The Effect of Financial Ratios on the Possibility of Financial Distress in Selected Manufacturing Companies Which Listed in Indonesia Stock Exchange

Lumbantobing, Rudolf

doi:10.2991/aebmr.k.200331.014

Cited by 5 publications

(7 citation statements)

References 9 publications

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“…Additionally, [12] delves into the predictive capabilities of traditional distress prediction models, particularly in identifying earlystage financial distress for firms. Leveraging financial ratios such as leverage, liquidity, profitability, and activity ratios [13]. Moreover, the impact of leverage on corporate financial distress has been studied, revealing a positive effect on financial distress measured by the Z-score [14].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Kadkhoda,

Amiri

2024

IEEE Access

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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.

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Section: Literature Reviewmentioning

confidence: 99%

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

Kadkhoda,

Amiri

2024

IEEE Access

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“…Prove that liquidity ratios have a significant effect in determining a company's financial distress.. According to (Lumbantobing, 2020), Debt ratio expresses how much the company's assets are financed by debt. This variable was proxied by debt to asset ratio (DAR).…”

Section: Current Ratiomentioning

confidence: 99%

Determination of Financial Distress in Transportation Equipment Industry: Evidence from Indonesia

2024

qas

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This study aims to measure the effect of leverage, current ratio, Operating expenses Operating income (OEOI), firm size, and sales growth on financial distress in Transportation Equipment Industry listed on the Indonesian stock exchange for the period 2018 to 2020.The uniqueness of this research is that it is a case study in a mature Transportation Equipment Industry company so that it examines in depth, detail, intensively, and thoroughly on variables, especially those related to financial distress.The analytical method used in this study is multiple regression analysis using the SPSS version 23. The results of this study provide an explanation of Leverage and Sales Growth partially having a negative effect, Current Assets, Operating expenses Operating income , and Firm Size. and a partial positive effect on financial distress. dependent financial distress can be explained by the independent variables (leverage, current ratio, OEOI, Firm size, and Sales growth) of 50.5 percent while 49.5 percent is influenced by other variables from this study.

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“…These findings corroborate the conclusions of prior research that the liquidity ratio has a strong negative influence on the likelihood of financial hardship. (Lumbantobing, 2020).…”

Section: Organization Movement (Tat) An Effect On Bond Ratingmentioning

confidence: 99%

Analysis of The Effect Earnings Management, Financial Ratios, Governance On Bond

Hartono

Krisdiana

Purwanggono

et al. 2022

Quant. Econ. Manag. Stud.

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The purpose of this study is to analyze the concentration on board profit factors, the proportion of liquidity, administrative ownership and review quality used to anticipate organizational security ratings. This study intends to provide experimental evidence that board income, monetary proportion and corporate administrative mechanisms affect security ratings. The subject of this exploration is an organization listed on the IDX and registered in Indonesia, PT. PEFINDO 2016-2019. Information check using strategic relapse investigation. The results showed that executive earnings, the proportion of liquidity, administrative ownership and review quality had an effect on bond ratings. While action proportion, market price proportion, institutional ownership and free head have no impact on security evaluation.

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The Effect of Financial Ratios on the Possibility of Financial Distress in Selected Manufacturing Companies Which Listed in Indonesia Stock Exchange

Cited by 5 publications

References 9 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

Determination of Financial Distress in Transportation Equipment Industry: Evidence from Indonesia

Analysis of The Effect Earnings Management, Financial Ratios, Governance On Bond

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