• The Journal of Fisheries Business Administration
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• v.27 no.1
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• pp.67-90
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• 1996

As an important factor of capitalism economics, price of commodities represents a certain country's economic index. For having correct price policy, there should be an appropriate mechanism to make and use systematic statistical data on price. Price statistics are made by indexes and price indexes are categorized into producer price index(PPI) and consumer price index(CPI). The Bank of Korea is publishing producer price index every year, but the producer price index contains some problems. These include as follows : (a) the impractical selection of fisheries products sample (b) uncorrect measure of aquatic products weights (c) investigating sample places. This study try to substitute producer price index of aquatic products and change construction of fisheries producer price index with experimental research on representative fisheries, weight of each fisheries, and suitability of investigating sample places. It is possible to improve practical fisheries producer price index with the results of this research. The findings are as follow. (a) It will be helpful for the government to make the fisheries price policy. (b) It can be used to understand trends of accurate price and price increase of aquatic products, and it's possible to compare with it other industrial indexes including the mining, agricultural, and manufacturing industry and understand relative price movement. (c) When free sales systems of fisheries products as expected, it will be helpful to analyze price movement of producing fisheries cooperatives, producing fisheries market and consuming fisheries market, analysis of market, and formation and consideration of budget. (d) It can be an important index to determine labor wage.

• Journal of Distribution Science
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• v.11 no.10
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• pp.63-72
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• 2013

Purpose - This study analyzes the relationship and dynamic interactions between stock price index, interest rate, price index, and housing price indices using Korean monthly data from 2000 to 2013, based on a VAR model. This study also examines Granger causal relationships among these variables in order to determine whether the time series of one is useful in forecasting another, or to infer certain types of causal dependency between stochastic variables. Research design, data, and methodology - We used Korean monthly data for all variables from 2000: M1 to 2013: M3. First, we checked the correlations among different variables. Second, we conducted the Augmented Dickey-Fuller (ADF) test and the co-integration test using the VAR model. Third, we employed Granger Causality tests to quantify the causal effect from time series observations. Fourth, we used the impulse response function and variance decomposition based on the VAR model to examine the dynamic relationships among the variables. Results - First, stock price Granger affects interest rate and all housing price indices. Price index Granger, in turn, affects the stock price and six metropolitan housing price indices. However, none of the Granger variables affect the price index. Therefore, it is the stock markets (and not the housing market) that affects the housing prices. Second, the impulse response tests show that maximum influence on stock price is its own, and though it is influenced a little by interest rate, price index affects it negatively. One standard deviation (S.D.) shock to stock price increases the housing price by 0.08 units after two months, whereas an impulse shock to the interest rate negatively impacts the housing price. Third, the variance decomposition results report that the shock to the stock price accounts for 96% of the variation in the stock price, and the shock to the price index accounts for 2.8% after two periods. In contrast, the shock to the interest rate accounts for 80% of the variation in the interest rate after ten periods; the shock to the stock price accounts for 19% of the variation; however, shock to the price index does not affect the interest rate. The housing price index in 10 periods is explained up to 96.7% by itself, 2.62% by stock price, 0.68% by price index, and 0.04% by interest rate. Therefore, the housing market is explained most by its own variation, whereas the interest rate has little impact on housing price. Conclusions - The results of the study elucidate the relationship and dynamic interactions among stock price index, interest rate, price index, and housing price indices using VAR model. This study could help form the basis for more appropriate economic policies in the future. As the housing market is very important in Korean economy, any changes in house price affect the other markets, thereby resulting in a shock to the entire economy. Therefore, the analysis on the dynamic relationships between the housing market and economic variables will help with the decision making regarding the housing market policy.

• Journal of Distribution Science
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• v.12 no.3
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• pp.75-83
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• 2014

Purpose - The oil price affects company value, which is the present value of the expected cash flow, by affecting the discount rate and cash flow. This study examines the nonlinear relationships between oil price and stock price using the AlphaShares Chinese Volatility Index as the threshold. Research design, data, and methodology - Data comprise daily closing values of the Shanghai Stock Exchange Composite Index, Shenzhen Stock Exchange Composite Index, and Hang Seng Index of ChinaWest Texas Intermediate crude oil spot price and AlphaShares Chinese Volatility Index from May 25, 2007 to May 24, 2012. The Threshold Error Correction Model is used. Results - The results demonstrate different relationships between the stock price index and oil price under different investor sentiments; however, the stock price index and oil price could adjust to a long-term equilibrium the long-term causality tests between them were all significant. Conclusions - The relationship between the WTI and HANG SENG Index is more significant than the Shanghai Composites Index and Shenzhen Composite Index, when using the AlphaShares Chinese Volatility Index (ASC-VIX) as the investor sentiment variable and threshold.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.977-985
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• 2021

