial data, machine learning models have also been widely used in various financial applications, such as asset pricing, bankruptcy prediction, high‐frequency trading, and inflation rate forecasting (Abdullah, 2021; Akyildirim et al, 2021; Cepni, Gupta, & Onay, 2022; Gu et al, 2021; Liu et al, 2022; Sulong et al, 2022; Tang et al, 2020; Uddin et al, 2021; Zhang et al, 2022). Following their success, in this paper, we exploit the powerful nonlinear modeling capacity of machine learning to improve the forecasting accuracy of NPLs.…”

Section: Introductionmentioning

confidence: 99%

Forecasting nonperforming loans using machine learning

Abdullah

Chowdhury

Uddin

et al. 2023

Journal of Forecasting

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Nonperforming loans play a critical role in financial institutions' overall performance and can be controlled by forecasting the probable nonperforming loans. This paper employs a series of machine learning techniques to forecast bank nonperforming loans on emerging countries' financial institutions. Using quarterly cross‐sectional data of 322 banks from 15 emerging countries, this study finds that advanced machine learning‐based models outperform simple linear techniques in forecasting bank nonperforming loans. Among all 14 linear and nonlinear models, the random forest model outperforms other models. It achieves a 76.10% accuracy in forecasting nonperforming loans. The result is robust in different performance metrics. The variable importance analysis reveals that bank diversification is the most critical determinant for future nonperforming loans of a bank. Additionally, this study revealed that macroeconomic factors are less prominent in predicting nonperforming loans compared with bank‐specific factors.

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The role of institutions in early-stage entrepreneurship: An explainable artificial intelligence approach

Graham,

Bonner

2024

Journal of Business Research

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Application of machine learning techniques to predict entrepreneurial firm valuation

Cited by 5 publications

References 64 publications

Revolutionizing Talent Acquisition in Indonesia's E-Commerce Industry: The Transformative Impact of AI and Machine Learning

Revolutionizing Talent Acquisition in Indonesia's E-Commerce Industry: The Transformative Impact of AI and Machine Learning

Forecasting nonperforming loans using machine learning

The role of institutions in early-stage entrepreneurship: An explainable artificial intelligence approach

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