Abstract :

This Research Aims To Evaluate The Collective Impact Of Fundamental And Technical Factors On Stock Market Behaviour And Investor Decisions, Leveraging Advanced Machine Learning Models To Enhance Prediction Accuracy And Reliability. The Study Integrates Key Financial Metrics Such As Earnings, Dividends, Revenue, And Net Income With Technical Indicators Like Chart Patterns, Trading Volumes, RSI, And Moving Averages. By Combining These Diverse Data Points, The Research Offers A Nuanced Understanding Of Stock Market Dynamics And Underscores The Importance Of Both Types Of Indicators In Predicting Market Movements. Data Was Collected From Reliable Financial Databases, Including Company Financial Statements And Stock Market Platforms. A Comprehensive Descriptive Analysis Revealed Significant Variations In Financial Performance Among Companies And Highlighted The Interconnections Between Fundamental And Technical Metrics. Multiple Regression Analysis Demonstrated The Significant Influence Of Fundamental Variables And Technical Indicators On Stock Prices, With Findings Showing Strong Positive Correlations Between Financial Performance And Favourable Technical Metrics. Machine Learning Models, Particularly The Random Forest Model, Were Employed To Predict Stock Prices. The Models Exhibited High Accuracy, With Minimal Prediction Errors, Demonstrating The Practical Utility Of Integrating Fundamental And Technical Analysis. Case Studies On Selected Equities Further Illustrated The Effectiveness Of These Models In Real-world Applications. The Research Concludes That Integrating Fundamental And Technical Factors Significantly Enhances The Accuracy Of Stock Market Predictions, Providing Valuable Insights For Investors And Portfolio Managers. The Studys Findings Pave The Way For Further Exploration Of The Synergistic Effects Of These Analyses, Contributing To The Advancement Of Financial Modelling And Market Prediction Methodologies.

Published:

16-6-2025

Issue:

Vol. 25 No. 6 (2025)

Page Nos:

465 - 473

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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