The Effect of Profitability, Liquidity, and Solvency on Financial Distress of Textile and Garment Companies in Indonesia

Maximillian, Nathaniel; Septina, Fanny

doi:10.31294/eco.v6i2.12933

Cited by 3 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the context of specific sectors, such as the textile and garment industry, network analysis has been employed to predict financial distress and its impact, aiming to determine the effect of profitability, liquidity, and solvency on financial distress in these sectors [49], [42]. Furthermore, network analysis has been applied to predict financial distress in various sectors, including infrastructure, utilities, transportation, and retail trade, using financial ratios and macroeconomic variables as independent variables [50], [51], [45], [52].…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction

Kadkhoda,

Amiri

2024

IEEE Access

View full text Add to dashboard Cite

Financial distress prediction is a crucial aspect of financial planning, particularly in the face of emerging uncertainties. This study introduces a novel methodology for predicting financial distress by amalgamating network analysis and machine learning techniques. The approach involves establishing two networks among companies based on their similarity and correlation in key financial indicators. The first network reflects similarity across five features, while the second captures correlation in the most critical feature. Subsequently, seven network-centric features are extracted and integrated into the dataset as new variables. Additionally, community detection algorithms are applied to cluster companies, with the resulting labels added as categorical variables. This process yields a modified dataset comprising both initial and network-based variables. Five classification algorithms are employed to forecast financial distress across three scenarios. Initially, models are trained using only the initial features. In subsequent scenarios, network-centric features from both similarity and correlation networks are incorporated, enhancing the predictive accuracy of machine learning models. Notably, features from the similarity network play a pivotal role in this improvement. The proposed model not only showcases superior predictive capabilities but also offers a holistic understanding of the dynamic interactions among financial entities. The results underscore the efficacy of network-based strategies in refining financial distress prediction models, providing valuable insights for decision-makers.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction

Kadkhoda,

Amiri

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Seringkali diukur dengan Debt to Equity Ratio (DER), solvabilitas indikator utama kapasitas perusahaan untuk memenuhi komitmen jangka panjangnya ditunjukkan dengan jelas dalam literatur melalui dampaknya yang kuat terhadap financial distress. Maximillian dan Septina (Maximillian & Septina, 2022), misalnya, mencatat bahwa solvabilitas memiliki pengaruh negatif dan signifikan terhadap financial distress, sehingga menekankan perlunya situasi solvabilitas yang kuat dalam mengurangi volatilitas keuangan.…”

Section: Solvabilitas Berpengaruh Signifikan Terhadap Financial Distressunclassified

Kepemilikan Manajerial sebagai Moderasi Pengaruh Kinerja Keuangan terhadap Financial Distress di Indonesia

Rahmansyah,

Hudzafidah,

Bahri

2024

RELASI

View full text Add to dashboard Cite

One of the main problems influencing Indonesian corporate sustainability is financial distress. The moderating effect of managerial ownership on the impact of financial performance—more especially, liquidity ratios (cash ratio), solvency ratios (DER), and profitability ratios (ROI)—on financial distress is investigated in this paper. Data from 32 food-related companies listed on the Indonesia Stock Exchange (IDX) spanning 2020 to 2023 is used in the study. Analyzed using SmartPLS, which combines inner and outer model assessments to ascertain validity, dependability, and variable influence, Higher profitability and solvency ratios clearly help to lower financial stress; managerial ownership and liquidity have no direct bearing on this regard. Furthermore, managerial ownership indicates that higher managerial ownership reduces financial instability when solvency rises, so improving the relationship between solvency and financial distress. Managerial ownership does not, however, help to moderate the effects of liquidity and profitability on financial distress. These results underline the need of managerial ownership in improving corporate resilience against financial distress.

show abstract

“…Dengan demikian, meskipun perusahaan menjadi semakin likuid karena punya tambahan aset lancar, hal itu tidak membantu mengurangi potensi financial distress. Hasil penelitian ini bertentangan dengan milik Chrissentia & Syarief (2018) dan Lumbantobing (2020) yang menunjukkan bahwa likuiditas yang diwakili oleh rasio lancar (Current Ratio) berpengaruh negatif signifikan terhadap financial distress, namun sejalan oleh penelitian dari Maximillian & Septina (2022) yang menunjukkan bahwa likuiditas berpengaruh positif signifikan terhadap financial distress.…”

Section: Pengaruh Likuiditas Terhadap Financial Distressunclassified

Analisis Faktor Penyebab Financial Distress pada PT Garuda Indonesia (Persero) TBK

Lalita Kusuma,

Purnamasari

2023

ECODEMICA

View full text Add to dashboard Cite

Bankcruptcy is a problem that is often faced by companies, included Garuda Indonesia. Garuda Indonesia is a big transportation company. However, its financial situation is not as good as it seems. Based on the data collected, Garuda Indonesia has some problems related to liquidity, leverage, and profitability. If the company doesn't realize its problems, the situation will only get worse. Therefore, the research goal is to find out the impact of liquidity, leverage, and profitability on Garuda Indonesia's (GIAA) financial distress, with the expectation that the company can take a serious step to fix its financial situation. The study uses quantitative methods using the secondary data. The sample of the research are GIAA's quarterly financial report from first quarter of 2012-fourth quarter of 2019 and uses purposive judgement sampling as a sampling method. The research shows that GIAA's financial distress influenced by liquidity, while profitability and leverage has nonsignificant effect on financial distress. The implication of this research is that the company now must manage its assets, in form of airplanes, because those assets can cause bigger cost to be borne if the company cannot manage them wisely.

show abstract

The Effect of Profitability, Liquidity, and Solvency on Financial Distress of Textile and Garment Companies in Indonesia

Cited by 3 publications

References 8 publications

A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction

A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction

Kepemilikan Manajerial sebagai Moderasi Pengaruh Kinerja Keuangan terhadap Financial Distress di Indonesia

Analisis Faktor Penyebab Financial Distress pada PT Garuda Indonesia (Persero) TBK

Contact Info

Product

Resources

About