한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제25권10호
  • /
  • Pages.1435-1440
  • /
  • 2021
  • /
  • 2234-4772(pISSN)
  • /
  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
권호 표지
DOI QR코드

DOI QR Code

빅데이터를 활용한 인공지능 주식 예측 분석

Stock prediction analysis through artificial intelligence using big data

  • Choi, Hun (Department of Information Management Systems, Catholic University of Pusan)
  • 투고 : 2021.08.11
  • 심사 : 2021.09.15
  • 발행 : 2021.10.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

저금리 시대의 도래로 인해 많은 투자자들이 주식 시장으로 몰리고 있다. 과거의 주식 시장은 사람들이 기업 분석 및 각자의 투자기법을 통해 노동 집약적으로 주식 투자가 이루어졌다면 최근 들어 인공지능 및 데이터를 활용하여 주식 투자가 널리 이용되고 있는 실정이다. 인공지능을 통해 주식 예측의 성공률은 현재 높지 않아 다양한 인공지능 모델을 통해 주식 예측률을 높이는 시도를 하고 있다. 본 연구에서는 다양한 인공지능 모델에 대해 살펴보고 각 모델들간의 장단점 및 예측률을 파악하고자 한다. 이를 위해, 본 연구에서는 주식예측 인공지능 프로그램으로 인공신경망(ANN), 심층 학습 또는 딥 러닝(DNN), k-최근접 이웃 알고리즘(k-NN), 합성곱 신경망(CNN), 순환 신경망(RNN), LSTM에 대해 살펴보고자 한다.

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.

키워드

과제정보

This paper was supported by RESEARCH FUND offered from Catholic University of Pusan in 2021

참고문헌

  1. M. J. Cheon and O. Lee, "A Study on the stock price prediction and influence factors through NARX neural network optimization," Journal of the Korea Academia-Industrial cooperation Society, vol. 21, no. 8, pp. 572-578, 2020.
  2. J. M. An and K. Yoon, "Intelligent Government in the Age of Artificial Intelligence: Opportunities and Challenges," in Proceeding of the 17th annual conference on The Korean Association For Regional Information Society, pp. 1-11, 2021.
  3. J. Choi and K. Nam, "The Theoretical Basis of Linguistics in the Age of Artificial Intelligence: The Legacy of Firth's(1957) Linguistic Theory in 21st-century Linguistics," Journal of East-West Humanities, vol. 15, no. 4, pp. 43-82, 2021.
  4. H. Y. Shin and S. H. Kweon, "An Evaluation of Determinants to Viewer Acceptance of Artificial Intelligence-based News Anchor," Journal of the Korea Contents Association, vol. 21, no. 4, pp. 205-219, 2021. https://doi.org/10.5392/JKCA.2021.21.04.205
  5. Y. Bengio, A. Courville, and P. Vincent, "Representation Learning: A Review and New Perspectives," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798-1828, 2013. https://doi.org/10.1109/TPAMI.2013.50
  6. D. H. Cho, H. S. Ryou, S. H. Jung, and K. J. Oh, "Using AI to develop forecasting model in IPO market," Journal of the Korean Data And Information Science Society, vol. 31, no.3, pp. 579-590, 2020. https://doi.org/10.7465/jkdi.2020.31.3.579
  7. Y. J. Song, J. W. Lee, and J. W. Lee, "Performance Evaluation of Price-based Input Features in Stock Price Prediction using Tensorflow," KIISE Transactions on Computing Practices, vol. 23, no. 11, pp. 625-631, 2017. https://doi.org/10.5626/KTCP.2017.23.11.625
  8. S. H. Chun, "The Effect of Data Size on the k-NN Predictability: Application to Samsung Electronics Stock Market Prediction," Journal of Intelligence and Information Systems, vol. 25, no. 3, pp. 239-251, 2019. https://doi.org/10.13088/JIIS.2019.25.3.239
  9. M. S. Lee and H. C. Ahn, "A Time Series Graph based Convolutional Neural Network Model for Effective Input Variable Pattern Learning : Application to the Prediction of Stock Market," Journal of Intelligence and Information Systems, vol. 24, no. 1, pp.167-181, 2018. https://doi.org/10.13088/JIIS.2018.24.1.167
  10. J. H. Cui and S. H. Kim, "Predicting Stock Prices Based on Neural Networks Around Earnings Announcements," Journal of The Korean Data Analysis Society, vol. 22, no. 6, pp. 2667-2678, 2020. https://doi.org/10.37727/jkdas.2020.22.6.2667
  11. I. T. Joo and S. H. Choi, "Stock Prediction Model based on Bidirectional LSTM Recurrent Neural Network," Journal of Korea Institute of Information, Electronics, and Communication Technology, vol. 11, no. 2, pp. 204-208, 2018. https://doi.org/10.17661/JKIIECT.2018.11.2.204
  12. D. G. Kim, "Suggestion of a New Volatility Index(VIX) in Stock Market," in Proceeding of the 23th semi-annual conference on The Korea Institute of Information and Communication Engineering, pp. 283-285, 2019.