• 한국컴퓨터정보학회논문지
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• 제13권5호
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• pp.95-102
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• 2008

'국가 주요 정보통신 기반시설과 목표시설'로 지정되어 있는 증권전산통신망의 차세대 네트워크화 준비는 국가경쟁력 강화와 국가경제력 향상을 위해 반드시 필요하다. 본 논문은 현재의 정부정책과 네트워크, 보안 및 증권전산망을 연구하고, 기존의 SONET/SDH기반의 증권전산망을 대체하여, 차세대 증권전산망은 국가간 GMG서비스를 위한 MPLS 기반의 ALL-IP서비스를 제공하도록 설계한다. 설계 시에 안정성, 표준화, 보안성 확장성의 기준을 설정하여, 현재와 차세대 증권전산망을 각 기준별로 비교한다. 또한 차세대 증권전산망 특징과 장점 및 대역폭과 QoS, 통신기술, 정보보호시스템 등에서 향상된 기대효과를 분석한다. 본 논문의 연구결과는 u-Korea를 이룩하는 국가경쟁력 강화와 국가경제력 향상에 기여할 것이다.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1201-1210
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• 2019

Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to have a direct influence on the stock markets globally. Given that the stock price data often contain both linear and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however, it provides an accurate and effective way to process autocorrelation and non-stationary data in time series forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price of the Shanghai composite index and Shenzhen component index.

• 경영과학
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• 제28권1호
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• pp.53-60
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• 2011

Time series analysis methods have been traditionally used in stock price prediction. However, most of the existing methods represent some methodological limitations in reflecting influence from external factors that affect the fluctuation of stock prices, such as oil prices, exchange rates, money interest rates, and the stock price indexes of other countries. To overcome the limitations, we propose a network based method incorporating the relations between the individual company stock prices and the external factors by using a graph-based semi-supervised learning algorithm. For verifying the significance of the proposed method, it was applied to the prediction problems of company stock prices listed in the KOSPI from January 2007 to August 2008.

• International journal of advanced smart convergence
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• 제8권2호
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• pp.132-139
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• 2019

We address the problem about forecasting the direction of stock price movement in the Korea market. Recently, the deep neural network is popularly applied in this area of research. In deep neural network systems, proper parameter initialization reduces training time and improves the performance of the model. Therefore, in our study, we propose a novel parameter initialization technique and apply this technique for the stock price movement prediction model. Specifically, we design a framework which consists of two models: a base model and a main prediction model. The base model constructed with LSTM is trained by using the large data which is generated by a large amount of the stock data to achieve optimal parameters. The main prediction model with the same architecture as the base model uses the optimal parameter initialization. Thus, the main prediction model is trained by only using the data of the given stock. Moreover, the stock price movements can be affected by other related information in the stock market. For this reason, we conducted our research with two types of inputs. The first type is the stock features, and the second type is a combination of the stock features and the Korea Composite Stock Price Index (KOSPI) features. Empirical results conducted on the top five stocks in the KOSPI list in terms of market capitalization indicate that our approaches achieve better predictive accuracy and F1-score comparing to other baseline models.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제3권1호
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• pp.7-12
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• 2003

In recent years, many attempts have been made to predict the behavior of bonds, currencies, stock or other economic markets. Most previous experiments used the neural network models for the stock market forecasting. The KOSPI 200 (Korea Composite Stock Price Index 200) is modeled by using different neural networks and fuzzy logic. In this paper, the neural network, the dynamic polynomial neural network (DPNN) and the fuzzy logic employed for the prediction of the KOSPI 200. The prediction results are compared by the root mean squared error (RMSE) and scatter plot, respectively. The results show that the performance of the fuzzy system is little bit worse than that of the DPNN but better than that of the neural network. We can develop the desired fuzzy system by optimization methods.

• International Journal of Computer Science & Network Security
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• 제22권2호
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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.

• International Journal of Computer Science & Network Security
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• 제23권4호
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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%.

• 지능정보연구
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• 제2권2호
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• pp.43-54
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• 1996

Financial markets are operating 24 hours a day throughout the world and interrelated in increasingly complex ways. Telecommunications and computer networks tie together markets in the from of electronic entities. Financial practitioners are inundated with an ever larger stream of data, produced by the rise of sophisticated database technologies, on the rising number of market instruments. As conventional analytic techniques reach their limit in recognizing data patterns, financial firms and institutions find neural network techniques to solve this complex task. Neural networks have found an important niche in financial a, pp.ications. We a, pp.y neural networks to Standard and Poor's (S&P) 500 stock index futures trading to predict the futures marker behavior. The results through experiments with a commercial neural, network software do su, pp.rt future use of neural networks in S&P 500 stock index futures trading.

• 경영과학
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• 제29권2호
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• pp.167-184
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• 2012

Business diversification is inevitable to survive under the current competitive business environments. The advent of new businesses makes corporate governance more complicated through corporate combinations. Recent introduction of new accounting standard, International Financial Reporting Standards(IFRS), accelerates the need for corporate governance analysis. This study analyses the complex corporate governance system and its relation to the business performance using social network analysis. Corporate inter-governance networks can be visualized easily in a social network diagram. 552 corporate governance data are empirically analysed in the Korean stock market. The changes in In-Degree between networks are positively related with the changes in corporate sales volume. We can find the same results using operating profits as corporate performance proxy. The results show that social network analysis technique can be applied to investments in the stock markets.

• 기술혁신학회지
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• 제2권3호
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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.