Pengaruh Likuiditas, Profitabilitas dan Leverage terhadap Prediksi Financial Distress

ABSTRACT The importance of financial management as a vital aspect of the company should be considered and managed properly if the company remains to maintain the sustainable business. Development of an early warning system model is needed to anticipate the financial distress, because this model can identify and improve the condition prior to the crisis. This research aims to examine the liquidity (CR, QR), profitability (ROI, ROE), leverage (DER, DAR), the effect of financial distress. The sample used in this research is the company's transportation, infrastructure and utilities sector in Indonesia Stock Exchange (BEI) for the period year 2012-2016. This research uses purposive sampling, which obtained 171 samples of observations from 39 companies. The hypothesis is examined by using regression logistic. The result shows that the ratio of ROI havesignificant effect on the company’s financial distress. While the CR, QR, ROE DER and DAR have no effect on the company’s financial distress. ABSTRAK Pentingnya pengelolaan keuangan sebagai aspek vital perusahaan benar-benar harus diperhatikan dan dikelola dengan baik jika perusahaan tetap untuk dapat menjaga kelangsungan hidup usahanya. Diperlukan pengembangan model sistem peringatan untuk mengantisipasi adanya financial distress, karena model ini dapat digunakan sebagai sarana untuk mengidentifikasikan bahkan untuk memperbaiki kondisi sebelum sampai pada kondisi krisis. Studi ini bertujuan untuk meneliti pengaruh likuiditas (CR, QR), profitabilitas (ROI, ROE), leverage (DER, DAR), terhadap prediksi financial distress pada perusahaan sektor transportasi, infrastruktur dan utilities yang terdaftar di Bursa Efek Indonesia periode tahun 2012-2016. Pengambilan sampel dengan menggunakan purposive sampling dan diperoleh sebanyak 171 sampel observasi dari 39 perusahaan. Pengujian hipotesis dilakukan dengan menggunakan regression logistik. Hasil pengujian hipotesis mununjukkan bahwa variabel ROI berpengaruh signifikan terhadap financial distress perusahaan. Sedangkan variabel CR, QR, ROE, DER dan DAR tidak berpengaruh terhadap financial distress perusahaan. JEL Classification: M40, G18

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Financial Early Warning System for Risk Detection and Prevention from Financial Crisis

Data Mining ◽

10.4018/978-1-4666-2455-9.ch082 ◽

2013 ◽

pp. 1559-1590

Author(s):

Nermin Ozgulbas ◽

Ali Serhan Koyuncugil

Keyword(s):

Financial Crisis ◽

Early Warning ◽

Financial Distress ◽

Business Performance ◽

Global Financial Crisis ◽

Stock Exchange ◽

Warning System ◽

Chi Square ◽

Risk Detection ◽

The Global Financial Crisis

Risk management has become a vital topic for all enterprises especially in financial crisis periods. All enterprises need systems to warn against risks, detect signs and prevent from financial distress. Before the global financial crisis that began 2008, small and medium-sized enterprises (SMEs) have already fought with important financial issues. The global financial crisis and the ensuring flight away from risk have affected SMEs more than larger enterprises When we consider these effects, besides the issues of poor business performance, insufficient information and insufficiencies of managers in finance education, it is clear that early warning systems (EWS) are vital for SMEs for detection risk and prevention from financial crisis. The aim of this study is to develop and present a financial EWS for risk detection via data mining. For this purpose, data of SMEs listed in Istanbul Stock Exchange (ISE) and Chi-Square Automatic Interaction Detector (CHAID) Decision Tree Algorithm were used. By using EWS, we determined the risk profiles and risk signals for risk detection and road maps for risk prevention from financial crisis.

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Early Warning System for SMEs as a Financial Risk Detector

Data Mining Applications for Empowering Knowledge Societies ◽

10.4018/978-1-59904-657-0.ch012 ◽

2009 ◽

pp. 220-238 ◽

Cited By ~ 8

Author(s):

Ali Serhan Koyuncugil

Keyword(s):

Data Mining ◽

Risk Management ◽

Early Warning ◽

Financial Management ◽

Early Warning System ◽

Quantitative Data ◽

Financial Risk ◽

Warning System ◽

Theoretical Background ◽

Effect Level

This chapter introduces an early warning system for SMEs (SEWS) as a financial risk detector which is based on data mining. In this study, the objective is to compose a system in which qualitative and quantitative data about the requirements of enterprises are taken into consideration, during the development of an early warning system. Furthermore, during the formation of system; an easy to understand, easy to interpret and easy to apply utilitarian model that is far from the requirement of theoretical background is targeted by the discovery of the implicit relationships between the data and the identification of effect level of every factor. Using the system, SME managers could easily reach financial management, risk management knowledge without any prior knowledge and expertise. In other words, experts share their knowledge with the help of data mining based and automated EWS.

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Establishment of a Financial Crisis Early Warning System for Domestic Listed Companies Based on Three Decision Tree Models

Mathematical Problems in Engineering ◽

10.1155/2020/8036154 ◽

2020 ◽

Vol 2020 ◽

pp. 1-7

Author(s):

Gang Wang ◽

Keming Wang ◽

Yingying Zhou ◽

Xiaoyan Mo ◽

Weilin Xiao

Keyword(s):

Financial Crisis ◽

Decision Tree ◽

Early Warning ◽

Financial Management ◽

Early Warning System ◽

Comprehensive Evaluation ◽

Warning System ◽

Predictive Ability ◽

Tree Models ◽

The Impact

The financial crisis is a realistic problem that the general enterprise must encounter in the process of financial management. Due to the impact of the COVID-19 and the Sino-US trade war, domestic companies with unsound financial conditions are at risk of shutdowns and bankruptcies. Therefore, it is urgently needed to study the financial warning of enterprises. In this study, three decision tree models are used to establish the financial crisis early warning system. These three decision tree models include C50, CART, and random forest decision trees. In addition, the ROC curve was used for comprehensive evaluation of the accuracy analysis of the model to confirm the predictive ability of each model. This result can provide reference for domestic financial departments and provide financial management basis for the investing public.

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