• 한국콘텐츠학회논문지
• /
• 제13권11호
• /
• pp.48-58
• /
• 2013

주식시장은 시간에 따라 계속 변하고 특별한 이유 없이 주가가 급등하거나 급락하는 사건들이 발생하기도 한다. 이런 이유로 주식시장은 복잡계로 인식되고 있으며 주가 변동을 예측하는 것은 어려운 일이다. 이 논문에서는 주식시장을 개별 주식들의 네트워크로 이해하고 시간에 따라 변하는 한국 주식시장 네트워크를 분석하였다. 코스피200 지수를 구성하는 137개 회사의 주식들을 대상으로 주식 사이의 상관관계를 측정한 결과 주식 간 상관관계가 매우 높을 때 주가가 급락하는 경향이 있는 것으로 나타났다. 또한, 우리는 이러한 네트워크 분석 결과를 바탕으로 주식 포트폴리오를 구성하는 방법을 제안한다. 제안 방법으로 구성된 포트폴리오의 효율성을 보이기 위해 실제 주식들을 대상으로 모의 투자 실험을 수행하였고, 마코위츠의 효율적 포트폴리오 구성 알고리즘을 이용해 구성한 포트폴리오의 수익률과 비교하였다. 실험 결과 제안 방법으로 구성된 포트폴리오는 평균적으로 약 10.6%의 수익률을 보였으며, 같은 기간 마코위츠의 효율적 포트폴리오의 수익률보다 약 3.7% 높으며, 코스피200 수익률보다 약 5.6% 정도 높게 나타났다.

• International Journal of Computer Science & Network Security
• /
• 제22권2호
• /
• pp.185-192
• /
• 2022

Forecasting and time series modelling plays a vital role in the data analysis process. Time Series is widely used in analytics & data science. Forecasting stock prices is a popular and important topic in financial and academic studies. A stock market is an unregulated place for forecasting due to the absence of essential rules for estimating or predicting a stock price in the stock market. Therefore, predicting stock prices is a time-series problem and challenging. Machine learning has many methods and applications instrumental in implementing stock price forecasting, such as technical analysis, fundamental analysis, time series analysis, statistical analysis. This paper will discuss implementing the stock price, forecasting, and research using prophet and LSTM models. This process and task are very complex and involve uncertainty. Although the stock price never is predicted due to its ambiguous field, this paper aims to apply the concept of forecasting and data analysis to predict stocks.

• 한국경영과학회지
• /
• 제23권4호
• /
• pp.213-224
• /
• 1998

Since the late 1980s, an Increasing number of neural network models have been studied in the areas of financial prediction and analysis. The purpose of this study is to Investigate the possibility of building a neural network model that is able to construct a profitable trading strategy in the Korean Stock Market. This study classifies stocks into the future market winners and losers from the publicly available accounting information and builds portfolios based on this information. The performances of the winner portfolios and the loser portfolios are compared with each other and against the market index. The empirical result of this research is consistent with the traditional fundamental analysis where it is claimed that the financial statements contain firm values that may not be fully reflected In stock prices without delay. Despite the supporting empirical evidence. It is somewhat Inconclusive as to whether or not the abnormal return in excess of market return is the result of the extra knowledge obtained in the neural network models derived from the historical accounting data. This research attempts to open another avenue using neural network models for searching for evidence against market efficiency where statistics and intuition have played a major role.

• International journal of advanced smart convergence
• /
• 제8권2호
• /
• pp.132-139
• /
• 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 Computer Science & Network Security
• /
• 제22권2호
• /
• pp.1-8
• /
• 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.

