Determinants of profitability in non-financial sectors: a panel data and machine learning analysis of Indonesian firms from 2012 to 2023

Christian, Boedy (2025) Determinants of profitability in non-financial sectors: a panel data and machine learning analysis of Indonesian firms from 2012 to 2023. Masters thesis, Universitas Pelita Harapan.

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Abstract

This study examines the determinants of profitability in Indonesia’s company from 2012 to 2023, using quarterly data from 816 companies listed on the Indonesia Stock Exchange. Return on Assets (ROA) and Earnings per Share (EPS) are employed as profitability measures, while firm size (LSIZE), company efficiency (CE), liquidity (LIQ), market power (MP), sales growth (SG), and sustainable growth rate (LSGR) are investigated as explanatory variables. Results from panel regression analysis reveal that except for LIQ, all variables are have positive significant impact on profitability. Meanwhile LIQ doesn’t have significant impact on profitability. The analysis is further refined using machine learning techniques, specifically Random Forest, XGBoost, and deep learning neural network that concludes the most important variable that influence ROA is company efficiency while the most important variable that influence EPS is firm size. These findings provide practical insights for management in the company to develop strategies to enhance profitability and stability for company. / Penelitian ini mengkaji faktor-faktor yang memengaruhi profitabilitas perusahaan di Indonesia dari tahun 2012 hingga 2023, dengan menggunakan data triwulanan dari 816 perusahaan yang terdaftar di Bursa Efek Indonesia. Return on Assets (ROA) dan Earnings per Share (EPS) digunakan sebagai ukuran profitabilitas, sementara ukuran perusahaan (LSIZE), efisiensi perusahaan (CE), likuiditas (LIQ), kekuatan pasar (MP), pertumbuhan penjualan (SG), dan tingkat pertumbuhan berkelanjutan (LSGR) dianalisis sebagai variabel independen. Hasil analisis regresi panel menunjukkan bahwa semua variabel kecuali LIQ berpengaruh positif signifikan terhadap profitabilitas. Sementara itu, LIQ tidak menunjukkan pengaruh yang signifikan. Analisis ini diperdalam dengan teknik machine learning, khususnya Random Forest, XGBoost, dan deep learning neural network, yang menyimpulkan bahwa variabel paling berpengaruh terhadap ROA adalah efisiensi perusahaan, sedangkan terhadap EPS adalah ukuran perusahaan. Temuan ini memberikan wawasan praktis bagi manajemen perusahaan untuk mengembangkan strategi dalam meningkatkan profitabilitas dan stabilitas perusahaan.
Item Type: Thesis (Masters)
Creators:
Creators
NIM
Email
ORCID
Christian, Boedy
NIM01804230011
christian.boedy81@gmail.com
UNSPECIFIED
Contributors:
Contribution
Contributors
NIDN/NIDK
Email
Thesis advisor
Budhidharma, Valentino
NIDN0330117604
valentino.budhidharma@uph.edu
Uncontrolled Keywords: profitability ; random forest ; xgboost ; deep learning neural network
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Divisions: University Subject > Current > Faculty/School - UPH Karawaci > Business School > Master of Management
Current > Faculty/School - UPH Karawaci > Business School > Master of Management
Depositing User: Boedy Christian
Date Deposited: 28 Jul 2025 02:45
Last Modified: 28 Jul 2025 02:45
URI: http://repository.uph.edu/id/eprint/70154

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