• The Journal of the Korea Contents Association
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• v.6 no.6
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• pp.26-32
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• 2006

Predictions on stock prices have been a hot issue in stock market as people get more interested in stock investments. Assuming that the stock price is moving by a trend in a specific pattern, we believe that a model can be derived from past data to describe the change of the price. The best model can help predict the future stock price. In this paper, our model derivation is based on automata over temporal data to which the model is explicable. We use Bayesian Information Criterion(BIC) to determine the best number of states of the model. We confirm the validity of Bayesian Information Criterion and apply it to building models over stock price indices. The model derived for predicting daily stock price are compared with real price. The comparisons show the predictions have been found to be successful over the data sets we chose.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.844-847
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• 2019

주가 예측은 상업적인 매력 때문에 많은 이목이 끌리는 분야이지만, 주가의 불확실성과 변동성 때문에 주가 예측은 어려운 작업이다. 최근에는 주가 예측 모델에 어텐션 메커니즘을 사용하여 주가 예측에 많은 인자들이 사용되어 생기는 성능 하락 문제를 해결하여 좋은 성능을 보여주는 연구가 존재한다. 본 연구에서는 그 모델 중 하나인 Dual-Stage Attention-Based Recurrent Neural Network(DARNN)의 어텐션 메커니즘을 변경해가며 어떤 어텐션 메커니즘이 주가 예측에 적합한지를 알아본다. KOSPI100 지수의 예측실험을 통해 location 스코어함수를 사용한 어텐션 메커니즘이 가장 뛰어난 성능을 보여주는 것을 확인하였고, 이는 기존의 스코어함수를 사용한 DARNN에 비해 약 10% 향상된 성능으로 스코어 함수가 모델의 중요한 영향을 끼치는 것을 확인하였다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.204-208
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• 2018

In this paper, we proposed and evaluated the time series deep learning prediction model for learning fluctuation pattern of stock price. Recurrent neural networks, which can store previous information in the hidden layer, are suitable for the stock price prediction model, which is time series data. In order to maintain the long - term dependency by solving the gradient vanish problem in the recurrent neural network, we use LSTM with small memory inside the recurrent neural network. Furthermore, we proposed the stock price prediction model using bidirectional LSTM recurrent neural network in which the hidden layer is added in the reverse direction of the data flow for solving the limitation of the tendency of learning only based on the immediately preceding pattern of the recurrent neural network. In this experiment, we used the Tensorflow to learn the proposed stock price prediction model with stock price and trading volume input. In order to evaluate the performance of the stock price prediction, the mean square root error between the real stock price and the predicted stock price was obtained. As a result, the stock price prediction model using bidirectional LSTM recurrent neural network has improved prediction accuracy compared with unidirectional LSTM recurrent neural network.

• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.603-608
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• 2008

In this paper an approach to building fuzzy model to predict daily open, close, high, and low stock prices is presented. One of prior problems in building a stock prediction model is to select most effective indicators for the stock prediction. The problem is overcome by the selection of information used in the analysis of stick-chart as the input variables of our fuzzy model. The fuzzy rules have the premise and the consequent, in which they are composed of trapezoidal membership functions, and nonlinear equations, respectively. DE(Differential Evolution) searches optimal fuzzy rules through an evolutionary process. To evaluate the effectiveness of the proposed approach numerical example is considered. The fuzzy models to predict open, high, low, and close prices of KOSPI(KOrea composite Stock Price Index) on a daily basis are built, and their performances are demonstrated and compared with those of neural network.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.541-544
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• 2021

본 논문은 어떤 기업의 주식 주문 정보를 담고 있는 호가창(limit order book)과 해당 기업과 관련된 뉴스 헤드라인을 사용하여 해당 기업의 주가 등락을 예측하는 딥러닝 기반 모델을 제안한다. 제안 모델은 호가창의 중기 변화와 단기 변화를 모두 고려하는 한편, 동기간 발생한 뉴스 헤드라인까지 예측에 고려함으로써 주가 등락 예측 정확도를 높인다. 제안 모델은 호가창의 변화의 특징을 CNN(convolutional neural network)으로 추출하고 뉴스 헤드라인을 Word2vec으로 생성된 단어 임베딩 벡터를 사용하여 나타낸 뒤, 이들 정보를 결합하여 특정 기업 주식의 다음 날 등락여부를 예측한다. NASDAQ 실데이터를 사용한 실험을 통해 제안 모델로 5개 종목(Amazon, Apple, Facebook, Google, Tesla)의 일일 주가 등락을 예측한 결과, 제안 모델은 기존 방법에 비해 정확도를 최대 17.14%, 평균 10.7% 향상시켰다.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.689-694
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• 2009

