• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

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 economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.29-43
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• 2012

Financial data such as stock index returns and exchange rates have the properties of heavy tail and asymmetry compared to normal distribution. When we estimate VaR using the GARCH model (with the conditional return distribution of normal) it shows the tendency of the lower estimation and clustering in the losses over the estimated VaR. In this paper, we argue that this problem can be resolved through the adaptation of the unbounded Johnson distribution as that of the condition return. We also compare this model with the GARCH with the conditional return distribution of normal and student-t. Using the losses exceed the ex-ante VaR, estimates, we check the validity of the GARCH models through the failure proportion test and the clustering test. We nd that the GARCH model with conditional return distribution of unbounded Johnson provides an appropriate estimation of the VaR and does not occur the clustering of violations.

• Journal of The Korean Digital Architecture Interior Association
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• v.12 no.3
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• pp.39-47
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• 2012

General concern over 'Quality of Life(QOL)' has caused many researches, which compare nations' or cities' QOL by the normative criteria proposed by themselves. The fact that these are characterized by subjectiveness makes this study have a purpose of trying to enhance the intersubjectiveness by means of quantitive analysis to find the factors on the QOL. This study uses statistical methods such as multiple regression and factor analysis based on the secondary data from the "2011 Seoul Survey". The survey includes many items, for example happiness index and satisfaction for work, amenity, etc.. And the analysis tells three findings as follows; Firstly, five subcategories of happiness have relative importance in the order of (1)financial condition, (2)health, (3)social activities, (4)community relationship and (5)family life. These generally constitute the first factor extracted by factor analysis and named 'abundance-family-intimacy factor.' Secondly, the 'abundance-family-intimacy factor' and the 'information-danger factor' among five factors(the others are 'learning-giving factor', 'local patriotism-hope for rise factor' and 'amenity-comfort factor') have statistically significant effect to QOL. Thirdly, the first factor has positive effect, but the second has negative to QOL. Note is needed to the fact that the items on SNS and internet belong to second factor and to the result that these make QOL deteriorate. These results should be considered as having limited meaning of statistical aspect. But accumulation of following studies by quantitive approach is anticipate to make more practical and general meaning.

• Management & Information Systems Review
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• v.32 no.1
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• pp.135-151
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• 2013

The study has been carried out to understand productivity-related factors that have been connected to improve financial independence rate of national university hospital. In order to achieve such research purpose as working out implications of efficient resource management, data from the four years between 2007 to 2010 of 12 national university hospitals were reviewed. According to the results, the hospitals came up with significant differences in Value Added to Total Asset, Value Added to Tangible Fixed Assets, Value Added to Personnel Expenses and Value Added Ratio to Patient Revenues. In addition, after the relation between the investment efficiency and profitability of the national university hospitals was investigated, it was learned that application of basic environmental factors would have an influence on a profitability index Operating Margin. As long as the basic environmental factors are adopted, Operating Margin of the national university hospitals is improved under the condition that the Value Added to Tangible Fixed Assets for Production and the Value Added to Personnel Expenses gets higher but the Value Added Ratio to Patient Revenues gets lowered.

• Korean Journal of Construction Engineering and Management
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• v.16 no.1
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• pp.101-109
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• 2015

The expected economic effect by investment was divided by square of real discount rate annually for change to present value. Thus, the impact of real discount rate on economic analysis is larger than other factors. The existing general method for prediction of real discount rate is application of average data during past certain period. This study proposed prediction method of real discount rate for accuracy improvement. First, the economic variables which impact on interest rate of business loan and consumer price of real discount rate were determined. The variables which impact on interest rate of business loan were selected to call rate and exchange rate. The variable which impact on consumer price index was selected to producer price index. Next, the effect relation was analyzed between real discount rate and selected variables. The significant effect relation were analyzed to exit. Lastly, the real discount rate was predicted from 2008 to 2010 based on related economic variables. The accuracy of prediction result was compared with actual data and average data. The real discount rate based on actual data, predicted data, and average data were analyzed to -1.58%, -0.22%, and 6.06%, respectively. Though the proposed method in this study was not considered special condition such as financial crisis, the prediction accuracy was much higher than result based on average data.

