• 산업경영시스템학회지
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• 제41권3호
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• pp.154-161
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• 2018

This study conducted to predict the stock market prices based on the assumption that internet news articles might have an impact and effect on the rise and fall of stock market prices. The internet news articles were tested to evaluate the accuracy by comparing predicted values of the actual stock index and the forecasting models of the companies. This paper collected stock news from the internet, and analyzed and identified the relationship with the stock price index. Since the internet news contents consist mainly of unstructured texts, this study used text mining technique and multiple regression analysis technique to analyze news articles. A company H as a representative automobile manufacturing company was selected, and prediction models for the stock price index of company H was presented. Thus two prediction models for forecasting the upturn and decline of H stock index is derived and presented. Among the two prediction models, the error value of the prediction model (1) is low, and so the prediction performance of the model (1) is relatively better than that of the prediction model (2). As the further research, if the contents of this study are supplemented by real artificial intelligent investment decision system and applied to real investment, more practical research results will be able to be developed.

• Asia pacific journal of information systems
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• 제30권1호
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• pp.31-52
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• 2020

Stock markets are popular investment avenues to people who plan to receive premium returns compared to other financial instruments, but they are highly volatile and risky due to the complex financial dynamics and poor understanding of the market forces involved in the price determination. A system that can forecast, predict the stock prices and automatically create a portfolio of top performing stocks is of great value to individual investors who do not have sufficient knowledge to understand the complex dynamics involved in evaluating and predicting stock prices. In this paper the authors propose a Stock prediction, Portfolio Generation and Selection model based on Machine learning algorithms, Artificial neural networks (ANNs) are used for stock price prediction, Mathematical and Statistical techniques are used for Portfolio generation and Un-Supervised Machine learning based on K-Means Clustering algorithms are used for Portfolio Evaluation and Selection which take in to account the Portfolio Return and Risk in to consideration. The model presented here is limited to predicting stock prices on a long term basis as the inputs to the model are based on fundamental attributes and intrinsic value of the stock. The results of this study are quite encouraging as the stock prediction models are able predict stock prices at least a financial quarter in advance with an accuracy of around 90 percent and the portfolio selection classifiers are giving returns in excess of average market returns.

• 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.

• The Journal of Asian Finance, Economics and Business
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• 제8권8호
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• pp.399-407
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• 2021

Stock movement is difficult to predict because it has dynamic characteristics and is influenced by many factors. Even so, there are some approaches to predict stock price movements, namely technical analysis, fundamental analysis, and sentiment analysis. Many researches have tried to predict stock price movement by utilizing these analysis techniques. However, the results obtained are varied and inconsistent depending on the variables and object used. This is because stock price movement is influenced by a variety of factors, and it is likely that those studies did not cover all of them. One of which is that no research considers the use of fundamental analysis in terms of currency exchange rates and the use of foreign stock price index movement related to the technical analysis. This research aims to predict stock price movements in Indonesia based on sentiment analysis, technical analysis, and fundamental analysis using Support Vector Machine. The result obtained has a prediction accuracy rate of 65,33% on an average. The inclusion of currency exchange rate and foreign stock price index movement as a predictor in this research which can increase average prediction accuracy rate by 11.78% compared to the prediction without using these two variables which only results in average prediction accuracy rate of 53.55%.

• 한국컴퓨터정보학회논문지
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• 제25권12호
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• pp.35-44
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• 2020

본 연구에서는 주가 결정 방법이 주가 경향 예측에 미치는 영향을 확인하기 위한 분석을 수행한다. 주식시장에서 성공적인 투자를 위해서는 주가의 상승과 하락을 정확하게 예측하는 것이 큰 도움이 되므로 주가 경향 예측에 관해 많은 연구가 진행되고 있다. 예를 들어 근래에는 SNS나 뉴스의 내용을 텍스트 마이닝을 이용하여 분석하고, 이를 이용한 주가 등락의 예측 방법이 제안되었으며 다양한 기계학습 기법들이 활용되고 있다. 그러나 주가의 경향을 '상승' 또는 '하락'으로 결정하는 방법은 제대로 분석된 적 없으며 일반적으로 쓰던 방법을 답습하고 있다. 이에 본 논문에서는 주가 경향 결정 방법을 이동평균을 이용해 일반화하고 주가 경향 결정 방법이 예측 정확도에 미치는 영향을 분석한다. 분석 결과, 다음 날의 주가 경향을 예측하는 경우, 주가 경향 결정방법에 따라 예측 정확도가 47%까지 차이가 남을 발견하였다. 또한 경향 결정에 사용되는 기준값 윈도우의 크기와 예측의 정확도는 비례 관계이며, 대상값 윈도우의 크기와 정확도는 반비례 관례임을 알 수 있었다.

