• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• KDI Journal of Economic Policy
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• v.34 no.3
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• pp.31-65
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• 2012

We study the sustainability of the current fiscal policy of Korea, and the effects of the social welfare policy expansion, which has been recently discussed among the political circles, on the government budget and the generational equity, using generational accounting. We follow the generational accounting approach, considering the fact that most of the social welfare policies are the entitlement programs, which imposes the limitation of the policy maker's discretion to control the cost of their provision. The social welfare expenditure will change due to the change in the policy environments of the future, such as population aging. Therefore, we need to take into account the government cash flow of the future as well as of the present to investigate its effects on the fiscal sustainability, which implies that the national debt or the budget balance is not a proper index for the investigation. Our findings are as follows. The current fiscal policies are not sustainable, and the long-term budgetary imbalance is shown very serious. The required tax adjustment, which is defined as the percentage change of tax burden required to attain the long-term budgetary balance, is very large. Unless the level of the government expenditure is properly controlled, the tax burden and the social contribution level will rise to the untolerable level. Moreover, the expansion of the social welfare policies, which has been discussed among the political circles, will substantially increase the fiscal burden of the future generations. Even though the provision of the free lunch to the primary and the secondary school students, the free child care, and the discounted college tuition do not increase the fiscal burden much, because their magnitude at present is not large and will decrease due to the decrease in the number of the newborns and the students resulting from the fall in the fertility rate, that of the free health care service will increase tax burden of the future generations very much, because the magnitude of the government expenditure needed at present is very large and the population aging will further increase the magnitude of the health care expenditure. The findings indicate that the structural reforms, to prevent the explosive increase in the social welfare expenditure in the future, are necessary before the implementation of the welfare policy expansion. In particular, the cost control of the social transfers to the elderly needs to be made, because the speed of the population aging of Korea is among the highest in the world. The findings also indicate that the budget balance or the national debt can cause the fiscal illusion, which makes the Korean government budget look sound, even though the fiscal policy will rapidly increase the social welfare expenditure in the future, as the population ages. The generational accounting, which takes into account the cash flow of the future as well as of the present, unlike the budgetary balance and the national debt, which shows the results of the government financial activities of the past and the present, is a useful method to overcome the fiscal illusion.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.109-125
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• 2019

As interest on intelligent search engines increases, various studies have been conducted to extract and utilize the features related to products intelligencely. In particular, when users search for goods in e-commerce search engines, the 'color' of a product is an important feature that describes the product. Therefore, it is necessary to deal with the synonyms of color terms in order to produce accurate results to user's color-related queries. Previous studies have suggested dictionary-based approach to process synonyms for color features. However, the dictionary-based approach has a limitation that it cannot handle unregistered color-related terms in user queries. In order to overcome the limitation of the conventional methods, this research proposes a model which extracts RGB values from an internet search engine in real time, and outputs similar color names based on designated color information. At first, a color term dictionary was constructed which includes color names and R, G, B values of each color from Korean color standard digital palette program and the Wikipedia color list for the basic color search. The dictionary has been made more robust by adding 138 color names converted from English color names to foreign words in Korean, and with corresponding RGB values. Therefore, the fininal color dictionary includes a total of 671 color names and corresponding RGB values. The method proposed in this research starts by searching for a specific color which a user searched for. Then, the presence of the searched color in the built-in color dictionary is checked. If there exists the color in the dictionary, the RGB values of the color in the dictioanry are used as reference values of the retrieved color. If the searched color does not exist in the dictionary, the top-5 Google image search results of the searched color are crawled and average RGB values are extracted in certain middle area of each image. To extract the RGB values in images, a variety of different ways was attempted since there are limits to simply obtain the average of the RGB values of the center area of images. As a result, clustering RGB values in image's certain area and making average value of the cluster with the highest density as the reference values showed the best performance. Based on the reference RGB values of the searched color, the RGB values of all the colors in the color dictionary constructed aforetime are compared. Then a color list is created with colors within the range of ${\pm}50$ for each R value, G value, and B value. Finally, using the Euclidean distance between the above results and the reference RGB values of the searched color, the color with the highest similarity from up to five colors becomes the final outcome. In order to evaluate the usefulness of the proposed method, we performed an experiment. In the experiment, 300 color names and corresponding color RGB values by the questionnaires were obtained. They are used to compare the RGB values obtained from four different methods including the proposed method. The average euclidean distance of CIE-Lab using our method was about 13.85, which showed a relatively low distance compared to 3088 for the case using synonym dictionary only and 30.38 for the case using the dictionary with Korean synonym website WordNet. The case which didn't use clustering method of the proposed method showed 13.88 of average euclidean distance, which implies the DBSCAN clustering of the proposed method can reduce the Euclidean distance. This research suggests a new color synonym processing method based on RGB values that combines the dictionary method with the real time synonym processing method for new color names. This method enables to get rid of the limit of the dictionary-based approach which is a conventional synonym processing method. This research can contribute to improve the intelligence of e-commerce search systems especially on the color searching feature.

