亚洲矿业的财务困境

Dinda Azzahra, Yunus Harjito, A. Suseno
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引用次数: 1

摘要

本研究旨在揭示和发现东南亚采矿业潜在的财务困境。对潜在财务困境的检测是根据财务比率进行的,包括盈利能力、流动性、杠杆率和运营能力。通过使用奥特曼模型修正或众所周知的Z-Score来代表财务困境。本研究中的人群是在东南亚证券交易所上市的矿业公司。方法在三年(2017-2019年)内,84家公司进行了有目的的抽样,以获得所使用人群的样本量,并获得了多达140个样本。本研究中使用的数据分析是使用Eviews 9软件进行的面板数据回归分析。结果表明,盈利能力对财务困境有正向影响,而经营能力对潜在财务困境有负向影响。但另外两个变量,即流动性和杠杆率,预计会影响财务困境,但尚未证明对财务困境的影响。印尼文摘要本研究旨在揭示和发现南亚采矿业潜在的财务困境。财务困境潜力的检测是根据财务比率进行的,包括盈利能力、流动性、杠杆率和运营能力。财务困境是使用奥特曼修正模型或通常称为Z-Score来练习的。这项研究的对象是一家在南亚效率交易所上市的矿业公司。有目的的抽样方法用于从使用的人群中获得样本量,并在3年(2017-2019)内获得了84家公司的140个样本。本研究中使用的数据分析是使用Eviews软件9对面板数据进行的回归分析。研究表明,盈利能力对财务困境有积极影响,而运营能力对财务危机潜力有负面影响。但另外两个变量——预计会影响财务困境的流动性和杠杆率——将但不会影响财务困境。
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DETEKSI FINANCIAL DISTRESS PADA INDUSTRI PERTAMBANGAN DI ASIA TENGGARA [DETECTION OF FINANCIAL DISTRESS IN THE MINING INDUSTRY IN SOUTHEAST ASIA]
This study aims to reveal and detect potential financial distress in the mining industry in Southeast Asia. Detection of potential financial distress is carried out on financial ratios, including profitability , liquidity , leverage and operating capacity. Financial distress proxied by using Altman Model Modification or popularly known as the Z-Score. The population in this study are mining companies listed on the Stock Exchange in Southeast Asia. Methods purposive sampling used to obtain the sample size of the population used and obtained as many as 140 samples were comprised of 84 companies over three years (2017-2019). Data Analysis used in this study is panel data regression analysis using software Eviews 9. The results showed that the profitability of a positive influence on financial distress and operating capacity negatively affect the potential financial distress . But two other variables namely liquidity and leverage that could be expected to affect the financial distress but not proven effect on the financial distress. Abstrak dalam Bahasa Indonesia Penelitian ini bertujuan untuk mengungkap dan mendeteksi terjadinya potensi financial distress pada industri pertambangan di Asia Tenggara. Deteksi potensi financial distress dilakukan terhadap rasio-rasio keuangan, diantaranya adalah profitabilitas , likuiditas , leverage , dan operating  capacity. Financial distress diproksikan dengan menggunakan Model Altman Modifikasi atau yang  populer dikenal dengan Z- Score . Populasi dalam penelitian ini adalah perusahaan pertambangan yang terdaftar di Bursa Efek se Asia Tenggara. Metode purposive sampling digunakan untuk memperoleh ukuran sampel dari populasi yang digunakan dan diperoleh sebanyak 140 sampel yang terdiri dari 84 perusahaan selama 3 tahun (2017-2019). Analisis data yang digunakan dalam penelitian ini adalah analisis regresi data panel dengan menggunakan Software Eviews 9 . Hasil penelitian menunjukkan bahwa profitabilitas berpengaruh positif terhadap financial distress dan operating capacity berpengaruh negatif terhadap potensi financial distress. Namun dua variabel lainnya yakni likuiditas dan leverage yang diduga dapat mempengaruhi financial distress akan tetapi tidak terbukti berpengaruh terhadap financial distress.
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