• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.95-107
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• 2020

This study analyzes the effect of corporate social responsibility (CSR) activity on investors' heterogeneous beliefs. The hypothesis of this study is based on the conflicting effects of CSR activities on firm value and earning's quality. Investors' heterogeneous beliefs used in the empirical analysis of this study are trading volume, and CSR activity is measured by the KEJI Index (Korea Economic Justice Institute Index). This study performs an empirical analysis using regression analysis including control variables. CSR activities are found to have a positive relationship with trading volume. This is consistent regardless of the low and high accounting information (earning's quality). It can be interpreted that Korea's CSR activity acts as an incentive to increase investors' heterogeneous beliefs about target companies. In other words, it implies that the investor judges CSR activities negatively when evaluating firm value. This study could have a policy implication in that it analyzes how CSR activities affect investors' decision-making. In other words, this study analyzed CSR activities from the perspective of shareholders. Therefore, this study is expected to provide useful information for policymaking by regulatory agencies. In particular, its contribution is to presents data that CSR activities can be a negative factor in evaluating firm values.

• Journal of Korea Trade
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• v.23 no.3
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• pp.1-19
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• 2019

Purpose - This research examines the effect of a foreign subsidiary on the productivity growth of a Two-way trading manufacturing firm in Korea. We explore firms engaged in both trade and FDI simultaneously to verify whether participation in GVC as a broad concept is an efficient internationalization strategy to increase the productivity of a Korean manufacturing firm. Design/methodology - Based on the firm-level data by utilizing the Survey of Business Activities from Statistics Korea, we examine the impact of vertically integrated foreign subsidiaries on the productivity of a manufacturing firm that exports and imports simultaneously. Findings - The results show that if a Two-way trading firm establishes one or more overseas subsidiaries, the total factor productivity growth increases. Moreover, the FDI effect is statistically significant when the destination country has an economically close relationship with Korea. However, these effects are disparate depending on the industrial competitiveness or market situation where the subsidiary is located. Nonetheless, the synergy effect resulting from industrial combination is represented in China and the USA only. Originality/value - As the importance of GVC has become more emphasized around the world. In spite of the scarcity of related domestic studies, we explored the effect of multinational manufacturing firms participating in GVC using firm-level data.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.34-46
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• 2021

In the domestic data exchanging market, unreasonable pricing of purchase data is consistently mentioned as a major obstacle in data trading. This is a problem caused by the inability to properly evaluate the value of data products due to lack of product information and experience in using them. In order to activate trading, the data exchanges need to provide information that allows consumers to comprehensively judge the value of data products in addition to prices. The cost-based, income-based, and market-based methods, which are mainly applied to data valuation, are insufficient as data valuation methods to stimulate trading and distribution because only price information, a result of valuation from a supplier's point of view, can be shared with consumers. This study aims to develop a measurable valuation method that allows data trading stakeholders (exchanges, suppliers, and consumers) to judge and share the value of data products from a common perspective. To this end, we identified the value drivers of data products, which are considered important in overseas data exchanges and related research, and derived an evaluation method that can quantitatively measure each value driver. In addition, evaluation criteria in the form of a rating table were developed using data products for transactions, and a value evaluation index was developed through stratification analysis (AHP) to enable relative value comparison. As a result of applying the evaluation criteria to actual data products, it was found that the evaluation values were differentiated according to the characteristics of individual data products, so it could be used as a relative value comparison tool.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

• 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.

• Proceedings of the CALSEC Conference
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• 2001.08a
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• pp.439-448
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• 2001

Data Alignment is achieved when all trading partners information systems are maintained automatically synchronising with the suppliers information systems on a continuing basis. Electronic catalogues facilitate the ongoing synchronisation of data between trading partners and large retailers in United States and Canada use electronic catalog in order to get rid of non-value added paperwork and manual reconciliation. Data Alignment will dramatically improve the effectiveness of E-Commerce and Supply Chain initiatives including electronic Marketplaces, Collaborative Planning and Forecasting and continuous replenishment processes.

