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Various studies have been conducted from the past to the present because stock price forecasts provide stability in the national economy and huge profits to investors. Recently, there have been many studies that suggest stock price prediction models using various input data such as macroeconomic indicators and emotional analysis. However, since each study was conducted individually, it is difficult to objectively compare each method, and studies on their impact on stock price prediction are still insufficient. In this paper, the effect of input data currently mainly used on the stock price is evaluated through the predicted value of the deep learning model and the error rate of the actual stock price. In addition, unlike most papers in emotional analysis, emotional analysis using the news body was conducted, and a method of supplementing the results of each emotional analysis is proposed through three emotional analysis models. Through experiments predicting Microsoft's revised closing price, the results of emotional analysis were found to be the most important factor in stock price prediction. Especially, when all of input data is used, error rate of ensembled sentiment analysis model is reduced by 58% compared to the baseline.

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기사명 저자명 페이지 원문 목차
웨어러블 단말의 가속도 센서를 이용한 수면 중 움직임 및 자세를 감지하는 방법 = A method for detecting movement and posture during sleep using an acceleration sensor of a wearable device 전영준, 김상혁, 강순주 p. 1-7

실내 미세먼지 및 소음 모니터링 시스템 설계 및 구현 = Design and implementation of an indoor particulate matter and noise monitoring system 조현태 p. 9-17

파킨슨 환자의 증상들을 데이터화하여 분석하고 관리할 수 있는 다양한 센서가 탑재된 웨어러블 디바이스 개발 = Development of wearable devices equipped with multi sensor that can analyze and manage symptoms of Parkinson's patients as data 김상혁, 전영준, 강순주 p. 19-24

IR-UWB 레이더와 Lomb-Scargle Periodogram을 이용한 비접촉 심박 탐지 = Non-contact heart rate monitoring using IR-UWB radar and Lomb-Scargle periodogram 변상선 p. 25-32

오픈 소스 기반의 정찰 및 탐색용 드론 프로그램 개발 = Development of the program for reconnaissance and exploratory drones based on open source 채범석, 김정환 p. 33-40

경량화된 임베디드 시스템에서 역 원근 변환 및 머신 러닝 기반 차선 검출 = Lane detection based on inverse perspective transformation and machine learning in lightweight embedded system 홍성훈, 박대진 p. 41-49

멀티코어 시스템에서 TLB Lockdown에 의한 TLB Miss 영향 분석 = Investigation on TLB miss impact through TLB lockdown in multi-core systems 송대영, 박시형, 김형신 p. 59-65

ISAR 영상을 이용한 효과적인 편대비행 표적식별 연구 = A study on effective identification of targets flying in formation ISAR images 차상빈, 최인오, 정주호, 박상홍 p. 67-76

뉴스 감성 앙상블 학습을 통한 주가 예측기의 성능 향상 = An accurate stock price forecasting with ensemble learning based on sentiment of news 김하은, 박영욱, 유시은, 정성우, 유준혁 p. 51-58

참고문헌 (24건) : 자료제공( 네이버학술정보 )

