• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.19-24
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• 2020

Artificial intelligence technology, which is the core of the 4th industrial revolution, is making intelligent judgments through deep learning techniques and machine learning that it is impossible to predict if it is applied to stock prediction beyond human capabilities. In US fund management companies, artificial intelligence is replacing the role of stock market analyst, and research in this field is actively underway. In this study, we use BLSTM to reduce errors that occur in unidirectional prediction of the existing LSTM method, reduce errors in predictions by predicting in both directions, and macroscopic indicators that affect stock prices, namely, economic growth rate, economic indicators, interest rate, analyze the trade balance, exchange rate, and volume of currency. To help stock investment by accurately predicting the target price of stocks by analyzing the PBR, BPS, and ROE of individual stocks after analyzing macro-indicators, and by analyzing the purchase and sale quantities of foreigners, institutions, pension funds, etc., which have the most influence on stock prices.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.221-233
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• 2014

Because people's interest of the stock market has been increased with the development of economy, a lot of studies have been going to predict fluctuation of stock prices. Latterly many studies have been made using scientific and technological method among the various forecasting method, and also data using for study are becoming diverse. So, in this paper we propose stock prices prediction models using sentiment analysis and machine learning based on news articles and SNS data to improve the accuracy of prediction of stock prices. Stock prices prediction models that we propose are generated through the four-step process that contain data collection, sentiment dictionary construction, sentiment analysis, and machine learning. The data have been collected to target newspapers related to economy in the case of news article and to target twitter in the case of SNS data. Sentiment dictionary was built using news articles among the collected data, and we utilize it to process sentiment analysis. In machine learning phase, we generate prediction models using various techniques of classification and the data that was made through sentiment analysis. After generating prediction models, we conducted 10-fold cross-validation to measure the performance of they. The experimental result showed that accuracy is over 80% in a number of ways and F1 score is closer to 0.8. The result can be seen as significantly enhanced result compared with conventional researches utilizing opinion mining or data mining techniques.

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.84-93
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• 2021

The purpose of this study is to analyze whether the spread of COVID-19 infectious diseases acts as a fear to investors and affects the direction and volatility of stock returns. The investor fear index was proposed using the domestic confirmed patient information of COVID-19, and the influence on stock prices was empirically analyzed. The direction and volatility models of stock prices used the Granger causality and GARCH models, respectively. The results of empirical analysis using the KOSPI index from February 20, 2020 to June 30, 2021 are as follows: First, the COVID-19 fear index showed causality to future stock prices. Second, the COVID-19 fear index has a negative effect on the volatility of KOSPI index returns. In future studies, it is necessary to document the cause by using individual business performance and stock price instead of the stock index.

• Journal of Convergence for Information Technology
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• v.11 no.1
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• pp.28-33
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• 2021

Since the stock price is a measure of the future value of the company, when analyzing the stock price, the company's growth potential, such as sales and profits, is considered and invested in stocks. In order to set the criteria for selecting stocks, institutional investors look at current industry trends and macroeconomic indicators, first select relevant fields that can grow, then select related companies, analyze them, set a target price, then buy, and sell when the target price is reached. Stock trading is carried out in the same way. However, general individual investors do not have any knowledge of investment, and invest in items recommended by experts or acquaintances without analysis of financial statements or growth potential of the company, which is lower in terms of return than institutional investors and foreign investors. Therefore, in this study, we propose a research method to select undervalued stocks by analyzing ROE, an indicator that considers the growth potential of a company, such as sales and profits, and predict the stock price flow of the selected stock through deep learning algorithms. This study is conducted to help with investment.

• Journal of the Korean Institute of Gas
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• v.4 no.4 s.12
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• pp.34-41
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• 2000

The purpose of this paper is to assess the R&D investment of Korea Gas Corporation (Kogas) by combining several measures including R&D needs, technology spillover effects, and technology stock at the corporate level. This study has revealed that Kogas has concentrated its R&D resources on the operation and maintenance of gas supply facilities, and technology groups, which have higher fulfillment of R&D needs and technology spillover effects, have been on the relatively greater level of technology stocks.

• Journal of Information Technology Applications and Management
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• v.19 no.1
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• pp.1-12
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• 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.

