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

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.