Integrating Technical & Fundamental Analysis with Machine Learning for Stock Market Investment Decisions

Author(s)	Mr. Saroj Kumar Mahto, Dr. Ram Singh
Country	India
Abstract	This study explores the integration of technical and fundamental analysis with machine learning to enhance stock market investment decision. By combining these analytical methods, we aim to improve the accuracy of market predictions and investment strategies. The study compares two models based on recommendation system with different approaches. The first approach includes the integration of technical and fundamental analysis. Input data involves computation of 3 most popular technical parameters using data from google finance (LTP, 52WH and Fib Retracement) and financial ratios (PE Ratio, Intrinsic value and Growth rate). The second approach includes Support Vector Regression (SVR) model as a recommendation system to select stocks from the list of NIFTY-500 Index of Indian Equity market. The results indicates that the hybrid approach outperforms traditional methods, offering a more profitable framework for investors.
Keywords	Technical Analysis, Fundamental Analysis, Machine learning, Support Vector Regression
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-02-24
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.69698

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International Journal For Multidisciplinary Research