• 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 the Korean housing association
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• v.13 no.5
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• pp.77-88
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• 2002

This article deals the investigations how to solve the social deficiencies of deteriorate apartments, which is a half cycle of a building and it goes slum clearance and redevelopment. And this proposes an active remodeling and design strategy, management, and housing policies for extending the usage of the resource. Most of apartment housing in Korea is built by the panel wall and slab structure system fur economic price. To remake is possible, even though not designed in flexibility and variation. The remodeling strategies are dwelling unification, transformation of two units to one or three units, addition of a room, changing into commercial and community required spaces, and reshaping of a envelop and facade by addition of a dwelling or dwellings, roof floors, change of materials and colors, and so on. And, all activities in structural aspect are proposed removal in upper part and addition in lower part of an apartment housing. Active remodeling cost a great deal compare to new construction, so any remodeling activities should be based on a minimal interfere and budgets to enhancing the quality in existing building. The final aim of an active remodeling is to enhance the quality in economic values, and to keep original state and to put on the new one in a small part. To promote the active and careful management and rehabilitation, it is necessary to give the positive incentive in terms of architectural law, bank loan, and any redevelopment project should get the remodeling record in national resources.

• The Journal of Fisheries Business Administration
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• v.42 no.3
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• pp.41-61
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• 2011

Marine protected Areas(MPAs) are specially designated zones of the sea that are designed to secure operation of ecosystem function and to restore marine ecosystem to the original state by excluding all detrimental human activities. MPAs have been proposed in many countries as means of realizing sustainable fisheries and recently MPAs are newly receiving attention as precautionary measure for global warming and climate change. The purpose of this paper is to examine the possibility of MPAs as fisheries management tools through a wide range of literature analysis and to suggest necessity of fisheries purpose of MPAs in Korea. Establishment of marine protected area can accompany various economic benefits such as restoration of marine environment, preservation of habitats, promotion of marine tourism and so on. Especially, a lot of case studies suggested that MPAs may bring out benefits to the fishing industry as a result of enhanced stocks. Fisheries benefits of MPAs on targeted species include increased abundance, increased mean individual size and age, increased reproductive output, enhanced recruitment inside and outside refuge, maintenance of genetic diversity of stocks, and enhanced fishery yields in adjacent fishing grounds, so called spill-over. MPAs for ecosystem conservation and protection of coastal wetland have been applied appropriately and effectively, however, the Korean MPAs system is still detective due to absence of fisheries purpose MPAs. Finally, suggestions for Korean MPAs can be summarized as following four recommendations; to establish number of small-scale MPAs rather than few large MPAs, to designate island and its surrounding areas as reserve, to consider MPA design with stock enhancement program, and to undertake co-management with Eochon-Gye, the traditional coastal community in Korea.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.56 no.1
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• pp.26-36
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• 2020

Thousands of pelagic and demersal fishes inhabit the waters around Korea and many of them are overexploited. One of the reasons is technological development, which increases the efficiency of the vessels continuously. The analysis was conducted to identify the change of fishing power index to develop the vessel and gear technology that may have improved the fishing efficiency of the otter trawl fishery from 1960s to 2010s. Gross tonnage was decreased stably, but horse power was increased annually. The perimeter of net mouth was somewhat longer, but little changed. Color fish finder was utilized from the mid-1960s. Hydraulic net drum were introduced in the early 1990s, and supply rate was gradually increased. Surveys on the supply and upgrading of fishing equipment utilized visiting research. Therefore, the relative fishing power index in the trawl fishery increased about two to three times in the 2010s compared to the 1980s. The results are expected to contribute to reasonable fisheries stock management.

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.3
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• pp.81-101
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• 2003

The financial statements purpose to provide useful information to decision-making process of business managers. The value-relevant information, however, embedded in the financial statement has been often overlooked in Korea. In fact, the financial statements in Korea have been utilized for nothing but account reports to Security Supervision Boards (SSB). The objective of this study is to develop earnings forecasting models through financial statement analysis using artificial intelligence (AI). AI methods are employed in forecasting earnings: artificial neural networks (ANN) for manufacturing industry and case~based reasoning (CBR) for banking industry. The experimental results using such AI methods are as follows. Using ANN for manufacturing industry records 63.2% of hit ratio for out-of-sample, which outperforms the logistic regression by around 4%. The experiment through CBR for banking industry shows 65.0% of hit ratio that beats the statistical method by 13.2% in holdout sample. Finally, the prediction results for manufacturing industry are validated through monitoring the shift in cumulative returns of portfolios based on the earning prediction. The portfolio with the firms whose earnings are predicted to increase is designated as best portfolio and the portfolio with the earnings-decreasing firms as worst portfolio. The difference between two portfolios is about 3% of cumulative abnormal return on average. Consequently, this result showed that the financial statements in Korea contain the value-relevant information that is not reflected in stock prices.

