• International Area Studies Review
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• v.12 no.2
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• pp.149-173
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• 2008

This paper empirically studies the relationship between R&D expenditures and firms value. First, we can conjecture that R&D expenditures are enhancing the firms value. Such findings depend on an existing research, which R&D expenditures are intangible asset rather than expenses. Although, under U.S. accounting standards, financial statements do not report intangible assets but costs. Second, we can conjecture that short-term, the rate of increase in R&D expenditures had negative influence on firms valuation, because such findings indicates that R&D spending of costs incur mis-pricing. But long-term, consistently R&D expenditures may attract investors on the stock market. Third, lately firms focus on capital efficiency management, such a firms R&D expenditures incur high ROE. Generally investors put too much confidence in capital efficiency management and high ROE may attract investors on the stock market. Finally, High-Tech through the R&D investment improve firms competitive advantage, by competitive advantage, firms have reduced cost and raised productivity in the end improve firms value.

• The Journal of the Korea Contents Association
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• v.19 no.6
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• pp.663-675
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• 2019

During the last decade, the dominant design of display has been LCD, and it has been led by Korean manufacturers. However, their leading positions has been recently threatened by Chinese manufacturers, Korean manufacturers are endeavoring to move toward next generation display technologies. They embarked on standards battle to win dominant design especially in the next generation display market for TVs by launching new technologies such as Quantum-dot display and OLED. While there are a number of factors of dominant design, it is expected that the technical attributes of the technology itself may be the most significant factor. For this reason, this research scrutinizes consumer preferences for technical attributes, and attempts to provide implications for standards battle in the display (for the TVs) sector. For this purpose, we employed a conjoint analysis for the preferences of potential consumers. The results show that potential consumers prefer displays with higher resolution, natural color, and durabillity.

• The Journal of Information Systems
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• v.30 no.2
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• pp.147-163
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• 2021

Purpose The study aims to predict real estate prices by utilizing regional characteristics. Since real estate has the characteristic of immobility, the characteristics of a region have a great influence on the price of real estate. In addition, real estate prices are closely related to economic development and are a major concern for policy makers and investors. Accurate house price forecasting is necessary to prepare for the impact of house price fluctuations. To improve the performance of our predictive models, we applied LSTM, a widely used deep learning technique for predicting time series data. Design/methodology/approach This study used time series data on real estate prices provided by the Ministry of Land, Infrastructure and Transport. For time series data preprocessing, HP filters were applied to decompose trends and SOM was used to cluster regions with similar price directions. To build a real estate price prediction model, SVR and LSTM were applied, and the prices of regions classified into similar clusters by SOM were used as input variables. Findings The clustering results showed that the region of the same cluster was geographically close, and it was possible to confirm the characteristics of being classified as the same cluster even if there was a price level and a similar industry group. As a result of predicting real estate prices in 1, 2, and 3 months, LSTM showed better predictive performance than SVR, and LSTM showed better predictive performance in long-term forecasting 3 months later than in 1-month short-term forecasting.

• Journal of Information Technology Services
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• v.19 no.6
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• pp.131-144
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• 2020

As the blockchain technology attracts attention, interest in cryptocurrency that is received as a reward is also increasing. Currently, investments and transactions are continuing with the expectation and increasing value of cryptocurrency. Accordingly, prediction for cryptocurrency price has been attempted through artificial intelligence technology and social sentiment analysis. The purpose of this paper is to develop a deep learning ensemble model for predicting the price fluctuations and one-day lag price of cryptocurrency based on the design science research method. This paper intends to perform predictive modeling on Ethereum among cryptocurrencies to make predictions more efficiently and accurately than existing models. Therefore, it collects data for five years related to Ethereum price and performs pre-processing through customized functions. In the model development stage, four LSTM models, which are efficient for time series data processing, are utilized to build an ensemble model with the optimal combination of hyperparameters found in the experimental process. Then, based on the performance evaluation scale, the superiority of the model is evaluated through comparison with other deep learning models. The results of this paper have a practical contribution that can be used as a model that shows high performance and predictive rate for cryptocurrency price prediction and price fluctuations. Besides, it shows academic contribution in that it improves the quality of research by following scientific design research procedures that solve scientific problems and create and evaluate new and innovative products in the field of information systems.

• The Journal of Fisheries Business Administration
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• v.52 no.4
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• pp.13-28
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• 2021

The purpose of this study is to compare short-term price predictive power among ARMA ARMAX and VAR forecasting models based on the MDM test using monthly consumer price data of frozen mackerel. This study also aims to help policymakers and economic actors make reasonable choices in the market on monthly consumer price of frozen mackerel. To analyze this study, the frozen wholesale prices and new consumer prices were used as variables while the price time series data were used from December 2013 to July 2021. Through the unit root test, it was confirmed that the time series variables employed in the models were stable while the level variables were used for analysis. As a result of conducting information standards and Granger causality tests, it was found that the wholesale prices and fresh consumer prices from the previous month have affected the frozen consumer prices. Then, the model with the highest predictive power was selected by RMSE, RMSPE, MAE, MAPE, and Theil's inequality coefficient criteria where the predictive power was compared by the MDM test in order to examine which model is superior. As a result of the analysis, ARMAX(1,1) with the frozen wholesale, ARMAX(1,1) with the fresh consumer model and VAR model were selected. Through the five criteria and MDM tests, the VAR model was selected as the superior model in predicting the monthly consumer price of frozen mackerel.

