Modeling Funding for Industrial Projects Using Machine Learning: Evidence from Morocco

Soukaina Laaouina () and Mimoun Benali
Additional contact information
Soukaina Laaouina: Laboratory of Research and Studies in Management, Entrepreneurship and Finance (LAREMEF), National School of Commerce and Management of Fez, Sidi Mohamed Ben Abdellah University, Fes 30050, Morocco
Mimoun Benali: Laboratory of Research and Studies in Management, Entrepreneurship and Finance (LAREMEF), National School of Commerce and Management of Fez, Sidi Mohamed Ben Abdellah University, Fes 30050, Morocco

JRFM, 2024, vol. 17, issue 4, 1-20

Abstract: Moroccan manufacturing companies investing in the metallurgical, mechanical, and electromechanical industries sector are among the contributors to the growth of the national economy. The projects they are awarded do not have the same specific features as those of operating activities within other companies. They share several common features, making them particularly complex to fund. In such circumstances, supervised machine learning seems to be a suitable instrument to help such enterprises in their funding decisions, especially given that linear regression methods are inadequate for predicting human decision making as human thinking is a complicated system and not linear. Based on 5198 industrial projects of 53 firms operating in the said sector, four machine learning models are used to predict the funding method for some industrial projects, including are decision tree, random forest, gradient boosting, and K-nearest neighbors (KNN). Among the four machine learning methods, the gradient boosting method appears to be most effective overall, with an accuracy of 99%.

Keywords: funding decision; industrial projects; supervised machine learning; gradient boosting (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1911-8074/17/4/173/pdf (application/pdf)
https://www.mdpi.com/1911-8074/17/4/173/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:17:y:2024:i:4:p:173-:d:1380560

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().