• Journal of Digital Convergence
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• v.14 no.10
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• pp.107-117
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• 2016

This study examines information efficiency of financial information on the firm value for the listed manufacturing companies in Korea stock market in terms of timing pattern of information. We set 3 different test periods based on the financial statement released years - the current year, 90 days before financial statement announcement and the next year. We introduce using the stepwise regression method to examine the effect of financial variables on the stock returns. The financial variables include profitability ratio, growth ratio, stability ratio, activity ratio and market valuation ratio. The results of the study showed that both growth and profitability ratio affected the current year stock returns, while stability and activity ratio affected the next year stock returns. Growth rate of total asset affects both current year and next year stock returns. Our findings imply that the period in which financial information is reflected in the firm value, could vary with the characteristics of financial information.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.50 no.4
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• pp.467-475
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• 2014

This study was performed to estimate optimum fishing mortality (F) and the age at first capture ($t_c$) for small yellow croaker in Korean waters. We first estimated optimum F and $t_c$ using traditional yield-per-recruit (YPR) analysis, and the results were 0.8/year and 2.5 years old, respectively. However, the individual fish price per unit weight of small yellow croaker in Korea increases dramatically by size. Thus, we developed an alternative method, which is called as production value-per-recruit (PPR) analysis. We developed two types of the PPR analysis, that is, the discrete function and the continuous function method. We estimated optimum F and $t_c$ using the two types of the PPR analysis and compared the results. The optimum F and $t_c$ from the discrete function method, were 0.3/year and 5.0 years old, respectively, while those from the continuous function method were 0.5/year and 3.5 years old, respectively. These PPR estimates were much more conservative for the stock management than the traditional YPR analysis, which can prevent the fish stock from the economic overfishing. As a result, the PPR analysis could be more proper approach for stock assessment in the case that the individual fish price per unit weight increases dramatically by size like small yellow croaker in Korea.

• Journal of Animal Science and Technology
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• v.57 no.3
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• pp.7.1-7.7
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• 2015

To meet the increasing demands of specific pathogen free (SPF) minipigs in biomedical researches, 8 pregnant Chinese Wuzhishan minipigs (WZSP) sows with clear background were chosen to obtain SPF WZSP by hysterectomy. At $111{\pm}days2$ of the pregnancy, piglets were aseptically taken out from the sows and artificially suckled for 40 to 45 days in the positive isolators. Then, the piglets defined as F0 were transferred to barrier environment and fed with standard feeds. The original SPF colony was formed for breeding by selected piglets from F0 group of 6-8 months old. Biological characteristics of SPF WZSP were collected and further compared to those of conventional (CV) WZSP, including growth performance, reproductive performance, hematology and blood biochemistry, and major pathogens detection. As a result, 61 F0 piglets were obtained from 8 candidate sows, and 55 out of them survived. After strictly selection, 35 F0 piglets were used to form the original SPF colony, which produced 14 litters of SPF piglets defined as F1. Piglet survival rates, growth performance, and reproductive performance of SPF WZSP were similar to CV WZSP. Some hematology and blood biochemistry parameters showed significant differences between SPF and CV WZSP. Eighteen kinds of pathogens were identified to be free in F0 and F1 SPF colony by repeated pathogen detections. In conclusion, we established a satisfied SPF WZSP colony maintaining original characteristics, free of controlled diseases, and being proved to be a suitable laboratory animal.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.52 no.3
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• pp.220-231
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• 2016

Yield-per-recruit (YPR) analysis is used to provide management guidance for the efficient use of a fish cohort. However, the individual fish price per unit weight of small yellow croaker (Larimichthys polyactis) or hairtail (Trichiurus lepturus) increases dramatically by size in Korea. Therefore, age-based production value-per-recruit (PPR) analysis has recently been developed (Zhang et al., 2014). Since age determination requires a substantial amount of money and time and it is even impossible for some fish species, it is difficult to obtain age information to apply the age-based PPR model. Thus, we attempted to develop an alternative method, which uses length data rather than age information, called the length-based PPR analysis. The results revealed that length-based PPR analysis was much more conservative for stock management than the YPR analysis. Furthermore, the PPR analysis was more economically beneficial than the YPR analysis, which can prevent the fish stock from the economic overfishing. In conclusion, the length-based PPR analysis could be a proper approach for stock assessment in the case that the individual fish price per unit weight increases dramatically by size, and this analysis is useful to obtain vital management parameters under data-deficient situation when traditional stock assessment methods are not applicable.