Open Access
ITM Web Conf.
Volume 40, 2021
International Conference on Automation, Computing and Communication 2021 (ICACC-2021)
Article Number 03020
Number of page(s) 5
Section Computing
Published online 09 August 2021
  1. F. Obeid Algorithmic Trading: High Frequency & Low Frequency Trading, FERM 6303: Financial Assets and Markets, (2018). [Google Scholar]
  2. E. Boehmer, K. Fong, J. Wu, International evidence on Algo. Trading, Journal of financial and quantitative analysis,(2015). [Google Scholar]
  3. M. Bogliardi, F. S. Canepa, G. Frisina, Multiple Spread Trading 60, An investment methodology neutral to financial market trends English Version, (2019). [Google Scholar]
  4. P. Rys, R. Slepaczuk, ML in algo. trading strategy opt. implementation and efficiency, Faculty of Economic Sciences, University of Warsaw, (2018). [Google Scholar]
  5. E. Sorhun, How Is a Machine Learning Algorithm Now-Casting Stock Returns? A Test for ASELSAN, Springer Science and Business Media LLC, (2019). [Google Scholar]
  6. N. Budhani, Dr. C. K. Jha, S. K. Budhani―Prediction of Stock Market Using Artificial Neural Network, International Conference of Soft Computing Techniques for Engineering and Technology (ICSCTET), (2014). [Google Scholar]
  7. M. Adya and F. Collopy, How effective are neural networks at fore-casting and prediction, A review and evaluation, J. Forecasting, vol. 17, (1998). [Google Scholar]
  8. M. R. Vargas, C. E. M. dos Anjos, G. L. G. Bichara and A.G. Evsukoff, “Deep Leaming for Stock Market Prediction Using Technical Indicators and Financial News Articles,” International Joint Conference on Neural Networks (IJCNN), (2018). [Google Scholar]
  9. Lo, Andrew W., H. Mamaysky and J. Wang. “Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, And Empirical Implementation,” Journal of Finance, (2000). [Google Scholar]
  10. F. Allen, R. Karjalainen, using genetic algorithms to find technical trading rules, Journal of financial economics, vol. 51, issue 2, (1999). [Google Scholar]
  11. K. Chen, Y. Zhou and F. Dai ―A LSTM-based method for stock returns prediction: A case study of China stock market, IEEE International Conference on Big Data (Big Data), (2015). [Google Scholar]
  12. H. Duzan, NSBM. Shariff, Ridge regression for solving the multicollinearity problem: review of methods and models, Journal of Applied Sciences, 15, (2015). [Google Scholar]
  13. T. Gao, Y. Chai and Y. Liu, “Applying long short-term memory neural networks for predicting stock closing price,” 8th IEEE International conference on software engineering and service science(ICSESS), (2017). [Google Scholar]
  14. D. Wei, “Prediction of Stock Price Based on LSTM Neural Network,” International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM), (2019). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.