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Feature Selection and Hyper-parameter Tuning Technique using Neural Network for Stock Market Prediction | ||
| Journal of Information Technology Management | ||
| دوره 12، Special Issue: The Importance of Human Computer Interaction: Challenges, Methods and Applications.، 2020، صفحه 89-108 اصل مقاله (1.19 M) | ||
| نوع مقاله: Special Issue: The Importance of Human Computer Interaction: Challenges, Methods and Applications | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2020.79368 | ||
| نویسندگان | ||
| Karanveer Singh1؛ Rahul Tiwari1؛ Prashant Johri1؛ Ahmed A. Elngar2 | ||
| 1School of Computer Science and Engineering, Galgotias University, Greater Noida, India. | ||
| 2Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef City, Egypt. | ||
| چکیده | ||
| The conjecture of stock exchange is the demonstration of attempting to decide the forecast estimation of a particular sector or the market, or the market as a whole. Every stock every investor needs to foresee the future evaluation of stocks, so a predicted forecast of a stock’s future cost could return enormous benefit. To increase the accuracy of the Conjecture of stock Exchange with daily changes in the market value is a bottleneck task. The existing stock market prediction focused on forecasting the regular stock market by using various machine learning algorithms and in-depth methodologies. The proposed work we have implemented describes the new NN model with the help of different learning techniques like hyperparameter tuning which includes batch normalization and fitting it with the help of random-search-cv. The prediction of the Stock exchange is an active area for research and completion in Numerai. The Numerai is the most robust data science competition for stock market prediction. Numerai provides weekly new datasets to mold the most exceptional prediction model. The dataset has 310 features, and the entries are more than 100000 per week. Our proposed new neural network model gives accuracy is closely 86%. The critical point, it isn’t easy with our proposed model with existing models because we are training and testing the proposed model with a new unlabeled dataset every week. Our ultimate aim for participating in Numerai competition is to suggest a neural network methodology to forecast the stock exchange independent of datasets with reasonable accuracy. | ||
| کلیدواژهها | ||
| Neural network؛ Stock market prediction؛ Numerai؛ NMR؛ Deep learning | ||
| مراجع | ||
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Al-Hmouz, R., Pedrycz, W., & Balamash, A. (2015). Description and prediction of time series: A general framework of Granular Computing. Expert Systems with Applications, 42(10), 4830-4839. doi:10.1016/j.eswa.2015.01.060
Babu, M., N.Geethanjali, & B.Satyanarayana, P. (2012, January 02). Clustering Approach to Stock Market Prediction. Retrieved from http://paper.researchbib.com/view/paper/59204
Bagheri, A., Peyhani, H. M., & Akbari, M. (2014). Financial forecasting using ANFIS networks with Quantum-behaved Particle Swarm Optimization. Expert Systems with Applications, 41(14), 6235-6250. doi:10.1016/j.eswa.2014.04.003
Bao, W., Yue, J., & Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short term memory. Plos One, 12(7). doi:10.1371/journal.pone.0180944
Bengio, Y. (2009). Learning Deep Architectures for AI. Foundations and Trends® in Machine Learning, 2(1), 1-127. doi:10.1561/2200000006
Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83, 187-205. doi:10.1016/j.eswa.2017.04.030
Ghosh, P., Neufeld, A., & Sahoo, J. K. (2020, April 21). Forecasting directional movements of stock prices for intraday trading using LSTM and random forests. Retrieved from https://arxiv.org/abs/2004.10178
Gunduz, H., Cataltepe, Z., & Yaslan, Y. (2017). Stock market direction prediction using deep neural networks. 2017 25th Signal Processing and Communications Applications Conference (SIU). doi:10.1109/siu.2017.7960512
Hiransha M, Dr. E. A. Gopalakrishnan, Vijay Krishna Menon, Dr. Soman K. P, (2018). NSE Stock Market Prediction Using Deep-Learning Models. Procedia Computer Science, 132, 1351-1362. doi:10.1016/j.procs.2018.05.050
Idrees, S. M., Alam, M. A., & Agarwal, P. (2019). A Prediction Approach for Stock Market Volatility Based on Time Series Data. IEEE Access, 7, 17287-17298. doi:10.1109/access.2019.2895252
Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., & Alfakeeh, A. S. (2020). Stock market prediction using machine learning classifiers and social media, news. Journal of Ambient Intelligence and Humanized Computing. doi:10.1007/s12652-020-01839-w
Khan, W., Malik, U., Ghazanfar, M. A., Azam, M. A., Alyoubi, K. H., & Alfakeeh, A. S. (2019). Predicting stock market trends using machine learning algorithms via public sentiment and political situation analysis. Soft Computing, 24(15), 11019-11043. doi:10.1007/s00500-019-04347-y
Kimoto, T., Asakawa, K., Yoda, M., & Takeoka, M. (1990). Stock market prediction system with modular neural networks. 1990 IJCNN International Joint Conference on Neural Networks. doi:10.1109/ijcnn.1990.137535
Kusuma, R. M., Ho, T., Kao, W., Ou, Y., & Hua, K. (2019, February 26). Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market. Retrieved from https://arxiv.org/abs/1903.12258
Lathuiliere, S., Mesejo, P., Alameda-Pineda, X., & Horaud, R. (2020). A Comprehensive Analysis of Deep Regression. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1-1. doi:10.1109/tpami.2019.2910523
Lee, J., Kim, R., Koh, Y., & Kang, J. (2019). Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network. IEEE Access, 7, 167260-167277. doi:10.1109/access.2019.2953542
Liu, G., & Wang, X. (2019). A Numerical-Based Attention Method for Stock Market Prediction With Dual Information. IEEE Access, 7, 7357-7367. doi:10.1109/access.2018.2886367
Minh, D. L., Sadeghi-Niaraki, A., Huy, H. D., Min, K., & Moon, H. (2018). Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network. IEEE Access, 6, 55392-55404. doi:10.1109/access.2018.2868970
Nguyen, T., & Yoon, S. (2019). A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning. Applied Sciences, 9(22), 4745. doi:10.3390/app9224745
Parmar, I., Agarwal, N., Saxena, S., Arora, R., Gupta, S., Dhiman, H., & Chouhan, L. (2018). Stock Market Prediction Using Machine Learning. 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC). doi:10.1109/icsccc.2018.8703332
Parmar, I., Agarwal, N., Saxena, S., Arora, R., Gupta, S., Dhiman, H., & Chouhan, L. (2018). Stock Market Prediction Using Machine Learning. 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC). doi:10.1109/icsccc.2018.8703332
Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259-268. doi:10.1016/j.eswa.2014.07.040
Qiu, J., Wang, B., & Zhou, C. (2020, January 03). Forecasting stock prices with long-short term memory neural network based on attention mechanism. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/31899770
Ren, R., Wu, D. D., & Liu, T. (2019). Forecasting Stock Market Movement Direction Using Sentiment Analysis and Support Vector Machine. IEEE Systems Journal, 13(1), 760-770. doi:10.1109/jsyst.2018.2794462
S Abdulsalam Sulaiman Olaniyi, Adewole, Kayode S. Jimoh, R. G. Stock Trend Prediction Using Regression Analysis – A Data Mining Approach, ARPN Journal of Systems and Software, Volume 1 No. 4, JULY 2011
Selvamuthu, D., Kumar, V., & Mishra, A. (2019). Indian stock market prediction using artificial neural networks on tick data. Financial Innovation, 5(1). doi:10.1186/s40854-019-0131-7
Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2017). Stock price prediction using LSTM, RNN and CNN-sliding window model. 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). doi:10.1109/icacci.2017.8126078
Son, Y., Noh, D., & Lee, J. (2012). Forecasting trends of high-frequency KOSPI200 index data using learning classifiers. Expert Systems with Applications, 39(14), 11607-11615. doi:10.1016/j.eswa.2012.04.015
Stoean, C., Paja, W., Stoean, R., & Sandita, A. (2019). Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations. Plos One, 14(10). doi:10.1371/journal.pone.0223593
Wang, Y., Liu, H., Guo, Q., Xie, S., & Zhang, X. (1970). Stock Volatility Prediction by Hybrid Neural Network: Semantic Scholar. Retrieved from https://www.semanticscholar.org/paper/Stock-Volatility-Prediction-by-Hybrid-Neural-Wang-Liu/310b54f1913ac93cb2817e810c62e92e6a65e326
Yuan, X., Yuan, J., Jiang, T., & Ain, Q. U. (2020). Integrated Long-Term Stock Selection Models Based on Feature Selection and Machine Learning Algorithms for China Stock Market. IEEE Access, 8, 22672-22685. doi:10.1109/access.2020.2969293 | ||
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