Analysis of Stock Market using Machine Learning
Keywords:Machine Learning, Deep Learning, Stock Market, RNN, LSTM
Machine Learning is a prominent area of research that emphasizes on finding patterns in existential data. The field of Machine Learning, can be concisely described as enabling computers to make productive predictions using previous experiences. As there is a large amount of information being available everywhere, it is very important to analyze this data in order to extract some useful information and thus developing algorithms based on this analysis. This can hence be done through data mining and Machine Learning. In addition to many other fields, Machine Learning models have broad applications in the field Bioinformatics. The complexity involved in biological analysis has led to the development of experienced Machine Learning methods. This research paper discusses the importance of a data-driven approach, compared to the formalization of traditional Artificial Intelligence and also primarily focuses on a key approach to forecast company's workflow using Machine learning.
S. O. Ojo, P. A. Owolawi, M. Mphahlele and J. A. Adisa, "Stock Market Behaviour Prediction using Stacked LSTM Networks*," 2019 International Multidisciplinary Information Technology and Engineering Conference (IMITEC), 2019, pp. 1-5, doi: 10.1109/IMITEC45504.2019.9015840.
S. Liu, G. Liao and Y. Ding, "Stock transaction prediction modeling and analysis based on LSTM," 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2018, pp. 2787-2790, doi: 10.1109/ICIEA.2018.8398183.
K. Chen, Y. Zhou and F. Dai, "A LSTM-based method for stock returns prediction: A case study of China stock market," 2015 IEEE International Conference on Big Data (Big Data), 2015, pp. 2823-2824, doi: 10.1109/BigData.2015.7364089.
W. Jhang, S. Gao, C. Wang and M. Hsieh, "Share Price Trend Prediction Using Attention with LSTM Structure," 2019 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2019, pp. 208-211, doi: 10.1109/SNPD.2019.8935806.
M. Faraz, H. Khaloozadeh and M. Abbasi, "Stock Market Prediction-by-Prediction Based on Autoencoder Long Short-Term Memory Networks," 2020 28th Iranian Conference on Electrical Engineering (ICEE), 2020, pp. 1-5, doi: 10.1109/ICEE50131.2020.9261055.
Y. Zeng and X. Liu, "A-Stock Price Fluctuation Forecast Model Based on LSTM," 2018 14th International Conference on Semantics, Knowledge and Grids (SKG), 2018, pp. 261-264, doi: 10.1109/SKG.2018.00044.
K. A. Althelaya, E. M. El-Alfy and S. Mohammed, "Evaluation of bidirectional LSTM for short-and long-term stock market prediction," 2018 9th International Conference on Information and Communication Systems (ICICS), 2018, pp. 151-156, doi: 10.1109/IACS.2018.8355458.
S. Kumar and D. Ningombam, "Short-Term Forecasting of Stock Prices Using Long Short Term Memory," 2018 International Conference on Information Technology (ICIT), 2018, pp. 182-186, doi: 10.1109/ICIT.2018.00046.
How to Cite
Copyright (c) 2021 Riteesh Mandi, Skanda G
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on any article in the International Journal of Engineering and Applied Physics is retained by the author(s) under the Creative Commons license, which permits unrestricted use, distribution, and reproduction provided the original work is properly cited.
Authors grant IJEAP a license to publish the article and identify IJEAP as the original publisher.
Authors also grant any third party the right to use, distribute and reproduce the article in any medium, provided the original work is properly cited.