• Journal of the Korean Applied Science and Technology
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• v.40 no.1
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• pp.24-34
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• 2023

This study sought to explore the online consumption propensity of sports goods by generation of the productive population and to provide basic data to predict the future consumption market by segmenting online consumers in the sports 4.0 market. Therefore, this survey was conducted on those who consumed sports goods among the generation-specific groups (Generation Y and above, Z) of the productive population, and a total of 478 people's data were applied to the final analysis. Data processing was conducted with SPSS statistics (ver.21.0), frequency analysis, exploratory factor analysis, correlation analysis of re-examination reliability, reliability analysis, and decision tree analysis. According to the online consumption propensity of sports goods by generation of the productive population, there is a high probability of being classified as Generation Z group if the factors of leisure, joy, and environment are high. In addition, the classification accuracy of such a model was 69.7%.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.2
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• pp.223-229
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• 2023

Seawater batteries are next-generation secondary batteries that use seawater as a cathode. They utilize marine resources to provide competitive prices, high eco-friendliness, and a structure suitable for marine applications. Based on these advantages, pouch types and prismatic types have been studied and developed assuming natural seawater exposure. However, because of the electrical characteristics of the secondary battery, its capacity and internal resistance vary depending on the use environment. These characteristics are not only utilized for predicting the life of a battery but also have a direct effect on the capacity and power suitable for a specific situation. Therefore, the internal resistance was analyzed in this study by measuring the capacity depending on the seawater battery use environment and the state-of-charge-open-circuit-voltage measurement method.

• The Journal of the Convergence on Culture Technology
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• v.4 no.2
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• pp.197-204
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• 2018

One of the hottest issues in our 21st century is AI. Just as the automation of manual labor has been achieved through the Industrial Revolution in the agricultural society, the intelligence information society has come through the SW Revolution in the information society. With the advent of Google 'Alpha Go', the computer has learned and predicted its own machine learning, and now the time has come for the computer to surpass the human, even to the world of Baduk, in other words, the computer. Machine learning ML (machine learning) is a field of artificial intelligence. Machine learning ML (machine learning) is a field of artificial intelligence, which means that AI technology is developed to allow the computer to learn by itself. The time has come when computers are beyond human beings. Many companies use machine learning, for example, to keep learning images on Facebook, and then telling them who they are. We also used a neural network to build an efficient energy usage model for Google's data center optimization. As another example, Microsoft's real-time interpretation model is a more sophisticated translation model as the language-related input data increases through translation learning. As machine learning has been increasingly used in many fields, we have to jump into the AI industry to move forward in our 21st century society.

• Journal of Korean Society of Forest Science
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• v.81 no.3
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• pp.255-262
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• 1992

Urguay Round(UR) has lots of implication in the forest product market as well as the other sectors of the economy. Chestnut, one of the major forest product in Korea, would be affected by free trade resulting from the agreement on UR. To establish effective policy measures dealing with negative effects of free trade, if any, the effect of UR on producers should be figured out. In this contest, the purposes of this study are (1) estimating the demand, supply and its price functions of this market and (2) forecasting the effect of UR on growers. Using econometric method, demand, supply and price function of this market are estimated. The total amount of yearly money loss of growers due to free trade from 1992 to 2001 are estimated for four different scenarios. In each scenario, it is assumed that the tariffication reduction is 30%, 40%, 50% and 90%. Yearly money loss of chestnut growers at the year 2001 are forecasted such as 14 billion won, 18 billion won, 24 billion won and 25 billion won for the rate of tariffication reduction of 30%, 40%, 50%, and 90%, respectively.

• Korean Journal of Weed Science
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• v.32 no.1
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• pp.44-51
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• 2012

This study was conducted to predict the rice yield loss and to determine the economic threshold levels for direct-seeding flooded rice cultivation from competition to the most serious perennial weeds, Cyperus serotinus Rottb. and Echinochloa crus-galli L. The rice yield loss model of C. serotinus and E. crus-galli were predicted as Y = 560 kg/(1+0.001883x), $r^2$=0.933, and Y = 507 kg/(1+0.001734x), $r^2$=0.867, respectively. In comparison of the competitiveness represented by parameter ${\beta}$, it was 0.001883 in C. serotinus and 0.001734 in E. crus-galli, respectively. Economic thresholds calculated using Cousens' equation were negatively related with the competitiveness of weed. The economic thresholds of C. serotinus and E. crus-galli were 15.5 and 2.3 plants per $m^2$, respectively.