• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.277-283
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• 2005

In general, stock prices do not follow normal distributions and mean trend indexes, volatility indexes, and volume indicators relating to these non-normal stock price are widely used as buy-sell strategies. These general buy-sell strategies are rather intuitive than statistical reasoning. The non-normality problem can be solved by normalizing process and statistical buy-sell strategy can be obtained by using mean trend and volatility indexes together with normalized stock prices. In this paper, buy-sell strategy based on mean trend and volatility index with normalized stock prices are proposed and applied to KOSPI200 data to see the feasibility of the proposed buy-sell strategy.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.523-531
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• 2021

Vietnam's Oil and gas industry make a significant contribution to the Gross Domestic Product of Vietnam. The ongoing COVID-19 pandemic has hit every industry hard, but perhaps the one industry which has taken the biggest hit is the global oil and gas industry. The purpose of this article is to examine how the COVID-19 pandemic affects the share price of the Vietnam Oil and Gas industry. The event study method applied to Oil and Gas industry index data around three event days includes: (i) The date Vietnam recognized the first patient to be COVID-19 positive was January 23, 2020; (ii) The second outbreak of COVID-19 infection in the community began on March 6, 2020; (iii) The date (30/3/2020) when Vietnam announced the COVID-19 epidemic in the whole territory. This study found that the share price of the Vietnam Oil and Gas industry responded positively after the event (iii) which is manifested by the cumulative abnormal return of CAR (0; 3] = 3.8% and statistically significant at 5 %. In the study, event (ii) has the most negative and strong impact on Oil and Gas stock prices. Events (i) favor negative effects, events (iii) favor positive effects, but abnormal return change sign quickly from positive to negative after the event date and statistically significant shows the change on investors' psychology.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.311-324
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• 2021

Investors have increasingly become interested in macroeconomic antecedents in order to better understand the investment environment and estimate the scope of profitable investment in equity markets. This study endeavors to examine the interdependency between the macroeconomic antecedents (international oil price (COP), Domestic gold price (GP), Rupee-dollar exchange rates (ER), Real interest rates (RIR), consumer price indices (CPI)), and the BSE Sensex and Nifty 50 index return. The data is converted into a natural logarithm for keeping it normal as well as for reducing the problem of heteroscedasticity. Monthly time series data from January 1992 to July 2019 is extracted from the Reserve Bank of India database with the application of financial Econometrics. Breusch-Godfrey serial correlation LM test for removal of autocorrelation, Breusch-Pagan-Godfrey test for removal of heteroscedasticity, Cointegration test and VECM test for testing cointegration between macroeconomic factors and market returns,] are employed to fit regression model. The Indian market returns are stable and positive but show intense volatility. When the series is stationary after the first difference, heteroskedasticity and serial correlation are not present. Different forecast accuracy measures point out macroeconomics can forecast future market returns of the Indian stock market. The step-by-step econometric tests show the long-run affiliation among macroeconomic antecedents.

• Journal of Navigation and Port Research
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• v.46 no.5
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• pp.441-448
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• 2022

The aim of this study was to examine determinants of secondhand Bulk carrier and Oil tanker prices. This study compiled S& P transaction data taken from the Clarksons Research during J anuary 2018 to April 2022 to see how independent variables influenced secondhand ship prices. In the secondhand ship pricing model of entire segments, size, age, and LIBOR showed significant effects on prices. A vessel built in J apan and Korea was traded at a higher price than a vessel built in other countries. In the bulk segment, size, age, Clarksea index, LIBOR, and inflation were meaningful variables. In the Tanker segment, unlike Bulk carrier, only size and age were useful variables. This study performed regression analyses for various sizes of Bulk carriers and Oil tankers. It verified that impacts of variables other than ship size and age were significantly associated with ship type and size while macroeconomic variables had no influence except for bulk carriers. By applying diverse variables affecting secondhand ship price estimation according to various sizes of Bulk carriers and Oil tankers, this study will expand the scope of practical application for investors. It also reaffirms prior research findings that the secondhand ship market is primarily market-driven.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.313-320
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• 2017

