• The Journal of Asian Finance, Economics and Business
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• 제8권12호
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• pp.271-282
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• 2021

In recent years, a significant number of empirical studies have examined the relationship between export and economic growth in India. However, this study analyses the relationship between exports and economic growth through the time series model. The main aim of this study is to investigate the causal relationship between exports and economic growth in India. The VAR model was used for the period 1961 to 2015 after verifying the stationarity of the variables through using Augmented Dickey-Fuller and Phillip-Perron tests. The Indian export sector has been found to have a significant and positive impact on economic growth and other long-term economic activities. The study also employed the Granger causality test to check the direction of causality and found that RXGS, RGDP, RPFC, and RGFC had a unidirectional relationship and RXGS and RMGS had a bidirectional relationship in long run. Also, the findings of this study suggest that a steady-state between exports and economic growth can be achieved in India over a long period. The overall outcome of this study provides a testimony of the fact that the export sector plays a vital role in economic growth in India and also leads to the long-term growth of other economic activities.

• The Journal of Asian Finance, Economics and Business
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• 제8권9호
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• pp.165-176
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• 2021

The paper has used the Solow-Swan growth model to analyze the long-term impact of credit market development and stock market development on economic growth in Asia from 2000 to 2019. The empirical model is performed with panel cointegration analysis by Common Correlated Effects (CCE) method with cross-sectional dependencies. The results find that there exists a cointegration relationship among stock market, credit market development, and economic growth. These results also show that financial structure improves the exact impact of financial development on economic growth, namely the opposite effect of stock market development and credit market development. Moreover, the Granger causality test reveals a bi-directional relationship between credit market development and economic growth, while only unidirectional causality from stock market development to economic growth for the whole group panel. And it is different for a specific country, according to Kónya's test. The view of the new structuralism does not apply in the Asian financial system when we estimate the Nonlinear Autoregressive Distributed Lag model (NARDL) to analyze the asymmetric relationship between financial structure and economic growth. On the whole, policymakers can draw on the findings to provide policy implications to improve their country's financial system as well as pursue the goal of sustainable economic growth.

• The Journal of Asian Finance, Economics and Business
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• 제8권5호
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• pp.325-334
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• 2021

This study aims to investigate whether economic growth is elevated by banking industry development in Jordan. The study adopts time-series econometric methodologies, which comprise the bounds testing approach within the autoregressive distributed lag (ARDL) and the conditional causality analysis. Consistent with the assumptions of the adopted methodology, the study utilized annual time-series data for a relatively long period of thirty-nine years, between 1980 and 2018. The empirical results show that Jordan's economic growth is strongly responsive in respect to any changes in banking industry development. Also, the results reveal the harmful impact of rising lending interest rate; as this rate increases, economic growth will decrease. The findings are in line with the conceptual arguments of the supply-leading hypothesis, which confirmed that banking development is considered as one of the main pillars that have stimulating effects on economic growth. The evidence of the current study may provide important implications for policymakers and bankers. Those professionals should work to maintain a stable regulatory system that enhances the banking system function in activating economic growth. Also, a considerable focus should be placed on designing a steady interest rate policy to avoid the inherently undesirable impacts of high-interest rates on the Jordanian economy.

• The Journal of Asian Finance, Economics and Business
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• 제8권4호
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• pp.1-9
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• 2021

UAE has rapidly developed into one of the leading global financial hubs, with significant transformations in its stock exchanges. In its attempt at economic diversification in the last two decades, the country has also taken a lead in the GCC region in introducing extensive reforms to attract FDI to the Emirates. However, oil price volatilities have posed a significant challenge to all oil-exporting countries. The main aim of this study is to explore the impact of economic diversification and oil price on the UAE stock market. The study applies Granger Causality and Vector Autoregressive Model on monthly Abu Dhabi stock exchange index, Dubai Fateh crude oil spot price, and FDI inflows during 2001-19. The short-term interbank rate has been included as a monetary policy variable. The results show a substantial difference between the two phases of reforms. Oil price and Abu Dhabi stock index show bidirectional relationship during 2001-09 but no causality was found during 2010-19. Furthermore, the second phase was characterized by unidirectional causation from FDI to ADX index. This study highlights FDI inflows as a key driver of stock market performance during the last decade and emphasizes the success of the intense reforms in the UAE initiated for the diversification of its economy.

• The Journal of Asian Finance, Economics and Business
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• 제8권3호
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• pp.191-199
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• 2021

The study examines the causal relationship between financial development and economic growth through trade openness for the leading ASEAN countries (Indonesia, the Philippines, Malaysia, Singapore, Thailand, and Vietnam). The study employs a panel data for the period of 25 years spanning from 1995 to 2015 for the six countries, yielding a balanced panel of 150 observations. Fixed effect model (FEM) and random effect model (REM) are used for the panel data, following the Hausman test performed for model selection. The trivariate Granger causality test is also used to check for possible relationship between the variables. The results show that REM is chosen based on the Hausman test result, suggesting that the trade openness has a positive association with growth whereas the financial development is positively, but insignificantly associated with growth. The reason for this is that the financial development and economic growth may be related to each other. The results are, then, further explored and confirmed by the causality test. That is, the financial development and the economic growth, through the trade openness, are found to have bidirectional positive relationships. This implies that there would be shortcomings when ignoring the presence of trade openness, which positively impacts the relationship between finance and growth.

