• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8B
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• pp.1211-1216
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• 2010

There is no reference grasp only embedded SW market because embedded SW is built in SW production. In this paper, In order to know only embedded SW market, we used estimation method size of the amount of production. We draw suitable industry cluster structure of embedded SW market estimation. As we estimated size of embedded SW market by industry cluster. And, We calculated importance of embedded SW by industry cluster and finally we estimated size of embedded SW market. Result of estimation, added values of embedded SW estimated about 27 trillion.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.9
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• pp.1535-1542
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• 2007

HTS (High Temperature Superconducting) Power devices has the several useful characteristics from the technical and economical viewpoint. Possible application to the utility industry have been widely discussed in various research projects. For the successful introduction of HTS power devices into power system, establishing a proper R&D and marketing strategies through estimating the future market size are necessary. However, quantitative estimates of how well HTS power devices will serve their markets have been lacking. In this paper, we propose a estimation methodology of future market size for HTS power divices such as cable, transformer, generator, and motor, and also evaluate the future international and domestic market size by using proposed methodology.

• Journal of Korea Technology Innovation Society
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• v.18 no.3
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• pp.387-415
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• 2015

In this study, we have proposed an estimation model of domestic market size using the linking between standard statistical classification systems, and reviewed the practical applicability of the model. The results of the mining and manufacturing survey of Statistics Korea conducted on the basis of KSIC (Korea Standard Industrial Classification) and Korea trade statistics based on HS (The Harmonized Commodity Description and Coding System; Harmonized System) classification were linked for the model by using the correspondence tables provided by Statistics Korea and United Nations Statistics Division. The most serious problem to adopt the integrated KSIC-ISIC-HS correspondence table for the estimation of domestic market size is the complex multiple linkages among KSIC and HS codes. In this study, we have suggested the method to divide the amount of trade corresponding to the HS codes linked to more than two ISIC codes based on the ratio of shipments corresponding to the ISIC codes as the weight. Then, it is possible to analyze the domestic market size of 125 ISIC codes in the manufacturing industry and to forecast the market size in the near future by using the model. Although the model has some limitations such as the difficulty in analysis on more subdivided items than ISIC items, the impossibility of the analysis on items in industries except for manufacturing, errors in the shipment due to some missing data, this study has significance in the sense that it provided the analysis method of domestic market size by using the most objective, reliable and sustainably useful data.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.691-700
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• 2023

Purpose: This study aims to analyze the diffusion pattern of the Mongolian mobile phone market. In particular, we used a generalized diffusion model to explore the factors affecting market potenial. Methods: We used three diffusion models to estimate the number of mobile subscribers in Mongolia. Based on the Logistic model with the best fitness, we introduced time-varying market potential and explored the influence of various independent variables such as GDP and inflation. Results: Among the basic diffusion models, the Logistic model was the best in terms of estimation performance and statistical significance. The estimation results of the Generalized Logistic model confirm that investment in the telecommunication sector has a significant positive effect on market potential. The estimation of the Generalized Logistic model effectively describes the continuous growth of the Mongolian telecommunications market until recently. Conclusion: We have analyzed the diffusion pattern of the Mongolian telecommunications market and found that the amount of investment in the sector leads to the growth of the market size. This study is original in terms of its subject - Mongolian telecommunications market and methodology - time-varying market potential.

• Journal of Korean Society of Water and Wastewater
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• v.23 no.6
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• pp.753-761
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• 2009

Bottled water market is growing at a rate of 10% per year in Korea. However, bottled water exhausts ground water. Korean government proposed to provide 'bottled tap water' at a low price in 2008. This study is the estimation of 'bottled tap water' market scale using binary logit model. we calculate that 'bottled tap water' market scale is from at least 92 billion won to 154 billion won by 150, 250 won per a bottled water, respectively. This paper indicates that scale of 'bottled tap water' market is a half of 'bottled water' market in 2007. This result provides that policy-makers with available and responsible information regarding the scale of 'bottled tap water' market.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Journal of the Korean Mathematical Society
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• v.61 no.5
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• pp.875-898
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• 2024

