• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.3
• /
• pp.977-985
• /
• 2021

The main aim of the study is to provide empirical evidence about the association between stock market exchange data and weighted price index. This research utilized monthly reported data from the Amman stock exchange market (ASE) and the Central Bank of Jordan (CBJ). The weighted price index was employed as the dependent variable and the independent variables were weighted price index (WPI), turnover ratio (TOR), number of trading days (NTD), price-earnings ratio (PER), and dividends yield ratio (DY). The time period of the study was from January 2015 to October 2020. The study's methodology follows a quantitative approach using the multiple regression method to test the hypotheses of the study. The final results of the study provided conclusive evidence that the market-weighted price index is strongly and positively correlated to three predetermined variables, namely; turnover ratio, price-earnings ratio, and dividend yield but no evidence was obtained for the effect of the number of trading days. The finding of the current study proved that the market price index is not only influenced by macro factors, but also by other variables assumed to not beneficial for the judgment of price index movements.

• Korean Journal of Construction Engineering and Management
• /
• v.16 no.2
• /
• pp.46-53
• /
• 2015

Electrical construction cost index is composed of the cost of albor and material. The producer price index is used to the cost of material. The Bank of Korea restructured the formation method and the basic period of the producer price index in 2013. Because fixed-weighted method can't faithfully reflect industrial structure changes. The weighted value and price index of fixed-weighted method is fixed on the basicp eriod. Electrical construction cost index is changed from fixed-weighted method to chain-weighted method in september 2014, because of these on the need. But the change of organization in formation method changes the weighted value. So there is the need of analysis about the statistical continuity of electrical construction cost index. This study is focused on the time series analysis between fixed-weighted and chain-weighted electrical construction cost index. We uses unit root test, cointegration test, regression analysis of long and short term equation, fitness for the estimation of static forecast as time series analysis. We verify that chain-weighted electrical construction cost index can be replaced to fixed-weighted construction cost index accounting analyses result. So users of it recognize that chain-weighted electrical construction cost index has statistical continuity.

• Korean Journal of Construction Engineering and Management
• /
• v.15 no.5
• /
• pp.49-60
• /
• 2014

Electrical construction cost index has been applied fixed-weighted method. But fixed-weighted method can't faithfully reflect industrial structure changes. Because the weighted value and price index of fixed-weighted method is fixed on the basic period. Electrical construction cost index is composed of the cost of labor and material. So it fluctuates sharply whenever the construction association of korea announces the laborer's wage of electrical construction. And it depends on only the producer price index changes that is related to electrical construction since then. So a study is focused on developing electrical construction cost index applied chain-weighted method. Because chain-weighted method can reflect the realities of the electrical construction and alleviate the sudden changes of labor cost with link index. We verify that chain-weighted method relieves the step states of electrical construction cost index applied fixed-weighted method.

• Journal of the Korean Statistical Society
• /
• v.8 no.2
• /
• pp.139-163
• /
• 1979

Utilization of the aggregation concept applied in economics has been a traditional way of describing the state of an economic system and of predicting the future economic conditions. In addition, certain aggregate variables have also played a crucial role as indicators of the business cycle. Quick examples would be the price index, the productivity changes, the industrial production index, GNP, and so on. The methods of aggregation could be either simple summations, like GNP, or sophisticated weighted average, like the price index.

• Korean Management Science Review
• /
• v.24 no.2
• /
• pp.115-126
• /
• 2007

Weekly minima of daily log returns of Korean composite stock price index 200 and its five industry-based business divisions over the period from January 1990 to December 2005 are fitted using two block-based extreme distributions: Generalized Extreme Value(GEV) and Generalized Logistic(GLO). Parameters are estimated using the probability weighted moments. Applicability of two distributions is investigated using the Monte Carlo simulation based empirical p-values of Anderson Darling test. Our empirical results indicate that both the GLO and GEV models seem to be comparably applicable to the weekly minima. These findings are against the evidences in Gettinby et al.[7], who claimed that the GEV model was not valid in many cases, and supported the significant superiority of the GLO model.

• KIPS Transactions on Software and Data Engineering
• /
• v.8 no.3
• /
• pp.123-128
• /
• 2019

This paper proposes the stock price prediction based on the artificial intelligence, where the model with recurrent convolution neural network (RCNN) layers is adopted. In the motivation of this prediction, long short-term memory model (LSTM)-based neural network can make the output of the time series prediction. On the other hand, the convolution neural network provides the data filtering, averaging, and augmentation. By combining the advantages mentioned above, the proposed technique predicts the estimated stock price of next day. In addition, in order to emphasize the recent time series, a custom weighted loss function is adopted. Moreover, stock data related to the stock price index are adopted to consider the market trends. In the experiments, the proposed stock price prediction reduces the test error by 3.19%, which is over other techniques by about 19%.

