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All Journal International Journal of Management Science and Information Technology (IJMSIT)
Karto, Andriani
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Development of Econometric Models for Financial Performance Forecasting in Companies Nugroho, Adi; Apriyane Patty, Maria; Karto, Andriani
International Journal of Management Science and Information Technology Vol. 4 No. 2 (2024): July - December 2024
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v4i2.3146

Abstract

This study develops an econometric model to predict corporate financial performance. The goal is to improve the accuracy of predictions by analysing relevant economic and financial variables. The model combines statistical and econometric analysis to identify significant input variables such as asset turnover, firm size, capital structure, and liquidity. The study also highlights the importance of external factors, such as environmental policies and knowledge management, that affect corporate financial performance. Using ARIMA and VAR models, the study shows that selecting the correct parameters, such as the number of lags, is critical to improving prediction accuracy. The developed model is evaluated based on RMSE, MAD, and MAPE metrics, which show that the econometric model offers more accurate predictions than the classical statistical model. These results contribute significantly to understanding corporate financial performance dynamics and can be a reliable tool in strategic decision-making across various industry sectors.
Development of Econometric Models for Financial Performance Forecasting in Companies Nugroho, Adi; Apriyane Patty, Maria; Karto, Andriani
International Journal of Management Science and Information Technology Vol. 4 No. 2 (2024): July - December 2024
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v4i2.3146

Abstract

This study develops an econometric model to predict corporate financial performance. The goal is to improve the accuracy of predictions by analysing relevant economic and financial variables. The model combines statistical and econometric analysis to identify significant input variables such as asset turnover, firm size, capital structure, and liquidity. The study also highlights the importance of external factors, such as environmental policies and knowledge management, that affect corporate financial performance. Using ARIMA and VAR models, the study shows that selecting the correct parameters, such as the number of lags, is critical to improving prediction accuracy. The developed model is evaluated based on RMSE, MAD, and MAPE metrics, which show that the econometric model offers more accurate predictions than the classical statistical model. These results contribute significantly to understanding corporate financial performance dynamics and can be a reliable tool in strategic decision-making across various industry sectors.
Co-Authors Adi Nugroho Apriyane Patty, Maria