• Journal of Digital Convergence
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• v.18 no.11
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• pp.267-272
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• 2020

Macroeconomics are one of the indicators that are preceded and analyzed when analyzing stocks because it shows the movement of a country's economy as a whole. The overall economic situation at the national level, such as national income, inflation, unemployment, exchange rates, currency, interest rates, and balance of payments, has a great affect on the stock market, and economic indicators are actually correlated with stock prices. It is the main source of data for analysts to watch with interest and to determine buy and sell considering the impact on individual stock prices. Therefore, economic indicators that impact on the stock price are analyzed as leading indicators, and the stock price prediction is predicted through deep learning-based prediction, after that the actual stock price is compared. If you decide to buy or sell stocks by analysis of stock prediction, then stocks can be investments, not gambling. Therefore, this research was conducted to enable automated stock trading by using macro-indicators and deep learning algorithms in artificial intelligence.

• International Area Studies Review
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• v.14 no.1
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• pp.55-76
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• 2010

This paper analyses interaction between yen/dollar exchange rates and NIKKEI index using bivariate GJR-GARCH(1,1) model. The data employed for the study is daily data series for the period of Jan. 4, 1995 through Aug. 30, 2009. One of main findings is that market inefficiency appears in the periods of financial crisis. Second, the volatility of exchange rates and stock returns has more increased in the wake of the volatility shock of the previous period during financial crisis than during non-financial crisis. Third, interestingly, the asymmetric volatility shock by bad news in those markets is bigger in financial crisis period than in non financial crisis. Fourth, in the period of current global financial crisis triggered by subprime mortgage crisis in U.S, volatility shock at the previous period is bigger than that of Asian financial crisis that happened in 1997. Lastly, the correlation between both returns of exchange rates and stock prices turns up positive according to the empirical estimation. This result may come from the fact that Japanese stock market does not have much attraction for international financial investment compared to stock markets of neighbouring countries like China, Korea and so on, while real sector's contribution to the economy is considered more importantly.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.785-794
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• 2002

Automatic stock trading systems should be able to solve various kinds of optimization problems such as market trend prediction, stock selection, and trading strategies, in a unified framework. But most of the previous trading systems based on supervised learning have a limit in the ultimate performance, because they are not mainly concerned in the integration of those subproblems. This paper proposes a stock trading system, called R-Trader, based on reinforcement teaming, regarding the process of stock price changes as Markov decision process (MDP). Reinforcement learning is suitable for Joint optimization of predictions and trading strategies. R-Trader adopts two popular reinforcement learning algorithms, temporal-difference (TD) and Q, for selecting stocks and optimizing other trading parameters respectively. Technical analysis is also adopted to devise the input features of the system and value functions are approximated by feedforward neural networks. Experimental results on the Korea stock market show that the proposed system outperforms the market average and also a simple trading system trained by supervised learning both in profit and risk management.

• Smart Media Journal
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• v.11 no.1
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• pp.38-45
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• 2022

A lot of research is being going until this day in order to obtain stable profit in the stock market. Trading algorithms are widely used, accounting for over 80% of the trading volume of the US stock market. Despite a lot of research, there is no trading algorithm that always shows good performance. In other words, there is no guarantee that an algorithm that performed well in the past will perform well in the future. The reason is that there are many factors that affect the stock price and there are uncertainties about the future. Therefore, in this paper, we propose a model using TimeGAN that predicts future returns well and selects algorithms that are expected to have high returns based on past records of the returns of algorithms. We use TimeGAN becasue it is probabilistic, whereas LSTM method predicts future time series data is deterministic. The advantage of TimeGAN probabilistic prediction is that it can reflect uncertainty about the future. As an experimental result, the method proposed in this paper achieves a high return with little volatility and shows superior results compared to many comparison algorithms.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.906-907
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• 2023

본 연구는 변동성이 높은 주식시장에서 안정적인 수익창출에 기여할 수 있는 주가예측 강화학 모델을 제안한다. DQN 알고리즘과 LSTM 신경망을 이용하여 시장의 흐름에 따라 전략을 달리하는 모델을 개발하고, 이를 활용한 주식 트레이딩 시스템의 유용성을 확인하고 발전 방향을 제시한다.

