Analisis Akurasi Model-Model Prediksi Financial Distress

This study aims to find the most accurate predictor model of financial distress. The company has the potential to go bankrupt. Bankruptcy can be predicted using an accurate predictor model as an early warning to anticipate financial distress. This research was conducted on the global retail industry which is included in Kantar's 2019 Top 30 Global Retails (EUR). The data in this study were taken from 60 annual reports for the 2018-2019 period and a sample of 30 on global retail companies. The accuracy rate is calculated by the number of correct predictions divided by the total data and multiplied by one hundred percent. This study compares four predictor models of financial distress, namely the Altman model, the Springate model, the Taffler model, and the Grover model. With the results of the study, the Grover model has the highest level of accuracy, which is 76.67%.

Analisis Model Altman, Grover, dan Zmijewski dalam Memprediksi Financial Distress Perusahaan pada Industri Manufaktur di Indonesia

This research was conducted to analyze the model altman score, grover score, zmijewski score, in predicting financial distress in the manufacturing industry. This type of research in this research is quantitative with descriptive methods. The object of this research is a company that has been delisted from the Indonesia Stock Exchange in the 2015-2018 period. The object-taking technique in this study was purposive sampling, amounting to 16. The results of this study indicate that the Altman model has an accuracy rate of 25%, Grover has an accuracy rate of 6.25%, Zmijewski has an accuracy rate of 50%. From the three bankruptcy analysis models used in this study it can be concluded that the Zmijewski model is best used as a bankruptcy detector with an accuracy rate of 50%. This is because a company that goes bankrupt has a tendency to generate a small net capital of its total assets, the company's ability to generate profit before interest and taxes from its assets is getting smaller, the lower the level of company sales using all of its assets, and the less likely the profit before tax can be cover current debts owned by the company.

Is It Possible to Make Money on Investing in Companies Manufacturing Solar Components? A Panel Data Approach

In the interests of the environment, many countries set limits on the use of non-renewable energy sources and promote renewable energy sources through policy and legislation. Consequently, the demand for components for renewable energy systems exhibits an upward trend. For this reason, managers, investors, and banks are interested in knowing whether investing in a business associated with the semiconductor and related device manufacturing sector, especially the photovoltaic (PV) systems manufacturers, is worthy of a penny. Using a sample for the period of 2015-2018, we apply a new approach to panel data, extending existing research using Classification Trees with the k-Nearest Neighbor and Altman model. Our aim is to analyze the financial conditions of enterprises to identify key indicators that distinguish companies producing PV system components (labeled “green, G”) from companies that do not manufacture PV components (“red, R”). Our results show that green companies can be distinguished from red companies at classification accuracies of 86% and 90% for CRT and CHAID algorithms in Classification Trees method and 93% for k-Nearest Neighbor method, respectively. Based on the Altman model and the analysis of crucial ratios, we also find that green businesses are characterized by lower financial performance although future ratio values may equal or exceed the values for the red companies if current upward trends are sustained. Therefore, investing in green companies presents a viable alternative.

Comparative Analysis of Altman, Springate, and Zmijewski Models in Predicting the Bankruptcy of Retail Companies in Indonesia and Singapore

This study aims to determine whether there is a difference in the prediction results between the Altman, Springate, and Zmijewski models and the most accurate prediction model for predicting the bankruptcy of retail companies in Indonesia and Singapore. This research is descriptive quantitative. The sampling method used was purposive sampling with 15 Indonesian retail companies and 15 Singapore retail companies. This study used descriptive analysis, normality test, and One Way ANOVA test using an SPSS program. The results showed that: 1) There are significant differences between the Altman model, Springate model, and Zmijewski model in Indonesian retail companies. 2) There is a significant difference between the Altman model, Springate model, and Zmijewski model in the Singapore retail companies. 3) The most accurate model in predicting the bankruptcy of Indonesian retail companies is the Zmijewski model. 4) The most accurate model in predicting the bankruptcy of Singapore retail companies is the Altman model.

Financial Distress Analysis in an Indonesian Textile Company

This research aimed to examine the health of textile companies by using the Altman Z-Score method. The Altman model is used to determine the effect on financial distress through Working Capital to Total Asset (WCTA), Retained Earning to Total Asset (RETA), Earning Before Interest and Tax to Total Asset (EBITA), Market Value of Equity to Book Value of Liabilities (MVEBL) and Sales to Total Asset (STA). The population in this study was textile companies for the period 2016-2019. The sample was 14 textile companies with a research time of 4 years resulting in 56 samples obtained by purposive sampling. The results indicated that WCTA, RETA, EBITA, MVEBL and STA had a simultaneous effect on financial distress, but they had no effect separately. Keywords: Altman Z-Score, financial distress, bankruptcy

Altman Model Verification Using a Multi-Criteria Approach for Slovakian Agricultural Enterprises

The Altman model is still one of the most widely used predictive models in the 21st century, and it aims to highlight the differences between bankrupt and healthy enterprises. This model has been modified several times; its most well-known forms are from 1968, 1983 and 1995. However, the use of the Altman Z-score for Slovak enterprises is more than questionable. The unsuitability of the model for the conditions of Slovak companies has been confirmed by several empirical surveys. The objective of this study was to verify the validation of these three variants of the Altman model, depending on how an unprosperous company is identified, using a sample of 996 agricultural enterprises operating in the Slovak Republic. Four indicators were selected for the identification of an unprosperous enterprise – economic results, total liquidity, equity, and economic value added – and they were monitored over the last year or, as the case may be, over the last three years from 2014 to 2016. Using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Coefficient of variation (CV) methods as an objective method for weight determination, a combination of the Altman model from 1968 and the negative total liquidity in the last reference year was determined to be the best. One of our main findings is that the way in which an unprosperous enterprise is identified is a significant factor affecting the overall reliability of the Altman model. The Altman model from 1968 and 1983 confirmed the differences resulting from the natural conditions in which the enterprises operate. The economic results and economic value added (EVA) proved to be inappropriate as indicators for defining an unprosperous enterprise in the conditions of the Slovak Republic.

