Artificial Neural Networks: A Deep Learning Approach in Financial Distress Prediction

DOI

10.2991/978-94-6463-587-4_12How to use a DOI?

Keywords

Deep Learning; Artificial Neural Networks; Financial Distress Prediction

Abstract

Predicting financial distress is a crucial thing in company management, so researchers are competing to develop methods that can produce high accuracy in predicting financial distress. One method that is being widely developed is Machine Learning. Machine Learning, which is a branch of artificial intelligence, is a method that applies learning to machines so that machines are not only able to behave in making decision but can also adapt to changes that occur. The branch of Machine Learning that has high power in prediction is Deep Learning. Artificial Neural Networks (ANN) is one of the most important and reliable parts of deep learning. ANN is a model inspired by how neurons in the human brain work. Every neuron in the human brain is interconnected and information flows from each neuron. In the ANN architecture, the stimulus will enter a neuron called the input layer, be processed in one or several hidden layers which will be weighted according to the activation function, then the results will be passed on to the output layer. ANN is predicted to have many advantages compared to other machine learning methods in predicting financial distress. This research applies ANN to predict Financial Distress for Property and Real Estate Sector Companies listed on the IDX. The research results show that ANN produces higher prediction accuracy, namely 88.88% compared to other Machine Learning methods such as SVM at 80.47% and PSO-SVM at 83.16%.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ni Wayan Dewinta Ayuni
AU  - Wayan Hesadijaya Utthavi
AU  - Ni Nengah Lasmini
PY  - 2024
DA  - 2024/12/01
TI  - Artificial Neural Networks: A Deep Learning Approach in Financial Distress Prediction
BT  - Proceedings of the International Conference on Sustainable Green Tourism Applied Science - Engineering Applied Science 2024 (ICoSTAS-EAS 2024)
PB  - Atlantis Press
SP  - 99
EP  - 108
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-587-4_12
DO  - 10.2991/978-94-6463-587-4_12
ID  - Ayuni2024
ER  -