Multi-Factor Influence on NIFTY50: A Machine Learning Approach

Abstract

A wide range of economic, technical, and sentiment-driven factors shape the Indian financial market, particularly benchmark indices like the NIFTY50. Using an analytical method that is based on machine learning, this study looks into how all of these factors affect the NIFTY50 index. By employing statistical techniques alongside modern algorithms, we examine a diverse set of macroeconomic variables, technical indicators, and global financial signals to identify the most significant contributors to NIFTY50 fluctuations.[1] We use correlation analysis, mutual information metrics, and recursive feature elimination (RFE) to evaluate the relationships between variables. These methods enable us to get rid of features that are redundant or less important. Not only are predictive but also feature importance assessments made using machine learning models like Random Forest, XGBoost, and LASSO regression. Model clarity and computational efficiency are also improved by employing dimensionality reduction methods like Principal Component Analysis (PCA).[2] The results show that the models\' predictive accuracy and overall performance are both improved and their complexity is reduced by selecting a refined set of influential features. This research contributes to a deeper understanding of market dynamics and offers practical insights for investors, analysts, and policymakers. In addition, it lays the groundwork for the creation of machine learning-powered intelligent financial decision-making systems that operate in real time.[1]

Introduction

The performance of stock indices like India’s NIFTY50 is influenced by a complex mix of internal and external factors, including macroeconomic indicators, technical signals, market sentiment, and global trends. Traditional financial models often fail to capture the nonlinear and interdependent nature of these influences. Machine learning (ML) techniques offer a more effective alternative by handling large datasets, uncovering hidden patterns, and adapting to market changes. This study applies ML models (Random Forest, XGBoost, LASSO regression) along with feature selection methods (Pearson correlation, mutual information, recursive feature elimination) and dimensionality reduction (PCA) to identify key factors affecting NIFTY50 and improve prediction accuracy. The goal is to develop a robust, data-driven predictive model for investors and policymakers in a dynamic market.

The literature review highlights various ML approaches for stock prediction, including regression models, random forests using financial ratios, and multi-source learning incorporating sentiment analysis from news and social media. These approaches demonstrate the importance of combining technical data with sentiment and event information to improve forecasting accuracy.

A typical AI prediction workflow involves collecting financial data (historical prices, economic indicators, sentiment), extracting and preprocessing relevant features, training ML models on historical data, testing on unseen data, and generating stock price predictions. This process is iterative, with models updated as new data arrives.

Future research directions focus on enhancing feature selection algorithms to reduce noise and overfitting, integrating textual sentiment data using NLP, developing multi-step and longer-term forecasting models, improving data preprocessing techniques, adopting financial domain-specific performance metrics, and validating models across different market conditions using ensemble approaches. These advancements aim to increase the accuracy, reliability, and practical value of AI-driven financial market forecasting, particularly for indices like the NIFTY50.

Conclusion

The multi-factor influence on the NIFTY50 index is examined using a machine learning-based framework that incorporates macroeconomic indicators, technical parameters, and global financial signals. The research successfully identifies key variables that have a significant impact on the movements of the index by employing feature correlation and selection methods like mutual information, Pearson correlation, and recursive feature elimination (RFE). Prediction accuracy and feature importance were evaluated using cutting-edge machine learning techniques like Random Forest, XGBoost, and LASSO regression. Additionally, principal component analysis (PCA) and other dimensionality reduction methods helped to keep the most important information while simultaneously reducing model complexity. Prediction accuracy and model efficiency are both enhanced by concentrating on a more refined set of influential features, as demonstrated by the experiments. In addition to providing a data-driven foundation for the creation of forecasting systems that are more accurate and intelligent, this research contributes to a deeper comprehension of the factors that influence NIFTY50 fluctuations. The findings can aid investors, analysts, and policymakers in making informed decisions in volatile market conditions. The incorporation of textual sentiment data, the investigation of deep learning architectures, and the expansion of prediction to multi-step and long-term horizons are all promising future research topics in this field.

References

[1] Stock Market Prediction of NIFTY 50 Index Applying Machine Learning TechniquesZahra Fathali ,Zahra Kodia &Lamjed Ben Said Article: 2111134 | Published online: 16 Sep 2022 [2] A multi-feature selection fused with investor sentiment for stock price predictionAuthor links open overlay panelKehan Zhen a b, Dan Xie a b c, Xiaochun Hu [3] Forecasting of NIFTY 50 Index Price by Using Backward Elimination with an LSTM Model by Syed Hasan Jafar 1Shakeb Akhtar 1,Hani El-Chaarani 2,*,Parvez Alam Khan 3 andRuaa Binsaddig [4] Survey of feature selection and extraction techniques for stock market predictionHtet Htet Htun, Michael Biehl & Nicolai Petkov volume 9, Article number: 26 (2023) [5] NIFTY 50 PREDICTIONUSING AI AND MACHINE LEARNING: A REVIEW Ms. Wayal Priti*1, Ms. Wani Aasavari*2, Ms. Mulik Siddhi*3, Dr. S.B. Lande*4 *1,2,3,4Department Of Electronics And Telecommunication VPKBIET Baramati Savitribai Phule Pune University, Maharashtra, India [6] Stock Market Prediction Using Machine Learning Algorithms K. Hiba Sadia, Aditya Sharma, Adarrsh Paul, SarmisthaPadhi, Saurav Sanyal [7] Ashish Sharma, Dinesh Bhuriya, Upendra Singh. \"Survey of Stock Market Prediction Using Machine Learning Approach\", ICECA 2017 [8] Loke.K.S. “Impact Of Financial Ratios And Technical Analysis On Stock Price Prediction Using Random Forests”, IEEE, 201 [9] Xi Zhang1, Siyu Qu1, Jieyun Huang1, Binxing Fang1, Philip Yu2, “Stock Market Prediction via Multi-Source Multiple Instance Learning.” IEEE 2018 [10] Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda by Ritika Chopra andGagan Deep SharmaUniversity School of Management Studies, Guru Gobind Singh Indraprastha University, Dwarka Sector 16-C, New Delhi 110078, India

Copyright

Copyright © 2025 Siddhi Mehta, Nimesh Vaidya, Dr. Vijay Gadhavi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.