In this paper an approach to building fuzzy models for predicting daily and weekly stock prices is presented. Predicting stock prices with traditional time series analysis has proven to be difficult. Fuzzy logic based models have advantage of expressing the input-output relation linguistically, which facilitates the understanding of the system behavior. In building a stock prediction model we bear a burden of selecting most effective indicators for the stock prediction. In this paper information used in traditional candle stick-chart analysis is considered as input variables of our fuzzy models. The fuzzy rules have the premises and the consequents composed of trapezoidal membership functions and nonlinear equations, respectively. DE(Differential Evolution) identifies optimal fuzzy rules through an evolutionary process. The fuzzy models to predict daily and weekly open, high, low, and close prices of KOSPI(KOrea composite Stock Price Index) are built, and their performances are demonstrated.

• KIISE Transactions on Computing Practices
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• v.23 no.11
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• pp.625-631
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• 2017

The stock price prediction for stock markets remains an unsolved problem. Although there have been various overtures and studies to predict the price of stocks scientifically, it is impossible to predict the future precisely. However, stock price predictions have been a subject of interest in a variety of related fields such as economics, mathematics, physics, and computer science. In this paper, we will study fluctuation patterns of stock prices and predict future trends using the Deep learning. Therefore, this study presents the three deep learning models using Tensorflow, an open source framework in which each learning model accepts different input features. We expand the previous study that used simple price data. We measured the performance of three predictive models increasing the number of priced-based input features. Through this experiment, we measured the performance change of the predictive model depending on the price-based input features. Finally, we compared and analyzed the experiment result to evaluate the impact of the price-based input features in stock price prediction.

• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.702-714
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• 2020

Stock investment is a personal investment technique that has gathered tremendous interest since the reduction in interest rates and tax exemption. However, it is risky especially for those who do not have expert knowledge on stock volatility. Therefore, it is well understood that accurate stock trend prediction can greatly help stock investment, giving birth to a volume of research work in the field. In order to compare different research works and to optimize hyper-parameters for prediction models, it is required to have an evaluation standard that can accurately assess performances of prediction models. However, little research has been done in the area, and conventionally used methods have been employed repeatedly without being rigorously validated. For this reason, we first analyze performance evaluation of stock trend prediction with respect to performance metrics and data composition, and propose a fair evaluation method based on prediction disparity ratio.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.367-374
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• 2021

Stock price prediction is a subject of research in various fields such as economy, statistics, computer engineering, etc. In recent years, researches on predicting the movement of stock prices by learning artificial intelligence models from various indicators such as basic indicators and technical indicators have become active. This study proposes a deep learning model that predicts the ups and downs of KOSPI from overseas indices such as S&P500, past KOSPI indices, and trading trends by KOSPI investors. The proposed model extracts a latent variable using a stacked auto-encoder to predict stock price fluctuations, and predicts the fluctuation of the closing price compared to the market price of the day by learning an LSTM suitable for learning time series data from the extracted latent variable to decide to buy or sell based on the value. As a result of comparing the returns and prediction accuracy of the proposed model and the comparative models, the proposed model showed better performance than the comparative models.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.165-171
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• 2020

Most of the stock price prediction research using text mining uses news and SNS data. However, there is a weakness that it is difficult to get honest and vivid information about companies from them. This paper deals with the problem of the prediction for the direction of stock price by doing text mining the online company reviews of internal staff indicating employee satisfaction. The comparative analysis of the prediction models for the direction of stock price showed the prediction model, which adds internal employee reviews, has better performance than those that did not. This paper presents the convergence study using natural language processing in financial engineering. In the field of stock price prediction, This paper pursued a new methodology that used employee satisfaction. In practice, it is expected to provide useful information in the field of forecasting stock price direction.