This study estimates the true price of an asset and finds the optimal bid/ask prices for market makers. We provide a novel state-space model based on the exponential Ornstein-Uhlenbeck volatility and the Heston models with Gaussian noise, where the traded price and volume are available, but the true price is not observable. An objective of this study is to use Bayesian filtering to estimate the posterior distribution of the true price, given the traded price and volume. Because the posterior density is intractable, we employ the guided particle filtering algorithm, with which adaptive rejection metropolis sampling is used to generate samples from the density function of an unknown distribution. Given a simulated sample path, the posterior expectation of the true price outperforms the traded price in estimating the true price in terms of both the mean absolute error and root-mean-square error metrics. Another objective is to determine the optimal bid/ask prices for a market maker. The profit-and-loss of the market maker is the difference between the true price and its bid/ask prices multiplied by the traded volume or bid/ask size of the market maker. The market maker maximizes the expected utility of the PnL under the posterior distribution. We numerically calculate the optimal bid/ask prices using the Monte Carlo method, finding that its spread widens as the market maker becomes more risk-averse, and the bid/ask size and the level of uncertainty increase.

• Journal of Navigation and Port Research
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• v.46 no.5
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• pp.441-448
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• 2022

The aim of this study was to examine determinants of secondhand Bulk carrier and Oil tanker prices. This study compiled S& P transaction data taken from the Clarksons Research during J anuary 2018 to April 2022 to see how independent variables influenced secondhand ship prices. In the secondhand ship pricing model of entire segments, size, age, and LIBOR showed significant effects on prices. A vessel built in J apan and Korea was traded at a higher price than a vessel built in other countries. In the bulk segment, size, age, Clarksea index, LIBOR, and inflation were meaningful variables. In the Tanker segment, unlike Bulk carrier, only size and age were useful variables. This study performed regression analyses for various sizes of Bulk carriers and Oil tankers. It verified that impacts of variables other than ship size and age were significantly associated with ship type and size while macroeconomic variables had no influence except for bulk carriers. By applying diverse variables affecting secondhand ship price estimation according to various sizes of Bulk carriers and Oil tankers, this study will expand the scope of practical application for investors. It also reaffirms prior research findings that the secondhand ship market is primarily market-driven.

• Asia pacific journal of information systems
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• v.30 no.2
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• pp.328-352
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• 2020

If a firm announces an investment in IT security, how the market value of its competitors reacts to the announcement? We try to shed light on this question through an event study design. To test the relationship, we collected 143 announcements on cybersecurity investment and measured the subsequent impact on 533 competitors' abnormal returns, spanning from 2000 to 2019. Our estimation results present that, on average, the announcements have no observable impact on the market value of announcing firms and competitors as well, which is consistent with findings of a prior study. Interestingly, however, the impact becomes evident when we classify our samples by industries (Finance vs. non-Finance or ICT vs. non-ICT) and firm size (Big vs. Small). We interpret our empirical findings through the lenses of contagion effect and competition effect between announcing firms and their competitors. Key finding of our study is that, for financial service firms, the effect resulting from the announcement on cybersecurity investment transfers to competitors in the same direction (i.e., contagion effect).

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.141-148
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• 2020

This paper analyzes factors affecting the debt maturity structure of enterprises listed on the Vietnam stock market. The panel data of research sample includes 549 non-financial listed enterprises on the Vietnam stock market from 2009 to 2019. The Generalized Least Square (GLS) tool is employed to address econometric issues and to improve the accuracy of the regression coefficients. In this research, debt maturity structure is the dependent variable. Capital structures, fixed assets, liquidity, firm size, asset maturity, profitability, corporate income tax, gross domestic product, inflation rate, credit growth scale are independent variables in the study. The model results show, that among the factors affecting the structure of debt maturity, the capital structure, asset structure, and firm size have the highest estimation coefficients, which shows that capital structure, asset structure, and firm size plays an important role in the decision-making process of debt maturity structure. The empirical results show that there are differences in the impact of these factors on the debt maturity structures in state-owned enterprises and non-state enterprises listed on the Vietnam stock market. The findings of this article are useful for business administrators, helping business managers make the right financial decisions to determine the target debt maturity structure in enterprises.