• Fashion, Industry and Education
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• v.14 no.2
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• pp.47-59
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• 2016

This research is concerned with comparing fluctuation in the Korean and the US economies in order to ascertain the degree to which the former is influenced by changes in the latter. The aim of this research is to explore business cycles, to examine consumer expenditure in Korea and the US, and to discover the relationships between business fluctuation indexes and overall expenditure. Statistical data from the national statistics of Korea and the US during period from 1990 to 2015 were used. The instrument included a measure of GDP, unemployment rates, GDP deflator rate (inflation rates), and household income and expenditure. For the average annual household expenditures, food, apparel and transportation expenditure data were compared across the two countries. Data were collected separately from different (though comparable) sources and were analyzed using relatively straight forward statistical techniques. It was found that Korean and the US consumers' income and expenditure were greatly affected by economic fluctuations. Total expenditure and the expenditures for food and transportation were much influenced by business fluctuation in the US, whereas, the expenditures for apparel were much influenced by business fluctuation in Korea.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.363-370
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• 2021

This study aims to revisit the issues and misconceptions about financial inclusion (FI) indices. For indices construction, this study uses two approaches: one approach following the methodology of Sarma (2008) which is based on UNDP methodology, while the other is the Dynamic Factor Model (DFM)-based index of Stock and Watson (2002) and Rehman et al. (2021). The data of 18 economies of Asia from 1997 till 2017 is used for indices construction and analysis. The authors constructed macro and micro-level financial inclusion indices based on the different types of financial inclusion indicators. Second, the authors have critically evaluated two different approaches, and the results show that Sarma (2008)-based index show financial inclusion's level, while DFM-based index reveal fluctuation in the current year's financial inclusion level due to the prior variations. For measuring the level of financial inclusion, the Sarma (2008) index is effective, while for forecasting the level of financial inclusion, the DFM approach is more appropriate. Furthermore, the micro and macro aspects of financial inclusion should be reflected in separate indices for better understanding and in-depth insights.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.5
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• pp.779-803
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• 2023

The growth of digital news media and the stock price index has resulted in economic fluctuations in the fashion industry. This study examines the impact of fashion industry news and macroeconomic changes on the Textile & Wearing Apparel KOSPI over the past five years. An auto-regressive integrated moving average exogenous time series model was conducted using the fashion industry stock market index, the news topic index, and macro-economic indicators. The results indicated the topics of "Cosmetic business expansion" and "Digital innovation" impacted the Textile & Wearing Apparel KOSPI after one week, and the topics of "Pop-up store," "Entry into the Chinese fashion market," and "Fashion week and trade show" affected it after two weeks. Moreover, the topics of "Cosmetic business expansion" and "Entry into the Chinese fashion market" were statistically significant in the macroeconomic environment. Regarding the effect relation of Textile & Wearing Apparel KOSPI, "Cosmetic business expansion," "Entry into the Chinese fashion market," and consumer price fluctuation showed negative effects, while the private consumption change rate, producer price fluctuation, and unemployment change rate had positive effects. This study analyzes the impact of media framing on fashion industry business cycles and provides practical insights into managing stock market risk for fashion companies.

• The Journal of Fisheries Business Administration
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• v.26 no.2
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• pp.75-92
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• 1995

This paper tried to estimate the pass - through of exchange rate changes to export price of fishery products using export price function. The results are as follows : 1) The variable of fluctuation of exchange rate of Won(equation omitted) to Yen(equation omitted)(variable E2) is more powerful explanatory variable than that of Won to U.S. dollar to explain the fluctiation of export price of fishery products(varible $P_{t}$)- 2) The variable of fish catches(variable K $P_{t}$) is also found to be a statistically significant varible but that of producer price index is not found. 3) The variable E2 have statistically a more influence on variable $P_{t}$ than variable K $P_{t.}$ 4) The estimation shows us that 1% of fluctuation of variable E2 could result in 0.9978% of fluctuation of variable $P_{t.}$

• Korean Journal of Construction Engineering and Management
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• v.14 no.5
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• pp.103-112
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• 2013

After 1990s, the influence of construction industry has been decreased on national economy and construction business condition has been changed on economic recession and boom repeatedly. Larger fluctuation of business condition makes a forecast of it to be more difficult. Uncertainty in business prediction results in damages on construction companies and stakeholders. Therefore, study on forecasting a construction business is very important. This study suggests the Construction Industry Production Index(CIPI) to predict a construction business in consider of temperature effects. The results show that construction business is much influenced by temperature effects certainly and GDP. With the CBFM, this study examines CIPI for 2013 with two scenarios: 1)with GDP growth rate of 3.5% 2)with GDP growth rate of 2.4%. Thus, CIPI would be used as the economic state index to display the construction business conditions. Also, CIPI will be utilized as basic methodology in the impact of climate change in the construction industry.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.285-294
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• 2022

Using the ARDL approach, this study examined the impact of macro factors on Vietnam's stock market in the short and long run from 2010 to 2021. The State Bank of Vietnam and the International Monetary Fund provided time series data for this study. Research results show that in the long run, money supply and exchange rate respectively affect the stock market. The money supply had a positive effect on the VN-Index, while the exchange rate showed the opposite effect. However, the study did not find a relationship between world oil price and interest rates on VN-Index in the long run. On the other hand, in the short term, there are relationships between variables; specifically, interest rates and exchange rates have a negative impact on the VN-Index, while the world oil price and the fluctuation of money supply M2 of the previous one and two months showed an impact in the same direction on this index. The differences in the regression results on the impact of exchange rate and oil price on the VN-Index compared to previous studies come from the characteristics of Vietnam's stock market, with the large capitalization of companies in the oil and gas sector, and the structure of Vietnam's economy with export heavily depends on FDI sector.

