• The Korean Journal of Financial Management
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• v.26 no.3
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• pp.171-198
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• 2009

The primary purpose of this study is to examine the impact of new BIS regulation, which is the preparations to incorporate not only credit risk but also market and operation risk, on the bank behaviors. As methodology, SUR(seemingly unrelated regression) and pool unit test are used in the empirical analysis of banks survived in Korea. It is employed that quarterly data of BIS capital ratio, ratio of standard and below loans to total loans, ratio of liquid assets to liquid liabilities, allowances for credit losses, real GDP, yields of corporate bonds(3years, AA) covering the period of 2000Q1~2009Q1. As a result, it could be indicated that effectiveness and promoting improvements of BIS capital regulation policy as follows; First, it is explicitly seen that weight of lending had decreased and specific gravity of international investment had increased until before BIS regulation is built up a step for revised agreement in late 2001. Second, after more strengthening of BIS standard in late 2002, banks had a tendency to decrease the adjustment of assets weighted risk through issuing of national loan that is comparatively low profitability. Also, it is implicitly sought that BIS regulation is a bit of a factor to bring about credit crunch and then has become a bit of a factor of economic stagnation. Third, as the BIS regulation became hard, it let have a effort to raise the soundness of a credit loan because of selecting good debtor based on its credit ratings. Fourth, it should be arranged that the market disciplines, the effective superintendence system and the sound environment to be able to raise enormous bank capital easily, against the credit stringency and reinforce the soundness of banks etc. in Korea capital market.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.4
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• pp.421-431
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• 2023

Sewer deterioration models can offer important information on prediction of future condition of the asset to decision makers in their implementing sewer pipe networks management program. In this study, Markov chain model was used to estimate sewer deterioration trend based on the historical structural condition assessment data obtained by CCTV inspection. The data used in this study were limited to Hume pipe with diameter of 450 mm and 600 mm in three sub-catchment areas in city A, which were collected by CCTV inspection projects performed in 1998-1999 and 2010-2011. As a result, it was found that sewers in sub-catchment area EM have deteriorated faster than those in other two sub-catchments. Various main defects were to generate in 29% of 450 mm sewers and 38% of 600 mm in 35 years after the installation, while serious failure in 62% of 450 mm sewers and 74% of 600 mm in 100 years after the installation in sub-catchment area EM. In sub-catchment area SN, main defects were to generate in 26% of 450 mm sewers and 35% of 600 mm in 35 years after the installation, while in sub-catchment area HK main defects were to generate in 27% of 450 mm sewers and 37% of 600 mm in 35 years after the installation. Larger sewer pipes of 600 mm were found to deteriorate faster than smaller sewer pipes of 450 mm by about 12 years. Assuming that the percentage of main defects generation could be set as 40% to estimate the life expectancy of the sewers, it was estimated as 60 years in sub-catchment area SN, 42 years in sub-catchment area EM, 59 years in sub-catchment area HK for 450 mm sewer pipes, respectively. For 600 mm sewer pipes, on the other hand, it was estimated as 43 years, 34 years, 39 years in sub-catchment areas SN, EM, and HK, respectively.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.141-154
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• 2014

