• Journal of The Korean Digital Architecture Interior Association
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• v.12 no.1
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• pp.89-98
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• 2012

While the housing transaction price of Seoul tends to be stagnant or declining in line with the housing market recession since 2007, the jeonse price keeps continual increase. Such flow of jeonse price change has a serious influence on ordinary person's housing stability seriously. Therefore, it is very meaningful in terms of social policy to analyze the trend of recent jeonse price change. This study aims to have an empirical analysis of structural characteristics of the trend of recent jeonse price change. After the review of various previous studies, this study selected housing jeonse price index, non-sold house quantity, jeonse vs. transaction price rate, and housing construction performance as analytical variables, and employed monthly time series resources from January 2007 to April 2011. As a result, when the housing supply reduced, the potential quantity for jeonse market reduced that occurred unbalance of supply and demand in jeonse market. In turn, it caused the increase of jeonse price. And, in case of jeonse vs. transaction price rate change, the rate increased which means the increase of required rate of return of invested demand. As such, the increase of market risk degenerates the investment sentiment which caused the reduction of quantity for jeonse market as a submarket.

• Journal of Distribution Science
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• v.19 no.1
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• pp.27-35
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• 2021

Purpose: This study is to investigate the effect of managerial ownership level in distribution and service companies on the stock price crash. The managerial ownership level affects the firm's information disclosure policy. If managers conceal or withholds business-related unfavorable factors over a long period, the firm's stock price is likely to plummet. In a similar vein, management's equity affects information opacity, and information asymmetry affects stock price collapse. Research design, data, and methodology: A regression analysis is conducted using the data on companies listed on the Korea Composite Stock Price Index (KOSPI) between 2012-2017 to examine the effect of the managerial ownership level on stock price crash risks. Results: Logistic and regression results indicate that the stock price crash risk was reduced as managerial ownership levels are increased. The managerial ownership level has a significant negative coefficient on stock price crash risk, negative conditional return skewness of firm-specific weekly return distribution, and asymmetric volatility between positive and negative price-to-earnings ratios. Conclusions: As the ownership and management align, the likeliness of withholding business-related information is reduced. This study's results imply that the stock price crash risk reduces as the managerial ownership level increases because shareholder and manager interests coincide, thereby reducing information asymmetry.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.5
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• pp.527-534
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• 2020

A significant factor in construction work is cost. At early- and advanced-stage design, costs should be calculated to derive realistic cost estimates according to unit price calculation. Based on these estimates, the economic feasibility of construction work is assessed, and whether to proceed is determined. Through the Korea Institute of Civil Engineering and Building Technology, the construction cost index has been calculated by indirect methods after both the producer price index and construction market labor have been reprocessed to easily adjust the price changes of construction costs in Korea, and the Institute has announced it since 2004. As of January 2000, however, the construction cost index was released, and this has a time constraint on the correction and use of past construction cost data to the present moment. Variables were calculated to compute a rough construction cost that utilized past construction costs through surveys of the producer price index and the construction market labor force consisting of the construction cost index. After significant independent variables among the many variables were selected through correlation analysis, the construction cost index from 1970 to 1999 was calculated and presented through multiple regression analysis. This study therefore has prominent significance in terms of proposing a method of calculating rough construction costs that utilize construction costs that pre-date the 2000s.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.47-56
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• 2020

The stock index is used not only as an economic indicator for a country, but also as an indicator for investment judgment, which is why research into predicting the stock index is ongoing. The task of predicting the stock price index involves technical, basic, and psychological factors, and it is also necessary to consider complex factors for prediction accuracy. Therefore, it is necessary to study the model for predicting the stock price index by selecting and reflecting technical and auxiliary factors that affect the fluctuation of the stock price according to the stock price. Most of the existing studies related to this are forecasting studies that use news information or macroeconomic indicators that create market fluctuations, or reflect only a few combinations of indicators. In this paper, this we propose to present an effective combination of the news information sentiment analysis and various macroeconomic indicators in order to predict the US Dow Jones Index. After Crawling more than 93,000 business news from the New York Times for two years, the sentiment results analyzed using the latest natural language processing techniques BERT and NLTK, along with five macroeconomic indicators, gold prices, oil prices, and five foreign exchange rates affecting the US economy Combination was applied to the prediction algorithm LSTM, which is known to be the most suitable for combining numeric and text information. As a result of experimenting with various combinations, the combination of DJI, NLTK, BERT, OIL, GOLD, and EURUSD in the DJI index prediction yielded the smallest MSE value.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1573-1583
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• 2016

