Identifying the Determinants of Financial Distress for Public Listed Companies in Malaysia

Ahmad Monir Abdullah

Abstract


ABSTRACT

 

Companies that face financial distress are always regarded as the root cause of enormous financial and economic losses for many stakeholders and at the same time, contribute to social unrest within the society. Identifying the determinants of financial distress in advance will bring many advantages to stakeholders so that they can manage their companies effectively. This study aimed to identify the determinants of financial distress for Malaysian public listed companies (PLC) by utilising financial ratios and market data. Additionally, this study focuses on finding a better distress prediction model between the traditional statistical approach that utilises a logistic regression and an artificial neural networks (ANN) model. Sixteen ratios were selected in the study and two techniques were used to assess the data of 192 Malaysian PLC. The empirical findings from this research show that current assets turnover (CAT), working capital to total assets (WCTA,) and retained earnings to total assets (RETA) display the highest ability to distinguish between financially distressed and non-distressed groups. The results also indicate that the mentioned variables possessed a high discriminant and predictive power. This study  also found that the ANN model has a higher predictive accuracy compared to the logistic regression model.

 

Keywords: Financial distress prediction; Malaysian public listed companies; emerging market; artificial neural networks; logistic regression analysis

 

ABSTRAK

 

Syarikat-syarikat yang menghadapi tekanan kewangan lazimnya  dianggap sebagai punca utama kerugian ekonomi yang besar bagi banyak pihak berkepentingan dan pada masa yang sama, menyumbang kepada pergolakan sosial dalam masyarakat. Mengenalpasti penyebab kesulitan kewangan pada peringkat awal akan membawa banyak kelebihan kepada pihak berkepentingan supaya mereka boleh mengurus syarikat mereka dengan berkesan. Kajian ini bertujuan untuk mengenal pasti penyebab kesulitan kewangan bagi syarikat-syarikat awam Malaysia dengan menggunakan nisbah kewangan dan data pasaran semasa. Selain itu, kami juga menumpukan kepada mencari model ramalan yang lebih baik antara pendekatan statistik tradisional yang menggunakan regresi logistik dan model rangkaian saraf buatan (ANN). Enam belas nisbah dipilih dalam kajian ini dan dua teknik digunakan untuk menilai 192 data syarikat-syarikat awam Malaysia. Penemuan empirikal dari kajian ini menunjukkan bahawa perolehan aset semasa (CAT)), modal kerja kepada jumlah aset (WCTA) dan pendapatan terkumpul kepada jumlah aset (RETA) menunjukkan keupayaan tertinggi untuk membezakan antara kumpulan kewangan yang bermasalah dan tidak bermasalah. Hasil kajian ini juga menunjukkan bahawa pembolehubah tersebut mempunyai kekuatan diskriminasi dan ramalan yang tinggi. Kami juga mendapati bahawa model ANN mempunyai ketepatan ramalan yang lebih tinggi berbanding dengan model regresi logistik.

Kata Kunci: Ramalan kesulitan kewangan; syarikat-syarikat awam Malaysia; pasaran baru muncul; rangkaian saraf buatan; analisis regresi logistik.


Keywords


Financial distress prediction, artificial neural networks, logistic regression analysis, multivariate discriminant analysis.

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