• Journal of Distribution Science
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• v.12 no.6
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• pp.49-57
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• 2014

Purpose - The world now recognizes environmental disruption as a serious issue when regarding growth-oriented strategies; therefore, environmental preservation issues become pertinent. Consequently, green distribution is continuously emphasized. However, studying the prediction and association of distribution and the environment is insufficient. Most existing studies about green distribution are about its necessity, detailed operation methods, and political suggestions; it is necessary to study the distribution service industry and environmental service industry together, for green distribution. Research design, data, and methodology - ARIMA (auto-regressive moving average model) was used to predict the environmental service and distribution service industries, and the Granger Causality Test based on VAR (vector auto regressive) was used to analyze the causal relationship. This study used 48 quarters of time-series data, from the 4th quarter in 2001 to the 3rd quarter in 2013, about each business type's production index, and used an unchangeable index. The production index about the business type is classified into the current index and the unchangeable index. The unchangeable index divides the current index into deflators to remove fluctuation. Therefore, it is easy to analyze the actual production index. This study used the unchangeable index. Results - The production index of the distribution service industry and the production index of the environmental service industry consider the autocorrelation coefficient and partial autocorrelation coefficient; therefore, ARIMA(0,0,2)(0,1,1)4 and ARIMA(3,1,0)(0,1,1)4 were established as final prediction models, resulting in the gradual improvement in every production index of both types of business. Regarding the distribution service industry's production index, it is predicted that the 4th quarter in 2014 is 114.35, and the 4th quarter in 2015 is 123.48. Moreover, regarding the environmental service industry's production index, it is predicted that the 4th quarter in 2014 is 110.95, and the 4th quarter in 2015 is 111.67. In a causal relationship analysis, the environmental service industry impacts the distribution service industry, but the distribution service industry does not impact the environmental service industry. Conclusions - This study predicted the distribution service industry and environmental service industry with the ARIMA model, and examined the causal relationship between them through the Granger causality test based on the VAR Model. Prediction reveals the seasonality and gradual increase in the two industries. Moreover, the environmental service industry impacts the distribution service industry, but the distribution service industry does not impact the environmental service industry. This study contributed academically by offering base line data needed in the establishment of a future style of management and policy directions for the two industries through the prediction of the distribution service industry and the environmental service industry, and tested a causal relationship between them, which is insufficient in existing studies. The limitations of this study are that deeper considerations of advanced studies are deficient, and the effect of causality between the two types of industries on the actual industry was not established.

• The Research Journal of the Costume Culture
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• v.2 no.1
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• pp.167-186
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• 1994

The purpose of his study is to investigate the relationship between the business cycle and the fashion of silhouette from 1956 to 1992. Correlation analysis an regression analysis were used to investigate the relation of them. In this study, the coincident composite index was used as business cycle and change of skirt in length and width, collar and pants in width wee thoroughly checked through graphs and photographs. The results of analysis are as follows. 1. When the economy is to ascend, the skirts are short and narrow. On the country, when the economy is descend, they are long and wide. 2. The business cycle gives influence on skirts line and with, that is, about 18%, 33% of total changes. 3. In change of fashion, skirts length and width had significant positive correlation and they showed a tendency to move together. On the other hand, the change of collar and patterns in width have no connection with business fluctuation. 4. The change of fashion is affected by the movement of itself. According to analysis that includes the trend of skirts, about 50%, and 35% of changes in skirts length and width were decided by them,. and about 52% and 35% of change in collar and patterns width were decided by them.

• Asian Journal of Business Environment
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• v.3 no.2
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• pp.17-22
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• 2013

Purpose - This dissertation is based on previous research, and analyzes processing trade, which constitutes a major section of foreign trade in Shandong Province. Research design, data, and methodology - The study uses the survey data on polarization, which is a vital index reflecting the unbalanced growth of regional economic development. The article introduces the processing trade polarization index, and the processing trade polarization fluctuation rate, to predict the geographical polarization posture and development trends in Shandong Province. Results -The development of processing trade in Shandong Province shows the level of gradient from east to west. The first-line growth pole has been formed and developed, and the initial formation of the diffusion mechanism has taken place. However, coordination problems in accompanying regional development have become increasingly prominent. Conclusions - This study focuses on the development of processing trade strategy and suggests overall coordination of development objectives, using non-balanced development goals. According to regional characteristics and development objectives of the processing trade in Shandong Province, the region around the city is divided into innovation diffusion region, enhanced growth areas, areas expected to undertake development, and areas to upgrade in four levels, given the different policy proposals.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.