It is necessary to check mutual correlations between related variables because housing prices are influenced by a lot of variables of the economy both internally and externally. In this paper, employing the Granger causality test, we have validated interrelated relationship between the variables. In addition, there is cointegration associations in the results of the cointegration test between the variables. Therefore, an analysis using a vector error correction model including an error correction term has been attempted. As a result of the empirical comparative analysis of the forecasting performance with ARIMA and VAR models, it is confirmed that the forecasting performance by vector error correction model is superior to those of the former two models.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.463-470
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• 2016

In analysing a time series on the frequency domain, the spectral estimator (or periodogram) is a very useful statistic to identify the periods of a time series. However, the spectral estimator is very sensitive in nature to outliers, so that the spectral estimator in terms of M-estimation has been studied by some researchers. Pak (2001) proposed an empirical method to choose a tuning parameter for the Huber's M-estimating function. In this article, we try to implement Pak's estimation proposal in the spectral estimator. We use the Korean housing price index as an example data set for comparing various M-estimating results.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.263-269
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• 2020

The purpose of this paper is to examine the impact of consumer price index, infrastructure, human resources, trade openness, and private credit on the attraction of foreign direct investment (FDI) in Tay Ninh province as well as to emphasize the important role of FDI in economic growth of developing areas. The research data was collected from Tay Ninh Statistical Office with 80 samples of a 20-year period from 2000 to 2019. Also, OLS regression method using Eviews software was employed to analyze the data obtained. The findings revealed that human resources, infrastructure and private credit have a positive and significant impact on FDI attraction in Tay Ninh province, while consumer price index was proven to affect FDI attraction negatively. Accordingly, competent authorities of Tay Ninh province should focus on stabilizing prices as well as implementing policies for developing local human resources and attracting high-quality personnel from foreign countries. Tay Ninh province also needs to pay more attention to information technology investment for synchronous development of infrastructure. Moreover, the State Bank of Tay Ninh branch needs to consider more credit sources to provide support packages for businesses, creating a strong basis for establishments to attract FDI for the province's economic development.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.448-459
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• 2021

In this paper, we propose a method of predicting the next-day stock price fluctuations of 10 KOSDAQ-listed companies in 5G, autonomous driving, and electricity sectors by training SVM, XGBoost, and LightGBM models from macroeconomic·financial market indicators, technical indicators, social interest indicators, and daily positive indices extracted from online news. In the three experiments to find out the usefulness of social interest indicators and daily positive indices, the average accuracy improved when each indicator and index was added to the models. In addition, when feature selection was performed to analyze the superiority of the extracted features, the average importance ranking of the social interest indicator and daily positive index was 5.45 and 1.08, respectively, it showed higher importance than the macroeconomic financial market indicators and technical indicators. With the results of these experiments, we confirmed the effectiveness of the social interest indicators as alternative data and the daily positive index for predicting stock price fluctuation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.13 no.21
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• pp.11-18
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• 1990

Because of the descent trend of the recent oil price and the ascent elements of the manufacturing price of public coal. the future demand of public coal is very obscured. In this paper, forecast the public coal demand by the regression analysis method reflected the policy and economic index of alternative energies.

• Journal of the Economic Geographical Society of Korea
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• v.13 no.4
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• pp.663-680
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• 2010

This paper aims at analysing Regional Patterns of Farmland Price Changes for the Farmland Reverse Mortgage System. Farmland Reverse Mortgage(FRM) is a system in which the aged farmers in the rural areas receive certain amount of money monthly through the liquidation of their own farmlands for the life time. Farmland price affects the farmland annuity considerably. In the future, if the farmland price goes down than the price when the borrower joined FRM, the borrower can get profits from the pension. Based on the results, the farmland price of Kyeonggi-do is strongly related to economic growth rates(index of industrial product). while that of Gyeongsangbuk-do is weakly related to economic variables including economic growth rates. Therefore, the expectation of farmland value rising rate will be higher in Kyeonggi-do than in Gyeongsangbuk-do. Thus the number of borrowers who want to join FRM in Gyeongsangbuk-do will be more than those in Kyeonggi-do.