부도예측 모형에서 뉴스 분류를 통한 효과적인 감성분석에 관한 연구

김찬송,신민수 한국IT서비스학회 2019 한국IT서비스학회지 Vol.18 No.1

Bankruptcy prediction model is an issue that has consistently interested in various fields. Recently, as technology for dealing with unstructured data has been developed, researches applied to business model prediction through text mining have been activated, and studies using this method are also increasing in bankruptcy prediction. Especially, it is actively trying to improve bankruptcy prediction by analyzing news data dealing with the external environment of the corporation. However, there has been a lack of study on which news is effective in bankruptcy prediction in real-time mass-produced news. The purpose of this study was to evaluate the high impact news on bankruptcy prediction. Therefore, we classify news according to type, collection period, and analyzed the impact on bankruptcy prediction based on sentiment analysis. As a result, artificial neural network was most effective among the algorithms used, and commentary news type was most effective in bankruptcy prediction. Column and straight type news were also significant, but photo type news was not significant. In the news by collection period, news for 4 months before the bankruptcy was most effective in bankruptcy prediction. In this study, we propose a news classification methods for sentiment analysis that is effective for bankruptcy prediction model.

Bankruptcy Type Prediction Using A Hybrid Artificial Neural Networks Model

Nam-ok Jo(조남옥),Hyun-jung Kim(김현정),Kyung-shik Shin(신경식) 한국지능정보시스템학회 2015 지능정보연구 Vol.21 No.3

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster.

Bankruptcy Prediction Modeling Using Qualitative Information Based on Big Data Analytics

Nam-ok Jo(조남옥),Kyung-shik Shin(신경식) 한국지능정보시스템학회 2016 지능정보연구 Vol.22 No.2

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm’s financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from e