• The Korean Journal of Financial Management
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• v.10 no.2
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• pp.263-301
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• 1993

최근의 연구결과에 의하면 분산이 시간에 따라 변화하여 이분산적(異分散的)이며, 시계열상관(時系列相關)이 존재하는 것으로 나타나고 있다. 일정(一定)한 분산을 가정하여 주식수익률(株式收益率)의 움직임을 설명하는 기존의 모형들은 주식수익률(株式收益率)을 예측하는데 편의(偏倚)(bias)를 가지게 되며, 또한 투자자(投資者)들에게 정확한 위험측정(危險測定)의 수단을 제공하지 못하고 있다. 따라서 본 연구는 우리나라 주식수익률(株式收益率)의 분산이 시간에 따라 변화하는지를 살펴보기 위해 종합주가지수(綜合株價指數) 및 규모별(規模別) 지수(指數)를 사용하여 ARCH 및 GARCH 모형을 추정하였다. 또한 기대수익률(期待收益率)과 조건부(條件附) 분산(分散)사이의 다기간(多期間)(intertemporal) 관계를 ARCH-M 및 GARCH-M 모형을 사용하여 추정하였다. 추정결과는 우리나라 주식시장에도 유의적인 ARCH 및 GARCH 효과, 즉 주식수익률이 매우 이분산적(異分散的)인 것으로 나타났다. 그리고 기대수익률(期待收益率)과 조건부(條件附) 분산(分散)사이의 관계에서 ARCH-M 모형과 GARCH-M 모형의 추정결과가 다르게 나타났으나 전체적으로 유의하지 않는 것으로 나타났다. 이러한 본 연구결과로 조건부(條件附) 분산모형(分散模型)을 통하여 기대수익률(期待收益率) 및 분산(分散)의 움직임을 더욱 잘 파악할 수 있을 것으로 생각되며, 따라서 주식수익률(株式收益率) 및 분산(分散)의 예측에 더 좋은 도구로 활용될 수 있을 것으로 생각된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.435-438
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• 2020

시장예측 문제를 해결하기 위하여 과거부터 꾸준한 연구가 진행되어왔다. 하지만 금융 시계열 데이터에는 분산이 일정하지 않으며 Non-stationarity 등 예측을 하는 것에 있어서 여러 가지 방해 요인이 존재한다. 또한 광범위한 데이터 변수는 기존에 사람이 직접 경험적으로 선택하는 것에 한계가 있기 때문에, 모델이 변수를 자동으로 추출할 수 있어야 한다. 본 논문에서는 여러 가지 금융 시계열 데이터의 문제를 고려하여 타임 스텝 정규화를 제안하며 자동 변수 추출을 위해 LSTM 형태의 오토 인코더 모델을 학습하였으며 LSTM 네트워크를 이용하여 시장 예측하는 모델을 제안한다. 해당 시스템은 실제 주식 거래나 시장 거래를 위하여 온라인 학습이 가능하며 긴 기간을 테스트 구간으로 실험한 결과 미래의 수익률을 예측하는 것에 있어서 우수한 성능을 보였다.

• Information Systems Review
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• v.23 no.1
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• pp.23-43
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• 2021

Recently, many studies have been conducted to increase the accuracy of stock price prediction by analyzing candlestick charts using artificial intelligence techniques. However, these studies failed to consider the time-series characteristics of candlestick charts and to take into account the emotional state of market participants in data learning for stock price prediction. In order to overcome these limitations, this study produced input data by combining volatility index and candlestick charts to consider the emotional state of market participants, and used the data as input for a new method proposed on the basis of combining variantion autoencoder (VAE) and attention mechanisms for considering the time-series characteristics of candlestick chart. Fifty firms were randomly selected from the S&P 500 index and their stock prices were predicted to evaluate the performance of the method compared with existing ones such as convolutional neural network (CNN) or long-short term memory (LSTM). The results indicated the method proposed in this study showed superior performance compared to the existing ones. This study implied that the accuracy of stock price prediction could be improved by considering the emotional state of market participants and the time-series characteristics of the candlestick chart.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• The Korean Journal of Financial Management
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• v.20 no.2
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• pp.235-261
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• 2003

We examine the relationship between the trading activities of Korea Stock Price Index (KOSPI) 200 futures contract and its underlying stock market volatility for about six years from May 1996 when the futures contract was introduced. The trading activities of the futures contracts are proxied by the volume and open interest, which are divided into expected and unexpected portions by using the previous data. The daily, intradilay, and overnight cash volatility is estimated by the GJR-GARCH model. We find a positive contemporaneous relationship between the intradaily stock market volatility and the unexpected futures volume while the relationship between the volatility and expected futures volume is weakly negative or non-existent. We also find that the unexpected futures volume strongly causes intradaily cash volatility. On the other hand, the overnight cash volatility causes the unexpected futures volume. The impulse responses between these variables are all positive. The result implies that during a trading time futures trading tends to increase the cash volatility while the unexpected overnight changes in cash volatility tends to increase the futures trading activities. We, however, find no association between the cash volatility and futures maturities.