scholarly journals ANALISIS UJI AKURASI MODEL GROVER, SPRINGATE, DAN ZMIJEWSKI DALAM MEMPREDIKSI KEBANGKRUTAN PERUSAHAAN DELISTED DI BEI

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Komang Agus Rudi Indra Laksmana ◽  
Ayu Darmawati
Keyword(s):  
Data Analysis ◽  
Prediction Model ◽  
Predictive Model ◽  
Financial Distress ◽  
Bankruptcy Prediction ◽  
Accurate Prediction ◽  
Accuracy Rate ◽  
The Future

This study aimed at analyzing how the results of the Grover, Springate and Zmijewski models predict the bankruptcy of PT Citra Maharlika Nusantara Corpora Tbk for the period of June 2013 - September 2016. This study also aimed at measuring the accuracy of the bankruptcy prediction model and determined which predictive model of the three models was the most accurate. From the data analysis, it was found that Springate model was the most accurate prediction model with 100% accuracy rate to predict the bankruptcy of PT Citra Maharlika Nusantara Corpora Tbk compared to the Grover model with an accuracy rate of 71.48% and Zmijewski model with the lowest accuracy rate of 21.48%. The limitations of this study was this study only carried out in one company, thus in the future it is expected that the model will be tested in more than one company and type of business sector.Keywords: Financial Distress, Grover, Springate, Zmijewski ModelsPenelitian ini bertujuan untuk menganalisis bagaimana hasil dari model Grover, Springate dan Zmijewski dalam memprediksi kebangkrutan PT Citra Maharlika Nusantara Corpora Tbk periode Juni 2013 – September 2016 serta mengukur tingkat akurasi model prediksi kebangkrutan tersebut dan menentukan model prediksi manakah diantara ketiga model tersebut yang paling akurat. Model Springate menjadi model prediksi paling akurat dengan tingkat akurasi 100% untuk memprediksi kebangkrutan PT Citra Maharlika Nusantara Corpora Tbk dibandingakan dengan model Grover dengan tingkat akurasi 71,48% dan model Zmijewski dengan tingkat akurasi paling rendah sebesar 21,48%.Keterbatasan penelitian ini terletak pada pengujian model pada satu perusahaan di satu unit sektor usaha, kedepan bisa dilakukan pengujian pada berbagai jenis sektor usaha.Kata kunci: Financial Distress, Model Grover, Springate, Zmijewski

Research of High-Speed Railway Running Economic Benefit Based on Gray Model

2012 ◽  
Vol 253-255 ◽  
pp. 1273-1277
Author(s):  
Xue Dong Du ◽  
Na Ren
Keyword(s):  
Data Analysis ◽  
Prediction Model ◽  
Economic Benefit ◽  
High Speed ◽  
Accurate Prediction ◽  
Gray Model ◽  
Practical Application ◽  
High Speed Railway ◽  
Railway Stations ◽  
Train Operation

The research of high-speed railway running economic benefit is important to timely know well the train operation state for the railway administration. A prediction model of high-speed railway running economic benefit is proposed in this article based on Gray model. The Gray model is a good example to make accurate prediction of the development of matters. According to the data analysis of Beijing and Shanghai railway stations, we can know that the result of prediction model is accurate, so the prediction based on Gray model is scientific and reasonable in the practical application.

scholarly journals A Higher-Order N-gram Model to Enhance Automatic Word Prediction for Assamese Sentences Containing Ambiguous Words

2019 ◽  
Vol 8 (6) ◽  
pp. 2921-2926 ◽  
Keyword(s):  
Prediction Model ◽  
Predictive Model ◽  
Failure Rate ◽  
Predictive Models ◽  
Traditional Model ◽  
Word Prediction ◽  
Ambiguous Words ◽  
The Future ◽  
Model Improvement ◽  
N Gram

Word prediction is a technique which tries to suggest the users’ words after knowing the few input letters of the user. This predictive model also tries to generate the future words or next words of a sentence by observing earlier words of the sentence. In this research, two problems are combined, one is word prediction and the next is handling of ambiguous words. A word prediction model predicts the future words of a sentence by using n-gram based model. In general, predictive models use unigram, bigram or trigram models to predict the next words. In case of sentences consisting of ambiguous words, the predictive model by using only bigram or trigram cannot perform well to predict the next words. To enhance this prediction for ambiguous words, maximum of six previous input words are observed and try to predict almost the exact words after the ambiguous words in those particular contexts. Different level of experiments are done and the results are compared for modified or enhanced prediction model with the traditional prediction model, improvement on accuracy and failure rate are found in the enhanced model. The accuracy of the Traditional Model is 60.68% on the hand the accuracy of the Enhanced Model is 66.88%. The failure rate of the Traditional Model is 32.35% and the Enhanced Model is 29.17%

scholarly journals Application of Altman Modified Z-Score to Predict Financial Distress in the Indonesian Telecommunications Industry

