• Journal of Korea Society of Industrial Information Systems
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• v.18 no.1
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• pp.81-89
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• 2013

The purpose of this study is to analyze the information effect about the pricing using the ETF price, the benchmark index, and the total tracking error between the ETF price and the benchmark index on the index ETF market and sector ETF markets. Furthermore, the total tracking error is distinguished between the market tracking error and the NAV tracking error. Summary of this study are as follows: First, While KODEX200 don't have impact factors on the price, the most sectors of ETF have the factors affecting the pricing decision. They are the day before the total tracking error or market tracking error. Second, for the ETF price of the most industry, we find that the day before the market tracking error have the price discovery function because it is a negative(-) coefficients. But NAV tracking error could not find such a feature. Finally, the sector ETF price of energy chemical, construction, IT, and semiconductor industries affected of the day before positive(+) impact by the benchmark index price.

• Smart Media Journal
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• v.11 no.3
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• pp.62-73
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• 2022

Researches on the prediction of domestic apartment sales price have been continuously conducted, but it is not easy to accurately predict apartment prices because various characteristics are compounded. Prior to predicting apartment sales price, the analysis of major factors, influencing on sale prices, is of paramount importance to improve the accuracy of sales price. Therefore, this study aims to analyze what are the factors that affect the apartment sales price in Gwangju, which is currently showing a steady increase rate. With 6 years of Gwangju apartment transaction price and various social factor data, several maching learning techniques such as multiple regression analysis, random forest, and deep artificial neural network algorithms are applied to identify major factors in each model. The performances of each model are compared with RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and R² (coefficient of determination). The experiment shows that several factors such as 'contract year', 'applicable area', 'certificate of deposit', 'mortgage rate', 'leading index', 'producer price index', 'coincident composite index' are analyzed as main factors, affecting the sales price.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.3
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• pp.499-506
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• 2017

Consideration of influential factors through division of capital market sector and interest rate sector to find and resolve the problems in current housing market and leasing market will become an important index to prepare measures for stabilization of housing sales market and housing lease market. Furthermore, a guideline will be provide you with preliminary data using Big Data to prepare for sudden price fluctuation because expected economic crisis, stock market situation, and uncertain future financial crisis can be predicted which may help anticipate real estate price index such as housing sales price index and housing lease price index.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.3
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• pp.154-161
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• 2018

This study conducted to predict the stock market prices based on the assumption that internet news articles might have an impact and effect on the rise and fall of stock market prices. The internet news articles were tested to evaluate the accuracy by comparing predicted values of the actual stock index and the forecasting models of the companies. This paper collected stock news from the internet, and analyzed and identified the relationship with the stock price index. Since the internet news contents consist mainly of unstructured texts, this study used text mining technique and multiple regression analysis technique to analyze news articles. A company H as a representative automobile manufacturing company was selected, and prediction models for the stock price index of company H was presented. Thus two prediction models for forecasting the upturn and decline of H stock index is derived and presented. Among the two prediction models, the error value of the prediction model (1) is low, and so the prediction performance of the model (1) is relatively better than that of the prediction model (2). As the further research, if the contents of this study are supplemented by real artificial intelligent investment decision system and applied to real investment, more practical research results will be able to be developed.

• Journal of Korea Port Economic Association
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• v.29 no.2
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• pp.63-79
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• 2013

The fundamental endeavor of this study is to investigate the asymmetric relationship between bunker price and Baltic Dry-bulk Index (hereafter BDI). Previous investigations employ linear form based analysis between oil price and BDI but we develop nonlinear and asymmetric cointegration method, which is properly able to capture the decreasing and increasing periods differently. The empirical results show there is no relationships in linear model (e.g. Engle and Granger's methods). On the contrary, our estimate reveals there is significant long-run relationship with asymmetric framework, which implies the necessity of nonlinear and asymmetric consideration to the bunker price analysis.

