Neural Network Prediction Analysis: The Financial Distress Case

This study aims to to test internal financial factor company to financial distress in the year 2018 using data historical 2012-2018. The independent variable taken of a set the initial of du Jardin variables in 2010. Secondary data was used in the study with 32 mining firms that listed on the Indone...

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Published inResearchers world - journal of arts science and commerce Vol. 9; no. 3; pp. 119 - 125
Main Authors Akbar, Rasyidi Faiz, Musdholifah, Musdholifah, Purwohandoko, Purwohandoko
Format Journal Article
LanguageEnglish
Published Malegaon Educational Research Multimedia & Publications 01.07.2018
Subjects
Algorithms
Back propagation
Bankruptcy
Brain research
Corporate profits
Discriminant analysis
Economic crisis
Financial analysis
International finance
Learning
Mining
Neural networks
Psychological distress
Research methodology
Researchers
Sampling
Stock exchanges
Studies
Variables
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Summary:This study aims to to test internal financial factor company to financial distress in the year 2018 using data historical 2012-2018. The independent variable taken of a set the initial of du Jardin variables in 2010. Secondary data was used in the study with 32 mining firms that listed on the Indonesia Stock Exchange in 2012-2018 with purposive the sampling method of. A prediction done by means of a utensil artificial analysis the skill of artificial neural network. The application of a method of neural network used backpropagation algorithm. Architecture neural network used 3 layers (1 for input layer, 1 for hidden layer, 1 for output layer). The activation function used a logarithm sigmoid (logsig). The value of mean square error (MSE) training a network is 0,001. The results of forecasting neural network that there is no financial distress in 2018 with accuracy of prediction reaches 84,375%. For the next researcher, it is expected to use several models and several sectors as a comparison so that it can be proven which model is the most accurate in predicting symptoms of financial distress.
ISSN:2229-4686
DOI:10.18843/rwjasc/v9i3/15