• Journal of Korea Trade
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• v.26 no.6
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• pp.20-40
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• 2022

Purpose - In recent times, the international trade environment has been changing rapidly, centering on the online market. In the post-COVID-19 era, small and medium-sized trading companies are facing the problem of not being properly provided with overseas market research, market trend analysis, and trade-related information. Cloud-based digital trade is being sought as an alternative to solve these problems; however, there is a lack of research on the intention to switch to digital trade among small and medium-sized trading companies. Therefore, this study empirically analyzes the intention to switch to digital trade based on the migration theory, and through this, attempts to identify each factor that affects the intention to switch to digital trade. Design/methodology - In this study, in order to identify factors influencing intention to switch to digital trade and innovation resistance of small and medium-sized trading companies, through previous research on migration theory and the PPM (Push, Pull, Mooring) model, each variable was selected for the purpose of the study. Based on this, a research model was established for the factors affecting switching to digital trade of small and medium-sized trading companies and empirically analyzed. In addition, considering the differences in the innovation propensity and maturity of information infrastructure of trading companies as the recipients of innovation, this study analyzes the moderating effect of the mooring effect and seeks ways to establish specific strategies according to the degree. Findings - As a result of empirical analysis, the pull effect was found to have the greatest influence on intention to switch to digital trade. However, the pull factor was found to have an effect on user resistance, and it was confirmed that it was a factor simultaneously inducing positive and negative consumption behaviors among users. In addition, it was found that the higher the company's innovation propensity, the higher the pull effect's influence on the intention to switch, and analysis showed that the push effect had no influence. In addition, companies with high information infrastructure maturity were expected to have a relatively high level of intention to switch compared to companies with low information infrastructure maturity, and the difference between the two groups was found not to be statistically significant. Originality/value - This study is a timely study in that it demonstrated the effect on the switching to cloud-based digital trade for small and medium-sized trading companies and that the cloud system related to digital trade is in full swing. There are academic implications in that it revealed that the pull effect is an important factor in the intention to switch to cloud service. Practical implications were presented in that small and medium-sized trading companies suggested ways to increase the value of the cloud system for switching to digital trade and a way to increase the switching ratio by minimizing the mooring effect. In addition, the study argues that active institutional support from the government is needed to activate cloud service.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.73
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• pp.91-110
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• 2017

Recently, world wide trading which support free trade will increase the economic volume size. It will grow the quality of life. But, the reduce of gap between the rich state and the poor one has always been risen the problem of one of welfare. Trough unregulated trade activities, multinational corporations succeeded in expanding the market globally. However, there were unfair acts such as infringement of serious rights of producer of low development countries. Fair trade has begun to pay fair value to them and to ease inequality, but, as time went by, the its idea became thinner, distorted in the market, or became a marketing tools. So, In this paper, I analyze the limitations and causes of fair trade and suggest directions for fair trade. This Study provided a causes of the limitation of fair trade and for the future, I'll suggest an alternative of limitation of fair trade.

• KDI Journal of Economic Policy
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• v.23 no.1_2
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• pp.149-191
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• 2001

In this paper, we propose a new bankruptcy algorithm. The proposed algorithm is comprised of four tasks. Task A is the procedure of soliciting bids, Task B is the procedure of allocating claims, Task C is the procedure of trading claims, and Task D is the procedure of exercising options and holding shareholders' meeting. Tasks A, B, and D are based on Bebchuk(1988) and Aghion, Hart, ad Moore(1992). This paper adds Task C, the procedure of trading claims. Claims are in the form of options which are written on the new shares of the bankrupt firm. Trading options expedites the process of finding the value of the bankrupt firm, and also it mitigates the problem of incomplete capital market by expanding the pool of new investors.

• Journal of Information Technology and Architecture
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• v.9 no.4
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• pp.365-376
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• 2012

We examine the value of information sharing in the context of supplier-buyer relationships after controlling for trading partners' opportunism. Given that trading partners' opportunism is not randomly chosen, we explicitly incorporate their self-selection process into our estimation procedure by employing Heckman's self-selection model. According to our analysis, firms that have built safeguards via mutual trust, commitments and information sharing experience less opportunistic risk in supplier-buyer relationships. Our findings also suggest that information sharing has a positive impact on firm performance after controlling for opportunism. Further, firms that are less exposed to trading partners' opportunistic risk have achieved a higher performance than others that are more exposed. Importantly, higher performance for those firms with less opportunistic risk is driven by safeguards in supplier-buyer relationships as well as information sharing. Our findings can be applied for systems analysts to design information systems of supplier-buyer transactions.