Browse > Article
http://dx.doi.org/10.6109/jkiice.2021.25.10.1435

Stock prediction analysis through artificial intelligence using big data  

Choi, Hun (Department of Information Management Systems, Catholic University of Pusan)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.10, 2021 , pp. 1435-1440 More about this Journal
Abstract
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.
Keywords
Stocks; Artificial intelligence; Artificial intelligence algorithms; Stock price prediction;
Citations & Related Records
연도 인용수 순위
  • Reference
1 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.
2 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.
3 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.   DOI
4 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.   DOI
5 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.   DOI
6 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.   DOI
7 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.   DOI
8 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.
9 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.   DOI
10 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.   DOI
11 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.
12 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.   DOI