• Journal of the Korea Institute of Building Construction
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• v.12 no.6
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• pp.624-635
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• 2012

Construction projects, including housing, are carried out over long periods of time. According to changes to the construction period, the cost of input materials and wages also changes. Therefore appropriate management is important in order to minimize cost risks caused by fluctuations in prices. In Korea, housing units are usually sold in lots prior to construction completion. Therefore, careful management of input elements such as materials and equipment that are sensitive to price fluctuations is very important. This study deals with how the price fluctuation of materials, labor, and equipment influences the change of housing cost and seeks a way for cost management through identifying key resources sensitive to price fluctuation. As a result, a change to the housing cost index multiplies depending on cost changes of materials and labor together. Labor costs are a major factor on the housing cost index. In addition, certain types of materials and labor input to housing construction greatly influence price fluctuations. Thus, it is found that managing those main cost factors is the key for effective cost management.

• Journal of Distribution Science
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• v.21 no.9
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• pp.103-114
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• 2023

Purpose: This paper aims to assess psychological heuristics' effectiveness on cumulative returns after significant stock price changes. Specifically, it compares availability and anchoring heuristics' empirical validity due to conflicting stock return predictions. Research Design, Data, and Methodology: This paper analyzes stock price changes of Korean distribution industry stocks in the KOSPI market from January 2004 to July 2022, where daily fluctuations exceed 10%. It evaluates availability heuristics using daily KOSPI index changes and tests anchoring heuristics using 52-week high and low stock prices as reference points. Results: As a result of the empirical analysis, stock price reversals did not consistently appear alongside changes in the daily KOSPI index. By contrast, stock price drifts consistently appeared around the 52-week highest stock price and 52-week lowest stock price. The result of the multiple regression analysis which controlled for both company-specific and event-specific variables supported the anchoring heuristics. Conclusions: For stocks related to the Korean distribution industry in the KOSPI market, the anchoring heuristics theory provides a consistent explanation for stock returns after large-scale stock price fluctuations that initially appear to be random movements.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1201-1210
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• 2019

Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to have a direct influence on the stock markets globally. Given that the stock price data often contain both linear and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however, it provides an accurate and effective way to process autocorrelation and non-stationary data in time series forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price of the Shanghai composite index and Shenzhen component index.

• Journal of Korean Society of Forest Science
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• v.99 no.4
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• pp.573-579
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• 2010

This study was carried out to investigate the relationship between visitor uses of national parks and economic variables, such as the index of industrial product and the consumer price index. The results from the Granger Causality test showed that the index of industrial product and the consumer price index influenced visitor use at national parks. Also the Impulse Response Analysis showed that the index of industrial product and the consumer price index greatly influenced national park visitor use in the short term as well as the long term. The study showed that national park visitor use was mainly influenced by variance decompositions. These results suggested that economic variables could be used to not only forecast the demand for recreation but also establish recreational policies.

• Survey Research
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• v.9 no.3
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• pp.1-21
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• 2008

The housing market in Korea had mainly consisted of Maemae(purchasing market) and Chonsei(rental market). Since 1997 foreign exchange crisis, the rental housing market has experienced substantial changes in preferred rental contracts between Chonsei and monthly-rent. Even though monthly-rent has taken a substantial portion of housing rental contracts, not yet reliable monthly-rent index has been developed. Furthermore, it isn't obvious to define monthly-rent because there are many types of monthly rent structures from full-monthly-rent to monthly-rent-with-variable-deposit. This study is the basic research of developing a housing price index of monthly-rent in accordance with the existing price index of Maemae and Chonsei in Korea. This research has been carried out with the following contents: (1) Constructing the actually desirable concept of monthly-rent through examining monthly-rental market in Korea. (2) Selecting the reasonable method to investigate monthly-rental market, especially monthly-rent-with-variable -deposit. (3) Designing monthly-rental market samples and calculating the price index of monthly-rent based on 2005 Census.