새마을금고 재무적 특성의 부실예측에 관한 연구

조희국 ( Hee Gook Cho ),김영수 ( Yeong Soo Kim ) 아시아.유럽미래학회 2011 유라시아연구 Vol.8 No.2

새마을금고(이하“금고”라 약칭함)는 대표적 서민금융기관으로서 지역 밀착경영을 통해 성장 발전해 왔으나 최근의 금융환경변화는 새마을금고의 지속가능 경영에 있어서 그 어느 때 보다도 강력한 경쟁력이 요구되고 있다. 금고가 위기를 극복하고 급변하는 금융환경 속에서 살아남기 위해서는 금고 내부에 존재하는 비효율적인 부실요소를 밝혀내어 이를 제거하고 개선하려는 노력이 이루어 져야 할 것이다. 따라서 부실을 사전에 예측하고 부실여부를 객관화하기 위한 부실예측모형을 도출하고 도출된 모형의 예측력과 유용성을 검증하였다. 이를 위해 표본금고 300개를 선정하여 이중 2008년 말 현재 경영실태평가(CAMEL평가)에 따라 부실금고로 분류된 60개의 금고와 우량금고로 분류된 금고 46개를 대상으로 2005년~2008년 기간 동안 주요 재무비율을 이용한 판별분석을 실시하여 부실예측모형을 도출하였다. 도출된 모형을 이용하여 194개 검증용 표본에 대하여 모형의 예측력을 검증한 결과 첫째, 총 19개 재무특성변수 중 부실예측에 유용한 변수는 총 6개 변수로 이들 변수들 가운데 금고의 부실여부 판별에는 안정성 및 유동성, 수익성, 자산규모, 활동성 지표들이 유용한 것으로 나타났다. 둘째, 판별함수에서 채택된 변수들이 안정성 및 수익성, 자산규모 위주의 변수들로 나타난 반면 과거에 평가모형이나 부실예측모형에 다수 선택된 성장성 관련 재무특성변수들이 포함되지 않았다는 것은 금융위기이후 성장성 보다는 안정성과 수익성 및 대형화가 부실화 여부를 판단하는 주요 변수가 되고 있음을 알 수 있었다. 셋째, 부실예측모형의 분석용 표본 예측력은 우량금고의 경우 -4년에 82.6%, -3년에 89.1%, -2년에 89.1%, -1년에 93.4%로 예측하여 예측년도에 가까워질수록 높게 나타났고, 부실금고를 정확하게 예측할 확률은 -4년도에 90.0%, -3년도에는 88.3%, -2년도에는 95.0%, -1년도에도 95.0%로 예측년도에 가까워 질수록 높게 나타났으며 우량금고 예측보다 부실금고의 예측평균이 더 높게 나타났다. 예측기간이 짧아질수록 부실금고와 우량금고의 예측력이 높아지는 것은 표본 기간 동안 금고의 경영실적이 좋아지면서 부실금고와 우량금고 구분이 명확해져 모든 예측력의 결과를 일정하게 잘 나타내 주고 있다고 판단 된다. 넷째, 전체적으로 분석용 표본의 예측력은 92.1%로 나타났으며 검증용 표본에 의한 평균 부실예측모형의 예측력이 81.0%로 나타났다. 따라서 본 연구의 모형이 금고 부실에 대한 조기예측 및 건전경영을 기대하는 금융 감독 당국에도 유용한 지표가 될 것이며, 이론적 근거를 제공하는데 유용한 연구가 될 것 이다. To predict the bankruptcy of Saemaeul Kumko (“Kumko”) and objectively determine its bankruptcy, a model of bankruptcy prediction was developed, and the predictive power and utility of the model were verified. For the purpose of the study, 300 sample kumkos were selected. They were then divided into 60 kumkos, which had been classified as bankrupt kumko, and 46 kumkos classified as blue chip, according to the CAMELS rating (assessment of the kumko’s management condition) conducted at the end of 2008. Then, a discriminant analysis was carried out to come up with the prediction model, using the major financial ratios from 2005 to 2008. Using the model, the results of testing the predictive power of the model regarding 194 samples are as follows: First, among the 19 financial variables, 6 were considered useful in bankruptcy prediction, among which such indicators as stability, liquidity, profitability, asset size, and activity were found to be useful in discriminating the bankruptcy of a kumko. Second, while variables based on stability, profitability, and asset size were adopted from the discriminant function, growth-related financial variables were not included even though they used to be selected for an assessment model or a model of bankruptcy prediction. This showed that since the financial crisis, stability, liquidity, and super-sizing have been critical factors in determining whether a kumko is bankrupt or not. Third, As for the predictive power of testing samples in the model, the predictive of power of the Good kumko appeared higher towards the year of prediction (Y): 82.6% in the Y-4 year, 89.1% inY-3, 89.1% in Y-2, and 93.4% in Y-1. The percentage of accurate prediction of bankruptcy also became higher towards the year of prediction with 90.0% in Y-4, 88.3% in Y-3, 95.0% in Y-2, and 95.0% in Y-1. The average prediction of the bankrupt kumkos was higher than that of the Good kumko ones. The predictive power of the blue-chip kumko was higher as the prediction period became shorter, because the kumko’s business results improved during the sampling period, yielding a clear distinction between the bankrupt and Good kumko and, in turn, the consistent results of the predictive power of all the kumkos. Fourth, the entire predictive power of the analysis sample was 92.1%, and that of the average bankruptcy prediction model was 81.0%. Therefore, this model will be a useful indicator for the financial authorities if they look forward to early predictions of bank bankruptcy and sound management. Additionally, this study will help provide theoretical foundations

부도예측을 위한 KNN 앙상블 모형의 동시 최적화

민성환(Sung-Hwan Min) 한국지능정보시스템학회 2016 지능정보연구 Vol.22 No.1

Bankruptcy involves considerable costs, so it can have significant effects on a countrys economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

RNN(Recurrent Neural Network)을 이용한 기업부도예측모형에서 회계정보의 동적 변화 연구

권혁건(Hyukkun Kwon),이동규(Dongkyu Lee),신민수(Minsoo Shin) 한국지능정보시스템학회 2017 지능정보연구 Vol.23 No.3

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company’s financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don’t take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN’s performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more lear

