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

• Journal of Technology Innovation
• /
• v.26 no.4
• /
• pp.1-28
• /
• 2018

With the growing number of emerging carmakers, automotive parts manufacturers have to penetrate into emerging markets. They can provide large existing carmakers with fully customized parts because of economies scale, but cannot do this for small emerging carmakers due to their small and highly volatile volume order. Once the order by an emerging carmaker is placed, a part manufacturer is exposed to high risks both of decrease in profit margin and high opportunity cost. The platform-based mass customization can be a solution for cost reduction, but the risks of volatility in volume hard to manage. Tackling this issue, we presents a method of optimizing the product portfolio to maximize profits while managing volatility of volume order by emerging carmakers at an affordable level. It is the first robust product portfolio method to keep the scaled deviation of profits at a fixed level under volume order uncertainty. Also, the effect of on the platform-based mass customization on cost is considered. This model can be a building block of conservative market penetration as well as product development strategy while minimizing the financial risks. We conducted an empirical study of a part manufacturer targeting on eighteen automobile manufacturers in North America, Europe and Asia with it powered lift gate.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2019.05a
• /
• pp.127-128
• /
• 2019

After the financial crisis in 2008, Korean ship-parts manufacturing companies faced the survival of companies due to the difficulty in securing the volume of orders by sharp dropping in orders from big-three Korean shipbuilders. In ship-parts industry, product diversification and overseas market entry are important targets. Based on the expert Delphi survey and SWOT analysis, this study analyzes the key factors of overseas advancement by the growth stage of ship-parts companies and suggests a systematic and efficient overseas marketing strategy. Also, we propose a systematic and efficient advancement into overseas market by suggesting the traditional offline marketing strategy of exporting materials, marketing and marketing, and suggesting the need to build an integrated online and offline platform.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.1
• /
• pp.123-130
• /
• 2021

With the recent developments in computer technology, there has been an increasing interest in the field of machine learning. This also has led to a significant increase in real business cases of machine learning theory in various sectors. In finance, it has been a major challenge to predict the future value of financial products. Since the 1980s, the finance industry has relied on technical and fundamental analysis for this prediction. For future value prediction models using machine learning, model design is of paramount importance to respond to market variables. Therefore, this paper quantitatively predicts the stock price movements of individual stocks listed on the KOSPI market using machine learning techniques; specifically, the reinforcement learning model. The DQN and A2C algorithms proposed by Google Deep Mind in 2013 are used for the reinforcement learning and they are applied to the stock trading strategies. In addition, through experiments, an input value to increase the cumulative profit is selected and its superiority is verified by comparison with comparative algorithms.

• International Area Studies Review
• /
• v.14 no.2
• /
• pp.79-105
• /
• 2010

This paper investigates empirically the impact of foreign investors on a firm's dividend and investment decision making in Korean stock market. Empirical results using the sample firms in non-financial firms listed in Korean stock market whose settlement month are December, we find that foreign investors who declared participation in management didn't exert significant impact on dividend increase. In the case of investment, foreign investors exerts significant impact on R&D investments. Using Hausman-Taylor Instrumental Variable method, we controlled endogeneity problem related with foreign ownership and dividend and investment policy. The contribution of this paper is that the purpose of foreign investors whether or not participate in management is the most critical point and the impacts of foreign investors on dividends and investment are different whether they participate in management or not.

• The Korean Journal of Applied Statistics
• /
• v.34 no.3
• /
• pp.461-475
• /
• 2021

Hidden Markov model (HMM) is a statistical model in which the system consists of two elements, hidden states and observable results. HMM has been actively used in various fields, especially for time series data in the financial sector, since it has a variety of mathematical structures. Based on the HMM theory, this research is intended to apply the domestic KOSPI200 stock index as well as the prediction of global stock indexes such as NIKKEI225, HSI, S&P500 and FTSE100. In addition, we would like to compare and examine the differences in results between the HMM and support vector regression (SVR), which is frequently used to predict the stock price, due to recent developments in the artificial intelligence sector.

