Analisis Rasio Keuangan Untuk Memprediksi Kondisi Financial Distress (Studi Empiris Pada Perusahaan Property dan Real Estate yang Terdaftar Di BEI Tahun 2018-2020)

Zulma, Gandy Wahyu Maulana; Wahyudi, Ilham; Sutyowati, Sutyowati

doi:10.32832/neraca.v17i1.7090

Cited by 1 publication

(1 citation statement)

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“…Additionally, the use of specific financial ratios and indicators, such as return on equity (ROE), debt-to-equity ratio (DER), and current ratio (CR), in machine learning models has been found to significantly impact the prediction of financial distress conditions [39]. This emphasizes the importance of feature selection and the incorporation of relevant financial variables in developing accurate distress prediction models.…”

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%