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Rianti Fifriani ◽  
Perdana Wahyu Santosa
Keyword(s):  
Financial Distress ◽  
Stock Exchange ◽  
Financial Statements ◽  
Telecommunications Industry ◽  
Bankruptcy Prediction ◽  
Z Score ◽  
Purposive Sampling ◽  
Modification Method ◽  
The Future ◽  
The Right

Bankruptcy prediction is needed to assess the prospect of going concern and sustainability of the corporations in the future. This study aims to predict the bankruptcy of corporates with the Altman Z-Score Modification model in the telecommunications industry in Indonesia. The data used are the financial statements of the telecommunications industry that listing on the Indonesia Stock Exchange for the period 2011-2015. Samples for this study uses purposive sampling according to company criteria. The results of the study using the Altman Z-score modification method found two potentially bankrupt companies, namely Bakrie Telecom, Tbk, and Smartfren, Tbk. While Indosat, Tbk, and XL Axiata, Tbk have high financial distress potential due to liquidity and profitability problems that tend to weaken. Meanwhile, Telkom Indonesia, Tbk, and Infracom Inovisi financial concessions are relatively healthy and have the right business expectations

scholarly journals ANALISIS PREDIKSI FINANCIAL DISTRESS DENGAN MENGGUNAKAN MODEL SRINGATE PADA PERUSAHAAN PROPERTI DAN REAL ESTATE YANG TERDAFTAR DI BEI PERIODE 2019-2020

2022 ◽  
Author(s):  
GOVERNANCE: JURNAL POLITIK LOKAL DAN PEMBANGUNAN
Keyword(s):  
Data Analysis ◽  
Discriminant Analysis ◽  
Real Estate ◽  
Financial Distress ◽  
Stock Exchange ◽  
Accuracy Rate ◽  
Purposive Sampling ◽  
Data Analysis Technique ◽  
Real Estate Sector ◽  
Analysis Technique

This study aims to financial distress predict and the level of accuracy using the Springate model in the property and real estate sector listed on the Indonesia Stock Exchange for the 2019-2020 period. The population of this study is all property and real estate companies listed on the Indonesia Stock Exchange for the 2019-2020 period, so the population of this study managed to find 66 companies. Samples were selected based on predetermined purposive sampling criteria. The sample selected according to the specified criteria is 37 companies. The data analysis technique used the Springate S-Score discriminant analysis technique. The results of the bankruptcy analysis using the Springate method, namely in 2019 before the onset of covid-19 there were 27 property and real estate companies in financial distress and 10 companies in healthy condition (non-financial distress). In 2020, during the COVID-19 pandemic, there were additional companies that were in financial distress, namely 34 companies and only 3 companies that remained in a healthy condition (non-financial distress). Based on the results of the analysis of the Springate method in predicting bankruptcy in property and real estate sector companies, it has an accuracy rate of 62.2%.

Predicting Financial Distress in the Indian Banking Sector: A Comparative Study Between the Logistic Regression, LDA and ANN Models

2021 ◽  
pp. 097215092110267
Author(s):  
Nandita Mishraz ◽  
Shruti Ashok ◽  
Deepak Tandon
Keyword(s):  
Prediction Model ◽  
Financial Distress ◽  
Prediction Accuracy ◽  
Banking Sector ◽  
Banking System ◽  
Bankruptcy Prediction ◽  
Linear Discriminant ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Indian Banks

Financial distress is a socially and economically significant issue that affects almost every firm across the world. Predicting financial distress in the banking industry can substantially aid in the reduction of losses and can help avoid misallocation of banks’ financial resources. Models for financial distress prediction of banks are being increasingly employed as important tools to identify early warning signals for the whole banking system. This study attempts to forecast the financial distress of commercial banks by developing a bankruptcy prediction model for banks. The sample size for the study is 75 Indian banks. Logistic, linear discriminant analysis (LDA) and artificial neural network (ANN) models have been applied on the last 5 years’ (2015–2019) data of these banks. Data analysis results reveal the logistic and LDA models exhibiting similar prediction accuracy. The results of the ANN prediction model exhibit better prediction accuracy. It is expected that the results of this study will be useful for managers, depositors, regulatory bodies and shareholders to better manage their interests in the banking sector of the country.

Predicting Financial Distress for Listed MENA Firms

2019 ◽  
Vol 9 (2) ◽  
pp. 51
Author(s):  
Osama El-ansari ◽  
Lina Bassam
Keyword(s):  
Prediction Model ◽  
Financial Distress ◽  
Historical Data ◽  
Bankruptcy Prediction ◽  
Mena Region ◽  
Macroeconomic Variables ◽  
Listed Firms ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Accounting Ratios

Financial distress prediction gives an early warning about defaulting risk for firms; thus, it is a real concern of the entire economy.Purpose: To examine the determinants of financial distress across MENA region countries, by using definitions of distress and historical data from active listed firms in the region.Methodology: logistic regression is run on firm-specific variables and a set of macroeconomic variables to develop a prediction model to examine the effect of these predictors on the probability of financial distress.Findings: it has been found that after controlling for country effects, accounting ratios, firm size, and macroeconomic variables provided an acceptable prediction model for listed MENA firms.Originality: a gap exists in the literature of developing countries’ prediction for financial distress. Many studies addressed bankruptcy prediction for a certain country in the region, however, a limited number of researches approached predicting distressed models for listed firms in the region.

