• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.1-8
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• 2022

This work provides a reliable and classified stocks dataset merged with Saudi stock news. This dataset allows researchers to analyze and better understand the realities, impacts, and relationships between stock news and stock fluctuations. The data were collected from the Saudi stock market via the Corporate News (CN) and Historical Data Stocks (HDS) datasets. As their names suggest, CN contains news, and HDS provides information concerning how stock values change over time. Both datasets cover the period from 2011 to 2019, have 30,098 rows, and have 16 variables-four of which they share and 12 of which differ. Therefore, the combined dataset presented here includes 30,098 published news pieces and information about stock fluctuations across nine years. Stock news polarity has been interpreted in various ways by native Arabic speakers associated with the stock domain. Therefore, this polarity was categorized manually based on Arabic semantics. As the Saudi stock market massively contributes to the international economy, this dataset is essential for stock investors and analyzers. The dataset has been prepared for educational and scientific purposes, motivated by the scarcity of data describing the impact of Saudi stock news on stock activities. It will, therefore, be useful across many sectors, including stock market analytics, data mining, statistics, machine learning, and deep learning. The data evaluation is applied by testing the data distribution of the categories and the sentiment prediction-the data distribution over classes and sentiment prediction accuracy. The results show that the data distribution of the polarity over sectors is considered a balanced distribution. The NB model is developed to evaluate the data quality based on sentiment classification, proving the data reliability by achieving 68% accuracy. So, the data evaluation results ensure dataset reliability, readiness, and high quality for any usage.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.2286-2291
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• 2011

Technology on the rolling stock is very composition and system engineering. We have to consider to many parameters on the rolling stock design. High speed train is interface between rolling stock and infra system of rail, signal, communication etc. For many years testing operation has experienced substantial growth based on various advanced new technology. Recently some problems was clearly stated on effective management, practical use and testing operation of the the rolling stock on the technology change. This paper presents some results of the study on necessary requirements of the operation testing for rolling stock technology change. We propose a general plan to suggest the operation testing and the parameters of the rolling stock on the technology change in the text.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.103-108
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• 2021

It is difficult to predict an increase or decrease of stock price because of uncertainty. Researches for prediction of stock price using AI technology have been done for a long time. Recently stock buy/sell recommend programs called by Robot Advisor using AI machine learning technology are used. In this paper, to develop a stock buy/sell recommend system using AI technology, an core engine of this system is designed. An analysis and design method of a stock buy/sell recommend system software using AI machine learning technology will be presented by showing user requirement analysis using object-oriented analysis method, flowchart and screen design.

• Journal of Korea Technology Innovation Society
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• v.6 no.3
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• pp.328-344
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• 2003

This paper is aimed at analyzing the effect of Information Technology (IT) investment on the output growth and Total Factor Productivity (TFP) of Korean stock industry. Data on 24 stock firms for the eleven years (1991-2001) are used for the analysis. It is identified that there are both direct and indirect impacts of IT investment of the Korean stock industry on output growth. The total effect on output growth is 1.34 percentage point per year, which divided into a direct effect of investment in IT on the output growth is 1.97 and an indirect effect on the TFP is -0.63 percentage points per year. Results show that IT investment cannot contribute to increased stock industry productivity. Therefore, the Korean stock industry has not benefited from increased investment on IT in increasing productivity, implying the so-called productivity paradox has existed during the period.

• Journal of Korea Technology Innovation Society
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• v.2 no.3
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• pp.145-160
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• 1999

This study analyzed the stock brokers to find how they use the automatic stock trading through internet based on a comparison of U.S.A and Korea. And we reviewed the changing appearance of internet stock trading with one year time lag. The internet stock trading in Korea had been started in 1998 but it was a common trading just after one year.

• Journal of Information Technology Applications and Management
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• v.17 no.3
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• pp.151-162
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• 2010

Recent developments in financial market liberalization and information technology are accelerating the interdependence of national stock markets. This study explores the information spillover effect of the US stock market on the overnight and daytime returns of the Korean stock market. We develop a profitable intra-day trading strategy based on the information spillover effect. Our study provides several important conclusions. First, an information spillover effect still exists from the overnight US stock market to the current Korean stock market. Second, Korean investors overreact to both good and bad news overnight from the US. Therefore, there are significant price reversals in the KOSPI 200 index futures prices from market open to market close. Third, the overreaction effect is different between weekdays and weekends. Finally, the suggested intra-day trading system based on the documented overreaction hypothesis is profitable.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.166-171
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• 2023

The Stock price analysis is an increasing concern in a financial time series. The purpose of the study is to analyze the price parameters of date, high, low, and news feed about the stock exchange price. Long short term memory (LSTM) is a cutting-edge technology used for predicting the data based on time series. LSTM performs well in executing large sequence of data. This paper presents the Long Short Term Memory Model has used to analyze the stock price ranges of 10 days and 20 days by exponential moving average. The proposed approach gives better performance using technical indicators of stock price with an accuracy of 82.6% and cross entropy of 71%.

• Proceedings of the KSR Conference
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• 2003.10a
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• pp.144-150
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• 2003

This paper shows the systematic design technology for development of new high speed rolling stock and the important item by the instance of G7 R＆D project titled "Development of High Speed Railway Technology". This paper also describes core technology and concrete examples of its application during the development of high speed rolling stock.

• Culinary science and hospitality research
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• v.13 no.4
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• pp.293-304
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• 2007

Using the high-pressure heating extraction process suitable for mass-production, crab stock(CS), red crab stock(RCS) and fish stock(FS) were manufactured and their quality and organic features were compared. The results are as follows. In mineral content, the content of essential amino acids, the content of delicious amino acids and preference, CS and RCS were rated higher than FS, and especially RCS had the most abundant essential amino acid content and had the highest rating in organic evaluation. Also, in terms of cost, RCS was more economical than regular CS, which is relatively expensive. Therefore, it is believed that replacing fish bones with RCS in making stock will be more cost-saving and have higher preference.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.6
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• pp.543-552
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• 2021

The study examines how foreign capital inflows affect stock market development in Pakistan for the period from July 2008 to June 2018. Several components of foreign capital inflows were used for empirical analysis, namely, foreign direct investment, foreign portfolio investment, and remittances. Further, market capitalization was used as a proxy for stock market development. The study uses an ARDL model for examining the long-run and short-run relationships between variables. We also analyze the bi-directional causality between the variables through the Granger causality test. Further, the presence of structural breaks was analyzed through the CUSUM and CUSUM Square test. The results suggest that in the long run, remittances have a positive and significant relationship with stock market development. However, foreign direct investment, foreign portfolio investment, and USD-PKR exchange rate do not have a significant impact on stock market development. The results also suggest that in the short run there is a negative relationship between FDI, USD-PKR exchange rate and market capitalization. Contrarily, we found a positive relationship between FPI and market capitalization. The results of Granger causality test suggest that remittances and USD-PKR exchange rate have a causal relationship with stock market development. Finally, we found no evidence of structural breaks in the dataset.