ITM Web Conf.
Volume 40, 2021International Conference on Automation, Computing and Communication 2021 (ICACC-2021)
|Number of page(s)||5|
|Published online||09 August 2021|
- F. Obeid Algorithmic Trading: High Frequency & Low Frequency Trading, FERM 6303: Financial Assets and Markets, (2018). [Google Scholar]
- E. Boehmer, K. Fong, J. Wu, International evidence on Algo. Trading, Journal of financial and quantitative analysis,(2015). [Google Scholar]
- M. Bogliardi, F. S. Canepa, G. Frisina, Multiple Spread Trading 60, An investment methodology neutral to financial market trends English Version, (2019). [Google Scholar]
- P. Rys, R. Slepaczuk, ML in algo. trading strategy opt. implementation and efficiency, Faculty of Economic Sciences, University of Warsaw, (2018). [Google Scholar]
- 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]
- 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]
- M. Adya and F. Collopy, How effective are neural networks at fore-casting and prediction, A review and evaluation, J. Forecasting, vol. 17, (1998). [CrossRef] [Google Scholar]
- 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]
- 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]
- F. Allen, R. Karjalainen, using genetic algorithms to find technical trading rules, Journal of financial economics, vol. 51, issue 2, (1999). [Google Scholar]
- 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]
- H. Duzan, NSBM. Shariff, Ridge regression for solving the multicollinearity problem: review of methods and models, Journal of Applied Sciences, 15, (2015). [Google Scholar]
- 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]
- D. Wei, “Prediction of Stock Price Based on LSTM Neural Network,” International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM), (2019). [Google Scholar]
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