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p-Index From 2021 - 2026
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This Author published in this journals
All Journal Journal of Managerial Sciences and Studies
Ilham SAM, Muchammad
Unknown Affiliation
Humanities Decision Sciences, Operations Research & Management Social Sciences

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Mitigating Market Power in Banking: Cost Efficiency and the Path Toward Consumer Welfare Mahjudin Mahjudin; Kristiawati, Indriana; Ilham SAM, Muchammad
Journal of Managerial Sciences and Studies Vol. 3 No. 2 (2025): Agustus: Journal of Managerial Sciences and Studies
Publisher : PT. Mawadaku Sukses Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61160/jomss.v3i2.80

Abstract

Purpose – This study investigates the association between cost efficiency and the welfare performance of Islamic banks in Indonesia, focusing on the social costs of market power. The analysis covers both privately owned and state-owned Islamic banks over the period 2009–2017. Design/methodology/approach – The research utilizes Ordinary Least Squares (OLS), Fixed Effects (FE) panel regression, and Quantile Regression (QR) models to account for unobserved heterogeneity and improve policy applicability. To address potential endogeneity and reverse causality, the Two-Stage Least Squares Instrumental Variable (2SLS-IV) method is employed. Findings – Empirical results demonstrate a significant positive link between cost efficiency and banks’ welfare performance, implying that improving efficiency can mitigate welfare losses. Furthermore, the impact of cost efficiency on welfare outcomes varies depending on the bank’s familiarity with local market dynamics. QR results highlight that while cost efficiency may not eliminate welfare losses at the lower quantiles (Q.25–Q.50), it remains a critical factor in minimizing such losses.
Forecasting Equity Market Performance: A Comparative Analysis of Linear Regression, Random Forest, and LSTM Approaches Dody Suhermawan; Didit Wiwaha, Krisna; Ilham SAM, Muchammad
Journal of Managerial Sciences and Studies Vol. 3 No. 2 (2025): Agustus: Journal of Managerial Sciences and Studies
Publisher : PT. Mawadaku Sukses Solusindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61160/jomss.v3i2.85

Abstract

This research explores the predictive capabilities of three distinct modeling approaches—Linear Regression, Random Forest, and Long Short-Term Memory (LSTM)—in forecasting stock prices using data from 29 companies, including the S&P 500 index, spanning from January 1, 2000, to June 27, 2024. Through the utilization of historical time-series data, the study evaluates model performance based on key statistical indicators: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The findings indicate that while Random Forest outperforms Linear Regression in terms of accuracy, the LSTM model consistently delivers superior results, attributed to its strength in capturing sequential dependencies within financial data. These insights contribute to the growing body of literature in financial analytics by highlighting the comparative strengths of traditional, ensemble-based, and deep learning methods for stock market prediction. Furthermore, the study opens up avenues for integrating advanced temporal models into future financial forecasting frameworks.
Co-Authors Didit Wiwaha, Krisna Dody Suhermawan Kristiawati, Indriana Mahjudin Mahjudin