• The Journal of Asian Finance, Economics and Business
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• 제9권1호
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• pp.13-24
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• 2022

The main goal of this study is to look at how South Korea can catch up to the rest of the world through policy-driven structural change and manufacturing revolutions. To achieve the objective, this study used annual data on real exports and real GDP from the World Development Indicator WDI of South Korea for the period 1960 to 2019. The study's goal is to use econometrics to detect this policy-driven structural change trend. Multiple nonlinear Granger causality test was used to accomplish this. The findings revealed structural breaks and nonlinearities in the dynamic link between South Korea's real GDP and real exports. Furthermore, results also show evidence of multiple structural breaks in South Korean data. South Korea's economic catch-up was the result of a constant reevaluation of industrial policies, readjustment, and structural change to constantly explore and utilize comparative advantage, realizing economies of scale at the global level, and reallocating and redistribution of resources towards productive sectors with high value-added output, according to econometric analysis. If South Korea would have not done this structural change this miracle to escape the middle-income trap would not have been possible. These findings support the descriptive evidence of structural change in favor of manufacturing revolutions and value addition industry development in South Korea.

• 유통과학연구
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• 제11권10호
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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.

• 토지주택연구
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• 제13권4호
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• pp.45-53
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• 2022

본 연구에서는 '아파트 가격'과 '인터넷 검색량' 간의 관계를 분석하였다. 선행 논문들이 '공표된 아파트 가격'과 '인터넷 검색량' 간의 관계만을 검정했던 것에 비해, 본 논문은 '공표된 아파트 가격'을 '본질적 가격 요소'와 '일시적 가격 요소'로 구분하여 '본질적 가격 요소와 인터넷 검색량' 간의 관계, '일시적 가격 요소와 인터넷 검색량' 간의 관계에 대해서도 분석했다는 것이 선행 연구들과의 차별적 측면이다. Granger 인과관계 분석 결과를 보면, '공표된 아파트 가격'과 '인터넷 검색량'이 서로 양방향의 Granger 인과관계를 갖는 것으로 나타났다. 선행논문들에서 연구가 이루어지지 않았던 부분으로서, 아파트 가격의 추세 요소인 '아파트 본질적 가격 요소'도 '인터넷 검색량'과 피드백적 관계를 보였다. 그리고 '아파트 일시적 가격 요소'는 '인터넷 검색량'에 대해 선행관계를 갖는 것으로 나타났다. 아파트 일시적 가격 요소도 인터넷 검색량과 관계가 있다는 것은 아파트시장 참여자들의 '일시적 심리적 측면, 과잉반응에 의해 발생되는 가격 요소'도 인터넷 검색량에 영향을 준다는 것을 의미한다. 본 연구 결과는 아파트 가격의 움직임이 시장참여자들의 관심에 영향을 준다는 의미를 제시하며, 부동산시장 분석 등에 있어서 가격의 움직임, 인터넷 검색량과 같은 자료를 활용해야 한다는 의미를 갖고 있다.

• 한국전자거래학회지
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• 제26권3호
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• pp.33-53
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• 2021

제4차 산업혁명의 확산과 코로나 19의 장기화로 인한 사회적 변화의 기로에서 한국 정부는 2020년 7월 디지털 뉴딜 정책을 발표했다. 디지털 뉴딜 정책은 데이터, 네트워크, 인공지능 기술을 중심으로 공공분야 및 산업의 디지털 전환을 가속화함으로써 새로운 비즈니스를 창출하는 것을 주요 과제로 삼고 있다. 그러나 급변하는 사회환경에서 기술의 미래 이익에 대한 정보비대칭은 정책의 방향과 효과에 대한 대중의 분석 능력의 차이를 야기할 수 있으며, 이로 인해 정책의 실질적 효과에 대한 불확실성이 발생하게 된다. 한편, 언론은 정부 정책을 대중에 전파하는 전달자 역할을 통해 담론 형성을 주도하며, 보도를 통해 특정 이슈에 대한 제반 지식을 대중에게 제공하는 역할을 한다. 즉, 특정 정책에 대한 언론의 보도량이 증가할수록 이슈 집중도는 높아지며, 이를 통해 대중의 의사결정에도 영향을 미치게 된다. 따라서 본 연구의 목적은 한국 정부의 디지털 뉴딜 정책에 대한 언론 보도량과 주식 시장의 동태적 관계를 그랜저 인과관계(Granger causality), 충격반응함수, 분산분해분석을 이용하여 검증하는 것이다. 이를 위해 디지털 뉴딜 정책에 대한 언론 보도량, 키워드 검색량과 KOSDAQ 상장 기업 중 디지털 뉴딜 정책과 관련이 있는 디지털 기술 기반 기업들의 일일주식회전율, 일일주가수익률, EWMA 변동성을 변수로 설정하였으며, 정책발표 시점 전후 60 거래일, 총 120 거래일 간의 데이터를 이용했다. 분석 결과, 언론 보도량은 키워드 검색량, 일일주식회전율, EWMA 변동성과 양방향 그랜저 인과관계가 존재하였으며, 언론 보도량의 증가는 디지털 뉴딜 정책에 대한 키워드 검색량에 높은 영향을 미치는 것으로 나타났다. 또한 언론 보도량에 대한 충격반응분석 결과 EWMA 변동성을 큰 폭으로 하락시키는 양상을 보였으며, 시간이 지날수록 영향력이 점차 증가하며 주식 시장의 변동성을 완화시키는 역할을 하는 것으로 나타났다. 본 연구의 분석 결과를 토대로 디지털 뉴딜에 대한 언론 보도량은 주식 시장과 유의한 동태적 관계가 있음을 확인할 수 있었다.

• Asia pacific journal of information systems
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• 제24권2호
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.