Title: Z-score and logistic model-based default probability prediction in India's manufacturing sector for economic growth insights

Authors: Richa Kaur; Meghna Sharma; D.D. Chaturvedi; Jimnee Deka

Addresses: Amity College of Commerce and Finance, Amity University, Noida, 201301, Uttar Pradesh, India ' Amity International Business School, Amity University, Noida, 201301, Uttar Pradesh, India ' Guru Gobind Singh College of Commerce, University of Delhi, North Campus, 110034, Delhi, India ' Amity International Business School, Amity University, Noida, 201301, Uttar Pradesh, India

Abstract: This study explores the prediction of default probability in India's manufacturing sector within the industrial metaverse, incorporating considerations of economic growth. The research analyses 124 Indian manufacturing firms listed on the Bombay Stock Exchange over five years (2018-2019 to 2022-2023), utilising multiple discriminant analysis (MDA) and logistic regression models to classify firms based on their default risk. Both models develop equations to differentiate between firms likely to default and those not, using predictor variables relevant to the sector. The study examines the discriminant coefficients, which indicate the importance of each predictor variable in assessing default probability. The results show no significant difference in classification accuracy between the MDA and logistic regression models, demonstrating strong specification and robustness. These findings imply that both techniques can forecast manufacturing defaults. The study uses ergonomics to show how the industrial metaverse affects financial predictions, offering a new perspective on improving manufacturing decision-making in India. The findings can help financial professionals improve their forecasting models and tactics in a changing industry.

Keywords: MDA; multiple discriminant analysis; logistic regression; economic growth; non-default; logit model; robustness; canonical discriminant coefficients; prediction; significant differences; classification accuracy.

DOI: 10.1504/IJTGM.2024.145836

International Journal of Trade and Global Markets, 2024 Vol.20 No.3/4, pp.180 - 208

Received: 22 Sep 2024
Accepted: 14 Dec 2024
Published online: 29 Apr 2025 *

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