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
1 E. F. FAMA, "Efficient Capital Markets a Review of Theory and Empirical Work," The Fama Portfolio, pp.76-121, 2021. 미소장
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3 T. Gao, Y. Chai, Y. Liu, "Applying Long Short Term Momory Neural Networks for Predicting Stock Closing Price," 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS). pp. 575-578, 2017. 미소장
4 J. G. De Gooijer, R. J. Hyndman, “25 years of IIF time Series Forecasting: a Selective Review,“ International Journal of Forecasting Vol. 22, No. 3, pp. 443–473, 2006. 미소장
5 S. Hochreiter, J. Schmidhuber, "Long Short-Term Memory," in Neural Computation, Vol. 9, No. 8, pp. 1735-1780, 1997. 미소장
6 K. Cho, B. Van Merriënboer, D. Bahdanau, “On the Properties of Neural Machine Translation:Encoder-decoder Approaches,” arXiv preprint arXiv:1409.1259., 2014. 미소장
7 Y. S. Kim, J. S. Cha, “Analysis of the Relationship Between Stock Prices and Macroeconomic Variables,"Bank Of Korea, pp. 99-12 ,1999 . 미소장
8 P. S. Cho, “The Stock Forecasting Model using Bidirectional LSTM and Reinforcement Learning,” PhD Thesis. Hanyang University. 2021. 미소장
9 D. H. Shin, K. H. Choi, C. B. Kim, “Deep Learning Model for Prediction Rate Improvement of Stock Price Using RNN and LSTM," The Journal of Korean Institute of Information Technology, Vol. 15, No. 10, pp. 9-16, 2017 미소장
10 S. H. Hong, “A Research on Stock Price Prediction Based on Deep Learning and Economic Indicators,“Journal of Digital Convergence Vol. 18. No. 11, pp. 267-272, 2020 미소장
11 W. Antweiler, M. Z. Frank, “Is all that talk just noise?,“ The Information Content of Internet Stock Message Board," J Finance Vol. 59, No. 3, pp. 1259–1294, 2004. 미소장
12 M. Baker, J. Wurgler, "Investor Sentiment and the Cross Section of Stock Returns," The Journal of Finance, Vol. 61, No. 4, pp. 1645-1680, 2006. 미소장
13 M. J. Kim, J. H. Ryu, D. H. Cha, M. K. Sim, "Stock Price Prediction Using Sentiment Analysis: from “Stock Discussion Room” in Naver," The Jounal of Society for e-Business Studies ,Vol. 25, No. 4, pp. 61-75, 2020 미소장
14 J. G. Koh, G. Y. Lee, I. J. Son, Y. R. Gwon, "Stock Price Index Prediction Program Using Deep Learning Techniques," Proceedings of the Korean Society of Computer Information Conference, pp. 525-526, 2021 미소장
15 S. H. Hong, "A Study on Stock Price Prediction System Based on Text Mining Method Using LSTM and Stock Market news," Journal of Digital Convergence, Vol. 18, No. 7, pp. 223-228, 2020 미소장
16 E. Alickovic, A.. Subasi, “Normalized Neural Networks for Breast Cancer Classification,” In: International Conference on Medical and Biological Engineering. Springer, Cham, pp. 519-524, 2019. 미소장
17 C. Hutto, E. Gilbert, "Vader: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text," Proceedings of the International AAAI Conference on Web and Social Media. Vol. 8. No. 1, pp. 216–225, Jan. 2015. 미소장
18 J. Devlin, M. W. Chang, K. Lee, K. Toutanova, “Bert:Pre-training of Deep Bidirectional Transformers for Language Understanding,” arXiv preprint arXiv:1810.04805, 2018. 미소장
19 D. Araci, "Finbert: Financial Sentiment Analysis with Pre-trained Language Models," arXiv preprint arXiv:1908.10063, 2019. 미소장
20 T. Mikolov, K. Chen, G. Corrado, J. Dean, “Efficient Estimation of Word Representations in Vector Space,”arXiv preprint arXiv:1301.3781., 2013. 미소장
21 I. S. Kang, “A Comparative Study on Using SentiWordNet for English Twitter Sentiment Analysis,“ Journal of Korean Institute of Intelligent Systems, Vol. 23, No. 4, pp. 317-324, 2013 . 미소장
22 T. Mikolov, K. Chen, G. Corrado, J. Dean, “Efficient Estimation of Word Representations in Vector Space,”arXiv preprint arXiv:1301.3781, 2013 미소장
23 T, Fischer, C. Krauss, "Deep Learning with Long Short-term Memory Networks for Financial Market Predictions," European Journal of Operational Research, Vol. 270, No. 2, pp. 654-669, 2018. 미소장
24 X. Shi, Z. Chen, H. Wang, D. Y. Yeung, “Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting,“ In: Advances in Neural Information Processing Systems, pp. 802–810, 2015. 미소장

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