• International Journal of Computer Science & Network Security
• /
• v.21 no.4
• /
• pp.255-263
• /
• 2021

Smart grid is a fully-automated, bi-directional, power transmission network based on the physical grid system, which combines sensor measurement, computer, information communication, and automatic control technology. Blockchain technology, with its security features, can be integrated with Smart Grids to provide secure and efficient power management and transmission. This paper dicusses the deployment of Blockchain technology in Smart Grid. It presents application areas and protocols in which blockchain can be applied to in securing smart grid. One application of each area is explored in detail, such as efficient peer-to-peer transaction, lower platform costs, faster processes, greater flexibility in power generation to transmission, distribution and power consumption in different energy storage systems, current barriers obstructing the implementation of blockchain applications with some level of maturity in financial services but concepts only in energy and other sectors. Wide range of energy applications suggesting a suitable blockchain architecture in smart grid operations, a sample block structure and the potential blockchain technicalities employed in it. Also, added with efficient data aggregation schemes based on the blockchain technology to overcome the challenges related to privacy and security in the smart grid. Later on, consensus algorithms and protocols are discussed. Monitoring of the usage and statistics of energy distribution systems that can also be used to remotely control energy flow to a particular area. Further, the discussion on the blockchain-based frameworks that helps in the diagnosis and maintenance of smart grid equipment. We have also discussed several commercial implementations of blockchain in the smart grid. Finally, various challenges have been discussed for integrating these technologies. Overall, it can be said at the present point in time that blockchain technology certainly shows a lot of potentials from a customer perspective too and should be further developed by market participants. The approaches seen thus far may have a disruptive effect in the future and might require additional regulatory intervention in an already tightly regulated energy market. If blockchains are to deliver benefits for consumers (whether as consumers or prosumers of energy), a strong focus on consumer issues will be needed.

• Journal of the Korea Convergence Society
• /
• v.12 no.7
• /
• pp.31-36
• /
• 2021

As the global economy stagnated due to the Corona 19 virus from Wuhan, China, most countries, including the US Federal Reserve System, introduced policies to boost the economy by increasing the amount of money. Most of the stock investors tend to invest only by listening to the recommendations of famous YouTubers or acquaintances without analyzing the financial statements of the company, so there is a high possibility of the loss of stock investments. Therefore, in this research, I have used artificial intelligence deep learning techniques developed under the existing automatic trading conditions to analyze and predict macro-indicators that affect stock prices, giving weights on individual stock price predictions through correlations that affect stock prices. In addition, since stock prices react sensitively to real-time stock market news, a more accurate stock price prediction is made by reflecting the weight to the stock price predicted by artificial intelligence through stock market news text mining, providing stock investors with the basis for deciding to make a proper stock investment.

• Journal of Intelligence and Information Systems
• /
• v.28 no.4
• /
• pp.287-308
• /
• 2022

The explosive growth of cryptocurrency, led by Bitcoin has emerged as a major issue in the financial market recently. As a result, interest in cryptocurrency investment is increasing, but the market opens 24 hours and 365 days a year, price volatility, and exponentially increasing number of cryptocurrencies are provided as risks to cryptocurrency investors. For that reasons, It is raising the need for research to reduct investors' risks by dividing cryptocurrency which is not suitable for recommendation. Unlike the previous studies of maximizing returns by simply predicting the future of cryptocurrency prices or constructing cryptocurrency portfolios by focusing on returns, this paper reflects the tendencies of investors and presents an appropriate recommendation method with interpretation that can reduct investors' risks by selecting suitable Altcoins which are recommended using Apriori algorithm, one of the machine learning techniques, but based on the similarity and association rules of Bitocoin.

• Knowledge Management Research
• /
• v.23 no.1
• /
• pp.27-46
• /
• 2022

The current era's focus is on the surge in real estate prices triggered by the global economic downturn. This study advocated STO-based dispersed investment for the MZ generation, who has less capital than earlier generations. Existing real estate investment methods were categorized into online, offline, and hybrid formats and the effectiveness of the suggested STO was given in this study through case analysis domestically and overseas. The entry of STO into the financial industry was positively proved, and the efficacy of blockchain technology was validated, through the investigation of the STO framework. The findings of this study are projected to revitalize the new real estate sector by actively supporting the access of the MZ generation into the current inflexible real estate investment market by the application of blockchain and reflecting MZ generation's investment propensity.