Abstract
Estimation of stock prices is very important for traders and investors to make intelligent decisions. In order to understand the relationship between previous stock prices and future prices, this paper gives an overview of Recurrent Neural Networks(RNNs) that are used in predicting stock prices. Recurrent Neural Networks are designed to identify relationships between past and future states of a time series and non-linear patterns in sequential data, in opposite to traditional methods like multiple linear regression. After collecting and preprocessing previous stock data, the RNN model is trained to estimate future stock prices. By differentiating actual values with expected values, the model’s accuracy is evaluated and market trends are displayed. This method is used on international markets as well as the National Stock Exchange(NSE) and Bombay Stock Exchange(BSE) in India, giving investors access useful data to help them make data-driven decisions.
References
- S. Selvin, R. Vinayakumar, E. A. Gopalakrishnan, V. K. Menon and K. P. Soman, "Stock price prediction using LSTM, RNN and CNN-sliding window model," 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017, pp. 1643-1647, doi: 10.1109/ICACCI.2017.8126078. keywords: {Companies;Time series analysis;Logic gates;Predictive models;Forecasting;Data models;Machine learning;Time series;Stock market;RNN;LSTM;CNN}
- Pawar, K., Jalem, R.S., Tiwari, V. (2019). Stock Market Price Prediction Using LSTM RNN. In: Rathore, V., Worring, M., Mishra, D., Joshi, A., Maheshwari, S. (eds) Emerging Trends in Expert Applications and Security. Advances in Intelligent Systems and Computing, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-2285-3_58
- Ghosh, A., Bose, S., Maji, G., Debnath, N., & Sen, S. (2019, September). Stock price prediction using LSTM on Indian share market. In Proceedings of 32nd international conference on (Vol. 63, pp. 101-110).
- Moghar, A., & Hamiche, M. (2020). Stock market prediction using LSTM recurrent neural network. Procedia computer science, 170, 1168-1173.
- Sunny, M. A. I., Maswood, M. M. S., & Alharbi, A. G. (2020, October). Deep learning-based stock price prediction using LSTM and bi-directional LSTM model. In 2020 2nd novel intelligent and leading emerging sciences conference (NILES) (pp. 87-92). IEEE.
- Mehtab, S., & Sen, J. (2020, November). Stock price prediction using CNN and LSTM-based deep learning models. In 2020 International Conference on Decision Aid Sciences and Application (DASA) (pp. 447-453). IEEE.
- Bhandari, H. N., Rimal, B., Pokhrel, N. R., Rimal, R., Dahal, K. R., & Khatri, R. K. (2022). Predicting stock market index using LSTM. Machine Learning with Applications, 9, 100320.
- Jahan, I., & Sajal, S. (2018). Stock price prediction using recurrent neural network (RNN) algorithm on time-series data. In 2018 Midwest instruction and computing symposium. Duluth, Minnesota, USA: MSRP.
- Mehar Vijh, Deeksha Chandola, Vinay Anand Tikkiwal, Arun Kumar, “Stock Closing Price Prediction using Machine Learning Techniques”, International Conference on Computational Intelligence and Data Science (ICCIDS 2019)
- Kishor Kumar Reddy C and Vijaya Babu B, “ISLGAS: Improved Supervised Learning in Quest using Gain Ratio as Attribute Selection Measure”, International Journal of Control Theory and Applications, 2016
- Sreelatha, Gavini, A. Vinaya Babu, and Divya Midhunchakkarvarthy. "A Survey on Cloud Attack Detection using Machine Learning Techniques." International Journal of Computer Applications 975 (2020): 8887.
- Cakra, Y. E., & Trisedya, B. D. (2015, October). Stock price prediction using linear regression based on sentiment analysis. In 2015 international conference on advanced computer science and information systems (ICACSIS) (pp. 147-154). IEEE.
- Sreelatha, G., Vinaya Babu, A., Midhunchakkarvarthy, D. (2021). Extended Equilibrium-Based Transfer Learning for Improved Security in Cloud Environment. In: Suma, V., Chen, J.IZ., Baig, Z., Wang, H. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-16-1395-1_4
