Nifty index: Integrating deep learning models for future predictions and investments
- Title
- Nifty index: Integrating deep learning models for future predictions and investments
- Creator
- Josephine V.L.H.; Moulya V.H.; Jeevananda S.; Prathuri J.R.
- Description
- The Indian stock market, led by the NSE and BSE, has witnessed remarkable growth, exemplified by the NIFTY 50 index surpassing INR 176 trillion in market capitalization. Post the transformative New Economic Policy reforms in 1991, the market underwent significant expansion due to increased accessibility. This chapter focuses on predicting Nifty index prices for the upcoming 10-day period, aiming to provide valuable insights for investment decisions. Despite the markets inherent complexity, exacerbated by various factors like economic conditions and investor sentiment, the objective of the research study is clear: to boost profitability, mitigate risk, and safeguard traders capital. Leveraging Long Short-Term Memory (LSTM) and Vector Autoregression (VAR) models, the research study rigorously evaluates prediction accuracy using the Root Mean Square Error (RMSE) metric. The study underscores the potential of deep learning techniques in achieving reasonable accuracy, especially for short-term forecasts, while acknowledging the markets inherent unpredictability. Notably, the findings demonstrate that the LSTM model excels in predicting Nifty Bank prices, with an impressive RMSE score of 242.55 compared to VAR models. Furthermore, optimal data splitting, at an 8:2 ratio, significantly enhances prediction accuracy across all models, emphasizing the critical role of high-quality data in training. In conclusion, this study unequivocally recommends LSTM as the preferred model for Nifty index price prediction, providing practitioners with a robust tool to navigate the complexities of the Indian stock market with enhanced precision and confidence. 2025 selection and editorial matter, Vivek S. Sharma, Shubham Mahajan, Anand Nayyar and Amit Kant Pandit; individual chapters, the contributors.
- Source
- Deep Learning in Engineering, Energy and Finance: Principals and Applications, pp. 308-335.
- Date
- 2024-01-01
- Publisher
- CRC Press
- Coverage
- Josephine V.L.H., School of Business and Management Christ University, Bengaluru, India; Moulya V.H., School of Business and Management Christ University, Bengaluru, India; Jeevananda S., School of Business and Management Christ University, Bengaluru, India; Prathuri J.R., MA Christ University, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-104026136-1; 978-103262772-4
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Josephine V.L.H.; Moulya V.H.; Jeevananda S.; Prathuri J.R., “Nifty index: Integrating deep learning models for future predictions and investments,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/17887.