개선된 배깅 앙상블을 활용한 기업부도예측

민성환(Sung-Hwan Min) 한국지능정보시스템학회 2014 지능정보연구 Vol.20 No.4

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

신협 부도예측모형에 관한 연구

남주하 ( Nam Joo-ha ),최재원 ( Choi Jae-won ) 국제지역학회 2017 국제지역연구 Vol.21 No.3

본 연구는 국내에서 처음으로 신용협동조합(신협)의 부도예측모형의 분석을 시도하였다. 1997년 국내 금융위기이후 신협은 심각한 부실화로 인해 327개 신협에 4조7천억여원의 공적자금이 투입된바 있다. 그러나 이러한 부실화의 역사적 경험에도 불구하고 아직까지 신협의 부도원인과 부도예측에 관한 연구가 거의 없는 실정이다. 부도예측모형의 분석을 위해 먼저 부도신협군으로는 2009~2016년 사이 부도가 났거나 사고가 난 신협 중 순자본비율이 0%이하인 26개 신협(이상치 제거 후)을 표본으로 선택하였다. 건전신협군으로는 2015년 기준 부도 및 사고가 없는 신협 중 630개 신협(Case1)과 순자본비율 6% 이상인 37개의 신협(Case2) 등 두 그룹을 표본으로 채택하였다. 분석대상이 되는 재무비율들은 과거 금융회사에 적용한 재무비율이 최대 30여개에 불과하였으나 본 연구에서는 60개로 확장하여 신협의 부도원인과 부도예측력을 높이는데 기여하였다. 기존 연구에서 사용한 재무변수들 외에 좀 더 다양한 수익성 변수, 자기자신과 타신협과의 상대적 성과변수, 변동성 지표 등을 새롭게 포함하였다. 로짓최우추정법에 의한 분석결과에 의하면 Case1과 Case2의 표본을 이용한 전반적 예측력은 각각 97.5%와 94.5%로 나타나 신협의 부실예방에 큰 도움이 될 것으로 판단된다. 다만 제1종오류가 높아 부도신협군과 건전신협군의 좀 더 다양하고 정교한 표본선택에 의한 추가적인 연구도 필요해 보인다. This study analyzed the bankruptcy prediction model of credit unions for the first time in Korea. Since the 1997 financial crisis, public fund of 4.7 trillion won injected into 327 the credit unions to prevent insolvency. However, in spite of the historical experience of the bankruptcy, there is no study on the cause of bankruptcy and the prediction of bankruptcy of the credit unions. For the analysis of bankruptcy prediction models, we selected 26 credit unions with a net capital ratio of 0% or less among the credit unions with defaults or accidents between 2009 and 2016 as a bankruptcy sample. As for non-bankruptcy sample, we selected 2 groups, including 630 credit unions (Case 1) and 37 credit unions (Case 2) with a net capital ratio of 6% or more, among the credit unions with no defaults and accidents in 2015. The financial ratios used in previous studies are only about 30, but this study expanded to 60, which contributed to the cause of bankruptcy and the prediction power of bankruptcy. Unlike previous studies, we included more diverse profitability variables, relative performance variables of own and other credit unions, and volatility indicators. Using the logistic maximum likelihood estimation method, the overall prediction power for the samples of Case1 and Case2 is 97.5% and 94.5%, respectively, which are helpful bankruptcy of credit unions. However, because of the high error of Type I, further study in the future are necessary by using a more diverse and sophisticated sample.

기계학습을 이용한 수출신용보증 사고예측

조재영(Cho, Jaeyoung),주지환(Joo, Jihwan),한인구(Han, Ingoo) 한국지능정보시스템학회 2021 지능정보연구 Vol.27 No.1

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altmans Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accur

Bankruptcy Predictions for Korea Medium-Sized Firms using Neural Networks and Case Based Reasoning

Han, Ingoo,Park, Cheolsoo,Kim, Chulhong 한국경영과학회 1996 한국경영과학회 학술대회논문집 Vol.- No.2

Prediction of firm bankruptcy have been extensively studied in accounting, as all stockholders in a firm have a vested interest in monitoring its financial performance. The objective of this paper is to develop the hybrid models for bankruptcy prediction. The proposed hybrid models are two phase. Phase one are (a) DA-assisted neural network, (b) Logit-assisted neural network, and (c) Genetic-assisted neural network. And, phase two are (a) DA-assisted Case based reasoning, and (b) Genetic-assisted Case based reasoning. In the variables selection, We are focusing on three alternative methods - linear discriminant analysis, logit analysis and genetic algorithms - that can be used empirically select predictors for hybrid model in bankruptcy prediction. Empirical results using Korean medium-sized firms data show that hybrid models are very promising neural network models and case based reasoning for bankruptcy prediction in terms of predictive accuracy and adaptability.