“Nifty 50 Price Forecasting with NLP Technique”

Authors

  • Vikrant Kamlakar Ingale, Vikas Kumar

Keywords:

NIFTY 50, Price Forecasting, Natural Language Processing (NLP), Random Forest, Machine Learning, Sentiment Analysis, Financial News, Social Media, X Posts, TF-IDF, Word Embeddings, Express.js, React, Python, Flask, High-Dimensional Data, Non-Linear Patterns, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R² Score, Interactive Charts, Real-Time Data, Financial Forecasting, Web Application, Deep Learning, Transformers, Personalized Dashboards, Investment Strategies

Abstract

This research proposes a novel approach to forecasting NIFTY 50 index prices by integrating Natural Language Processing (NLP) with a Random Forest model within a user-friendly web application, distinct from existing methodologies. The platform enables users to input NIFTY 50-related queries, extract sentiment from diverse textual sources such as financial reports and social media discussions (e.g., posts on X, accessed as of May 23, 2025), and visualize price trends through dynamic charts. Built with Express.js, the back-end connects to external APIs for real-time sentiment data and stores processed features in a database. A Random Forest model, developed using Python and Flask, processes NLP-generated features (e.g., TF-IDF vectors and custom word embeddings) combined with historical NIFTY 50 data to predict price movements. The model’s strength in managing high-dimensional, non-linear relationships ensures accurate forecasting, with results displayed on a React-based front-end. Performance is assessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R² score, demonstrating superior predictive power compared to traditional statistical models. This approach uniquely leverages sentiment-driven insights to enhance financial forecasting. Future improvements may include incorporating advanced NLP techniques like transformer models, real-time sentiment monitoring, and user-specific features such as custom alerts and portfolio trackers. This project showcases the innovative fusion of NLP, Random Forest, and web technologies to empower financial decision-making with actionable, data-driven insights.

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References

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Published

18.03.2025

How to Cite

Vikrant Kamlakar Ingale. (2025). “Nifty 50 Price Forecasting with NLP Technique”. International Journal of Intelligent Systems and Applications in Engineering, 13(1s), 248–257. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7633

Issue

Vol. 13 No. 1s (2025)

Section

Research Article

License

Creative Commons License

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