scholarly journals ANALISIS SURVIVAL DALAM MEMPREDIKSI KONDISI FINANCIAL DISTRESS

2020 ◽  
pp. 112
Author(s):  
Muhammad Sanyasa Tyaga ◽  
Farida Titik Kristanti
Keyword(s):  
Regression Model ◽  
Financial Distress ◽  
Agency Costs ◽  
Time Series Data ◽  
Series Data ◽  
Internal Factors ◽  
Cox Proportional Hazard ◽  
Managerial Agency ◽  
Cox Proportional Hazard Regression ◽  
Internal And External Factors

Companies are required to be able to maintain the survival of the company so that the company's goals can be achieved properly. Financial distress is one of the factors that causes companies to not be able to maintain their survival so that the company's goals are not achieved. Factors that cause the company is in a state of distress are internal and external factors. This study uses internal factors such as financial ratios, size, agency costs and external factor is inflation. This research method is a quantitative method using time series data. The regression model used is the cox proportional hazard regression model. Determination of the sample using purposive sampling so that 81 samples were used in this study. Based on the results of partial tests, leverage, liquidity, activity, company size, managerial agency costs do not have a significant effect on financial distress. Only inflation has a significant positive effect on financial distress.

scholarly journals ANALISIS GENDER DIVERSITY, CORPORATE GOVERNANCE DAN LEVERAGE DALAM MEMPREDIKSIKAN KONDISI FINANCIAL DISTRESS PADA FAMILY BUSINESS

2021 ◽  
pp. 1015
Author(s):  
Siti Ratu Rodiah ◽  
Farida Titik Kristanti
Keyword(s):  
Regression Model ◽  
Family Business ◽  
Financial Distress ◽  
Time Series Data ◽  
Gender Diversity ◽  
Institutional Ownership ◽  
Series Data ◽  
Internal Factors ◽  
Management Ownership ◽  
Positive Effect

Economic growth in the world is currently experiencing a very rapid increase, so companies must make strategies to be able to compete in facing challenges to survive. Financial distress is one of the factors that causes the company to be unable to achieve its goals so that the company cannot maintain its life. This study uses internal factors, namely gender diversity, institutional ownership, management ownership, and independent commissioners and external factors are leverage in family business. This research method is a quantitative method using time series data. The regression model used is a logistic regression model. Purposive sampling is the method used so that 80 samples were used in this study. Based on the partial test, institutional ownership, management ownership and independent commissioners have no significant effect on financial distress. Only gender diversity and leverage have a significant positive effect on financial distress.

scholarly journals PERBANDINGAN MODEL REGRESI COX PROPORTIONAL HAZARD MENGGUNAKAN METODE BRESLOW DAN EFRON (Studi Kasus: Penderita Stroke di RSUD Tugurejo Kota Semarang)

2019 ◽  
Vol 8 (1) ◽  
pp. 93-105
Author(s):  
Eri Setiani ◽  
Sudarno Sudarno ◽  
Rukun Santoso
Keyword(s):  
Blood Pressure ◽  
Systolic Blood Pressure ◽  
Regression Model ◽  
Diastolic Blood Pressure ◽  
Proportional Hazard ◽  
Hazard Regression ◽  
Cox Proportional Hazard ◽  
History Of ◽  
Cox Proportional Hazard Regression ◽  
Sugar Levels

Cox proportional hazard regression is a regression model that is often used in survival analysis. Survival analysis is phrase used to describe analysis of data in the form of times from a well-defined time origin until occurrence of some particular even or end-point. In analysis survival sometimes ties are found, namely there are two or more individual that have together event. This study aims to apply Cox model on ties event using two methods, Breslow and Efron and determine factors that affect survival of stroke patients in Tugurejo Hospital Semarang. Dependent variable in this study is length of stay, then independent variables are gender, age, type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure, blood sugar levels, and BMI. The two methods give different result, Breslow has four significant variables there are type of stroke, history of hypertension, systolic blood pressure, and diastolic blood pressure, while Efron contains five significant variables such as type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure and blood sugar levels. From the smallest AIC criteria obtained the best Cox proportional hazard regression model is Efron method. Keywords: Stroke, Cox Proportional Hazard Regression model, Breslow method, Efron method.

Corporation financial distress prediction with deep learning: analysis of public listed companies in Malaysia

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zulkifli Halim ◽  
Shuhaida Mohamed Shuhidan ◽  
Zuraidah Mohd Sanusi
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Financial Distress ◽  
Time Series Data ◽  
Series Data ◽  
Learning Models ◽  
Content Type ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Gated Recurrent Unit