• Asia Marketing Journal
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• v.12 no.2
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• pp.83-110
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• 2010

Dissatisfied customers may express their dissatisfaction behaviorally. These behavioral responses may impact the firms' profitability. How do we model the impact of self regulatory orientation on emotions and subsequent customer behaviors? Obviously, the positive and negative emotions experienced in these situations will influence the overall degree of satisfaction or dissatisfaction with the service(Zeelenberg and Pieters 1999). Most likely, these specific emotions will also partly determine the subsequent behavior in relation to the service and service provider, such as the likelihood of complaining, the degree to which customers will switch or repurchase, and the extent of word of mouth communication they will engage in(Zeelenberg and Pieters 2004). This study investigates the antecedents, consequences of negative consumption emotion and the moderate effect of attribution processing in an integrated model(self regulatory mode → specific emotions → behavioral responses). We focused on the fact that regret and disappointment have effects on consumer behavior. Especially, There are essentially two approaches in this research: the valence based approach and the specific emotions approach. The authors indicate theoretically and show empirically that it matters to distinguish these approaches in services research. and The present studies examined the influence of two regulatory mode concerns(Locomotion orientation and Assessment orientation) with making comparisons on experiencing post decisional regret and disappointment(Pierro, Kruglanski, and Higgins 2006; Pierro et al. 2008). When contemplating a decision with a negative outcome, it was predicted that high (vs low) locomotion would induce more disappointment than regret, whereas high (vs low) assessment would induce more regret than disappointment. The validity of the measurement scales was also confirmed by evaluations provided by the participating respondents and an independent advisory panel; samples provided recommendations throughout the primary, exploratory phases of the study. The resulting goodness of fit statistics were RMR or RMSEA of 0.05, GFI and AGFI greater than 0.9, and a chi-square with a 175.11. The indicators of the each constructs were very good measures of variables and had high convergent validity as evidenced by the reliability with a more than 0.9. Some items were deleted leaving those that reflected the cognitive dimension of importance rather than the dimension. The indicators were very good measures and had convergent validity as evidenced by the reliability of 0.9. These results for all constructs indicate the measurement fits the sample data well and is adequate for use. The scale for each factor was set by fixing the factor loading to one of its indicator variables and then applying the maximum likelihood estimation method. The results of the analysis showed that directions of the effects in the model are ultimately supported by the theory underpinning the causal linkages of the model. This research proposed 6 hypotheses on 6 latent variables and tested through structural equation modeling. 6 alternative measurements were compared through statistical significance test of the paths of research model and the overall fitting level of structural equation model and the result was successful. Also, Locomotion orientation more positively influences disappointment when internal attribution is high than low and Assessment orientation more positively influences regret when external attribution is high than low. In sum, The results of our studies suggest that assessment and locomotion concerns, both as chronic individual predispositions and as situationally induced states, influence the amount of people's experienced regret and disappointment. These findings contribute to our understanding of regulatory mode, regret, and disappointment. In previous studies of regulatory mode, relatively little attention has been paid to the post actional evaluative phase of self regulation. The present findings indicate that assessment concerns and locomotion concerns are clearly distinct in this phase, with individuals higher in assessment delving more into possible alternatives to past actions and individuals higher in locomotion engaging less in such reflective thought. What this suggests is that, separate from decreasing the amount of counterfactual thinking per se, individuals with locomotion concerns want to move on, to get on with it. Regret is about the past and not the future. Thus, individuals with locomotion concerns are less likely to experience regret. The results supported our predictions. We discuss the implications of these findings for the nature of regret and disappointment from the perspective of their relation to regulatory mode. Also, self regulatory mode and the specific emotions(disappointment and regret) were assessed and their influence on customers' behavioral responses(inaction, word of mouth) was examined, using a sample of 275 customers. It was found that emotions have a direct impact on behavior over and above the effects of negative emotions and customer behavior. Hence, We argue against incorporating emotions such as regret and disappointment into a specific response measure and in favor of a specific emotions approach on self regulation. Implications for services marketing practice and theory are discussed.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.141-156
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• 2013