• Journal of Information Technology Applications and Management
• /
• 제19권1호
• /
• pp.1-12
• /
• 2012

Data mining is the process of searching and analyzing large quantities of data for finding out meaningful patterns and rules. Artificial Neural Network (ANN) is one of the tools of data mining which is becoming very popular in forecasting the future values. Some of the areas where it is used are banking, medicine, retailing and fraud detection. In finance, artificial neural network is used in various disciplines including stock market forecasting. In the stock market time series, due to high volatility, it is very important to choose a model which reads volatility and forecasts the future values considering volatility as one of the major attributes for forecasting. In this paper, an attempt is made to develop two models - one using feed forward back propagation Artificial Neural Network and the other using Autoregressive Conditional Heteroskedasticity (ARCH) technique for forecasting stock market returns. Various parameters which are considered for the design of optimal ANN model development are input and output data normalization, transfer function and neuron/s at input, hidden and output layers, number of hidden layers, values with respect to momentum, learning rate and error tolerance. Simulations have been done using prices of daily close of Sensex. Stock market returns are chosen as input data and output is the forecasted return. Simulations of the Model have been done using MATLAB$^{(R)}$ 6.1.0.450 and EViews 4.1. Convergence and performance of models have been evaluated on the basis of the simulation results. Performance evaluation is done on the basis of the errors calculated between the actual and predicted values.

• 경영과학
• /
• 제29권2호
• /
• pp.167-184
• /
• 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.

• Asia pacific journal of information systems
• /
• 제30권4호
• /
• pp.719-740
• /
• 2020

The stock market changes continuously as new information emerges, affecting the judgments of investors. Online news articles are valued as a traditional window to inform investors about various information that affects the stock market. This paper proposed new ways to utilize online news articles with technical indicators. The suggested hybrid model consists of three models. First, a self-attention-based convolutional neural network (CNN) model, considered to be better in interpreting the semantics of long texts, uses news content as inputs. Second, a self-attention-based, bi-long short-term memory (bi-LSTM) neural network model for short texts utilizes news titles as inputs. Third, a bi-LSTM model, considered to be better in analyzing context information and time-series models, uses 19 technical indicators as inputs. We used news articles from the previous day and technical indicators from the past seven days to predict the share price of the next day. An experiment was performed with Korean stock market data and news articles from 33 top companies over three years. Through this experiment, our proposed model showed better performance than previous approaches, which have mainly focused on news titles. This paper demonstrated that news titles and content should be treated in different ways for superior stock price prediction.

• International Journal of Computer Science & Network Security
• /
• 제24권2호
• /
• pp.113-123
• /
• 2024

Stock market news sentiment analysis (SA) aims to identify the attitudes of the news of the stock on the official platforms toward companies' stocks. It supports making the right decision in investing or analysts' evaluation. However, the research on Arabic SA is limited compared to that on English SA due to the complexity and limited corpora of the Arabic language. This paper develops a model of sentiment classification to predict the polarity of Arabic stock news in microblogs. Also, it aims to extract the reasons which lead to polarity categorization as the main economic causes or aspects based on semantic unity. Therefore, this paper presents an Arabic SA approach based on the logistic regression model and the Bidirectional Encoder Representations from Transformers (BERT) model. The proposed model is used to classify articles as positive, negative, or neutral. It was trained on the basis of data collected from an official Saudi stock market article platform that was later preprocessed and labeled. Moreover, the economic reasons for the articles based on semantic unit, divided into seven economic aspects to highlight the polarity of the articles, were investigated. The supervised BERT model obtained 88% article classification accuracy based on SA, and the unsupervised mean Word2Vec encoder obtained 80% economic-aspect clustering accuracy. Predicting polarity classification on the Arabic stock market news and their economic reasons would provide valuable benefits to the stock SA field.

• 한국컴퓨터정보학회논문지
• /
• 제22권11호
• /
• pp.111-116
• /
• 2017

This paper proposes a data mining approach to predicting stock price direction. Stock market fluctuates due to many factors. Therefore, predicting stock price direction has become an important issue in the field of stock market analysis. However, in literature, there are few studies applying data mining approaches to predicting the stock price direction. To contribute to literature, this paper proposes comparing single classifiers and ensemble classifiers. Single classifiers include logistic regression, decision tree, neural network, and support vector machine. Ensemble classifiers we consider are adaboost, random forest, bagging, stacking, and vote. For the sake of experiments, we garnered dataset from Korea Stock Exchange (KRX) ranging from 2008 to 2015. Data mining experiments using WEKA revealed that random forest, one of ensemble classifiers, shows best results in terms of metrics such as AUC (area under the ROC curve) and accuracy.