• Journal of Korea Trade
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• v.25 no.4
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• pp.17-36
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• 2021

Purpose - This study provides useful information to stakeholders by forecasting the tramp shipping market, which is a completely competitive market and has a huge fluctuation in freight rates due to low barriers to entry. Moreover, this study provides the most effective parameters for Baltic Dry Index (BDI) prediction and an optimal model by analyzing and comparing deep learning models such as the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM). Design/methodology - This study uses various data models based on big data. The deep learning models considered are specialized for time series models. This study includes three perspectives to verify useful models in time series data by comparing prediction accuracy according to the selection of external variables and comparison between models. Findings - The BDI research reflecting the latest trends since 2015, using weekly data from 1995 to 2019 (25 years), is employed in this study. Additionally, we tried finding the best combination of BDI forecasts through the input of external factors such as supply, demand, raw materials, and economic aspects. Moreover, the combination of various unpredictable external variables and the fundamentals of supply and demand have sought to increase BDI prediction accuracy. Originality/value - Unlike previous studies, BDI forecasts reflect the latest stabilizing trends since 2015. Additionally, we look at the variation of the model's predictive accuracy according to the input of statistically validated variables. Moreover, we want to find the optimal model that minimizes the error value according to the parameter adjustment in the ANN model. Thus, this study helps future shipping stakeholders make decisions through BDI forecasts.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.409-411
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• 2014

There have been various advanced research on how changes of analysts' profit forecasts affect stock prices. Also, consensus, which is usually drawn by the arithmetic mean of profit forecasts, has been widely harnessed among investors in stock market. Recently, it is emphasized to reflect the internal factors of individual forecasts to raise the accuracy of consensus. Based on national and international research, this study proposes a new methodology in consensus by applying statistically meaningful factors in computation.

• Asia pacific journal of information systems
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• v.11 no.4
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• pp.99-111
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• 2001

The prediction of stock price index is a very difficult problem because of the complexity of stock market data. It has been studied by a number of researchers since they strongly affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network(BPN). Finally, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.1
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• pp.17-24
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• 2024

Recently, retail technology has been developed by the rapid evolution of e-commerce and a representative example is ESL technology. In this study, we investigate ESL technology, market status and forecasts, and analyze the competitive structure between relational companies. Market analysis refers to data from market reports of Marketsandmarkets and Research, and internet media. In ESL, the display field is predicted to account for 43% of the total market in 2026, and is converting from LCD to electronic paper. The segmented type is becoming more advanced into the full-graphic type, and CAGR of 18.7% for 3-7 inches and 20.6% for 7-10 inches is predicted. The demand for ESL is greatest in North America and Europe, but CAGR is the highest in the Asia-Pacific region at 19.1%. Since ESL technology has a lot of overlap with semiconductor and display technology, the Asia-Pacific region is relatively advantageous, and this has led to rapid growth of domestic companies. However, it is expected that competition from European companies that are actually owned by Chinese companies will increase in the future, so continuous technological development and new market development are necessary.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.8A
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• pp.1115-1125
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• 1999

This paper forecasts the number of GMPCS(Global Mobile Communications by Satellite) subscribers in Korea. Since GMPCS adopts nor only a new tecnology cor proved in the market yet, bot also a global service principle, it's service market involves a great deal of nucertainties in terms of technological and regulatory perspectives. This paper develops a modified diffusion which considers those uncertainties by identifying three environmental group of tactors. The parameters of the model are estimated through a scenario-based approach. By assuming a pessimistic and an optimistic scenarios with three environmental group of factors, the model forecasts 4,000 and 7,000 substcribers in the first year, and then 100,000 and 600,000 subscribers in 2005 respectively. The sensitivity analysis of the model also gives an implication of the future market growth. In the early period, regulatoyu and technological issues are found to be relatively important, but, in the later period, the interconnection issues and price-competitiveness will become increasingly important.

• Management Science and Financial Engineering
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• v.14 no.2
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• pp.89-104
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• 2008

Growth curves including Bass, Logistic and Gompertz functions are widely used in forecasting the market demand. Nonlinear least square method is often adopted for estimating the model parameters but it is difficult to set up the starting value for each parameter. If a wrong starting point is selected, the result may lead to erroneous forecasts. This paper proposes a method of selecting starting values for model parameters in estimating some growth curves by nonlinear least square method through grid search and transformation into linear regression model. Resealing the market data using the national economic index makes it possible to figure out the range of parameters and to utilize the grid search method. Application to some real data is also included, where the performance of our method is demonstrated.

• The Journal of Information Technology and Database
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• v.6 no.1
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• pp.73-88
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• 1999

There have been a series of debates to determine whether it would be possible to forecast dynamic systems such as stock markets. Recently the introduction of chaos theory has allowed many researchers to bring back this issue. Their main concern was whether the behavior of stock markets is chaotic or not. These studies, however, present divergent opinions on this question, depending upon the method applied and the data used. And the issue of predictability based on the nonlinear, chaotic nature was not dealt extensively. This paper is to test the nonlinear nature of the Korea stock market and accordingly attempts to predict its behavior. The result indicates that our stock market represents a chaotic behavior. We also found out based on our simulation that executing buy/sell transactions based upon forecasts which were derived using the local approximation method outperforms the decision of holding without a buy/sell transaction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.652-655
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• 2003

This paper represents the electronic money market forecast. The cash, check and traditional settlement means transfer the electronic money with information technique. This paper forecasts not only the use of electronic money markets but also the scale of electronic money. So, the purpose of this study is to forecast and analyze the present situation of electronic money market.

• Environmental and Resource Economics Review
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• v.14 no.1
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• pp.201-220
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• 2005

Recently, we have been witnessing new records of crude oil price hikes. One question which naturally arises would be the possibility and accuracy of forecasting crude oil prices. This study tries to answer the relative predictability of futures prices compared to the forecasts based on experts system. Using WTI crude oil spot and futures prices, this study performs simple statistical comparisons in forecasting accuracy and a formal test of differences in forecasting errors. According to statistical results, WTI crude oil futures market turns out to be equally efficient relative to EIA experts system. Consequently, WTI crude oil futures market could be utilized as a market-based tool for price forecasting and/or resource allocation for both of petroleum producers and consumers.

• Proceedings of the KIEE Conference
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• summer
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• pp.698-700
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• 2004

Electric power restructuring offers a major change to the vertically integrated utility monopoly. Deregulation has had a great impact on the electric power industry in various countries. Bidding competition is one of the main transaction approaches after deregulation. The energy trading levels between market participants is largely dependent on the short-term price forecasts. This paper presents the short-term System Marginal Price (SMP) forecasting implementation using backpropagation Neural Network in competitive electricity market. Demand and SMP that supplied from Korea Power Exchange (KPX) are used by a input data and then predict SMP. It needs to analysis the input data for accurate prediction.