• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1063-1064
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• 2023

인류는 주가를 과학적으로 예측하기 위해 수많은 학문적 노력을 기울여왔지만, 아직까지도 풀지 못한 난제로 남아 있다. 이에 본 연구에서는 깊은 수학적 원리에 기반하고 알파고 등에서 인간을 능가하는 성능을 보여준 강화학습 기술을 주식 트레이딩에 적용한 RLTA 모델을 제안하고, 실험을 통해 그 유용성을 입증하였다.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.36-46
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• 2019

If stock market is efficient, any well-devised trading rule can't consistently outperform the average stock market returns. This study aims to verify whether the strategy based on bid-ask volume information can beat the stock market. I suggested a day trading strategy using order imbalance indicator and empirically analyzed its profitability with the KOSPI 200 index futures data from 2001 to 2018. Entry rules are as follows: If BSI is over 50%, enter buy order, otherwise enter sell order, assuming that stock price rises after BSI is over 50% and stock price falls after BSI is less than 50%. The empirical results showed that the suggested trading strategy generated very high trading profit, that is, its annual return runs to minimum 71% per annum even after the transaction costs. The profit was generated consistently during 18 years. This study also improved the suggested trading strategy applying the genetic algorithm, which may help the market practitioners who trade the KOSPI 200 index futures.

• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.329-338
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• 2022

As the non-face-to-face economic situation developed due to the COVID-19 pandemic, untact stock groups appeared in the stock market. This study proposed the Korea COVID-19 fear index following the spread of infectious diseases in the COVID-19 pandemic situation and analyzed the influence on the untact stock and contact stock returns. The results of the empirical analysis are as follows. First, as a result of the Granger causality analysis using the Korea COVID-19 fear index, significant causality was found in the return of contact stocks such as Korean Air, Hana Tour, CJ CGV, and Paradise. Second, as a result of stock price prediction based on the LSTM model, Kakao, Korean Air, and Naver's prediction performance was high. Third, the investment performances of the Alexander filter entry rule using the predicted stock price were high in Naver futures and Kakao futures. This study can find a difference from previous studies in that it analyzed the influence of the spread of the COVID-19 pandemic on untact and contact stocks in the COVID-19 situation where the non-face-to-face economy is in full swing.

• Management & Information Systems Review
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• v.33 no.1
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• pp.45-63
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• 2014

A trading system is a computer trading program that automatically submits trades to an exchange. Mechanical a trading system to execute trade is spreading in the stock market. However, a trading system to trade a single asset might occur instability of the profit because payoff of this system is determined a asset movement. Therefore, it is necessary to develop a trading system that is trade two assets such as a pair trading that is to sell overvalued assets and buy the undervalued ones. The aim of this study is to propose a relative value based trading system designed to yield stable and profitable profits regardless of market conditions. In fact, we propose a procedure for building a trading system that is based on the rough set analysis of indicators derived from a price ratio between two assets. KOSPI 200 index futures and S&P 500 index futures are used as a data for evaluation of the proposed trading system. We intend to examine the usefulness of this model through an empirical study.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.147-155
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• 2022

In this study, we developed a system to dynamically balance a daily stock portfolio and performed trading simulations using gradient boosting and genetic algorithms. We collected various stock market data from stocks listed on the KOSPI and KOSDAQ markets, including investor-specific transaction data. Subsequently, we indexed the data as a preprocessing step, and used feature engineering to modify and generate variables for training. First, we experimentally compared the performance of three popular gradient boosting algorithms in terms of accuracy, precision, recall, and F1-score, including XGBoost, LightGBM, and CatBoost. Based on the results, in a second experiment, we used a LightGBM model trained on the collected data along with genetic algorithms to predict and select stocks with a high daily probability of profit. We also conducted simulations of trading during the period of the testing data to analyze the performance of the proposed approach compared with the KOSPI and KOSDAQ indices in terms of the CAGR (Compound Annual Growth Rate), MDD (Maximum Draw Down), Sharpe ratio, and volatility. The results showed that the proposed strategies outperformed those employed by the Korean stock market in terms of all performance metrics. Moreover, our proposed LightGBM model with a genetic algorithm exhibited competitive performance in predicting stock price movements.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.255-267
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• 2011

The aim of this study is to utilize system trading for making investment decisions and use technical analysis and Dynamic Time Warping (DTW) to determine similar patterns in the frequency of stock data and ascertain the optimal timing for trade. The study will examine some of the most common patterns in the futures market and use DTW in terms of their frequency (10, 30, 60 minutes, and daily) to discover similar patterns. The recognized similar patterns were verified by executing trade simulation after applying specific strategies to the technical indicators. The most profitable strategies among the set of strategies applied to common patterns were again applied to the similar patterns and the results from DTW pattern recognition were examined. The outcome produced useful information on determining the optimal timing for trade by using DTW pattern recognition through system trading, and by applying distinct strategies depending on data frequency.

