Prediksi Financial Distress Perusahaan Go Publik di Bursa Efek Indonesia Dengan Menggunakan Logit Model (Studi Kasus pada Perusahaan Tercatat di Bursa Efek Indonesia)
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Institusion
Universitas Katolik Darma Cendika
Author
Aminati, Diah
Subject
HF5601 Accounting
Datestamp
2021-01-25 06:26:34
Abstract :
The company's expertise in predicting the condition of financial distress is the most important factor because by knowing the condition of a company's financial distress early on, then it can be taken to anticipate the conditions that lead to bankruptcy. This thesis is about the Ratio Analysis of Financial Condition Predicting Corporate Financial Distress noted that going public on the Indonesia Stock Exchange. Which are at issue is whether financial ratios using the logit regression significant effect on the condition of financial distress of listed companies? Are there differences in financial ratios of listed companies experiencing financial distress and non financial distress that go public on the Indonesia Stock Exchange? And how the prediction accuracy by using a logit model to determine the companies that experience financial distress and non financial distress above 60%? This study is useful to test the ability of the ratio of efficiency, profitability, Financial Lavarage and firm size dummy variables in predicting the emergence of the condition of financial distress on listed firms that go public. The research sample consists of 58 companies consisting of the observation period 2008-2010 with details of 29 companies that have non-financial distress kondosi and 29 companies that experienced financial distress. The research was based on a quantitative approach using logistic regression with SPSS. Variables that significantly affect the S/TA, EQ/TA, Size Companies. The most influential variables of the model obtained in other research is S/TA. The second variable is the biggest influence is the size of the Company. The third most influential variable is positive EQ / TA. Hypotheses are tested using the analysis model independent variables in 2008-2010. The results of data analysis using logistic regression analysis states that the model generates the prediction accuracy of 86%, and variable levels of the company's sales trend, the size of the company, and the company's total assets have a significant effect on the probability of the condition of financial distress prediction with a significance level of 5%.
The company's expertise in predicting the condition of financial distress is the most important factor because by knowing the condition of a company's financial distress early on, then it can be taken to anticipate the conditions that lead to bankruptcy. This thesis is about the Ratio Analysis of Financial Condition Predicting Corporate Financial Distress noted that going public on the Indonesia Stock Exchange. Which are at issue is whether financial ratios using the logit regression significant effect on the condition of financial distress of listed companies? Are there differences in financial ratios of listed companies experiencing financial distress and non financial distress that go public on the Indonesia Stock Exchange? And how the prediction accuracy by using a logit model to determine the companies that experience financial distress and non financial distress above 60%? This study is useful to test the ability of the ratio of efficiency, profitability, Financial Lavarage and firm size dummy variables in predicting the emergence of the condition of financial distress on listed firms that go public. The research sample consists of 58 companies consisting of the observation period 2008-2010 with details of 29 companies that have non-financial distress kondosi and 29 companies that experienced financial distress. The research was based on a quantitative approach using logistic regression with SPSS. Variables that significantly affect the S/TA, EQ/TA, Size Companies. The most influential variables of the model obtained in other research is S/TA. The second variable is the biggest influence is the size of the Company. The third most influential variable is positive EQ / TA. Hypotheses are tested using the analysis model independent variables in 2008-2010. The results of data analysis using logistic regression analysis states that the model generates the prediction accuracy of 86%, and variable levels of the company's sales trend, the size of the company, and the company's total assets have a significant effect on the probability of the condition of financial distress prediction with a significance level of 5%.
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