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All Journal Global Academy of Business Studies
Brady Rikumahu
Telkom University, Indonesia
Economics, Econometrics & Finance

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The impact of capital structure, investment growth, and liquidity on financial performance of automotive companies and its components on the Indonesia Stock Exchange (2018-2022) Riska Mandasari; Brady Rikumahu
Global Academy of Business Studies Vol. 1 No. 3 (2025): January
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/gabs.v1i3.3568

Abstract

Purpose: This study analyzes the influence of capital, investment, and liquidity structures on the financial performance of automotive companies and their components listed on the Indonesia Stock Exchange (IDX) between 2018 and 2022. Research methodology: Multiple regression analysis was used, and data were obtained from the company's annual financial statements from 2018 to 2022. The findings of this study are expected to provide important insights for financial managers and stakeholders in managing company finances and serve as a reference for investors and creditors in evaluating the financial performance of a company. Results: The results show that capital structure has a significantly positive effect on financial performance, while investment and liquidity do not significantly affect it. Conclusion: Capital structure significantly improves the financial performance of automotive companies, whereas investment and liquidity show no significant effects. Therefore, optimizing the capital structure is crucial for strengthening financial strategies in the Indonesian automotive sector. Limitation: The study's limitation is that it focuses solely on automotive companies listed on the Indonesia Stock Exchange during the specified time frame. Contribution: The study's contribution is that it provides empirical evidence regarding the impact of capital structure, investment growth, and liquidity on financial performance in the Indonesian automotive industry, which can inform future research and practical applications in this context.
Prediction of financial distress in transportation and logistics companies before, during and after the Covid-19 pandemic listed on the Indonesia Stock Exchange Sinta Dewi; Brady Rikumahu
Global Academy of Business Studies Vol. 1 No. 2 (2024): October
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/gabs.v1i2.3576

Abstract

Purpose: This study aims to predict financial distress in transportation and logistics companies before, during, and after the Covid-19 pandemic. Research Methodology: A total of 23 transportation and logistics companies were selected as the research sample using purposive sampling method. Secondary data from audited financial statements for 2019–2023 were analyzed. Three financial ratios—Current Ratio (CR), Return on Assets (ROA), and Debt-to-Asset Ratio (DAR)—were used as input variables. An Artificial Neural Network (ANN) with a multilayer perceptron backpropagation algorithm was employed to train and test the prediction models. The best architecture was identified by comparing the model performance across variations in hidden neurons. Results: The results reveal that companies reported as financially distressed have lower average values for the three ratios than companies not experiencing financial distress, making them suitable input variables. The best artificial neural network architecture in this study included an input layer with 60 neurons, a hidden layer with 15 neurons, and an output layer with a single neuron. This architecture achieved a training performance mean square error (MSE) of 0.125004 and an R-value of 50.00%. The study's findings suggest that 12 companies are likely to experience financial distress. Conclusions: Financial ratios are effective indicators of distress, and ANN models can predict potential bankruptcy with a reasonable accuracy. Limitations: This study is limited to three financial ratios and a single sector, which may not fully capture the broader determinants of financial distress. Contribution: This study contributes to the financial distress prediction literature by applying ANN to transportation and logistics firms in Indonesia and offers practical tools for stakeholders to anticipate risks and design preventive strategies.
Co-Authors Riska Mandasari Sinta Dewi