• Journal of Korean Society of Transportation
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• v.19 no.5
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• pp.21-33
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• 2001

Intelligent Transport Systems (ITS) was first introduced in Korea early 1990's, and Korean government has put a lot of efforts for flourishing it in the entire nation. Regardless of these efforts, private participation is not active enough to accelerate ITS implementation in Korea. Expert group made every endeavor to analyze the current situation, and found out some phenomena. It may be summarized as two folds. Firstly, private sector has a lack of confidence on the future ITS market. Budget in the strategic plan is the only publication and guide that private sector can refer to, and it merely indicates deployment costs. Secondly, direction and procedure of R&D are not well defined. It implies that private sector takes too much risk when they invest for R&D. This research, therefore, focuses on the first issues. Concretely, the goal of the project was to establish and analyze the model for estimation the future ITS market side. Author reviewed both quantitative and qualitative models, and concluded that diffusion model in qualitative model was suitable for ITS market estimation. According to model calibration. it is estimated that 14 trillion Won was the market size in 2020 under normal condition. Impact of this result may seduce Information Technology(IT) related private companies into ITS market. Although this research couldn't cover various topics, it nay dedicate in boosting ITS in Korea. Also, it will be a good starting point for further study for the advancement of ITS.

• Journal of the Korea Institute of Building Construction
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• v.14 no.6
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• pp.631-637
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• 2014

Recently, following the application of modular housing techniques to not only residential sector, but also to business sector, the scope of modular housing market b expanding. In the case of other developed countries, such markets are entering into the maturity stage, though the market in Korea is not fully formed yet. Thus, it is difficult to check its trend to estimated mid- to long-term prospects of the market. In this context, the study predicted demand of the modular housing market by using a non-linear prediction model based on time series analysis. To get the prospects for the modular housing market, the quantity of housing supply was estimated based on the estimated quantity of newly built housings, and assumed that a portion of the supplied quantity would be the demand for modular housings. Based on the assumption of demand for modular housings, several scenarios were analyzed and the prospects of the modular housing market was obtained by utilizing the non-linear prediction model.

• 한국데이터정보과학회:학술대회논문집
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• 2002.06a
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• pp.119-128
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• 2002

In this paper we consider the concepts and types of e-commerce, and we compare the predictors and its methods of e-commerce market size by survey institutes. Also we will make some policy for improving the reliability of the predictors of e-commerce market size from this study.

• International Area Studies Review
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• v.14 no.1
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• pp.99-119
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• 2010

This study explores the natural gas market and the oil market in the U.S. and the European oil market. It focuses on two kinds of analyses; one is to confirm whether there is the predictive power between spot and futures within homogeneous commodity market(or inter-heterogeneous commodity market) through Granger-causality test in terms of the return rate and the volatility. The other is to examine the spot price stabilizing effect of futures price through regression analysis. When it comes to the predictive power of inter-commodity market, there was a conflicting aspect between the return rate of spot and futures. Overall, however, its statistical significance was low. With respect to the volatility, we found that the natural gas market has little influence on the oil market unlike the predictive power of oil market on natural gas market. Concerning the return rate of the predictive power within homogeneous commodity market, we found that the return rate of spot has the predictive power on futures only in the European market. In addition, we identified that there is feedback between spot and futures in the all commodity markets regarding volatility. As a result of the spot price stabilizing effect analysis of futures price, futures volatility increased the spot volatility.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.51-59
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• 2024

This study examines the relationship between the release of the news and the individual stock returns. Investors utilize a variety of information sources to maximize stock returns when establishing investment strategies. News companies publish their articles based on stock recommendation reports of analysts, enhancing the reliability of the information. Defining release of a stock-recommendation news article as an event, we examine its economic impacts and propose a binary classification model that predicts the stock return 10 days after the event. XGBoost and LightGBM models are applied for the study with accuracy of 75%, 71% respectively. In addition, after categorizing the recommended stocks based on the listed market(KOSPI/KOSDAQ) and market capitalization(Big/Small), this study verifies difference in the accuracy of models across four sub-datasets. Finally, by conducting SHAP(Shapley Additive exPlanations) analysis, we identify the key variables in each model, reinforcing the interpretability of models.