scholarly journals KETEPATAN MODEL PREDIKSI FINANCIAL DISTRESS PADA PERUSAHAAN RETAIL DI INDONESIA

2019 ◽  
Vol 4 (2) ◽  
pp. 117-127
Author(s):  
Kartikasari Kartikasari ◽  
Diyah Santi Hariyani
Keyword(s):  
Data Analysis ◽  
Financial Distress ◽  
Stock Exchange ◽  
Secondary Data ◽  
Financial Statements ◽  
Inferential Statistics ◽  
Accuracy Rate ◽  
Data Analysis Technique ◽  
Analysis Technique ◽  
Score Model

Abstract This study aims to analyze the condition of Financial Distress in retail companies listed on the Indonesian Stock Exchange in 2015-2017 using the Ohlson Model, Fulmer Model, CA-Score Model and Zavgren Model. The data used in this study was secondary data derived from the financial statements of retail companies found on the Indonesian Stock Exchange. The data analysis technique used was inferential statistics and Kruskal-Wallis test. The results of this study indicated that Ohlson's model is best at predicting Financial Distress in retail companies in Indonesia with an accuracy rate of 83.33%, CA-Score Model with 30% accuracy rate, as well as the Fulmer Model and Zavgren Model with an accuracy rate of 0.00 %    

scholarly journals ANALISIS KOMPARASI MODEL PREDIKSI FINANCIAL DISTRESS ALTMAN, SPRINGATE, GROVER DAN OHLSON PADA PERUSAHAAN MANUFAKTUR YANG TERDAFTAR DI BURSA EFEK INDONESIA PERIODE 2011-2013

2018 ◽  
Vol 20 (3) ◽  
pp. 446
Author(s):  
Rini Tri Hastuti
Keyword(s):  
Prediction Model ◽  
Financial Distress ◽  
Manufacturing Industry ◽  
Stock Exchange ◽  
Descriptive Analysis ◽  
Bankruptcy Prediction ◽  
Manufacturing Companies ◽  
Test Analysis ◽  
Analysis Techniques ◽  
Kolmogorov Smirnov

This study purpose to determine the most accurate bankruptcy prediction model in order to suitable for use in its application to manufacturing companies in Indonesia, and to determine whether there is a difference Altman models with Springate models, Altman models with Grover models, and Altman models with Ohlson models. Scope of the study is limited to manufacturing industry companies listed on the Indonesia Stock Exchange during the years 2011-2013. This study comparing four bankruptcy prediction model by using statistic descriptive analysis techniques also Kolmogorov-Smirnov normality test and  paired  test  analysis  techniques  sample  t-test with  the  help  of  SPSS  program. Conclusion of this study showed significant differences between the models Altman with Springate models, Altman models with Grover  models, and Altman models with Ohlson models. And the highest level of accuracy achieved by the Grover models.

scholarly journals BANKRUPTCY PREDICTION ANALYSIS USING THE ZMIJEWSKI MODEL (X-SCORE) AND THE ALTMAN MODEL (Z-SCORE)

2020 ◽  
Vol 1 (5) ◽  
pp. 794-806
Author(s):  
Viciwati Viciwati
Keyword(s):  
Financial Distress ◽  
Stock Exchange ◽  
Secondary Data ◽  
Bankruptcy Prediction ◽  
Z Score ◽  
Accuracy Rate ◽  
Financial Reports ◽  
Normality Test ◽  
The Difference ◽  
Score Model

This study aims to identify and analyze the accurate models of Financial Distress in retail companies listed on the Indonesian Stock Exchange in 2014-2018 using the Zmijewski (X-Score) and Altman (Z-Score) Model. The sample used is 70. This study uses secondary data from the 2014-2018 annual financial reports. This study tested the hypothesis using the normality test and the Kruskal Wallis test or the difference test using SPSS version 26. The results of this study indicate that the Zmijewski (X-Score) model is the model that has the highest accuracy rate in predicting bankruptcy with an accuracy rate of 90%.

scholarly journals Construction of a Predictive Model for MLB Matches

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 102-112
Author(s):  
Chia-Hao Chang
Keyword(s):  
Markov Process ◽  
Prediction Model ◽  
Equilibrium Point ◽  
Predictive Model ◽  
Major League Baseball ◽  
Market Equilibrium ◽  
Accurate Prediction ◽  
Expected Return ◽  
Betting Odds ◽  
Process Method

The main purpose of this article was to define a model that could defeat the online bookmakers’ odds, where the betting item considered was the first five innings of major league baseball (MLB) matches. The betting odds of online bookmakers have two purposes: first, they are used to quantify the amount of profit made by the bettors; second, they are regarded as a market equilibrium point between multiple bookmakers and bettors. If the bettors have a more accurate prediction model than the system used to produce betting odds, it will create a positive expected return for the bettors. In this article, we used the Markov process method and the runner advancement model to estimate the expected runs in an MLB match for the teams based on the batting lineup and the pitcher.

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