Analisis Model Altman, Model Zmijewski dan Model Ohlson Untuk Memprediksi Financial Distress

Penelitian ini bertujuan untuk memprediksi tingkat financial distress pada PT. Krakatau Steel (persero) Tbk yang merupakan perusahaan BUMN atau Badan Usaha Milik Negara yang bergerak dibidang . Perusahaan BUMN membantu untuk pertumbuhan ekonomi Indonesia. Oleh sebab itu, setiap perusahaan bersaing untuk mendapatkan profit yang besar. Jika perusahaan tidak bisa mendapatkan profit perlu diketahui lebih dalam apakah kondisi keuangan perusahaan baik-baik saja atau mengalami suatu masalah. Kesulitan keuangan bisa saja meimpa perusahaan manapun dan jika tidak segera ditangani akan mengakibatkan perushaan mengalami kebangkrutan. Prediksi financial distress merupakan salah satu cara yang harus dilakukan perusahaan agar mengetahui kondisi keuangan perusahannya. Pada penelitian ini digunakan tiga model untuk memprediksi financial distress pada PT. Krakatau Steel (persero) Tbk. Berdasarkan hasil penelitian menggunakan model altman PT. Krakatau Steel (persero) Tbk pada tahun 2012 berada di posisi grey zone, sedangkan dari tahun 2013-2018 perusahaan berada diposisi distress zone yang artinya perusahaan meglami kesulitan keuangan (financial distress). Pada model zmijewski, dari tahun 2012-2018 perusahaan dinyatakan tidak mengalami kebangkrutan meskipun perusahaan mengalami laba setelah pajak yang bernilai negatif, total utang yang meningkat juga kewajiban lancar yang mengalami kenaikan. Pada model ohlson, tahun 2012 perusahaan diprediksi mengalami kebangkrutan dan pada tahun 2013- 2018 perusahaan dinyatakan tidak mengalami kebangkrutan meskipun perusahaan mengalami total tang yang meningkat, menurunnya modal kerja perusahaan, serta laba yang didapat perusahaan bernilai negatif.

USE OF THE BLAND-ALTMAN PLOT FOR GRAPHICAL DEMONSTRATION OF RESULTS IN THE SHARING ECONOMY

Today, the sharing economy is a widely discussed topic where people ask themselves what it is, how it works, and what it brings to the society. It occurs in many social spheres; this research paper focuses on the sharing economy in the field of transport. The sharing economy is a phenomenon of the 21st century; the fact that it is a very current topic is also evidenced by the amount of related research published in the databases. This paper aims to apply the BlandAltman model, commonly used in medicine, to the sharing economy. Its aim is to point out the functionality and possibilities of using non-traditional approaches in various areas of study. The introduction describes the sharing economy and the latest publications dealing with it in the field of transport, with a focus on Uber. The methodology section describes the Bland-Altman model and the authors who discuss it. In the results section, we offer the results of the application of the Bland-Altman model on the data obtained from 110 Uber rides in Prague. In addition to this method, regression analysis was also applied. The discussion and conclusion section summarizes the results and mentions essential publications in the field.

Prediksi Kebangkrutan Menggunakan Altman Z-Score dan Zmijewski pada PT Prasidha Aneka Niaga Tbk Periode 2014-2018

The growth of the company listed on the IDX is experiencing the highest achievement as the most listed exchange of IPO in the ASEAN region. The fluctuation of the profit generated by PT. Prasidha Aneka Niaga Tbk. which tends to suffer losses in the year of research, the research aims to predict bankruptcy at PT. Prasidha Aneka Niaga Tbk. using the Altman Z-Score and Zmijewski method. The method of study that was used is a descriptive method. The data used is based on the annual financial report. The results showed that bankruptcy predictions using the Altman model of the company's Z-Score were in the grey area and had potential bankruptcy category. While bankruptcy predictions using Zmijewski the company is in a healthy category. Based on the said calculations, the company has produced different results, but judging from the ratio of each model the company is in position of vigilant bankruptcy.

Analysis of The Bankruptcy of Companies with Altman Model and Ohlson Model

Often companies that have been operating for a certain period forced to disperse because of increased financial distress that caused bankruptcy. There are two models that can be used to predict bankruptcy of companies, that is Altman model (Z-score) and Ohlson model. This study aims to determine the accuracy of the Altman model (Z-Score) and Ohlson's model in predicting bankruptcy of delisting companies in Indonesia Stock Exchange for 2015-2019 period.The population in this study were all of delisting companies in Indonesia Stock Exchange for 2015-2019 period, totaled 17 companies. The number of samples used in this study were 8 companies, by using purposive sampling method. Data analysis used data processing application SPSS version 25. The results showed that accuracy of the Altman model is 58.3%, while the Ohlson model is 79.2%. The conclusion of this research Ohlson model has the highest accuracy that compared to Altman model in predicting bankruptcy at delisting companies in Indonesia Stock Exchange for 2015-2019 period, with accuracy values of Ohlson model is 79.2% and 58.3% for the Altman model. For further researchers, it is expected to increase the number of samples of companies studied and extend the research periods in order to provides more accurate results, and combining the Altman and Ohlson models with other bankruptcy prediction models that can be applied in companies in Indonesia.