Abstract :

The Predictability Of Stock Market Trends Has Long Been A Focal Point Of Financial Economics And Computational Intelligence, Driven By The Challenge Of Navigating Market Volatility And High Noise Levels. Traditional Econometric Models Often Fall Short When Capturing The Non-linear, Dynamic Relationships Inherent In Financial Time-series Data. This Study Investigates The Efficacy Of Integrating Machine Learning (ML) And Deep Learning (DL) Frameworks To Enhance The Accuracy Of Stock Trend Predictions. We Implement A Comparative Analysis Utilizing Traditional ML Algorithms, Such As Support Vector Machines (SVM), Random Forests, And Gradient Boosting, Alongside Advanced DL Architectures, Including Long Short-Term Memory (LSTM) Networks And Gated Recurrent Units (GRU). The Proposed Methodology Leverages Multi-source Data, Combining Historical Price Metrics, Technical Indicators Like Moving Averages And The Relative Strength Index (RSI), And Macro-economic Variables. Furthermore, Natural Language Processing (NLP) Is Applied To Perform Sentiment Analysis On Financial News Articles And Social Media Feeds, Extracting Market Sentiment As An Auxiliary Feature. Preprocessing Techniques, Including Min-max Normalization, Rolling-window Features, And Principal Component Analysis (PCA), Are Executed To Reduce Dimensionality And Mitigate Overfitting. Experimental Evaluations Are Conducted On Historical Data From Major Indices, Such As The S&P 500 And NASDAQ, Spanning A Multi-year Horizon. The Performance Of The Predictive Models Is Rigorously Assessed Using Classification And Regression Metrics, Including Accuracy, Precision, F1-score, And Root Mean Squared Error (RMSE). The Empirical Results Demonstrate That While Traditional Machine Learning Models Provide Robust Baselines, Deep Learning Architectures, Particularly LSTMs, Excel At Capturing Long-term Temporal Dependencies. Moreover, The Fusion Of Quantitative Technical Indicators With Qualitative Sentiment Features Yields A Statistically Significant Improvement In Directional Trend Prediction. The Hybrid Model Developed In This Research Outperforms Individual Standalone Models, Offering A More Resilient Framework Against Sudden Market Shocks. Ultimately, This Research Underscores The Transformative Potential Of Synergistic AI Methodologies In Financial Forecasting, Providing Actionable Insights And Enhanced Decision-support Systems For Institutional Investors, Algorithmic Traders, And Portfolio Managers Seeking To Optimize Risk-adjusted Returns In Volatile Trading Environments.

Published:

22-5-2026

Issue:

Vol. 26 No. 5 (2026)

Page Nos:

1455-1463

Section:

Articles

License:

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

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