Price fluctuation contract is imperative and of paramount essence in the construction industry as it provides adequate relief and cushioning for changes in the prices of input resources during construction. As a result, several methods have been devised to better help in arriving at fair recompense in the event of price chang es. However, stakeholders often appear not to be satisfied with the existing methods of fluctuation evaluation, ostensibly because of the challenges associated with them. The aim of this study was to identify the challenges to usage of building construction cost indices in Ghana. Data was gathered from contractors and quantity surveying firms. The study utilized survey questionnaire approach to elicit responses from the contractors and the consultants. Data gathered was analyzed scientifically, using the Relative Importance Index (RII) to rank the problems associated with the existing methods. The findings revealed the following among others; late release of data; inadequate recovery of costs; and work items of interest not included in the published indices as the main challenges of the existing methods. This study will provide useful lessons for policy makers and practitioners in decision making towards the usage and improvement of available indices.

• Korean System Dynamics Review
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• v.8 no.2
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• pp.175-190
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• 2007

Stock market volatility largely depends on firms' value and growth opportunities. However, with the globalization of world economy, the effect of the synchronization in major countries is gaining its importance. Also, domestically, the business cycle and cash market of the country are additional factors needed to be considered. The main purpose of this research is to attest the application and usefulness of System Dynamics as a general stock market forecasting tool. Throughout this research, System Dynamics suggests a conceptual model for forecasting a KOSPI(Korea Composite Stock Price Index), taking the factors of the composite stock price indexes in traditional researches. In conclusion of this research, System Dynamics was proved to bean appropriate model for forecasting the volatility and direction of a stock market as a whole. With its timely adaptability, System Dynamic overcomes the limit of traditional statistic models.

• Korean Journal of Agricultural Science
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• v.39 no.1
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• pp.43-51
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• 2012

Hanwoo, Korean cattle, is traditionally an important livestock to farmers and also important meat to consumers. Recently, to make more efficient production system and provide cheaper and high-quality hanwoo meat, the scale-up via brand is emphasized. However, the price of hanwoo is getting higher and the price increase is considered to occur due to marketing margins in the distribution process. In this study, factors affecting the hanwoo marketing margin are analyzed using 2004-2009 monthly data. In the Cochrane-Orcutt estimation of marketing margin, hanwoo production, other domestic production and import, income, output index, market share of major retailers, and market share of hanwoo brands shows statistical significance in the result. The results shows basic factors of the marketing margins to hanwoo and gives some implications to the management system of hanwoo brand like a sophisticated market segmentation and a differentiated promotion.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.7
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• pp.381-386
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• 2003

To provide a specified power quality under electric market system is becoming an important issue for customers and utility company. However, there is no realistic infra-structure to design a power system for the specified power quality. Present electric market is operating under the economic point of view. The low power price could be attractive, but the effect of low price could result the lower power quality for the long time and threat power system security. This paper presents a model which conceptualize the dynamic power quality control mechanism to minimize total cost of a society which is affected electric power quality. This model aims to produce a basic infra-structure to balance cost and quality under the electric market system.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.355-366
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• 2006

Customer Satisfaction (CS) in Auto insurance market is the important factor which makes customer loyalty and retention increase. Recently On-line companies are threatening the existing Off-line companies with taking advantage of the low price through cut-offing the price by internet marketing. Therefore, the CS is becoming an indispensable survival strategy to the Off-line companies. Under these circumstances, this study finds out what the CS factors are in the auto insurance market, and produces levels of CS, customer loyalty and satisfaction Index of each category. The purpose of this study is to suggest the strategic improvement factor for elevating CS level and strategic direction for CS management by CS portfolio analysis based on the survey result.

• Journal of information and communication convergence engineering
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• v.18 no.3
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• pp.167-175
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• 2020

The net income per unit area from crop production could be the most critical consideration for agricultural producers during cultivation planning. This paper proposes a scheme for predicting the net income per unit area based on machine learning and related calculations. This scheme predicts rice production and operation costs by applying climate and price index data. The rice price is also predicted by applying rice production and operation cost data. Finally, these predicted results are employed to calculate the predicted net income, which is compared with the actual net income. Consequently, the proposed scheme shows a meaningful degree of conformity, which indicates the potential of machine learning for predicting various aspects of agricultural production.