• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.73-84
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• 2020

The study aims to investigate the dual effects of corruption control on economic growth in relationship with the stock market and trade openness in developing countries. The study used difference S-GMM method on the dynamic panel data model in the period (2002-2017) with data collected from the World Bank. The study discovers the dominant impacts of corruption control in the relationship with the stock market on economic growth. At the same time, the study also confirms the overwhelming impact of corruption control in the relationship between trade openness and economic growth in the developing countries. In addition, the study shows that inefficient stock markets in developing countries will not promote economic growth. Meanwhile, the long-standing credit market has a positive impact on economic growth. With the strong development of stock market and trade openness in the period (2002-2017), control on corruption in developing countries does not get better in time with the increase in demand. The findings of this study suggest a number of solutions to strengthen corruption control, leading to the increased efficiency on the stock market and as well as encouraging the positive effects of trade openness to contribute to promoting economic growth in developing countries.

• Current Research on Agriculture and Life Sciences
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• v.31 no.2
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• pp.113-123
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• 2013

The carbon-offset mechanism based on forest management has been recognized as a meaningful tool to sequestrate carbons already existing in the atmosphere. Thus, with an emphasis on the forest-originated carbon-offset mechanism, the accurate measurement of the carbon stock in forests has become important, as carbon credits should be issued proportionally with forest carbon stocks. Various remote sensing techniques have already been developed for measuring forest carbon stocks. Yet, despite the efficiency of remote sensing techniques, the final accuracy of their carbon stock estimations is disputable. Therefore, minimizing the uncertainty embedded in the application of remote sensing techniques is important to prevent questions over the carbon stock evaluation for issuing carbon credits. Accordingly, this study reviews the overall procedures of carbon stock evaluation-related remote sensing techniques and identifies the problematic technical issues when measuring the carbon stock. The procedures are sub-divided into four stages: the characteristics of the remote sensing sensor, data preparation, data analysis, and evaluation. Depending on the choice of technique, there are many disputable issues in each stage, resulting in quite different results for the final carbon stock evaluation. Thus, the establishment of detailed standards for each stageis urgently needed. From a policy-making perspective, the top priority should be given to establishinga standard sampling technique and enhancing the statistical analysis tools.

• 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.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.

• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.169-176
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• 2009

Rule discovery is an operation that discovers patterns frequently occurring in a given database. Rule discovery makes it possible to find useful rules from a stock database, thereby recommending buying or selling times to stock investors. In this paper, we discuss storage structures for efficient processing of queries in a system that recommends stock investments. First, we propose five storage structures for efficient recommending of stock investments. Next, we discuss their characteristics, advantages, and disadvantages. Then, we verify their performances by extensive experiments with real-life stock data. The results show that the histogram-based structure improves the query performance of the previous one up to about 170 times.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2759-2769
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• 2018

The behavioral finance view on the existence of asset pricing anomalies is based on two factors: investors' sentiment and limits to arbitrage. This paper tries to examine the effect of investors' sentiment on the stock price in the Korean stock market. In order to measure investors' sentiment, we constructed the sentiment index using principal component of five sentiment variables. By using sentiment index as an additional independent variable to three risk factors, impacts of the sentiment index on individual stocks and 25 portfolios sorted by BM-size are examined. Main results found are as follows: 1) not only all three risk factors show positive impacts on the return of individual stock, but also the sentiment index has a positive impact. SI alone explains 15% of individual return variation. 2) among four independent variables, the most important factor turned out to be the market risk factor and investors' sentiment has better explanatory power on stock price than the size effect. 3) after controlling the market risk factor, the coefficient of the sentiment index for the smallest size and highest book/market value portfolios is significantly positive. 4) all the coefficients of the sentiment index for 25 portfolios sorted by BM-size have significant positive value after controlling size or (and) value.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.11-19
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• 2021

This study aims to analyze the causality relationship between sectoral stock markets (agricultural, financial, industrial, and mining sectors) and economic growth in the short and long term as well as to analyze whether it has similar types or not. The data used is quarterly time-series data (first quarter 2009 to fourth 2019). To determine the causality relationship, this study conducts a variable and multivariate causality test. The results of the varying granger causality test show that there is only a one-way relationship, where the economic growth of the agriculture sector affects its shares. A one-way relationship also occurs in stocks of the industrial sector, which has an influence on economic growth. The multivariate causality test shows that the economic growth of the agricultural sector has a two-way causality relationship, and it also exists between the industrial sector and the financial sector stock markets. The two-way causality relationship between the stock market and sectoral economic growth is a convergence towards long-term equilibrium. The findings of this study suggest that the government through the Financial Services Authority and the Indonesia Stock Exchange have to maintain stability in the stock market as a supporter of the national economy.

• The Journal of Asian Finance, Economics and Business
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• v.2 no.3
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• pp.17-22
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• 2015

This paper examines the effect of impulsiveness of euro on Indian stock market. In order to examine the problem, we select rupee-euro exchange rates and S&P CNX NIFTY and BSE30 SENSEX to represent stock price. We select euro as it considered as second most widely used currency at the international level after dollar. The data are collected a daily basis over a period of 3-Apr-2007 to 30-Mar-2012. The statistical and time series properties of each and every variable have examined using the conventional unit root such as ADF and PP test. Adopting a generalized autoregressive conditional heteroskedasticity (GARCH) and exponential GARCH (EGARCH) model, the study suggests a negative relationship between exchange rate and stock prices in India. Even though India is a major trade partner of European Union, the study couldn't find any significant statistical effect of fluctuations in Euro-rupee exchange rates on stock prices. The study also reveals that shocks to exchange rate have symmetric effect on stock prices and exchange rate fluctuations have permanent effects on stock price volatility in India.

• 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.

• The Journal of Information Technology and Database
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• v.6 no.1
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• pp.73-88
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• 1999

There have been a series of debates to determine whether it would be possible to forecast dynamic systems such as stock markets. Recently the introduction of chaos theory has allowed many researchers to bring back this issue. Their main concern was whether the behavior of stock markets is chaotic or not. These studies, however, present divergent opinions on this question, depending upon the method applied and the data used. And the issue of predictability based on the nonlinear, chaotic nature was not dealt extensively. This paper is to test the nonlinear nature of the Korea stock market and accordingly attempts to predict its behavior. The result indicates that our stock market represents a chaotic behavior. We also found out based on our simulation that executing buy/sell transactions based upon forecasts which were derived using the local approximation method outperforms the decision of holding without a buy/sell transaction.