The main aim of the study is to provide empirical evidence about the association between stock market exchange data and weighted price index. This research utilized monthly reported data from the Amman stock exchange market (ASE) and the Central Bank of Jordan (CBJ). The weighted price index was employed as the dependent variable and the independent variables were weighted price index (WPI), turnover ratio (TOR), number of trading days (NTD), price-earnings ratio (PER), and dividends yield ratio (DY). The time period of the study was from January 2015 to October 2020. The study's methodology follows a quantitative approach using the multiple regression method to test the hypotheses of the study. The final results of the study provided conclusive evidence that the market-weighted price index is strongly and positively correlated to three predetermined variables, namely; turnover ratio, price-earnings ratio, and dividend yield but no evidence was obtained for the effect of the number of trading days. The finding of the current study proved that the market price index is not only influenced by macro factors, but also by other variables assumed to not beneficial for the judgment of price index movements.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.16 no.6
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• pp.63-75
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• 2013

The purpose of the study is investigating the price fluctuation of landscaping plants in the Information on Commodity Prices(ICP) and the posted price fluctuation of landscaping plants of Public Procurement Service(PPS) recent 10 years. It also provides the basic information which can be applied to production and sales of landscaping plants, comparing with general price index. The major findings of the study are as follows. First, The price of investigated plants of PPS has increased about 4.56% in average recent 10 years. Among this increase, of evergreen tree was predominant. On the other hand, landscaping trees price of ICP has increased about only 2.34% in average. Secondly, The result shows that average price of investigated plants of PPS is positively related with the price of ICP. For this reason, we found that prices of ICP and of PPS move together in most case. However, we found that there are no relation between Consumer Price Index(CPI), Producer Price Index(PPI) and Agricultural Price Index(API). Therefore, price fluctuation of landscaping trees moves regardless of normal price fluctuation in general. Third, even though result shows that price index of evergreen trees, deciduous trees and shrubs are weakly related with normal price index partly, it was not high enough to be significant. According to the result, we found that price of landscaping plants is not related with market situation. For this reason, we thought that there are some difficulties for the reasonable production and sales of landscaping plants because the price is somewhat decided by rule of thumb. Therefore, understanding the composition of cost and making prediction by price fluctuation available are needed so that it can be practically conducive to reasonable production and sales.

• The Korean Fashion and Textile Research Journal
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• v.18 no.3
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• pp.301-316
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• 2016

Due to the dramatic increase in consumers' price sensitivity and growing importance for global retailers to create relevant price strategies, this study investigates the global pricing strategy of the main SPA brands such as ZARA, H&M and UNIQLO. Based on price information shown on official website, the study developed SPA brand index by using exchange rates in terms of US dollars and ratio of differences between the local price and the US price. These figures were compared with GDP per person data in order to analyze each brand's price level against the income level. The study also compared SPA brand index with Big Mac index to identify the difference in price levels between the fast fashion market and the fast food market. ZARA and H&M were mostly targeting Middle East and Asia as a high-price market when considering index only. After taking the income level into account, however, Asia came out be the highest price market and Middle East was similar to the US market. On the other hand, UNIQLO targeted Asia as the lowest price market and the US and EU as the highest in terms of index only. But, Asia came out to be the highest price zone after considering the income level while the price of the US and EU was reasonable. Comparison with Big Mac Index indicated that most of Asia had a higher price level of the fashion market than the food market, whereas most European countries had a similar or high-price level of food market.

• 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 Distribution Science
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• v.17 no.1
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• pp.11-19
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• 2019

Purpose - We document the impact of economic policy uncertainty (EPU) in the US and China on the dynamic spillover effect of macroeconomics such as stock price, housing price in Korea. Research design, data, and methodology - We use the nine variables to analyze the effect which produces a result among the EPU indexes of the US and China on economic variables which is the consumer price index (CPI), housing purchase price composite index, housing lease price, the stock price index in banking industry, construction industry and distribution industry, and composite leading indicator from January 1995 to December 2016 with the Vector Error Correction Model. Result - The US EPU index has significantly a negative relation on the CPI, housing purchase price index, housing lease price index, the stock price index in banking industry, construction industry, and distribution industry in Korea. Conclusions - We find the dynamic effect of the EPU indexes in the US and China on the macroeconomics returns in Korea. This study has an empirical evidence that the economy market in Korea is influenced by the EPU index of the US rather than it of China. The higher EPU, the more risky the economy of in Korea.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.427-436
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• 2000

The prediction of stock price index is a very difficult problem because of the complexity of the stock market data it data. It has been studied by a number of researchers since they strong1y 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). Fina1ly, 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.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.143-150
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• 2020

This study aims to investigate the effect of oil price and exchange rate on the two Vietnamese stock market indices: VN index and HXN index. This study uses the daily data from August 1^st 2000 to October 25^th 2019 of the two Vietnamese stock indices: VN index and HNX index, the two oil price indices: BRENT and WTI, and the two exchange rates: US dollar to Vietnamese dong and Euro to Vietnamese dong. Due to the presence of heteroskedasticity in our data, we use GARCH (1,1) regression model to perform our analysis. Our findings show that the oil price has a significant positive effect on the two Vietnamese stock market indices. In terms of the stock index volatility, both the VN index and HNX index volatilities are negatively impacted by the return of oil price. While the conclusion about the impact of oil price remained consistent through all three robustness tests, the effect of exchange rate on Vietnamese stock market indices is not consistent. We find thatchanges of the USD/VND exchange rate significantly impact the return and volatility of HNX index only in GARCH (1,1) setting. Our analysis also survives a number of robustness tests.