• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.63-79
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• 2022

Recently, various studies have been conducted on stock price prediction using machine learning and deep learning techniques. Among these studies, the latest studies have attempted to predict stock prices using limit order books, which contain buy and sell order information of stocks. However, most of the studies using limit order books consider only the trend of limit order books over the most recent period of a specified length, and few studies consider both the medium and short term trends of limit order books. Therefore, in this paper, we propose a deep learning-based prediction model that predicts stock price more accurately by considering both the medium and short term trends of limit order books. Moreover, the proposed model considers news headlines during the same period to reflect the qualitative status of the company in the stock price prediction. The proposed model extracts the features of changes in limit order books with CNNs and the features of news headlines using Word2vec, and combines these information to predict whether a particular company's stock will rise or fall the next day. We conducted experiments to predict the daily stock price fluctuations of five stocks (Amazon, Apple, Facebook, Google, Tesla) with the proposed model using the real NASDAQ limit order book data and news headline data, and the proposed model improved the accuracy by up to 17.66%p and the average by 14.47%p on average. In addition, we conducted a simulated investment with the proposed model and earned a minimum of $492.46 and a maximum of $2,840.93 depending on the stock for 21 business days.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.541-544
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• 2021

본 논문은 어떤 기업의 주식 주문 정보를 담고 있는 호가창(limit order book)과 해당 기업과 관련된 뉴스 헤드라인을 사용하여 해당 기업의 주가 등락을 예측하는 딥러닝 기반 모델을 제안한다. 제안 모델은 호가창의 중기 변화와 단기 변화를 모두 고려하는 한편, 동기간 발생한 뉴스 헤드라인까지 예측에 고려함으로써 주가 등락 예측 정확도를 높인다. 제안 모델은 호가창의 변화의 특징을 CNN(convolutional neural network)으로 추출하고 뉴스 헤드라인을 Word2vec으로 생성된 단어 임베딩 벡터를 사용하여 나타낸 뒤, 이들 정보를 결합하여 특정 기업 주식의 다음 날 등락여부를 예측한다. NASDAQ 실데이터를 사용한 실험을 통해 제안 모델로 5개 종목(Amazon, Apple, Facebook, Google, Tesla)의 일일 주가 등락을 예측한 결과, 제안 모델은 기존 방법에 비해 정확도를 최대 17.14%, 평균 10.7% 향상시켰다.

• Journal of Sericultural and Entomological Science
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• v.28 no.2
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• pp.48-51
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• 1986

Resistance to the flacherie virus(FV) and the densonucleosis virus(DNV) of 10 Japanese lines and 10 Chinese lines used for hybrids was tested and the results obtained are as follows ; 1. Hansang #1 showed the highest resistance to the FV among the tested Japanese lines whereas Mudeung was of lowest resistance. In Chinese lines tested on the resistance to the FV, Jam118 was the highest while Jam 116 was the lowest. 2. In Japanese lines tested on the resistance to the DNV, it was shown that Jam 117, Gyeongchy, Mudeung, Hansaeng #1 and Hansaeng #3 were of the complete resistance but Jam 115 showed the lowest resistance. On the other hand, all the Chinese lines tested showed the complete resistance to the DNV.

• Journal of Sericultural and Entomological Science
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• v.20 no.2
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• pp.32-35
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• 1978

The infectious flacherie viruses are serious diseases in the silkworm, which affects the cocoon crops. However, there is only a few study on this diseases up-to-date in Korea and, in this experiment, the authors investigated the resistance of the silkworm varieties to flacherie and Ina-flacherie viruses by peroral infection. A cross, Hansaeng ＃1$\times$ Harisaeng ＃2, showed the highest resistance to the flacherie virus a Mudeung$\times$Geumho showed the lowest resistance among the examined varieties. It seemed that the varietal difference of resistance against Ina-flacherie virus was appeared and Jam117$\times$ Jam118, Gyeongchu$\times$Yeonil, Mudeung$\times$Geumho, Hansaeng＃1$\times$Hansaeng＃2 and Hansaeng＃3 $\times$ Hansaeng ＃4 showed non susceptibility by peroral infection. However it was shown that Jam115 $\times$Jam116 had the lowest resistance to Ina-flacherie virus.