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首页> 外文会议>International Conference on Information and Communication Technology >A Cross Model Telco Industry Financial Distress Prediction in Indonesia: Multiple Discriminant Analysis, Logit and Artificial Neural Network
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A Cross Model Telco Industry Financial Distress Prediction in Indonesia: Multiple Discriminant Analysis, Logit and Artificial Neural Network

机译：印度尼西亚的跨模型电信行业财务困境预测：多重判别分析，Logit和人工神经网络

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摘要

The competition between telco industries is getting stronger and expects telco companies to continually strengthen the fundamentals of it's management, so that they will have the ability to survive in the competition with other companies. Incapability to anticipate the global changing trends would lead to a decrease in company business value, which in turn led to business losses. Bankruptcy is a very essential issue which must be aware by the company, the bankruptcy of a company can be measured and evaluated from financial statement data, by analyzing financial statements, the company management can take immediate action to restructure debt due to the effects of the liquidation of bankruptcy could be detrimental to creditors and investors. Academic research may provide a model to prevent bankruptcy indispensable in Indonesia, some of the bankruptcy prediction model uses financial data prior to predict financial difficulties. The study compared three models in this research, there are: Altman model, Ohlson model and Artificial Neural Network Backpropagation. This research aims to compare financial distress prediction model with the most appropriate application in Indonesia's Telecommunication sector. Comparisons were made by analyzing the accuracy level of each model. The samples used were three telecommunication company that listed on Indonesia Stock Exchange in the period 2013-2017. In summary, the prediction models used in this research can be used to help investors and company management to predict business failure probability.
机译：电信行业之间的竞争越来越激烈，并期望电信公司不断加强其管理的基础，以便它们能够在与其他公司的竞争中生存。无法预测全球变化趋势将导致公司业务价值下降，进而导致业务损失。破产是公司必须意识到的一个非常重要的问题，可以从财务报表数据中衡量和评估公司的破产，通过分析财务报表，公司管理层可以立即采取行动来重组债务，这是由于破产的影响。破产清算可能对债权人和投资者不利。学术研究可能提供了防止印度尼西亚不可缺少的破产的模型，一些破产预测模型在预测财务困难之前使用财务数据。该研究比较了本研究中的三种模型，分别是：Altman模型，Ohlson模型和人工神经网络反向传播。本研究旨在将财务困境预测模型与印度尼西亚电信行业中最合适的应用进行比较。通过分析每个模型的准确性水平进行比较。使用的样本是在2013-2017年期间在印度尼西亚证券交易所上市的三家电信公司。总之，本研究中使用的预测模型可用于帮助投资者和公司管理层预测业务失败的可能性。

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