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Introduction

Artificial Intelligence (AI) has been increasingly used in various industries to improve decision-making processes and optimize outcomes. In the field of finance, AI has shown great promise in portfolio optimization, where the goal is to construct a portfolio of assets that maximizes returns while minimizing risks. This thesis aims to explore the use of AI techniques in portfolio optimization and analyze their effectiveness in achieving superior investment performance.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objectives of Study
1.5 Limitations 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 Portfolio Optimization
2.2 Traditional Approaches to Portfolio Optimization
2.3 Artificial Intelligence in Finance
2.4 AI Techniques for Portfolio Optimization
2.5 Challenges in Portfolio Optimization
2.6 Empirical Studies on AI in Portfolio Optimization
2.7 Comparison of AI and Traditional Methods
2.8 Theoretical Framework
2.9 Research Gaps
2.10 Summary

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Analysis
3.4 Sampling Techniques
3.5 Variables and Measures
3.6 Research Model
3.7 Hypotheses
3.8 Data Processing
3.9 Reliability and Validity
3.10 Summary

Chapter 4: Discussion of Findings
4.1 Descriptive Analysis
4.2 Statistical Analysis
4.3 Interpretation of Results
4.4 Comparison of AI and Traditional Models
4.5 Implications for Portfolio Managers
4.6 Recommendations for Future Research
4.7 Limitations of the Study
4.8 Conclusion

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Research Contribution
5.3 Practical Implications
5.4 Theoretical Implications
5.5 Limitations of the Research
5.6 Recommendations for Practitioners
5.7 Recommendations for Future Research
5.8 Conclusion

Thesis Overview

The use of artificial intelligence in portfolio optimization has gained significant attention in the finance industry due to its potential to enhance investment decision-making processes. This thesis aims to investigate the effectiveness of AI techniques in optimizing investment portfolios and compare them with traditional approaches. The research will provide valuable insights into the benefits and challenges of incorporating AI in portfolio management and offer recommendations for practitioners and future research in this area.

Overall, this thesis will contribute to the existing literature on AI in finance and provide a comprehensive analysis of its use in portfolio optimization. By examining empirical studies and theoretical frameworks, the research aims to bridge the gap between theory and practice and offer practical implications for portfolio managers. Through a systematic research methodology and rigorous data analysis, this thesis will provide a valuable resource for academics, professionals, and policymakers interested in leveraging AI for improved investment outcomes.

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