The adoption of International Financial Reporting Standards (IFRS) is the one of important issues in the recent accounting research because the change from local GAAP (Generally Accepted Accounting Principles) to IFRS has a substantial effect on accounting information. Over 100 countries including Australia, China, Canada and the European Union member countries adopt IFRS (International Financial Reporting Standards) for financial reporting purposes, and several more including the United States and Japan are considering the adoption of IFRS (International Financial Reporting Standards). In Korea, 61 firms voluntarily adopted Korean International Financial Reporting Standard (K-IFRS) in 2009 and 2010 and all listed firms mandatorily adopted K-IFRS (Korea-International Financial Reporting Standards) in 2011. The adoption of IFRS is expected to increase financial statement comparability, improve corporate transparency, increase the quality of financial reporting, and hence, provide benefits to investors This study investigates whether recognized accounts receivable discounting (AR discounting) under Korean International Financial Reporting Standard (K-IFRS) is more value relevant than disclosed AR discounting under Korean Generally Accepted Accounting Principles (K-GAAP). Because more rigorous standards are applied to the derecognition of AR discounting under K-IFRS(Korea-International Financial Reporting Standards), most AR discounting is recognized as a short term debt instead of being disclosed as a contingent liability unless all risks and rewards are transferred. In this research, I try to figure out industrial responses to the changes in accounting rules for the treatment of accounts receivable toward more strict standards in the recognition of sales which occurs with the adoption of Korea International Financial Reporting Standard. This study examines whether accounting information is more value-relevant, especially information on accounts receivable discounting (hereinafter, AR discounting) is value-relevant under K-IFRS (Korea-International Financial Reporting Standards). First, note that AR discounting involves the transfer of financial assets. Under Korean Generally Accepted Accounting Principles (K-GAAP), when firms discount AR to banks before the AR maturity, firms conventionally remove AR from the balance-sheet and report losses from AR discounting and disclose and explain the transactions in the footnotes. Under K-IFRS (Korea-International Financial Reporting Standards), however, most firms keep AR and add a short-term debt as same as discounted AR. This process increases the firms' leverage ratio and raises the concern to the firms about investors' reactions to worsening capital structures. Investors may experience the change in perceived risk of the firm. In the study sample, the average of AR discounting is 75.3 billion won (maximum 3.6 trillion won and minimum 18 million won), which is, on average 7.0% of assets (maximum 38.6% and minimum 0.002%), 26.2% of firms' accounts receivable (maximum 92.5% and minimum 0.003%) and 13.5% of total liabilities (maximum 69.5% and minimum 0.004%). After the adoption of K-IFRS (Korea-International Financial Reporting Standards), total liabilities increase by 13%p on average (maximum 103%p and minimum 0.004%p) attributable to AR discounting. The leverage ratio (total liabilities/total assets) increases by an average 2.4%p (maximum 16%p and minimum 0.001%p) and debt-to-equity ratio increases by average 14.6%p (maximum 134%p and minimum 0.006%) attributable to the recognition of AR discounting as a short-term debt. The structure of debts and equities of the companies engaging in factoring transactions are likely to be affected in the changes of accounting rule. I suggest that the changes in accounting provisions subsequent to Korea International Financial Reporting Standard adoption caused significant influence on the structure of firm's asset and liabilities. Due to this changes, the treatment of account receivable discounting have become critical. This paper proposes an intelligent diagnostic system for estimating negative impact on stock value with self-organizing maps and case based reasoning. To validate the usefulness of this proposed model, real data was analyzed. In order to get the significance of this proposed model, several models were compared to the research model. I found out that this proposed model provides satisfactory results with compared models.

• Korean Journal of Social Welfare
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• v.59 no.2
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• pp.5-38
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• 2007

This study attempts to develop a theory on the evolving process of wealthy major donors' sharing lives in Korea through a grounded theory approach. To conduct this study, the researchers have in-depth interviews with 11 exemplary wealthy major donors who have more than one million US dollars in his or her own asset and donate more than ten thousand US dollars annually. In data analysis, this study identifies 161 concepts on the evolving process of wealthy major donors' sharing lives; and the concepts are categorized with 33 sub-categories and 14 categories. In the paradigm model on the evolving process of wealthy major donors' sharing lives, it is identified that the central phenomenon, 'practicing sharing lives as noblesse oblige', is related with the causal conditions such as 'learning through memories and observation', 'realizing my duties', and 'emphasizing'; and the central phenomenon is related with the contingent conditions such as 'being sensitive to external evaluation', 'having limited information on giving', 'distrusting donation related environments'. The action/interactional sequences such as 'utilizing relationships' and 'strengthening active participation' are accomplished by moderating conditions such as 'having internal and external supports' and 'guiding by firm conviction'. It reveals that as a result, wealthy major donors enjoy the feeling of becoming a ideal and true wealthy person, establish sharing lives as firm and major parts of overall lives, and experience strong desires for better future and society. In this study, 'generous sharing that shares personal heritages and social benefits' is analyzed as a core category; it shows that sharing of wealthy major donors is related to the characteristics of generosity practice based on moral self-benefiting rather than complete altruistic characteristics or self-sacrificial characteristics. The process analysis reveals that it has the following stages: first, initial giving by exposure to causes or requests; second, routine practice of giving; third, evolution of practice of giving with gradual expansion in quantities and qualities; and fourth, living with giving. In the process, the following four types are identified: devoted wealthy donors for sharing, wealthy donors practicing sharing in daily life, wealthy donors practicing sharing with learning on external stimulus, and wealthy donors practicing sharing on empathy. Finally, this study discusses both meanings of identifying and developing a theory on the evolving process of wealthy major donors' sharing lives and implications of the research results in cultivating and developing potential wealthy major donors in Korea.

• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.239-276
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• 2009

The paper investigates the possible relationship between earnings prediction by security analysts and special ownership ties that link security companies those analysts belong to and firms under analysis. "Security analysts" are known best for their role as information producers in stock markets where imperfect information is prevalent and transaction costs are high. In such a market, changes in the fundamental value of a company are not spontaneously reflected in the stock price, and the security analysts actively produce and distribute the relevant information crucial for the price mechanism to operate efficiently. Therefore, securing the fairness and accuracy of information they provide is very important for efficiencyof resource allocation as well as protection of investors who are excluded from the special relationship. Evidence of systematic distortion of information by the special tie naturally calls for regulatory intervention, if found. However, one cannot presuppose the existence of distorted information based on the common ownership between the appraiser and the appraisee. Reputation effect is especially cherished by security firms and among analysts as indispensable intangible asset in the industry, and the incentive to maintain good reputation by providing accurate earnings prediction may overweigh the incentive to offer favorable rating or stock recommendation for the firms that are affiliated by common ownership. This study shares the theme of existing literature concerning the effect of conflict of interests on the accuracy of analyst's predictions. This study, however, focuses on the potential conflict of interest situation that may originate from the Korea-specific ownership structure of large conglomerates. Utilizing an extensive database of analysts' reports provided by WiseFn(R) in Korea, we perform empirical analysis of potential relationship between earnings prediction and common ownership. We first analyzed the prediction bias index which tells how optimistic or friendly the analyst's prediction is compared to the realized earnings. It is shown that there exists no statistically significant relationship between the prediction bias and common ownership. This is a rather surprising result since it is observed that the frequency of positive prediction bias is higher with such ownership tie. Next, we analyzed the prediction accuracy index which shows how accurate the analyst's prediction is compared to the realized earnings regardless of its sign. It is also concluded that there is no significant association between the accuracy ofearnings prediction and special relationship. We interpret the results implying that market discipline based on reputation effect is working in Korean stock market in the sense that security companies do not seem to be influenced by an incentive to offer distorted information on affiliated firms. While many of the existing studies confirm the relationship between the ability of the analystand the accuracy of the analyst's prediction, these factors cannot be controlled in the above analysis due to the lack of relevant data. As an indirect way to examine the possibility that such relationship might have distorted the result, we perform an additional but identical analysis based on a sub-sample consisting only of reports by best analysts. The result also confirms the earlier conclusion that the common ownership structure does not affect the accuracy and bias of earnings prediction by the analyst.

• Cartoon and Animation Studies
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• s.26
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• pp.131-156
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• 2012

As the Global Age has arrived, the domain of festivals has expanded to fulfill the role of being not only a tourist attraction but of being a factor that determines the image and identity of cities, and the factor of enhancing the brand value of a particular city is being focused upon. The city of Bucheon, which aims to be a culture oriented city, is attempting to utilize the Bucheon International Comics Festival as a cultural asset for the revitalization of the city. This study has as its purpose the development of an evaluation index model on the brand value of the Bucheon International Comics Festival and research being conducted based on the developed evaluation index model on the awareness level of the citizens of Bucheon of the festival. In regards to this, the theoretical background was examined and the index model was developed based on precedent research. Based on this, a survey of 1,000 citizens of Bucheon was conducted in this study. This study conducted a survey targeting 500 persons, dividing them into 2 groups according to whether they participated in the festival. The survey of this study established 9 evaluation categories for the International Comics Festival evaluation index model which consists of demographic research and participation motivation, value of comics, festival brand awareness and association image, perceived product quality and loyalty for the festival, internationality of the festival and urban activation. Each survey question is composed of 5 points scale measurement. As a result of the survey, 'for an education of children' was the highest for the participation motivation, and 'not knowing of the festival information' was the highest for the reason of not having participated. The industrial value was evaluated as the highest among the value of comics by the both two groups, and it was studied that there was perception gap for the festival according to whether they participated in the festival for each survey question. It was revealed that the level of awareness of the Bucheon International Comics Festival was "normal," the "city revitalization" index and the "value of comics" index were relatively high and the "international character of the festival" index was the lowest. Furthermore, it was shown that there were differences in the awareness of the established categories of the developed evaluation index model based on whether or not there was participation in the festival. This study comprehensively organizes these analytical results and derives implications which can be used as data for the criteria of the development of future strategy for the Bucheon International Comics Festival.