• 한국정보과학회논문지:시스템및이론
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• 제37권6호
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• pp.354-367
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• 2010

시계열 데이터에서 패턴을 분석하는 기법은 많은 발전이 이루어져 오고 있다. 그러나 주식시장의 경우 시계열 데이터임에도 불구하고 패턴 분석 및 예측은 많은 연구가 이루어지지 않고 있으며 예측도가 매우 낮다. 그 이유는 주가의 등락 자체가 본질적으로 무작위하다고 하면 어떠한 과학적 방법으로도 그 예측은 불가능하다. 본 연구에서는 주가의 등락이 보여주는 무작위성의 정도를 Kolmogorov 복잡도를 이용해 측정하여 그 무작위의 정도와 본 논문에서 제시한 반 전역정렬(semi-global alignment)로 예측할 수 있는 주가의 예측의 정확간의 깊은 상관관계가 있음을 보인다. 이를 위해서 주가지수의 등락을 양자화된 문자열로 변환하고 그 문자열의 Kolmogorov 복잡도를 이용해 주가 변동의 무작위성을 측정하였다. 우리는 KOSPI 주식 데이터 28년 690개의 데이터를 수집하여 이를 실험용 데이터로 사용하여 본 논문에서 제시한 방법의 의미를 평가하였다. 그 결과 Kolmogorov 복잡도가 높은 경우에는 변동 예측이 어려우며, Kolmogorov 복잡도가 낮은 경우에는 주식 변동 예측은 가능하나 3종류의 예측율에 대해서 투자자들이 관심이 많은 등락 예측율은 단기 예측은 12% 이상의 예측율을 보일 수 없으며, 장기 예측의 경우 54%의 예측율로 수렴함을 확인하였다.

• 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.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1231-1242
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• 2019

This paper presents a two-dimensional attention-based long short-memory (2D-ALSTM) model for stock index prediction, incorporating input attention and temporal attention mechanisms for weighting of important stocks and important time steps, respectively. The proposed model is designed to overcome the long-term dependency, stock selection, and stock volatility delay problems that negatively affect existing models. The 2D-ALSTM model is validated in a comparative experiment involving the two attention-based models multi-input LSTM (MI-LSTM) and dual-stage attention-based recurrent neural network (DARNN), with real stock data being used for training and evaluation. The model achieves superior performance compared to MI-LSTM and DARNN for stock index prediction on a KOSPI100 dataset.

• The Journal of Asian Finance, Economics and Business
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• 제8권1호
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• pp.1-6
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• 2021

This research examines the stock market index determinants and the prediction using the FFT curve fitting of the Jakarta Stock Exchange (JKSE) Composite Index during the COVID-19 pandemic. This paper has used daily data of Jakarta Stock Exchange (JKSE) Composite Index, interest rate, and exchange rate from 15 October 2019 to 15 September 2020, and a total of 224 observations, retrieved from Indonesia Stock Exchange (IDX), Indonesia Statistics Central Bureau and Observation & Research of Taxation. The study covers descriptive statistics, multicollinearity test, hypothesis tests, determination test, and prediction using FFT curve fitting. The results unveil four fresh and robust evidence. Partially, the interest rate has affected positively and significantly the stock market index. Partially, the exchange rate has affected negatively and significantly the stock market index. The F-test result, interest rate, and exchange rate have significantly affected the stock market index (JKSE) simultaneously. Furthermore, the FFT curve fitting has predicted that the stock market fluctuates and increases over time. The results have shown a strong influence of the independent variables and the dependent variable. The value of Adjusted R-Square is 0.719, which means that the independent variables have simultaneously impacted the dependent variable for 71.9%; other factors have influenced the remaining 28.1%.

• 한국콘텐츠학회논문지
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• 제18권9호
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• pp.623-632
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• 2018

주가 이동 방향의 정확한 예측이 주식 매매에 관한 전략적 의사결정에 중요한 역할을 할 수 있기 때문에 투자자와 연구자 모두의 관심이 높다. 주가 이동 방향에 관한 기존 연구들을 종합해보면, 주식 시장에 따라서 그리고 예측 기간에 따라서 다양한 변수가 고려되고 있음을 알 수 있다. 이 연구에서는 한국 주식 시장을 대표하는 지수와 주식들을 대상으로 이동 방향 예측 기간에 따라서 어떤 데이터마이닝 기법의 성능이 우수한 것인지를 분석하고자 하였다. 특히, 최근 공개경쟁에서 활발히 사용되며 그 우수성이 입증되고 있는 익스트림 그라디언트 부스팅 기법을 주가 이동 방향 예측 문제에 적용하고자 하였으며, SVM, 랜덤 포리스트, 인공 신경망과 같이 기존 연구에서 우수한 것으로 보고된 데이터마이닝 기법들과 비교하여 분석하였다. 12년간 데이터를 사용하여 1일 후에서 5일 후까지의 이동 방향을 예측하는 실험을 통해서, 예측 기간과 종목에 따라서 선택된 변수들에 차이가 있으며, 1-4일 후 예측에서는 익스트림 그라디언트 부스팅이 다른 기법들과 부분적으로 동등함을 가지면서도 가장 우수함을 확인하였다.