Researchers in welfare services of local government in Korea have rather been on isolated issues as disables, childcare, aging phenomenon, etc. (Kang, 2004; Jung et al., 2009). Lately, local officials, yet, realize that they need more comprehensive welfare services for all residents, not just for above-mentioned focused groups. Still cases dealt with focused group approach have been a main research stream due to various reason(Jung et al., 2009; Lee, 2009; Jang, 2011). Social Security Information System is an information system that comprehensively manages 292 welfare benefits provided by 17 ministries and 40 thousand welfare services provided by 230 local authorities in Korea. The purpose of the system is to improve efficiency of social welfare delivery process. The study of local government expenditure has been on the rise over the last few decades after the restarting the local autonomy, but these studies have limitations on data collection. Measurement of a local government's welfare efforts(spending) has been primarily on expenditures or budget for an individual, set aside for welfare. This practice of using monetary value for an individual as a "proxy value" for welfare effort(spending) is based on the assumption that expenditure is directly linked to welfare efforts(Lee et al., 2007). This expenditure/budget approach commonly uses total welfare amount or percentage figure as dependent variables (Wildavsky, 1985; Lee et al., 2007; Kang, 2000). However, current practice of using actual amount being used or percentage figure as a dependent variable may have some limitation; since budget or expenditure is greatly influenced by the total budget of a local government, relying on such monetary value may create inflate or deflate the true "welfare effort" (Jang, 2012). In addition, government budget usually contain a large amount of administrative cost, i.e., salary, for local officials, which is highly unrelated to the actual welfare expenditure (Jang, 2011). This paper used local government welfare service data from the detailed data sets linked to the Social Security Information System. The purpose of this paper is to analyze the factors that affect social welfare spending of 230 local authorities in 2012. The paper applied multiple regression based model to analyze the pooled financial data from the system. Based on the regression analysis, the following factors affecting self-funded welfare spending were identified. In our research model, we use the welfare budget/total budget(%) of a local government as a true measurement for a local government's welfare effort(spending). Doing so, we exclude central government subsidies or support being used for local welfare service. It is because central government welfare support does not truly reflect the welfare efforts(spending) of a local. The dependent variable of this paper is the volume of the welfare spending and the independent variables of the model are comprised of three categories, in terms of socio-demographic perspectives, the local economy and the financial capacity of local government. This paper categorized local authorities into 3 groups, districts, and cities and suburb areas. The model used a dummy variable as the control variable (local political factor). This paper demonstrated that the volume of the welfare spending for the welfare services is commonly influenced by the ratio of welfare budget to total local budget, the population of infants, self-reliance ratio and the level of unemployment factor. Interestingly, the influential factors are different by the size of local government. Analysis of determinants of local government self-welfare spending, we found a significant effect of local Gov. Finance characteristic in degree of the local government's financial independence, financial independence rate, rate of social welfare budget, and regional economic in opening-to-application ratio, and sociology of population in rate of infants. The result means that local authorities should have differentiated welfare strategies according to their conditions and circumstances. There is a meaning that this paper has successfully proven the significant factors influencing welfare spending of local government in Korea.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.19
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• pp.224-242
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• 2003

The author believes that the main task of study in international trade usage and practice is the management of transactional risks involved in international sale of goods. They are foreign exchange risks, transportation risks, credit risk, risk of miscommunication, etc. In most cases, these risks are more serious and enormous than those involved in domestic sales. Historically, the merchant adventurers organized the voyage abroad, secured trade finance, and went around the ocean with their own or consigned cargo until around the $mid-19^{th}$ century. They did business faceto-face at the trade fair or the open port where they maintained the local offices, so-called "Trading House"(商館). Thererfore, the transactional risks might have been one-sided either with the seller or the buyer. The bottomry seemed a typical arrangement for risk sharing among the interested parties to the adventure. In this way, such organizational arrangements coped with or bore the transactional risks. With the advent of ocean liner services and wireless communication across the national border in the $19^{th}$ century, the business of merchant adventurers developed toward the clear division of labor; sales by mercantile agents, and ocean transportation by the steam ship companies. The international banking helped the process to be accelerated. Then, bills of lading backed up by the statute made it possible to conduct documentary sales with a foreign partner in different country. Thus, FOB terms including ocean freight and CIF terms emerged gradually as standard trade terms in which transactional risks were allocated through negotiation between the seller and the buyer located in different countries. Both of them did not have to go abroad with their cargo. Instead, documentation in compliance with the terms of the contract(plus an L/C in some cases) must by 'strictly' fulfilled. In other words, the set of contractual documents must be tendered in advance of the arrival of the goods at port of discharge. Trust or reliance is placed on such contractual paper documents. However, the container transport services introduced as international intermodal transport since the late 1960s frequently caused the earlier arrival of the goods at the destination before the presentation of the set of paper documents, which may take 5 to 10% of the amount of transaction. In addition, the size of the container vessel required the speedy transport documentation before sailing from the port of loading. In these circumstances, computerized processing of transport related documents became essential for inexpensive transaction cost and uninterrupted distribution of the goods. Such computerization does not stop at the phase of transportation but extends to cover the whole process of international trade, transforming the documentary sales into less-paper trade and further into paperless trade, i.e., EDI or E-Commerce. Now we face the other side of the coin, which is data security and paperless transfer of legal rights and obligations. Unfortunately, these issues are not effectively covered by a set of contracts only. Obviously, EDI or E-Commerce is based on the common business process and harmonized system of various data codes as well as the standard message formats. This essential feature of E-Commerce needs effective coordination of different divisions of business and tight control over credit arrangements in addition to the standard contract of sales. In a few word, information does not alway invite "trust". Credit flows from people, or close organizational tie-ups. It is our common understanding that, without well-orchestrated organizational arrangements made by leading companies, E-Commerce does not work well for paperless trade. With such arrangements well in place, participating E-business members do not need to seriously care for credit risk. Finally, it is also clear that E-International Commerce must be linked up with a set of government EDIs such as NACCS, Port EDI, JETRAS, etc, in Japan. Therefore, there is still a long way before us to go for E-Commerce in practice, not on the top of information manager's desk.

