• 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.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.65-74
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• 2021

The rapid change in gold price is an issue of concern in the global economy and financial markets. Gold has been used as a means for trading and transaction around the world for long period of time and it plays an integral role in monetary, business, commercial and financial activities. More importantly, it is used as economic measure for the global economy and will continue to play an important economic vital role - both locally and globally. There has been an explosive growth in demand for efficient and effective scheme to predict gold price due its volatility and fluctuation. Hence, there is need for the development of gold price prediction scheme to assist and support investors, marketers, and financial institutions in making effective economic and monetary decisions. This paper primarily proposed an intelligent based system for predicting and characterizing the gold market trend. The simulation result shows that the proposed intelligent gold price scheme has been able to predict the gold price with high accuracy and precision, and ultimately it has significantly reduced the prediction error when compared to baseline neural network (NN).

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.1-8
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• 2014

Cloud computing helps big data processing to make various information using IT resources. The government has to start the RPS(Renewable Portfolio Standard) and induce the production of electricity using renewable energy equipment. And the government manages system to gather big data that is distributed geographically. The companies can purchase the REC(Renewable Energy Certificate) to other electricity generation companies to fill shortage among their duty from the system. Because of the RPS use voluntary competitive market in REC trade and the prices have the large variation, RPS is necessary to predict the equitable REC price using RPS big data. This paper proposed REC price prediction method base on fuzzy logic using the price trend and trading condition infra in REC market, that is modeled in cloud computing environment. Cloud computing helps to analyze correlation and variables that act on REC price within RPS big data and the analysis can be predict REC price by simulation. Fuzzy logic presents balanced REC average trading prices using the trading quantity and price. The model presents REC average trading price using the trading quantity and price and the method helps induce well-converged price in the long run in cloud computing environment.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.14-19
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• 2008

The paper presents an intelligent time series model to predict uncertain electricity market price in the deregulated industry environment. Since the price of electricity in a deregulated market is very volatile, it is difficult to estimate an accurate market price using historically observed data. The parameter of an intelligent time series model is obtained based on the simultaneous perturbation stochastic approximation (SPSA). The SPSA is flexible to use in high dimensional systems. Since prediction models have their modeling error, an error compensator is developed as compensation. The SPSA based intelligent model is applied to predict the electricity market price in the Pennsylvania-New Jersey-Maryland (PJM) electricity market.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.3
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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.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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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.

• Korean Management Science Review
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• v.28 no.1
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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.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.3
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• pp.123-128
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• 2019

This paper proposes the stock price prediction based on the artificial intelligence, where the model with recurrent convolution neural network (RCNN) layers is adopted. In the motivation of this prediction, long short-term memory model (LSTM)-based neural network can make the output of the time series prediction. On the other hand, the convolution neural network provides the data filtering, averaging, and augmentation. By combining the advantages mentioned above, the proposed technique predicts the estimated stock price of next day. In addition, in order to emphasize the recent time series, a custom weighted loss function is adopted. Moreover, stock data related to the stock price index are adopted to consider the market trends. In the experiments, the proposed stock price prediction reduces the test error by 3.19%, which is over other techniques by about 19%.