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Stock Market Trend Prediction using Dueling Deep Q-Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 11 |
| Year of Publication: 2022 |
| Authors: N. Vijaya Lakshmi, R. Velumani |
10.5120/ijca2022921914
|
N. Vijaya Lakshmi, R. Velumani . Stock Market Trend Prediction using Dueling Deep Q-Network. International Journal of Computer Applications. 184, 11 ( May 2022), 1-4. DOI=10.5120/ijca2022921914
Abstract
The goal of the project is to develop a new technique to predict the stock trend, Because the stock market is so important to acountry's economic prosperity, anticipating changes in market behavior has become crucial to shareholders stock market is one of the popular ways to earn the passive income, A successful prediction of stocks future price is challenging because it could either give significant profit or significant loss for the investors. Prediction plays a vital role in the investment of stocks, so, we can maximize the profit, if the system takes the right action, there are many works on stock prediction using supervised learning, but they are not accurate, so to maximize the profit, we use dueling deep Q-learning, which helps to select the best action at a best particular state.
References
- P. Somani, S. Talele and S. Sawant, "Stock market prediction using Hidden Markov Model," 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference, Chongqing, 2014, pp. 89-92.
- K. Abhishek, A. Khairwa, T. Pratap and S. Prakash, "A stock market prediction model using Artificial Neural Network," 2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT'12), 2012, pp. 1-5, doi: 10.1109/ICCCNT.2012.6396089.
- A. Gupta and B. Dhingra, "Stock market prediction using Hidden Markov Models," 2012 Students Conference on Engineering and Systems, 2012, pp. 1-4, doi: 10.1109/SCES.2012.6199099.
- T. Leangarun, P. Tangamchit and S. Thajchayapong, "Stock Price Manipulation Detection using Generative Adversarial Networks," 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India, 2018, pp. 2104-2111, doi: 10.1109/SSCI.2018.8628777.
- N. V. G. Raju, S. S. S. Padullaparti and S. P. R. Allam, "Inclination of Tech Stocks using Time Series Analysis and Prophecy of Returns using Recurrent Neural Network," 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2020, pp. 792-795, doi: 10.1109/ICSSIT48917.2020.9214170.
- Fangqiong Luo, Jiansheng Wu and Kesong Yan, "A novel nonlinear combination model based on Support Vector Machine for stock market prediction," 2010 8th World Congress on Intelligent Control and Automation, 2010, pp. 5048-5053, doi: 10.1109/WCICA.2010.5554607.
- Y. Lin, H. Guo and J. Hu, "An SVM-based approach for stock market trend prediction," The 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, 2013, pp. 1-7.
- Z. Hu, J. Zhu and K. Tse, "Stocks market prediction using Support Vector Machine," 2013 6th International Conference on Information Management, Innovation Management and Industrial Engineering, Xi'an, 2013, pp. 115-118.
- X. Chen and Z. He, "Prediction of Stock Trading Signal Based on Support Vector Machine," 2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA), Nanchang, China, 2015, pp. 651-654, doi: 10.1109/ICICTA.2015.165.
- M. Usmani, S. H. Adil, K. Raza and S. S. A. Ali, "Stock market prediction using machine learning techniques," 2016 3rd International Conference on Computer and Information Sciences (ICCOINS), Kuala Lumpur, 2016, pp. 322-327.
- H. N. Shah, "Prediction of Stock Market Using Artificial Intelligence," 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), Bombay, India, 2019, pp. 1-6, doi:10.1109/I2CT45611.2019 .9033776.
- K. S. Loke, "Impact of financial ratios and technical analysis on stock price prediction using random forests," 2017 International Conference on Computer and Drone Applications (IConDA), Kuching, Malaysia, 2017, pp. 38-42, doi: 10.1109/ICONDA.2017.8270396.
- 8.N. V. G. Raju, S. S. S. Padullaparti and S. P. R. Allam, "Inclination of Tech Stocks using Time Series Analysis and Prophecy of Returns using Recurrent Neural Network," 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2020, pp. 792-795, doi: 10.1109/ICSSIT48917.2020.9214170.
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