• Journal of Internet Computing and Services
• /
• v.9 no.1
• /
• pp.129-135
• /
• 2008

This paper presents a methodology to forecast KOSPI index by extracting fuzzy rules based on the neural network with weighted fuzzy membership functions (NEWFM) and the minimized number of input features using the distributed non-overlap area measurement method. NEWFM classifies upward and downward cases of KOSPI using the recent 32 days of CPPn,m (Current Price Position of day n for n-1 to n-m days) of KOSPI. The five most important input features among CPPn,m and 38 wavelet transformed coefficients produced by the recent 32 days of CPPn,m are selected by the non-overlap area distribution measurement method. For the data sets, from 1991 to 1998, the proposed method shows that the average of forecast rate is 67.62%.

• Journal of Industrial Convergence
• /
• v.21 no.1
• /
• pp.87-93
• /
• 2023

Determining the timing of buying and selling in stock investment is one of the most important factors to increase the return on stock investment. Buying low and selling high makes a profit, but buying high and selling low makes a loss. The price is determined by the quantity of buying and selling, which determines the price of a stock, and buying and selling is also related to corporate performance and economic indicators. The fear and greed index provided by CNN uses seven factors, and by assigning weights to each element, the weighted average defined as greed and fear is calculated on a scale between 0 and 100 and published every day. When the index is close to 0, the stock market sentiment is fearful, and when the index is close to 100, it is greedy. Therefore, we analyze the trading criteria that generate the maximum return when buying and selling the US S&P 500 index according to CNN fear and greed index, suggesting the optimal buying and selling timing to suggest a way to increase the return on stock investment.

• East Asian Economic Review
• /
• v.27 no.3
• /
• pp.213-242
• /
• 2023

This paper investigates whether the price of cryptocurrency is determined by the US dollar index, the price of investment assets such gold and oil, and the implied volatility of the KOSPI. Overall, the returns on cryptocurrencies are best predicted by the trading volume of the cryptocurrency both in-sample and out-of-sample. The estimates of gold and the dollar index are negative in the return prediction, though they are not significant. The dollar index, gold, and the cryptocurrencies seem to share characteristics which hedging instruments have in common. When investors take notice of the imminent market risks, they increase the demand for one of these assets and thereby increase the returns on the asset. The most notable result in the out-of-sample predictability is the predictability of the returns on value-weighted portfolio by gold. The empirical results show that the restricted model fails to encompass the unrestricted model. Therefore, the unrestricted model is significant in improving out-of-sample predictability of the portfolio returns using gold. From the empirical analyses, we can conclude that in-sample predictability cannot guarantee out-of-sample predictability and vice versa. This may shed light on the disparate results between in-sample and out-of-sample predictability in a large body of previous literature.

• Management & Information Systems Review
• /
• v.37 no.3
• /
• pp.27-47
• /
• 2018

In this study, we examine how land use zoning affects the land price controlling other variables such as road-facing condition of the land, land form, land age after its development and land size. We employ geographically weighted regression analysis which reflects spatial dependency as methodology with a data sample of land transaction price data of Jangyu, a new town, in Korea. The results of our empirical analysis show that the respective coefficients of traditional regression and geographically weighted regression are not significantly different. However, after calculating Moran's Index with residuals of both OLS and GWR models, we find that Moran's Index of GWR decreases around 26% compared to that of OLS model, thus improving the problem of spatial autoregression of residuals considerably. Unlike our expectation, though, in both traditional regression and geographically weighted regression where residential exclusive area is used as a reference variable, the dummy variable of the residential land for both housing and shops shows a negative sign. This may be because the residential land for both housing and shops is usually located in the level area while the residential exclusive area is located at the foot of a mountain or on a gentle hill where the residents can have good quality air and scenery. Although the utility of the residential land for both housing and shops is higher than its counterpart's since it has higher floor area ratio, amenity which can be explained as high quality of air and scenery in this study seems to have higher impact in purchase of land for housing. On the other hand, land for neighbourhood living facility seems to be valued higher than any other land zonings used in this research since it has much higher floor area ratio than the two land zonings above and can have a building with up to 5 stories constructed on it. With regard to road-facing condition, land buyers seem to prefer land which faces a medium-width road as expected. Land facing a wide-width road may have some disadvantage in that it can be exposed to noise and exhaust gas from cars and that entrance may not be easy due to the high speed traffic of the road. In contrast, land facing a narrow road can be free of noise or fume from cars and have privacy protected while it has some inconvenience in that entrance may be blocked by cars parked in both sides of the narrow road. Finally, land age variable shows a negative sign, which means that the price of land declines over time. This may be because decline of the land price of Jangyu was bigger than that of other regions in Gimhae where Jangyu, a new town, also belong, during the global financial crisis of 2008.