• Korean Management Science Review
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• v.31 no.1
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• pp.49-61
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• 2014

We investigate the hedging effectiveness of incorporating single-stock futures into the corresponding stocks. Investing in only stocks frequently causes too much risk when market volatility suddenly rises. We found that single-stock futures help reduce the variance and risk levels of the corresponding stocks invested. We use daily prices of Korean stocks and their corresponding futures for the time period from December 2009 to August 2013 to test the hedging effect. We also use system trading technique that uses automatic trading program which also has several simulation functions. Moving average strategy, Stochastic's strategy, Larry William's %R strategy have been considered for hedging strategy of the futures. Hedging effectiveness of each strategy was analyzed by percent reduction in the variance between the hedged and the unhedged variance. The results clearly showed that examined hedging strategies reduce price volatility risk compared to unhedged portfolio.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.436-438
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• 2017

자본주의의 꽃인 주식시장은 파생시장에 의해 영향을 받고 있으며, 파생시장은 지수옵션 상품에 의해 영향을 받고 있다. 최근 들어 시스템 트레이딩에 대한 관심이 점점 더해가고 있으며 투자자에게 컴퓨터 시스템과 매매 전략에 대한 이해를 요구하고 있다. 지수옵션 시장은 만기일을 기준으로 마치 파도와 같이 순간순간 살아 움직이고 있다. 옵션에 대한 효과적인 관점은 투자자에게 확률 높은 매력적인 전략을 제공하며 옵션의 움직임을 전체적으로 해석할 수 있게 한다, 그리고 궁극적으로 옵션가의 예측을 가능하게 한다. 행사가와 방향성에 의한 개별 옵션은 함수로 해석될 수 있다. 다양한 입력값에 의해 가격이라는 하나의 출력값이 결정되는 구조이다. 입력값에는 지수, 시간, 거래량 의 세가지 카테고리로 이루어진다. 이중 거래량은 예측이 가능한데, 개별 옵션이 아닌 앙상불의 경우 출력값으로 처리될 수 있다. 하지만 앙상불 옵션에서 개별 옵션가는 경직성을 가지게 되어 예상가의 차이에 의한 압력이 발생하게 된다. 이 압력은 이후의 지수변화에 핵심적인 에너지로 작용할 수 있다. 압력의 측정은 다양한 방법이 있을 수 있는데, 본 논문에서는 뉴로-퍼지 시스템을 이용한 예측값과의 차이를 측정하여 계산하였다. 일단 학습된 뉴로-퍼지 시스템은 가격을 예측하게 되며, 실제 가격과의 괴리는 압력으로 해석할 수 있다.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.199-207
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• 2019

This study aims to analyze the profitability of Robo-Advisors portfolio combined with the analysts' forecasts on the Korean stock prices. Sample stocks are 8 blue-chips and sample period is from 2003 to 2019. Robo-Advisor portfolio was suggested using the Black-Litterman model combined with the analysts' forecasts and its profitability was analyzed. Empirical result showed the suggested Robo-Advisor algorithm produced 1% annual excess return more than that of the benchmark. The study documented that the analysts' forecasts had an economic value when applied in the Robo-Advisor portfolio despite the prevalent blames from investors. The profitability on small or medium-sized stocks will need to be analyzed in the Robo-Advisor context because their information is relatively less known to investors and as such is expected to be strongly influenced by the analysts' forecasts.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.123-130
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• 2022

Pair trading is a statistical arbitrage investment strategy. Traditionally, cointegration has been utilized in the pair exploring step to discover a pair with a similar price movement. Recently, the clustering analysis has attracted many researchers' attention, replacing the cointegration method. This study tests a clustering-driven pair trading investment strategy in the Korean stock market. If a pair detected through clustering has a large spread during the spread exploring period, the pair is included in the portfolio for backtesting. The profitability of the clustering-driven pair trading strategies is investigated based on various profitability measures such as the distribution of returns, cumulative returns, profitability by period, and sensitivity analysis on different parameters. The backtesting results show that the pair trading investment strategy is valid in the Korean stock market. More interestingly, the clustering-driven portfolio investments show higher performance compared to benchmarks. Note that the hierarchical clustering shows the best portfolio performance.