• The Journal of Industrial Distribution & Business
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• v.1 no.1
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• pp.13-21
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• 2010

Since Korea establish diplomatic ties with China in 1992, korea and China have had rapid progress in most of field as politic, economy, society and culture through basing on cultural commonality and geographical adjacency. Especially, China is the biggest trading partner to korea, and also Korea is third-biggest trading country to China. They become strategic cooperating relation in 2008. Currently, in terms of international trade relation, WTO/DDA negotiation is proceeding in difficulty, but FTA has been growing and extending in the world, and the two country, china and korea, have been competitively trying wide and active FTA negotiation promotion. After Financial crisis in 1997, according to the requirement of local economic cooperation, China has shown the interest to several countries since the conclusion of FTA treaty with ASEAN in 2005. China also makes the active afford to conclude FTA with Korea. Last May 28th, this was mentioned in the meeting between president Lee and Premier Wen Jiabao, so it is anticipated that the negotiation for FTA will be started in the near future. There are many political suggestions and concerns in terms of way of negotiation korea would choose. Some economist said that "'Continuous FTA aimed at long-term protocol should be promoted between korea and China and negotiated includingly'" However, this research claims that commodity exchange, service, and investment areas should be included and it has to be comprehensive package settlement style in negotiation. This research has found out the characteristics of China's negotiation and implications through the China's existed FTA negotiation examples. Currently, China has taken Continuous or a phase-negotiation method to ASEAN, Pakistan, Chile and some other developing country and to advanced countries like New Zealand or Singapore, comprehensive package settlement method is used in FTA negotiation. In consider of the FTA negotiation between Korea and China, Korea has some problems in the commodity change area in agriculture maket's opening. While, for china, the issues would happen in service trade area, especially when encountering finance and communication industries are opened, China's economy could be exposed to some risk. In result, Korea should expand its negotiation range from commodity trade to service trade, in order to exchange both issues, then the negotiation will be concluded more easily. In other word, for FTA, korea should follow comprehensive package settlement way that is similar to New zealand and Singapore case. Through this kind of method, Korea can expect effect of creating trade, conversion of it and preoccupancy of service field in china's market against the advanced countries like Usa, Europe and Japan. Also, to have a successful FTA negotiation, korea should find out china's policy for FTA negotiation. With this information, korea will be able to suggest the way to make a profit. Systematic analysis and comparison about previous negotiation cases of china are needed before the negotiation begin.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

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. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.

• Journal of Korea Entertainment Industry Association
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• v.14 no.4
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• pp.237-250
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• 2020

The purpose of this study was to investigate the relationship between the performance factors of dance performance and the intention to revisit the audience, and to investigate the structural relationship between viewing satisfaction and viewing value. For data processing, SPSS Ver. 21.0 and AMOS Ver. The program of 21.0 was used. Structural relationships were analyzed using a two-step approach, and the significance of the effects was verified using bootstrapping. In addition, a full mediating effect and a partial mediating effect were presented using the three-step regression analysis mediating effect. The results of the study are as follows. First, it was found that the viewing factors influenced the viewing satisfaction and the viewing value. Second, it was found that viewing satisfaction had an intention to revisit and influenced the viewing value. It was also found that the viewing value had an effect on the intention to revisit. Third, in the relationship between the viewing factors of the dance performance and the viewing value, it was found that the viewing satisfaction had a partial mediating effect. Fourth, it was found that the attendance factor of the dance performance was not related to the intention to revisit. However, it was found that the satisfaction of viewing and the value of viewing had a complete mediating effect in relation to the viewing factors of dance performances and the intention to revisit. Through these studies, the dance performance should overcome the inherent limitations of space-time limitations and present basic data for establishing a mid- to long-term marketing strategy that can respond quickly.