[논문]인공신경망 기반 호텔 부도예측모형 개발

최성주; 이상원

doi:10.9708/jksci.2014.19.10.125

[국내논문] 인공신경망 기반 호텔 부도예측모형 개발
A Development of Hotel Bankruptcy Prediction Model on Artificial Neural Network 원문보기

韓國컴퓨터情報學會論文誌 = Journal of the Korea Society of Computer and Information, v.19 no.10, 2014년, pp.125 - 133

최성주 (원광대학교 대학원 정보관리학과) , 이상원 (원광대학교 정보전자상거래학부)

초록
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본 논문에서는 호텔경영을 위한 인공신경망 기반의 부도예측 모형을 개발한다. 부도예측 모형은 호텔에서 관리하는 사업장의 사업성과 이터를 바탕으로 부도 가능성을 평가하여 호텔 전체사업의 부도를 예측하는 특징을 가진다. 부도예측을 위한 전통적인 통계기법은 다변량 판별분석이나 로짓분석 등이 있는데, 본연구는 이들보다 우수한 예측정확성을 갖는 인공신경망 기법을 이용해서 연구를 진행하였다. 이를 위해 우선 우수기업 100개와 도산기업 100개를 선정하여 전체 실험데이터를 구성하고, 뉴로쉘이라는 인공신경망 도구를 이용하여 부도예측모형을 구성하였다. 본 모형 설계와 실험은 서비스드 레지던스 호텔에서 관리하는 각 브랜치의 부도예측과 재무건전성을 판단하기에 효율성이 높아 호텔 경영의 의사결정에 많은 도움이 될 것이다.

Abstract ▼ AI-Helper

This paper develops a bankruptcy prediction model on an Artificial Neural Network for hotel management. A bankruptcy prediction model has a specific feature to predict a bankruptcy of the whole hotel business after evaluate bankruptcy possibility on the basis of business performance data of each branch. here are many traditional statistical models for bankruptcy prediction such as Multivariate Discriminant Analysis or Logit Analysis. However, we chose Artificial Neural Network because the method has accuracy rates of prediction better than those of other methods. We first selected 100 good enterprises and 100 bankrupt enterprises as experimental data and set up a bankruptcy prediction model by use of a tool for Artificial Neural Network, NeuroShell. The model and its experiments, which demonstrated high efficiency, can certainly provide great help in decision making in the field of hotel management and in deciding on the bankruptcy or financial solidity of each branch of serviced residence hotel.

Keyword

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제안 방법

The chapter is composed of two parts: one attempts to model bankruptcy prediction; the other provides the results of experiments using the models. For the design of the model, we select a tool, NeuroShell, for artificial neural networks, and establish bankruptcy prediction models. And then, for the experiment, we use data from 100 failed enterprises that went into bankruptcy between 2007 and 2009 as well as 100 successful enterprises that were not subject to bankruptcy.
An experiment utilizing our bankruptcy prediction model is performed in two phases: developing experimental models and analyzing their results.
The four groups are Group-A for Univariate Analysis, Group-B for Logit Analysis, Group C for Multivariate Discriminant Analysis, and Group D for Artificial Neural Network (Table 3). Group A collected input variables by performing Univariate Analysis on the basis of a t-test and selecting 8 variables with top significant level. Group B collected 8 input variables by performing Logit Analysis on the basis of an optional variable-selection method.
After preprocessing for experiments, we performed experiments with the above input variables for each group, Group A, Group B, Group C, and Group D, in NeuroShell. In order to increase the reliability of the experiment, we measured the accuracy rates of bankruptcy 10 times and calculated the average values of each group.

대상 데이터

And then, for the experiment, we use data from 100 failed enterprises that went into bankruptcy between 2007 and 2009 as well as 100 successful enterprises that were not subject to bankruptcy. While using NeuroShell on the Artificial Neural Network, we allocated the 200 enterprises into 100 examples for training, 50 examples for testing, and 50 examples for verification. In the last chapter, we make conclusions by explaining both our research contributions and limitations, with a consideration of further studies.
The Ascott Limited is a Singapore company that has grown to be the world’s largest international serviced residence owner-operator. It has over 23,000 operating serviced residence units in key cities in the Asia Pacific, European, and Gulf regions, as well as over 10,000 units that are under development, making a total of more than 33,000 units in over 200 properties. The company operates three brands: Ascott, Citadines, and Somerset.
For the experiment, this research used data from 100 failed enterprises that went bankrupt between 2007 and 2009 as well as 100 successful enterprises that did not fall into bankruptcy. Using NeuroShell on the Artificial Neural Network, we allocated 200 enterprises into 100 examples for training, 50 examples for testing, and 50 examples for verification.
For the experiment, this research used data from 100 failed enterprises that went bankrupt between 2007 and 2009 as well as 100 successful enterprises that did not fall into bankruptcy. Using NeuroShell on the Artificial Neural Network, we allocated 200 enterprises into 100 examples for training, 50 examples for testing, and 50 examples for verification.

이론/모형

Group B collected 8 input variables by performing Logit Analysis on the basis of an optional variable-selection method. Group C collected 8 input variables by performing Multivariate Discriminant Analysis on the basis of an optional variable-selection method. Group D collected 8 input variables by performing Artificial Neural Network on NeuroShell.

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상세보기
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상세보기
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상세보기
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