• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1435-1440
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• 2021

With the advent of the low interest rate era, many investors are flocking to the stock market. In the past stock market, people invested in stocks labor-intensively through company analysis and their own investment techniques. However, in recent years, stock investment using artificial intelligence and data has been widely used. The success rate of stock prediction through artificial intelligence is currently not high, so various artificial intelligence models are trying to increase the stock prediction rate. In this study, we will look at various artificial intelligence models and examine the pros and cons and prediction rates between each model. This study investigated as stock prediction programs using artificial intelligence artificial neural network (ANN), deep learning or hierarchical learning (DNN), k-nearest neighbor algorithm(k-NN), convolutional neural network (CNN), recurrent neural network (RNN), and LSTMs.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.121-134
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• 2021

As interest in stock markets is growing recently in Korea, more investors in their twenties are using the information provided by personal broadcasting channels on YouTube. This study explores how watching the stock market channels on YouTube influences watching behaviors and investment intentions. A structural equation modeling analysis of survey data from 219 adults in the twenties revealed that the perceived credibility of and para-social interaction with the YouTube influencers affected continued viewing intentions. The findings also showed that the effect of identification with the influencers on viewing satisfaction was more prominent when identification mediated the effect of para-social interaction on viewing satisfaction. Theoretical and practical implications were discussed.

• Management & Information Systems Review
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• v.33 no.1
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• pp.83-97
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• 2014

This study examined the effects institutional block ownership on the stock market liquidity in Korean Stock Market. The two measures of institutional block ownership are used. They are the percentage of a stock owned by institutional blockholder and the number of institutional blockholder that own the stock. This study used the Amihud(2002) illiquidity measure to measure stock market liquidity. The results are as fellows. First, this study showed that the number of institutional blockholder is significantly negatively correlated with the Amihud(2002) illiquidity measure in the analysis which is used the whole data. But we found no a consistent results between the number of institutional blockholder and the Amihud(2002) illiquidity measure in the grouped institutional blockholder's number analysis. This indicates that the effects institutional blockholder on market liquidity is not simple. Second, this study showed that the percentage of a stock owned by institutional blockholder are negatively related with Amihud(2002) illiquidity measure, especially revealed statistically significant in the group 3(11.71%~17.38%) and group 4(7.45%~11.65%). This results suggest that the institutional blockholder have positive effect on the market liquidity in the group 3 and 4. Third, the significance of the percentage of institutional block ownership and the number of institutional block ownership in explaining illiquidity are more showed in the term of the global financial crisis(2008) than the before and the after of the global financial crisis.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.472-482
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• 2012

There are many investors in the stock market, and more and more people get interested in the stock investment. In order to avoid risks and make profit in the stock investment, we have to determine several aspects using various information. That is, we have to select profitable stocks and determine appropriate buying/selling prices and holding period. This paper proposes a data mining tool for the investors' decision support. The data mining tool makes stock investors apply machine learning techniques and generate stock price prediction model. Also it helps determine buying/selling prices and holding period. It supports individual investor's own decision making using past data. Using the proposed tool, users can manage stock data, generate their own stock price prediction models, and establish trading policy via investment simulation. Users can select technical indicators which they think affect future stock price. Then they can generate stock price prediction models using the indicators and test the models. They also perform investment simulation using proper models to find appropriate trading policy consisting of buying/selling prices and holding period. Using the proposed data mining tool, stock investors can expect more profit with the help of stock price prediction model and trading policy validated on past data, instead of with an emotional decision.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.441-450
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• 2014

