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Introduction

Artificial neural networks (ANN) have gained significant attention in recent years due to their ability to effectively analyze complex data and make predictions in various fields, including stock market forecasting. The use of ANN in stock market forecasting has shown promising results, as they can capture intricate patterns and relationships in historical data that are difficult to be identified by traditional statistical methods. This thesis aims to explore the application of ANN in predicting stock market trends and making informed investment decisions.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of Artificial Neural Networks
2.2 Applications of Artificial Neural Networks in Stock Market Forecasting
2.3 Traditional Methods vs. Artificial Neural Networks in Stock Market Forecasting
2.4 Factors influencing Stock Market Trends
2.5 Evaluation Metrics for Stock Market Forecasting
2.6 Challenges and Limitations of using Artificial Neural Networks in Stock Market Forecasting
2.7 Recent Advances in Artificial Neural Networks for Stock Market Forecasting
2.8 Studies on the Effectiveness of Artificial Neural Networks in Stock Market Forecasting
2.9 Regulatory Framework for Stock Market Forecasting
2.10 Future Trends in Artificial Neural Networks for Stock Market Forecasting

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Model Selection
3.5 Training and Testing
3.6 Performance Evaluation Metrics
3.7 Hyperparameter Tuning
3.8 Validation Techniques

Chapter 4: Discussion of Findings
4.1 Analysis of Results
4.2 Comparison with Traditional Methods
4.3 Interpretation of Model Performance
4.4 Sensitivity Analysis
4.5 Robustness of the Model
4.6 Limitations of the Study
4.7 Recommendations for Future Research
4.8 Implications for Stock Market Investors

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Practical Implications
5.4 Future Research Directions
5.5 Conclusion

Thesis Overview on Artificial Neural Networks for Stock Market Forecasting

Artificial neural networks (ANN) have emerged as a powerful tool for stock market forecasting, owing to their ability to learn complex patterns from historical data and make accurate predictions. This thesis investigates the application of ANN in predicting stock market trends and its effectiveness in assisting investors in making informed decisions. The literature review highlights the evolution of ANN in stock market forecasting and evaluates its performance against traditional methods. The research methodology outlines the data collection process, model selection, training, testing, and performance evaluation metrics used in the study. The findings are discussed in detail, with a focus on the analysis of results, comparison with traditional methods, and implications for stock market investors. The conclusion summarizes the key findings, contributions of the study, practical implications, and future research directions in the field of ANN for stock market forecasting.

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