PurposeIn the previous study of financial distress prediction, deep learning techniques performed better than traditional techniques over time-series data. This study investigates the performance of deep learning models: recurrent neural network, long short-term memory and gated recurrent unit for the financial distress prediction among the Malaysian public listed corporation over the time-series data. This study also compares the performance of logistic regression, support vector machine, neural network, decision tree and the deep learning models on single-year data.Design/methodology/approachThe data used are the financial data of public listed companies that been classified as PN17 status (distress) and non-PN17 (not distress) in Malaysia. This study was conducted using machine learning library of Python programming language.FindingsThe findings indicate that all deep learning models used for this study achieved 90% accuracy and above with long short-term memory (LSTM) and gated recurrent unit (GRU) getting 93% accuracy. In addition, deep learning models consistently have good performance compared to the other models over single-year data. The results show LSTM and GRU getting 90% and recurrent neural network (RNN) 88% accuracy. The results also show that LSTM and GRU get better precision and recall compared to RNN. The findings of this study show that the deep learning approach will lead to better performance in financial distress prediction studies. To be added, time-series data should be highlighted in any financial distress prediction studies since it has a big impact on credit risk assessment.Research limitations/implicationsThe first limitation of this study is the hyperparameter tuning only applied for deep learning models. Secondly, the time-series data are only used for deep learning models since the other models optimally fit on single-year data.Practical implicationsThis study proposes recommendations that deep learning is a new approach that will lead to better performance in financial distress prediction studies. Besides that, time-series data should be highlighted in any financial distress prediction studies since the data have a big impact on the assessment of credit risk.Originality/valueTo the best of authors' knowledge, this article is the first study that uses the gated recurrent unit in financial distress prediction studies based on time-series data for Malaysian public listed companies. The findings of this study can help financial institutions/investors to find a better and accurate approach for credit risk assessment.

scholarly journals PENDUGAAN PARAMETER MODEl DISTRIBUTED LAG POLA POLINOMIAL MENGGUNAKAN METODE ALMON

2021 ◽  
Vol 15 (4) ◽  
pp. 761-772
Author(s):  
Fitria Virgantari ◽  
Wilda Rahayu
Keyword(s):  
Regression Model ◽  
Time Series Data ◽  
Operating Cost ◽  
Series Data ◽  
Operating Income ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Lag Model ◽  
Forecast Time ◽  
The Impact

The distributed lag model is a regression  model that describes the relationship between the dependent variable of a given period and the independent variables of a certain or previous periods. The model can be used to determine the impact of the independent variable to dependent variables over time and forecast time series data for the next periods. There are two forms of distributed lag model that have been widely proposed in the estimation of distributed lag regression model. The first form  is proposed by Koyck and the second form by Almon. This paper aims to apply the Almon model to examine the effect of  the ratio of BOPO (Operating Cost and Operating Income) to the ROA (Return on Asset) of a government bank based on quarterly data, to estimate its parameters, to examine the feasibility of the model, and to predict the next quarter.  Results shows that distributed lag model is  = 10.110 - 0.078  + 0.015  + 0.026  – 0.045  with Yt is ROA, and Xt is the ratio BOPO  on the 1st quarter until the previous 3 quarters. The model is quite good according to the determination coefficient that is 0.75, no autocorrelation in the model, t test and F test are also significant. Based on the model, the value of ROA ratio next quarter predicted 4.63%. The decrease in profitability ROA ratio is due to an increase in interest expense while interest income can not compensate

scholarly journals KAJIAN MODEL PERAMALAN KUNJUNGAN WISATAWAN MANCANEGARA DI BANDARA KUALANAMU MEDAN TANPA DAN DENGAN KOVARIAT

2019 ◽  
Vol 3 (1) ◽  
pp. 18-32
Author(s):  
Isti Rochayati ◽  
Utami Dyah Syafitri ◽  
I Made Sumertajaya ◽  
Indonesian Journal of Statistics and Its Applications IJSA
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Model Identification ◽  
Series Data ◽  
Explanatory Variables ◽  
Foreign Tourist ◽  
Currency Exchange ◽  
Inflation Rates ◽  
Internal And External Factors

Foreign tourist arrivals could be considered as time series data. Modelling these data could make use of internal and external factors. The techniques employed here to model these time series data are SARIMA, SARIMAX, VARIMA, and VARIMAX. SARIMA is a model for seasonal data and VARIMA is a model for multivariate time series data. If some explanatory variables are incorporated and have significant influence on the response, the former two models become SARIMAX and VARIMAX respectively. Three stages of creating the model are model identification, parameter estimation, and model diagnostics. The variables used in this study were foreign tourist visits, international passenger arrivals, inflation rates, currency exchange rates, and Gross Regional Domestic Product (GRDP) over the period of 2010-2017. All four models fulfill their model assumptions and therefore could be applied. The best model of foreign tourist arrivals was VARIMA with the value of MAPE testing data = 6.123.

scholarly journals An Adaptive Model of Student Performance Using Inverse Bayes

2014 ◽  
Vol 1 (3) ◽  
pp. 154-156 ◽  
Author(s):  
Charles Lang
Keyword(s):  
Student Performance ◽  
Student Behavior ◽  
Time Series Data ◽  
Bayes Theorem ◽  
Series Data ◽  
Adaptive Model ◽  
Bayesian Algorithm ◽  
Credit Score ◽  
Internal Student ◽  
Internal And External Factors

I propose a coherent framework for the use of Inverse Bayesian estimation to summarize and make predictions of student behavior in adaptive educational settings. The Inverse Bayes Filter utilizes Bayes theorem to estimate the relative impact of contextual factors and internal student factors on student performance using time series data across a range of possible dimensions. The Inverse Bayesian algorithm treats the student as a Bayesian learner; her partial credit score or confidence is proportional to her prior knowledge and how she interprets her environment. Once the algorithm has weighted internal and external factors this information is used to make a prediction about the student’s next attempt.

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