• Korean Management Science Review
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• v.20 no.1
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• pp.25-36
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• 2003

Now that many organizations have invested a tremendous amount of money and efforts to operate Web sites on the Internet, there is a strong demand to understand the effectiveness of such investments. In other words, one of most frequent and important questions about their Web sites is "Will the current Web site management policy be effective enough to have more visitors come to our Web site\ulcorner" In this paper, a system which predicts the degree of user interest in the future to Web sites is constructed. The degree of user interest to a Web site is defined to be the visit counts for the Web site in the system. With higher the visit counts, the related site is considered to be more interesting. However, the figures of the visit counts themselves cannot explain properly the degree of user Interest in the future to the related Web sites (i.e. the effectiveness of the related Web sites). Therefore, the system also uses mechanisms which use the concept of the Moving Averages, which have been used frequently in the stock exchanges. In this paper. two prediction mechanisms are proposed and compared. The first mechanism uses the Golden Cross/the Dead Cross of the Moving Averages, while the second mechanism uses the changes of upward/downward direction of the Moving Averages. Experimental results show that the two prediction mechanisms proposed in this paper predict the degree of user interest in the future to the related Web sites very well in most cases. However, the first one is considered to be better than the second one In the sense that the second one is too much sensitive to the changes of visit counts.it counts.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.4
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• pp.53-63
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• 2013

Together with soaring interest on Big Data, now there are vigorous reports that unearth various social values lying underneath those data from a number of application areas. Among those reports many are using such data as Internet search histories from Google site, social relationships from Facebook, and transactional or locational traces collected from various ubiquitous devices. Many of those researches, however, are conducted based on the data sets that are accumulated over the North American and European areas, which means that direct interpretation and application of social values exhibited by those researches to the other areas like Korea can be a disturbing task. This research has started from a validation study against Korean environment of the former paper which says an investment strategy that exploits up and down of Google search volume on a carefully selected set of terms shows high market performance. A huge difference between North American and Korean environment can be eye witnessed via the distinction in profit rates that are exhibited by the corresponding set of search terms. Two sets of search terms actually presented low correlation in their profit rates over two financial markets. Even in an experiment which compares the profit rates with two different investment periods with the same set of search terms showed no such meaningful result that outperforms the market average. With all these results, we cautiously conclude that establishing an investment strategy that exploits Internet search volume over a specified word set needs more conscious approach.

• Journal of Korean Society of Forest Science
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• v.99 no.6
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• pp.802-807
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• 2010

The objective of this study was to estimate potential of domestic long-term wood supply by using fuzzy linear programming (FLP). In order to construct a numerical formula model, maximization of total timber production was used for the objective function. Size limit of harvesting and sustained yield were used as the constraints. The results of comparison between LP and FLP were shown that LP is more suitable than FLP in terms of the amount of timber production and final forest stock. However, as long-term sustained yield was limitedly achieved by using LP, FLP was more desirable for prediction of potential wood supply. According to the results of this study, the potential of annual domestic wood supply was estimated about 10.5 million cubic meters. Gyeong buk, Jeon nam, Gangwon and Gyeong nam province were highly ranked in order of provincial potential of wood supply.

• Journal of Korea Port Economic Association
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• v.33 no.4
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• pp.99-116
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• 2017

This paper analyzes the characteristics of change in economic indicators logistics business performance indicators in Japan over the past decade. We compare the differences in management performance of groups related to logistics business strategy. This is because we want to show that the logistics business strategy is reflected in the management performance. Research methods include correlation analysis, crossover analysis, and variance analysis. The main results are as follows. First, logistic companies' sales are highly correlated with economic indicators such as GDP, trade value, and stock price. Second, there is a correlation between the business sectors and the proportion of tangible assets. It is understood that different business strategies are appropriate for each industry and each period. Third, the effects of business strategy variables on business performance variables were significant. In particular, the interaction effect of three variables showed a difference in the effect on the yield. The results of this study provide a better understanding of how logistics companies achieve a high performance in the changing economic environment over the past decade.

• Asia-Pacific Journal of Business
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• v.5 no.2
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• pp.51-64
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• 2014

Strategic bundling of Human Resource Management (HRM) practices among themselves works together as an entire HRM system rather than individual HRM practices to achieve organizational objectives. The bundles of HR practices support the effectiveness of one another assuming the effectiveness of any practice depends on other practices in place. It is said that the greater the total degree of bundling among the various components of HRM policies and practices, the more will be the organizational outcomes. Realizing these facts, this study aimed to explore the level of strategic bundling and examined the impact of such bundling on organizational performance to the publicly listed companies of Nepal. This empirical study is based on description and exploratory design for which data collected through the questionnaire based on 5-point liker scale. Total population of the study at the time of data collection are 234 organizations publicly listed in Stock Exchange of Nepal. Questionnaire is distributed to all organizations listed, response received from 105 organizations, as a unit of analysis, which is fairly good response. The study of strategic bundling of HRM practices perhaps the first study in Nepal, found that only 32 percent organizations have followed high bundling HR practices and these high bundling organizations are significantly different with low bundling organizations. Business organizations are trying to practice being close association of HRM policies and practices within them except labor relation with employee participation and business strategies. Supporting to the international literature, strategic bundling of HRM practices among themselves shows statistically significant effects on quality of product or services, labor productivity, financial performance, employee satisfaction, rate of innovation, employee commitment and market share.