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Introduction
Neuroevolution is a promising field that combines neural networks and evolutionary algorithms to optimize the structure and weights of neural networks. This innovative approach has been applied in various fields, including financial forecasting, to improve prediction accuracy and decision-making processes. In this thesis, we will explore the application of neuroevolution in financial forecasting and analyze its effectiveness in predicting future financial trends.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of the 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 financial forecasting
2.2 Traditional forecasting methods
2.3 Neural networks in financial forecasting
2.4 Evolutionary algorithms in financial forecasting
2.5 Neuroevolution in financial forecasting
2.6 Studies on neuroevolution in financial forecasting
2.7 Advantages of neuroevolution in financial forecasting
2.8 Challenges of neuroevolution in financial forecasting
2.9 Future trends in neuroevolution for financial forecasting
2.10 Summary of literature review

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection
3.3 Data preprocessing
3.4 Neural network architecture
3.5 Evolutionary algorithm parameters
3.6 Training and testing procedure
3.7 Performance evaluation metrics
3.8 Ethical considerations

Chapter 4: Discussion of Findings
4.1 Analysis of neural network performance
4.2 Comparison of neuroevolution with traditional methods
4.3 Impact of evolutionary algorithms on prediction accuracy
4.4 Interpretation of results
4.5 Implications for financial forecasting
4.6 Recommendations for future research
4.7 Limitations of the study

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Practical implications
5.4 Suggestions for future research
5.5 Conclusion

Thesis Overview:
Neuroevolution, the combination of neural networks and evolutionary algorithms, has shown great potential in improving financial forecasting accuracy. This thesis investigates the application of neuroevolution in financial forecasting and evaluates its effectiveness compared to traditional methods. By reviewing existing literature, analyzing research methodologies, discussing findings, and providing a comprehensive conclusion, this thesis aims to contribute to the understanding of neuroevolution in financial forecasting and its implications for future research and practical applications.

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