• 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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.489-497
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• 2000

After IMF situation, the money market environment is changing rapidly. Therefore, many companies including financial institutions and many individual investors are concerned about forecasting the money market, and they make an effort to insure the various profit and hedge methods using derivatives like option, futures and swap. In this research, we developed a prototype of forecasting system for KOSPI 200 option, especially call option, trading using artificial neural networks(ANN), To avoid the overfitting problem and the problem involved int the choice of ANN structure and parameters, we employed the ANN ensemble approach. We conducted two types of simulation. One is conducted with the hold signals taken into account, and the other is conducted without hold signals. Even though our models show low accuracy for the sample set extracted from the data collected in the early stage of IMF situation, they perform better in terms of profit and stability than the model that uses only the theoretical price.

• Environmental and Resource Economics Review
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• v.21 no.2
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• pp.301-319
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• 2012

The primary purpose of Renewable Portfolio Standard (RPS) is to facilitate investment in renewable energy technology. Since emission trading program has similar purpose, it is conceivable to attempt to link RPS and emission trading program through interlinked markets. RPS in Korea with single REC and emission allowance markets has particular advantages for constructing linkages between two markets. This paper provides a real option model to examine investment effects of linkage of RPS to the trading program. Emission permit price and REC price are assumed to follow stochastic processes and renewable investment is irreversible. The result shows that linked market provides further incentive for renewable investment by raising managerial flexibility for power companies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7672-7676
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• 2015

This study examines the dynamic relations between housing price and trading volume in a set of apartment markets in Republic of Korea to explore the informational role of trading volume in predicting the price volatility. Using monthly index data, EGARCH model is utilized to test for volume effect. To estimate the EGARCH-based volatility, two different sets of region are applied for the monthly return. Strong evidence has been found towards housing turnover leading price volatility, this supports previous studies on financial sector(s). These findings also support that trading volume in the housing market contains information on investor sentiment which, in turn, has a valuation effect on the price.

• Journal of the Korea Convergence Society
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• v.12 no.6
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• pp.9-16
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• 2021

In the past, there have been various studies on predicting the stock market by machine learning techniques using stock price data and financial big data. As stock index ETFs that can be traded through HTS and MTS are created, research on predicting stock indices has recently attracted attention. In this paper, machine learning models for KOSPI's up and down predictions are implemented separately. These models are optimized through a grid search of their control parameters. In addition, a hybrid machine learning model that combines individual models is proposed to improve the precision and increase the ETF trading return. The performance of the predictiion models is evaluated by the accuracy and the precision that determines the ETF trading return. The accuracy and precision of the hybrid up prediction model are 72.1 % and 63.8 %, and those of the down prediction model are 79.8% and 64.3%. The precision of the hybrid down prediction model is improved by at least 14.3 % and at most 20.5 %. The hybrid up and down prediction models show an ETF trading return of 10.49%, and 25.91%, respectively. Trading inverse×2 and leverage ETF can increase the return by 1.5 to 2 times. Further research on a down prediction machine learning model is expected to increase the rate of return.

• Environmental and Resource Economics Review
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• v.15 no.4
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• pp.591-642
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• 2006

This paper intends to analyze economic impacts of commitment period and initial allocation in emissions trading using computable general equilibrium (CGE) modeling. The fully dynamic CGE model with perfect foresight assumption is employed to illustrate (i) how a model displays economic impact of $CO_2$ regulation upon different commitment periods: one-year budget clearing vs. 5-year commitment period, (ii) how major 8 energy-intensive industries respond to different ways to allocate initial allowances. According to the results of the analysis, it IS found that market players are motivated to bank the permits and tend to sell permits in earlier stage and to buy permits in later stage of commitment period. This implies that banking allows permit trading within a commitment period, which supports the conclusions of Kling and Rubin (1997). Other findings are related to efficiency. That is, emissions trading surpasses command and control, in terms of economic efficiency and longer terms of commitment period converge on lower permit price, In long term, initial allocation based on value-added performs the lowest GDP loss among different initial allocations.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.1-6
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• 2019

Neural networks have been used to predict the direction of stock index movement from past data. The conventional research that predicts the upward or downward movement of the stock index predicts a rise or fall even with small changes in the index. It is highly likely that losses will occur when trading ETFs by use of the prediction. In this paper, a neural network model that predicts the movement direction of the daily KOrea composite Stock Price Index (KOSPI) to reduce ETF trading losses and earn more than a certain amount per trading is presented. The proposed model has outputs that represent rising (change rate in index ${\geq}{\alpha}$), falling (change rate ${\leq}-{\alpha}$) and neutral ($-{\alpha}$ change rate < ${\alpha}$). If the forecast is rising, buy the Leveraged Exchange Traded Fund (ETF); if it is falling, buy the inverse ETF. The hit ratio (HR) of PNN1 implemented in this paper is 0.720 and 0.616 in the learning and the evaluation respectively. ETF trading yields a yield of 8.386 to 16.324 %. The proposed models show the better ETF trading success rate and yield than the neural network models predicting KOSPI.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.3
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• pp.627-637
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• 2015

As game item trading becomes more popular with the rapid growth of online game market, the market for trading game items by cash has increased up to KRW 1.6 trillion. Thanks to this active market, it has been easy to turn these items and game money into real money. As a result, some malicious users have often attempted to steal other players' rare and valuable game items by using their account. Therefore, this study proposes a detection model through analysis on these account thieves' behavior in the Massive Multiuser Online Role Playing Game(MMORPG). In case of online game identity theft, the thieves engage in economic activities only with a goal of stealing game items and game money. In this pattern are found particular sequences such as item production, item sales and acquisition of game money. Based on this pattern, this study proposes a detection model. This detection model-based classification revealed 86 percent of accuracy. In addition, trading patterns when online game identity was stolen were analyzed in this study.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.367-374
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• 2021

Stock price prediction is a subject of research in various fields such as economy, statistics, computer engineering, etc. In recent years, researches on predicting the movement of stock prices by learning artificial intelligence models from various indicators such as basic indicators and technical indicators have become active. This study proposes a deep learning model that predicts the ups and downs of KOSPI from overseas indices such as S&P500, past KOSPI indices, and trading trends by KOSPI investors. The proposed model extracts a latent variable using a stacked auto-encoder to predict stock price fluctuations, and predicts the fluctuation of the closing price compared to the market price of the day by learning an LSTM suitable for learning time series data from the extracted latent variable to decide to buy or sell based on the value. As a result of comparing the returns and prediction accuracy of the proposed model and the comparative models, the proposed model showed better performance than the comparative models.

• Korean Business Review
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• v.19 no.1
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• pp.81-92
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• 2006

This study investigates the relation between institutional investor's net purchase and the volatility of KOSPI. Some portion of volatility in stock prices comes from noise trading of irrational traders. Observed volatility may be defined as the sum of the portion caused by information arrival, fundamental volatility, and the portion caused by noise trading, transitory volatility. This study decomposes the observed volatility into fundamental volatility and transitory volatility using Kalman filtering method. Most studies investigates the effect on the observed volatility. In contrast to other studies, this study investigates the effect on the fundamental volatility and transitory volatility individually. Estimation results show that institutional investor's net purchase was not significantly related to all kinds of volatility(observed volatility, fundamental volatility and transitory volatility). This means that institutional investor's net purchase did not increase noise trading.