The stock market is constantly changing and sometimes the stock prices unaccountably plummet or surge. So, the stock market is recognized as a complex system and the change on the stock prices is unpredictable. Recently, many researchers try to understand the stock market as the network among individual stocks and to find a clue about the change of the stock prices from big data being created in real time from Internet. We focus on the correlation between the stock prices and the human interactions in Internet especially in the stock message boards. To uncover this correlation, we collected and investigated the articles concerning with 57 target companies, members of KOSPI200. From the analysis result, we found that there is no significant correlation between the stock prices and the article volume, but the strength of correlation between the article volume and the stock prices is relevant to the stock return. We propose a new method for recommending stock portfolio base on the result of our analysis. According to the simulated investment test using the article data from the stock message boards in 'Daum' portal site, the returns of our portfolio is about 1.55% per month, which is about 0.72% and 1.21% higher than that of the Markowitz's efficient portfolio and that of the KOSPI average respectively. Also, the case using the data from 'Naver' portal site, the stock returns of our proposed portfolio is about 0.90%, which is 0.35%, 0.40%, and 0.58% higher than those of our previous portfolio, Markowitz's efficient portfolio, and KOSPI average respectively. This study presents that collective human behavior on Internet stock message board can be much helpful to understand the stock market and the correlation between the stock price and the collective human behavior can be used to invest in stocks.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.727-728
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• 2009

최근 컴퓨터와 인터넷의 발달로 과거 창구거래를 이용하던 방법에서 HTS(Home Trading System)을 이용하여 거래하게 됨으로써 개인투자자들도 쉽게 주식투자를 할 수 있게 되었다. 그러나 개인들이 방대한 양의 과거 데이터를 분석하기에는 상당한 어려움이 있다. 본 논문에서는 주식 데이터베이스로부터 과거 특정 종목들 간 연관성을 추출하여 투자자들로 하여금 주식 선별에 참고가 될 수 있는 방안에 관하여 논의한다. 기존의 논문에서 제안된 과거 패턴을 이용하여 미래의 주가변화를 예측하는 것과 달리, 종목들 간에 연관성을 통하여 하나의 테마가 형성 되었을 때 주도주의 변화로 관련주의 변화를 파악하여 투자에 유익한 정보를 제공하는데 목적이 있다.

• The Korean Journal of Financial Management
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• v.7 no.2
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• pp.165-186
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• 1990

• Journal of Digital Convergence
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• v.13 no.11
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• pp.45-55
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• 2015

This study used for KOSPI, KOSDAQ, entertainment culture and digital contents index that is related to cultural contents industry. There was investigated the each stock price index and return trends for a total 597 weeks to July 2015 from March 2004. They looked the content-related stocks about investment worth to comparative analysis the return, volatility, correlation, synchronization phenomena etc. of each stock index. When we saw the growth potential of the cultural contents industry forward, looked forward to the investment possibility of related stocks. Analysis Result cultural content related stocks showed a higher rate after the last 2008 global financial crisis. Recent as high interest in the cultural contents industry, we could see that the investment merit increases slowly. In the future, the cultural content industry is expected to continue to evolve. The increase of investments value in the cultural content related businesses is much expectation.

• Journal of the Korea Convergence Society
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• v.6 no.4
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• pp.127-132
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• 2015

This study attempts to empirically investigate if relative momentum strategy is effective in the Korean stock market. The sample of the study is comprised of companies which are traded in both Kospi and Kosdaq stock markets in Korea for the period between 2001~2014. The study observes that the momentum strategy buying past winner stocks and selling past loser stocks is negatively correlated with the value strategy buying value stocks with high book to market ratio and selling glamour stocks with low book to market ratio. And each strategy is alternatively effective from period to period. The study demonstrates that the momentum strategy is effective when both strategies which are negatively correlated are treated as one system by estimating Fama and French's[1] 3 factor regression model.

• Journal of Families and Better Life
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• v.22 no.3
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• pp.11-21
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• 2004

This study examined factors associated with the ownership of stock investments and the amount of stock investments of households using the 2001 National Survey of Family Income and Expenditure by National Statistical Office. Households with large amounts of income, savings, and liabilities were more likely to invest in stocks and have large amounts of stock investments. Also, households with young and male householders, highly educated householders, a number of children in school, and housing ownership were more likely to invest in stocks and have large amounts of stock investments. On the other hand, self employed households and dual income households were less likely to invest in stocks and have small amounts of stock investments.