• Journal of Digital Convergence
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• v.13 no.5
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• pp.237-244
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• 2015

Export-led policies, FTA signed and economics of scale through a variety of market-oriented policies, such as regulations to improve market grew constantly. Accordingly, the correct decision making accurately analyze the economics market for decision, a problem has been an important issue in predicting. For accurate analysis and decision-making of the most common indicators of the stock market by proposing a number of indicators of economic transformation techniques were applied to the convergence model combining estimation and forecasts problem confirmed its effectiveness. Experimental result, gave the model estimation method to apply a transform to show the valid combinations proposed model state estimation result was confirmed in a very similar exercise aspect of the physical problem and the KOSPI index prediction.

• The Journal of the Korea Contents Association
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• v.18 no.9
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• pp.623-632
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• 2018

Both investors and researchers are attentive to the prediction of stock price movement directions since the accurate prediction plays an important role in strategic decision making on stock trading. According to previous studies, taken together, one can see that different factors are considered depending on stock markets and prediction periods. This paper aims to analyze what data mining techniques show better performance with some representative index and stock price datasets in the Korea stock market. In particular, extreme gradient boosting technique, proving itself to be the fore-runner through recent open competitions, is applied to the prediction problem. Its performance has been analyzed in comparison with other data mining techniques reported good in the prediction of stock price movement directions such as random forests, support vector machines, and artificial neural networks. Through experiments with the index/price datasets of 12 years, it is identified that the gradient boosting technique is the best in predicting the movement directions after 1 to 4 days with a few partial equivalence to the other techniques.

• The Journal of Bigdata
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• v.7 no.1
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• pp.193-212
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• 2022

The Bass Diffusion Model is one of the most successful models in marketing research, and management science in general. Since its publication in 1969, it has guided marketing research on diffusion. This paper illustrates the usage of the Bass diffusion model, using mobile cellular subscription diffusion as a context. We fit the bass diffusion model to three large developed markets, South Korea, Japan, and China, and the emerging markets of Vietnam, Thailand, Kazakhstan, and Mongolia. We estimate the parameters of the bass diffusion model using the nonlinear least square method. The diffusion of mobile cellular subscriptions does follow an S-curve in every case. After acquiring m, p, and q parameters we use k-Means Cluster Analysis for grouping countries into three groups. By clustering countries, we suggest that diffusion rates and patterns are similar, where countries with emerging markets can follow in the footsteps of countries with developed markets. The purpose was to predict the timing and the magnitude of the market maturity and to determine whether the data follow the typical diffusion curve of innovations from the Bass model.

• Korea Real Estate Review
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• v.27 no.3
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• pp.71-86
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• 2017

The purpose of this study was to apply the deep running method to real estate price index predicting and to compare it with the time series analysis method to test the possibility of its application to real estate market forecasting. Various real estate price indices were predicted using the DNN (deep neural networks) and LSTM (long short term memory networks) models, both of which draw on the deep learning method, and the ARIMA (autoregressive integrated moving average) model, which is based on the time seies analysis method. The results of the study showed the following. First, the predictive power of the deep learning method is superior to that of the time series analysis method. Second, among the deep learning models, the predictability of the DNN model is slightly superior to that of the LSTM model. Third, the deep learning method and the ARIMA model are the least reliable tools for predicting the housing sales prices index among the real estate price indices. Drawing on the deep learning method, it is hoped that this study will help enhance the accuracy in predicting the real estate market dynamics.

• Journal of Korea Port Economic Association
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• v.35 no.3
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• pp.61-72
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• 2019

In order to enhance the competitiveness of the container shipping industry and promote its development, based on the empirical analyses using multivariate time series models, this study aims to suggest a few strategies related to the dynamics of the container shipping market. It uses the vector autoregressive (VAR) and vector error correction (VEC) models as analytical methodologies. Additionally, it uses the annual trade volumes, fleets, and freight rates as the dataset. According to the empirical results, we can infer that the most exogenous variable, the trade volume, exerted the highest influence on the total dynamics of the container shipping market. Based on these empirical results, this study suggests some implications for ship investment, freight rate forecasting, and the strategies of shipping firms. Concerning ship investment, since the exogenous trade volume variable contributes most to the uncertainty of freight rates, corporate finance can be considered more appropriate for container ship investment than project finance. Concerning the freight rate forecasting, the VAR and VEC models use the past information and the cointegrating regression model assumes future information, and hence the former models are found better than the latter model. Finally, concerning the strategies of shipping firms, this study recommends the use of cycle-linked repayment scheme and services contract.