• 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.

• 대한산업공학회지
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• 제39권6호
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• pp.535-545
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• 2013

The goal of stock investment is earning high rate or return with stability. To accomplish this goal, using a portfolio that distributes stocks with high rate of return with less variability and a stock price prediction model with high accuracy is required. In this paper, three methods are suggested to require these conditions. First of all, in portfolio re-balance part, Max-Return and Min-Risk (MRMR) model is suggested to earn the largest rate of return with stability. Secondly, Entering/Leaving Rule (E/L) is suggested to upgrade portfolio when particular stock's rate of return is low. Finally, to use outstanding stock price prediction model, a model based on Semi-Supervised Learning (SSL) which was suggested in last research was applied. The suggested methods were validated and applied on stocks which are listed in KOSPI200 from January 2007 to August 2008.

• 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.

• 대한임베디드공학회논문지
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• 제17권1호
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• pp.51-58
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• 2022

Various studies have been conducted from the past to the present because stock price forecasts provide stability in the national economy and huge profits to investors. Recently, there have been many studies that suggest stock price prediction models using various input data such as macroeconomic indicators and emotional analysis. However, since each study was conducted individually, it is difficult to objectively compare each method, and studies on their impact on stock price prediction are still insufficient. In this paper, the effect of input data currently mainly used on the stock price is evaluated through the predicted value of the deep learning model and the error rate of the actual stock price. In addition, unlike most papers in emotional analysis, emotional analysis using the news body was conducted, and a method of supplementing the results of each emotional analysis is proposed through three emotional analysis models. Through experiments predicting Microsoft's revised closing price, the results of emotional analysis were found to be the most important factor in stock price prediction. Especially, when all of input data is used, error rate of ensembled sentiment analysis model is reduced by 58% compared to the baseline.

• 한국정보전자통신기술학회논문지
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• 제11권2호
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• pp.204-208
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• 2018

본 논문에서는 시계열 데이터인 주가의 변동 패턴을 학습하고, 주가 가격을 예측하기 적합한 주가 예측 딥러닝 모델을 제시하고 평가하였다. 일반신경망에 시계열 개념이 추가되어 은닉계층에 이전 정보를 기억시킬 수 있는 순환신경망이 시계열 데이터인 주가 예측 모델로 적합하다. 순환신경망에서 나타나는 기울기 소멸문제를 해결하며, 장기의존성을 유지하기 위하여, 순환신경망의 내부에 작은 메모리를 가진 LSTM을 사용한다. 또한, 순환신경망의 시계열 데이터의 직전 패턴 기반으로만 학습하는 경향을 보이는 한계를 해결하기 위하여, 데이터의 흐름의 역방향에 은닉계층이 추가되는 양방향 LSTM 순환신경망을 이용하여 주가예측 모델을 구현하였다. 실험에서는 제시된 주가 예측 모델에 텐서플로우를 이용하여 주가와 거래량을 입력 값으로 학습을 하였다. 주가예측의 성능을 평가하기 위해서, 실제 주가와 예측된 주가 간의 평균 제곱근 오차를 구하였다. 실험결과로는 단방향 LSTM 순환신경망보다, 양방향 LSTM 순환신경망을 이용한 주가예측 모델이 더 작은 오차가 발생하여 주가 예측 정확성이 향상되었다.

• 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.

• Asia pacific journal of information systems
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• 제11권4호
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• pp.99-111
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• 2001

The prediction of stock price index is a very difficult problem because of the complexity of stock market data. It has been studied by a number of researchers since they strongly affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network(BPN). Finally, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

• Communications for Statistical Applications and Methods
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• 제10권1호
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• pp.153-166
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• 2003

This study presents the time-divided forecasting model to integrate evolutionary optimization algorithm and change point detection based on artificial neural networks (ANN) for the prediction of (Korea) stock price index. The genetic algorithm(GA) is introduced as an evolutionary optimization method in this study. The basic concept of the proposed model is to obtain intervals divided by change points, to identify them as optimal or near-optimal change point groups, and to use them in the forecasting of the stock price index. The proposed model consists of three phases. The first phase detects successive change points. The second phase detects the change-point groups with the GA. Finally, the third phase forecasts the output with ANN using the GA. This study examines the predictability of the proposed model for the prediction of stock price index.

• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.945-953
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• 2007

In this paper, we propose a smoothing and thus noise-reducing method of data sequences for stock price prediction with hidden Markov models, HMMs. The suggested method just uses simple moving average. A proper average size is obtained from forecasting experiments with stock prices of bank sector of Korean Exchange. Forecasting method with HMM and moving average smoothing is compared with a conventional method.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제8권3호
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• pp.123-128
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• 2019

본 논문에서는 RCNN (recurrent convolution neural network) 계층 모델을 채택한 인공 지능에 기반을 둔 주가 예측을 제안한다. LSTM (long-term memory model) 기반 신경망은 시계열 데이터의 예측에 사용된다. 다른 한편, 컨볼루션 신경망은 데이터 필터링, 평균화 및 데이터 확장을 제공한다. 제안된 주가 예측에서는 위에서 언급 한 장점들을 RCNN 모델에서 결합하여 적용함으로써 다음날의 주가 종가를 예측한다. 그리고 최근의 시계열의 데이터를 강조하기 위해 커스텀 가중치 손실 함수가 채택되었다. 또한 시장의 상황을 반영하기 위해 주가 인덱스에 관련된 데이터를 입력으로 포함하였다. 제안된 주가 예측 방식은 실제 주가를 대상으로 한 실험에서 3.19%로 테스트 오차를 줄였으며, 다른 방법보다 약 19%의 성능 향상을 거둘 수 있었다.