• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.121-145
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• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.

• Journal of the Korean Society of Clothing and Textiles
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• v.32 no.12
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• pp.1915-1926
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• 2008

Although we know that images of a country or an industry are influential factors in product evaluation, there has been little discussion about the channels through which consumers form a country or an industry image. Guided by the assumption that the image of the Korean fashion industry conveyed through U.S. media will likely affect the evaluation of Korean fashion products, we decided to examine articles published between January 1998 and June 2008 in Woman's Wear Daily(WWD), a prestigious U.S. daily trade newspaper covering all aspects of the national and international fashion business. By using the "Korean", we found 329 relevant articles. Through content analysis, we identified the aspects of the Korean fashion industry that have been considered salient to U.S. fashion media professionals. We set up categories based on the contents of the articles that discussed segments of the supply chain of the fashion industry. We found more comments on the Korean fashion industry as fiber and fabrics supplier or apparel manufacturer than in any other categories, which reflects that South Korea has been traditionally one of the most attractive sourcing countries for the U.S. fashion business. We identified significantly less coverage on the designing, branding, marketing, and retailing aspects of the Korean fashion industry. Due to economic boom in Korea, the country's fashion industry is recognized as having a highly fashion-conscious market that can afford the world's premium brands. However, the industry is viewed as being rather vulnerable to changes in the macro economic environment.

• Journal of Distribution Science
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• v.11 no.10
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• pp.73-79
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• 2013

Purpose - This study examines the impact of oil price volatility on economic activities in Korea. The new millennium has seen a deregulation in the crude oil market, which invited immense capital inflow into Korea. It has also raised oil price levels and volatility. Drawing on the recent theoretical literature that emphasizes the role of volatility, this paper attends to the asymmetric changes in economic growth in response to the oil price movement. This study further examines several key macroeconomic variables, such as interest rate, production, and inflation. We come to the conclusion that oil price volatility can, in some part, explain the structural changes. Research design, data, and methodology - We use two methodological frameworks in this study. First, in regards to the oil price uncertainty, we use an Exponential-GARCH (Exponential Generalized Autoregressive Conditional Heteroskedasticity: EGARCH) model estimate to elucidate the asymmetric effect of oil price shock on the conditional oil price volatility. Second, along with the estimation of the conditional volatility by the EGARCH model, we use the estimates in a VECM (Vector Error Correction Model). The study thus examines the dynamic impacts of oil price volatility on industrial production, price levels, and monetary policy responses. We also approximate the monetary policy function by the yield of monetary stabilization bond. The data collected for the study ranges from 1990: M1 to 2013: M7. In the VECM analysis section, the time span is split into two sub-periods; one from 1990 to 1999, and another from 2000 to 2013, due to the U.S. CFTC (Commodity Futures Trading Commission) deregulation on the crude oil futures that became effective in 2000. This paper intends to probe the relationship between oil price uncertainty and macroeconomic variables since the structural change in the oil market became effective. Results and Conclusions - The dynamic impulse response functions obtained from the VECM show a prolonged dampening effect of oil price volatility shock on the industrial production across all sub-periods. We also find that inflation measured by CPI rises by one standard deviation shock in response to oil price uncertainty, and lasts for the ensuing period. In addition, the impulse response functions allude that South Korea practices an expansionary monetary policy in response to oil price shocks, which stems from oil price uncertainty. Moreover, a comparison of the results of the dynamic impulse response functions from the two sub-periods suggests that the dynamic relationships have strengthened since 2000. Specifically, the results are most drastic in terms of industrial production; the impact of oil price volatility shocks has more than doubled from the year 2000 onwards. These results again indicate that the relationships between crude oil price uncertainty and Korean macroeconomic activities have been strengthened since the year2000, which resulted in a structural change in the crude oil market due to the deregulation of the crude oil futures.

• Information Systems Review
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• v.18 no.2
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• pp.173-198
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• 2016

Financial distress can damage stakeholders and even lead to significant social costs. Thus, financial distress prediction is an important issue in macroeconomics. However, most existing studies on building a financial distress prediction model have only considered idiosyncratic risk factors without considering systematic risk factors. In this study, we propose a prediction model that considers both the idiosyncratic risk based on a financial ratio and the systematic risk based on a business cycle. Ultimately, we build several IT artifacts associated with financial ratio and add them to the idiosyncratic risk factors as well as address the imbalanced data problem by using an oversampling technique and synthetic minority oversampling technique (SMOTE) to ensure good performance. When considering systematic risk, our study ensures that each data set consists of both financially distressed companies and financially sound companies in each business cycle phase. We conducted several experiments that change the initial imbalanced sample ratio between the two company groups into a 1:1 sample ratio using SMOTE and compared the prediction results from the individual data set. We also predicted data sets from the subsequent business cycle phase as a test set through a built prediction model that used business contraction phase data sets, and then we compared previous prediction performance and subsequent prediction performance. Thus, our findings can provide insights into making rational decisions for stakeholders that are experiencing an economic crisis.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.1-27
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• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.