• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.4
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• pp.26-39
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• 2016

Korea's main manufacturing industries, which have led its economy for the past three decades, are faced with a serious downturn and loss of competitive advantages due to the current economic depression, China's rise, and the drop of oil prices. Korean business firms must adopt the paradigm shift in their value strategies, along with a government-led industrial restructuring in order to gain sustainable competitive advantages. Business firms allocate their limited resources between value creation and value appropriation, however, what effect does strategic emphasis on value creation versus value appropriation have on a business firm's financial performance? This paper empirically addresses this issue by examining the effect of shifts in strategic emphasis on stock return. Furthermore, this study examines appropriate choices of strategic emphasis to gain differential financial performance. The data set used in this regression analysis comes from the KISLINE database of NICE Information Service. The variables that form the basis of this analysis are stock return, ROA, and Strategic Emphasis [(advertising expenditures-R&D expenditures)/assets]. The interactive effect with situational factors regarding the firm and the type of technological environment in which the firm is operating was also analyzed. Our results show that investors acknowledge a shift of strategic emphasis as a sign of stock valuation. In comparison to US, Korean business firms have weak value creation capabilities in high-technology industries, and weak value appropriation capabilities in low-technology industries. This proves Korean firms are fast followers in the global market. Our findings suggest that Korean firms have to adopt a balanced value strategy, nurturing value creation and developing value appropriation for overcoming the current economic downturn and becoming a first mover in the dawn of "Industry 4.0."

• Korean System Dynamics Review
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• v.14 no.3
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• pp.25-49
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• 2013

As global competition for green car, that is environmentally friendly car, is getting tougher, the governments and the related industries are putting their core efforts in its diffusion. However, the green car sales are disappointing so far. To overcome the gridlock, it is necessary to develop concrete analytical framework to understand the diffusion process. Based on causal loop analysis from the previous work, we have identified main variables and relationships of them in the diffusion process and developed a stock-flow diagram and mathematical formula for the main components. The model would be applied for further quantitative simulation on the diffusion process of green car and other innovative goods as well. Also, we have suggested constructive insights for the policy makers and for the related industries. First, it is important to increase consumers' willingness to consider through marketing and word of mouth to accelerate the diffusion process. Second, in the perspective of the industry, the market share of green car should be increased at the earliest possible stage and this could be done by enhancing each components of green car attractiveness(e.g. price, driving range, social infra). Third, companies should develop a balanced investment between consumer and technology sector through a flexible financial policy. Fourth, the government continuously has the role of investing in the related R&D and social infra building. We expect the green car diffusion model and related formula from the research can provide meaningful tools to analyze the diffusion process of other new and innovative goods based on its deep researched literature review.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.207-212
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• 2004

This paper presents a reinforcement learning framework for stock trading systems. Trading system parameters are optimized by Q-learning algorithm and neural networks are adopted for value approximation. In this framework, cooperative multiple agents are used to efficiently integrate global trend prediction and local trading strategy for obtaining better trading performance. Agents Communicate With Others Sharing training episodes and learned policies, while keeping the overall scheme of conventional Q-learning. Experimental results on KOSPI 200 show that a trading system based on the proposed framework outperforms the market average and makes appreciable profits. Furthermore, in view of risk management, the system is superior to a system trained by supervised learning.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.3
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• pp.211-219
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• 2010

This paper designs the dynamic symbol automatic trading system in Korean option market. This system is based on Multichart program which is convenient and efficient system trading tool. But the Multichart has an important restriction which has only one constant symbol per chart. This restriction causes very useful strategies impossible. The proposed design uses global variables, signal chart selection and position order exchange. So an automatic trading system with dynamic symbol works on Multichart program. To verify the proposed system, BS(Buythensell)-SB(Sellthenbuy) strategies are tested which uses the change of open-interest of stock index futures within a day. These strategies buy both call and put option in ATM at start candle and liquidate all at 12 o'clock and then sell both call and put option in ATM at 12 o'clock and also liquidate all at 14:40. From 23 March 2009 to 31 May 2010, 301-trading days, is adopted for experiment. As a result, the average daily profit rate of this simple strategies riches 1.09%. This profit rate is up to eight times of commision price which is 0.15 % per option trade. If the method which raises the profitable rate of wining trade or lower commission than 0.15% is found, these strategies make fascinated lossless trading system which is based on the proposed dynamic symbol automatic trading system.

• KDI Journal of Economic Policy
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• v.37 no.1
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• pp.19-44
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• 2015

This paper examines the effect of the global financial crisis on corporate investment in Korea. Specifically, the crisis was considered to have possibly constrained firm-level investment as the negative shock to the credit supply dramatically unfolded. As Duchin et al. (2010) demonstrated, if a negative supply-side shock is evident during a crisis period, larger cash holdings before the crisis will lead to fewer constraints to corporate investment, or vice versa. In order to investigate the supply-side effect of the crisis, we use firm-level financial data, including firms listed on the Korean stock market as well as small and medium-sized enterprises. We find that corporate investment declined significantly after the crisis, even if we control for factors associated with the demand side, such as contemporaneous capital productivity and cash flow. More importantly, the decline is positively and significantly related to cash holdings before the crisis, implying the negative effect of a credit supply shock. Small and medium enterprises experienced relatively sharp investment declines compared to those of larger firms, and the relationship between pre-crisis cash amounts and the degree of investment decline is greater than that in large firms. Additionally, we examine whether the negative effect persists up to the present, finding evidence that the cash-investment relationship continues in small and medium-sized enterprises.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.104-109
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• 2010