• Management & Information Systems Review
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• v.37 no.3
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• pp.27-47
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• 2018

In this study, we examine how land use zoning affects the land price controlling other variables such as road-facing condition of the land, land form, land age after its development and land size. We employ geographically weighted regression analysis which reflects spatial dependency as methodology with a data sample of land transaction price data of Jangyu, a new town, in Korea. The results of our empirical analysis show that the respective coefficients of traditional regression and geographically weighted regression are not significantly different. However, after calculating Moran's Index with residuals of both OLS and GWR models, we find that Moran's Index of GWR decreases around 26% compared to that of OLS model, thus improving the problem of spatial autoregression of residuals considerably. Unlike our expectation, though, in both traditional regression and geographically weighted regression where residential exclusive area is used as a reference variable, the dummy variable of the residential land for both housing and shops shows a negative sign. This may be because the residential land for both housing and shops is usually located in the level area while the residential exclusive area is located at the foot of a mountain or on a gentle hill where the residents can have good quality air and scenery. Although the utility of the residential land for both housing and shops is higher than its counterpart's since it has higher floor area ratio, amenity which can be explained as high quality of air and scenery in this study seems to have higher impact in purchase of land for housing. On the other hand, land for neighbourhood living facility seems to be valued higher than any other land zonings used in this research since it has much higher floor area ratio than the two land zonings above and can have a building with up to 5 stories constructed on it. With regard to road-facing condition, land buyers seem to prefer land which faces a medium-width road as expected. Land facing a wide-width road may have some disadvantage in that it can be exposed to noise and exhaust gas from cars and that entrance may not be easy due to the high speed traffic of the road. In contrast, land facing a narrow road can be free of noise or fume from cars and have privacy protected while it has some inconvenience in that entrance may be blocked by cars parked in both sides of the narrow road. Finally, land age variable shows a negative sign, which means that the price of land declines over time. This may be because decline of the land price of Jangyu was bigger than that of other regions in Gimhae where Jangyu, a new town, also belong, during the global financial crisis of 2008.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.7 no.4
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• pp.21-32
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• 2012

The current beauty service of Korea is upgrading its outside appearance from traditional small-shop image and its speed of change and competition are more severe than ever before. The top priority trait of beauty management is based on man-power resource, and personnel-management is the most important thing that more than anything else. Compared with other arenas of business, personnel management plays a key role in beauty industry. As a result, this research begins with the issue of personnel management. In addition, I will show that the management leadership of beauty service affects organization civil actions and changing jobs in the beauty industry employees, and the leadership can lower changing job rate to some extent. The role and duty of business manager is very significant because the dependence of man-power resource is magnificent in beauty service. In spite of rapid changing of economic surroundings, the management of beauty service is still working without any detail plans. It is no exaggeration to say that the most of beauty service managers are lack of leadership conception. Although the demands of customers are changing everyday, the frequent changing of jobs among talented employees drops customers credibility and high-quality service. From a management viewpoint, re-training cost of employees leads to economic loss and the company suffers from financial problems. As a result, it creates vicious circle in the beauty industry. Today, the management atmosphere in the beauty service is hard to get its right track because of the cutthroat competitions of beauty industry and difficulties of hiring promising employees. It has been said that "Nothing ventured, Nothing gained" Above all, the beauty service requires job speciality and it produces economic profits. Therefore, the managers desperately need to change the way they regard those with job changers. In this research shows the principal index of the leadership type of beauty business managers how much affects its employees' long-term working condition. The new concept of leadership, such as alternative suggestions, setting goals, and organization civil action will reorganize the stereo-type frame. I will also investigate the factors of job changing and intention of the beauty service employees for the development option of the beauty industry.