Authors : Tudimilla Dheeraj Kumar Chary; K. Venkata Kavya; N. Nagarjun Reddy

Volume/Issue : Volume 9 - 2024, Issue 4 - April

Google Scholar : https://shorturl.at/7wJtc

Scribd : https://shorturl.at/nhRgA

DOI : https://doi.org/10.38124/ijisrt/IJISRT24APR1602

Abstract : The advent of deep learning techniques, particularly Long short-term memory (LSTM) networks, has sparked a revolution in the realm of stock price prediction. This paper proposes a novel approach to revolutionize stock price prediction by harnessing the power of LSTM networks. Traditional methods of predicting stock prices have often relied on simplistic models or technical indicators, which may struggle to capture the intricate dynamics of financial markets. In contrast, LSTM networks offer the capability to effectively capture temporal dependencies and nonlinear relationships in time series data, making them well-suited for stock price prediction tasks. In this study, we leverage LSTM networks to develop a robust and accurate model for predicting stock prices. We employ a comprehensive dataset comprising historical stock prices, trading volumes, and other relevant financial indicators to train and evaluate our LSTM model. Through extensive experimentation and evaluation, we demonstrate the superior predictive performance of our proposed LSTM-based approach compared to conventional methods. Furthermore, we explore various techniques to enhance the robustness and generalization capability of our model, including feature engineering, hyperparameter tuning, and ensemble methods. Our findings highlight the effectiveness of LSTM networks in capturing complex patterns inherent in stock price data, thereby offering valuable insights for investors, traders, and financial analysts. Overall, this research contributes to the ongoing advancement of stock price prediction methodologies and underscores the potential of LSTM networks in revolutionizing predictive analytics in financial markets. By harnessing the power of deep learning techniques, we aim to empower stakeholders with more accurate and reliable forecasts, ultimately facilitating informed decision-making and driving positive outcomes in the realm of finance.

References :

1. X. Li, Y. Li, X.-Y. Liu, D. Wang, “Risk management via anomaly circumvent mnemonic deep learning for midterm stock prediction.” in Proceedings of 2nd KDD Workshop on Anomaly Detection in Finance (Anchorage ’19), 2019.
2. P. Chang, C. Fan, and C. Liu, “Integrating a piece-wise linear representation method and a neural network model for stock trading points prediction.” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 39, 1 (2009), 80–92.
3. Akita, Ryo, et al. “Deep learning for stock prediction using numerical and textual information.” IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS). IEEE, 2016.
4. Li, Xiaodong, et al. “Does summarization help stock prediction? A news impact analysis.” IEEE Intelligent Systems 30.3 (2015): 26-34.
5. Ding, Xiao, et al. “Deep learning for event-driven stock prediction.” Twenty-fourth International Joint Conference on Artificial Intelligence. 2015.
6. Hutto, Clayton J., and Eric Gilbert. “Vader: A parsimonious rule-based model for sentiment analysis of social media text.” Eighth International AAAI Conference on Weblogs and Social Media, 2014.
7. Ji, Zhanglong, Zachary C. Lipton, and Charles Elkan. “Differential privacy and machine learning: a survey and review.” arXiv preprint arXiv:1412.7584 (2014).
8. Abadi, Martin, et al. “Deep learning with differential privacy.” Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, ACM, 2016.
9. McMahan, H. Brendan, and Galen Andrew. “A general approach to adding differential privacy to iterative training procedures.” arXiv preprint arXiv:1812.06210 (2018).
10. Lecuyer, Mathias, et al. "Certified robustness to adversarial examples – differential privacy." arXiv preprint arXiv:1802.03471 (2018).
11. Hafezi, Reza, Jamal Shahrabi, and Esmaeil Hadavandi. "A bat-neural network multi-agent system (BNNMAS) for stock price prediction: A case study of DAX stock price."Applied Soft Computing, 29 (2015)
12. Chang, Pei-Chann, Chin-Yuan Fan, and Chen-Hao Liu. "Integrating a piecewise linear representation method and a neural network model for stock trading points prediction." IEEE Transactions on Systems, Man, and Cybernetics, (2008)
13. Gers, Felix A., Nicol N. Schraudolph, and Jürgen Schmidhuber. "Learning precise timing with LSTM recurrent networks." Journal of Machine Learning Research 3. Aug (2002)
14. Qin, Yao, et al. "A dual-stage attention-based recurrent neural network for time series prediction." arXiv preprint arXiv:1704.02971 (2017).
15. Malhotra, Pankaj, et al. "Long short-term memory networks for anomaly detection in time series." Proceedings. Presses universitaires de Louvain, 2015.
16. Sak, Ha¸sim, Andrew Senior, and Françoise Beaufays. "Long short-term memory recurrent neural network architectures for large scale acoustic modeling." Fifteenth annual conference of the International Speech Communication Association, 2014.
17. Kingma, Diederik P., and Jimmy Ba. "Adam: A method for stochastic optimization." arXiv preprint arXiv:1412.6980 (2014).
18. Box, George EP, et al. Time series analysis: forecasting and control. John Wiley & Sons, 2015.
19. Pang, Bo, and Lillian Lee. "Opinion mining and sentiment analysis." Foundations and Trends in Information Retrieval 2.1–2 (2008)
20. Cambria, Erik. "Affective computing and sentiment analysis." IEEE Intelligent Systems 31.2 (2016)