Management innovation as a strategic alternative to create competitive advantages in the global market, is being actively progressed by conglomerates such as Samsung, LG and Toyoda as they cut a brilliant figure in the 21 century by using various innovation methods. However, the domestic railway sectors, in particular urban railway depots have neglected management innovation as they undertake Operation and Management in non-competitive settings of a corporate enterprise. This paper analyzes the critical success factors that have switched the most obsolete depots to a benchmark model for domestic railway depots based on the remodeling businesses undertaken in Gunja rolling stock maintenance office, Korea's first urban railway depot, along with 5S 6sigma program.

• Journal of Arbitration Studies
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• v.12 no.2
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• pp.221-262
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• 2003

In recent years, information technological revolution of using Internet in the Stock Market has shown reconsideration of Securities and Exchange Law. Since the 1998 Taipei Conference, ILA have studied upon it and presented 6th Interim Report during the 2000 London Conference. This paper summarizes a part of the 6th Interim Report. The Internet has created a medium to which traditional principles of jurisdiction may not have full relevance. To understand the jurisdictional issues posed by this new medium, we should first review the traditional principles of jurisdiction under international, U. S. law and Other Countries law. In previous reports, the general principles of jurisdiction in international law have been described. These principles are, however, difficult to apply in the context of Internet and other forms of computerized securities trading. Regulators all over the world are trying to address and solve jurisdictional issues raised by the Internet. The traditional rules of jurisdiction in international law, grounded as they are on a geography-based approach, may be in the process of being undermined by the Internet and other global networks allowing for remote access to information and to markets.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.446-453
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• 2018

The study examines corporate research & development (R&D) expenditure in modern finance. Firms may face one of the essential issues to maintain their optimal levels of R&D expenditures in order to increase corporate profit. Accordingly, financial determinants that may influence R&D spending are statistically tested for firms listed on the KOSPI stock market during the period from 2010 to 2015. Financial determinants which may discriminate between firms in high-growth and low-growth industries are examined on a relative basis. Explanatory variables including one-period lagged R&D expenses (Lag_RD), cross-product term between the Lag_RD and type of industry (as a dummy variable), and advertising expenses (ADVERTISE) significantly influenced corporate R&D intensity. Moreover, high-growth firms in domestic capital markets showed higher Lag_RD, profitability (PROF) and foreign equity ownership (FOS) than their counterparts in low-growth sectors, whereas low-growth firms had higher market-value based leverage (MLEVER) and ADVERTISE. Overall, these results are expected to influence decision-making of firms concerning the optimal level of R&D expenditure, which may in turn enhance shareholder wealth.

• Korea Real Estate Review
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• v.28 no.1
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• pp.91-104
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• 2018

In this study, the occurrence and degree of herding behavior as a market participant behavior in a housing market were analyzed. For the analysis method, the actual sales price was applied in the CSAD (Cross-sectional Absolute Deviation) model, which has been used the most of late for herding behavior analysis. For the analysis contents, these were subdivided into region, elapsed year, size, and market condition to analyze the regionality and the internal and external factors. For the study results, first, there was no herding behavior in the entire region of Seoul. By region, herding behavior occurred in the downtown, southeast, and northwest regions, which coincided with the results of the precedent study (Ngene et al., 2017). Second, in the market analysis by elapsed year, herding behavior was captured in dilapidated dwellings. By size, herding behavior was observed in small-scale ($60m^2$ or less) apartments and in $85m^2$ or higher and less than $102m^2$ national housing units. Third, during the time of the global financial crisis, herding behavior was not observed in all the regions, whereas when the market situations were in a boom cycle, it was observed in the northwest region. These results suggest that there is a difference from the stock market, where in a period of recession, herding behavior occurs intensively with the expanding fear of incurring losses. This study is significant in that it analyzed the market participant behaviors in the behavioral economic aspects to better understand the abnormal phenomenon in a housing market, and in that it additionally provides a psychological factor - market participant behavior - in market analysis.

• Information Systems Review
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• v.22 no.4
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• pp.21-39
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• 2020

Recently, deep learning has shown high performance in various applications such as pattern analysis and image classification. Especially known as a difficult task in the field of machine learning research, stock market forecasting is an area where the effectiveness of deep learning techniques is being verified by many researchers. This study proposed a deep learning Convolutional Neural Network (CNN) model to predict the direction of stock prices. We then used the feature selection method to improve the performance of the model. We compared the performance of machine learning classifiers against CNN. The classifiers used in this study are as follows: Logistic Regression, Decision Tree, Neural Network, Support Vector Machine, Adaboost, Bagging, and Random Forest. The results of this study confirmed that the CNN showed higher performancecompared with other classifiers in the case of feature selection. The results show that the CNN model effectively predicted the stock price direction by analyzing the embedded values of the financial data