• Journal of the Economic Geographical Society of Korea
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• v.17 no.3
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• pp.502-516
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• 2014

This study has decomposed the Gini coefficient using Korean Labor & Income Panel Study data and empirically analyzed the impact of demographic characteristics and source-specific income of householder on the household income gap using panel analysis. The scope of areas were divided into 'nationwide,' 'metropolitan areas,' and 'non-metropolitan areas,' and the period before and after the global financial crisis was examined. The analysis findings are as follows. First, when the entire period was examined by income source using Gini decomposition with division of areas into 'nationwide,' 'metropolitan areas,' and 'non-metropolitan areas', the following results were revealed. The absolute and relative contribution level of property income to the gross income was the largest in the category of 'nationwide' and 'metropolitan areas,' while the contribution level of earned income was the largest in the category of 'non-metropolitan areas'. In addition, property income worsened the household income gap the most in the category of 'nationwide' and 'metropolitan areas.' Second, property income worsened the household income gap less after the financial crisis than before the crisis. It is probably because the price of real estate skyrocketed before the global financial crisis, worsening the household income gap, whereas the price drop after the crisis temporarily alleviated the gap. Third, a correlation analysis revealed that households with older householders whose education is high school graduation or below had relatively low gross income, and households with higher source-specific income, especially earned income, had relatively high gross income. Fourth, when the household income determinants were compared through panel analysis with division of areas into 'nationwide,' 'metropolitan areas,' and 'non-metropolitan areas,' the following results were obtained. While the impact of earned income, financial income, and other incomes was greater in non-metropolitan areas than in metropolitan areas, the impact of property income was greater in metropolitan areas than in non-metropolitan areas. To reduce the income gap, the government should impose higher taxes on the high-income class and provide tax benefits to the low-income class, with efforts to create a wide variety of jobs. In addition, since income inequality gets worse as the proportion of incomes generated through asset holdings becomes higher, the government should focus on stabilizing property prices while paying attention to the regional differentiation when carrying out related policies.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.123-136
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• 2014

Recently, online shopping has further developed as the use of the Internet and a variety of smart mobile devices becomes more prevalent. The increase in the scale of such shopping has led to the creation of many Internet shopping malls. Consequently, there is a tendency for increasingly fierce competition among online retailers, and as a result, many Internet shopping malls are making significant attempts to attract online users to their sites. One such attempt is keyword marketing, whereby a retail site pays a fee to expose its link to potential customers when they insert a specific keyword on an Internet portal site. The price related to each keyword is generally estimated by the keyword's frequency of appearance. However, it is widely accepted that the price of keywords cannot be based solely on their frequency because many keywords may appear frequently but have little relationship to shopping. This implies that it is unreasonable for an online shopping mall to spend a great deal on some keywords simply because people frequently use them. Therefore, from the perspective of shopping malls, a specialized process is required to extract meaningful keywords. Further, the demand for automating this extraction process is increasing because of the drive to improve online sales performance. In this study, we propose a methodology that can automatically extract only shopping-related keywords from the entire set of search keywords used on portal sites. We define a shopping-related keyword as a keyword that is used directly before shopping behaviors. In other words, only search keywords that direct the search results page to shopping-related pages are extracted from among the entire set of search keywords. A comparison is then made between the extracted keywords' rankings and the rankings of the entire set of search keywords. Two types of data are used in our study's experiment: web browsing history from July 1, 2012 to June 30, 2013, and site information. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The original sample dataset contains 150 million transaction logs. First, portal sites are selected, and search keywords in those sites are extracted. Search keywords can be easily extracted by simple parsing. The extracted keywords are ranked according to their frequency. The experiment uses approximately 3.9 million search results from Korea's largest search portal site. As a result, a total of 344,822 search keywords were extracted. Next, by using web browsing history and site information, the shopping-related keywords were taken from the entire set of search keywords. As a result, we obtained 4,709 shopping-related keywords. For performance evaluation, we compared the hit ratios of all the search keywords with the shopping-related keywords. To achieve this, we extracted 80,298 search keywords from several Internet shopping malls and then chose the top 1,000 keywords as a set of true shopping keywords. We measured precision, recall, and F-scores of the entire amount of keywords and the shopping-related keywords. The F-Score was formulated by calculating the harmonic mean of precision and recall. The precision, recall, and F-score of shopping-related keywords derived by the proposed methodology were revealed to be higher than those of the entire number of keywords. This study proposes a scheme that is able to obtain shopping-related keywords in a relatively simple manner. We could easily extract shopping-related keywords simply by examining transactions whose next visit is a shopping mall. The resultant shopping-related keyword set is expected to be a useful asset for many shopping malls that participate in keyword marketing. Moreover, the proposed methodology can be easily applied to the construction of special area-related keywords as well as shopping-related ones.