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Introduction:
Insider trading is a crucial issue in financial markets, as it involves the illegal buying or selling of securities based on non-public, material information about a company. Detecting insider trading is a challenging task, given the complex and constantly evolving nature of financial markets. To address this issue, various algorithms have been developed to detect suspicious trading activities that may indicate insider trading. This thesis aims to explore and evaluate these insider trading detection algorithms to provide insights for regulators and market participants.

1.1 Introduction
1.2 Background of the study
1.3 Problem statement
1.4 Objective of the study
1.5 Limitation of the study
1.6 Scope of the study
1.7 Significance of the study
1.8 Structure of the thesis
1.9 Definition of terms

Chapter Two: Literature Review
– Overview of insider trading
– Regulatory frameworks for insider trading
– Existing insider trading detection algorithms
– Evaluation of insider trading detection algorithms
– Challenges in detecting insider trading
– Advances in machine learning for insider trading detection
– Case studies of insider trading scandals
– Ethical implications of insider trading
– Relationship between insider trading and market efficiency
– Future directions in insider trading detection research

Chapter Three: Research Methodology
– Data collection methods
– Data preprocessing techniques
– Feature selection and extraction methods
– Evaluation metrics for algorithm performance
– Experimental design
– Implementation of insider trading detection algorithms
– Comparison of different algorithms
– Statistical analysis of results

Chapter Four: Discussion of Findings
– Analysis of algorithm performance
– Comparison of different detection algorithms
– Interpretation of results
– Insights for market participants and regulators
– Limitations of the study
– Recommendations for future research

Chapter Five: Conclusion and Summary
– Recap of key findings
– Contribution to insider trading detection research
– Implications for market participants and regulators
– Future research directions
– Conclusion

Thesis Overview:
Insider trading detection algorithms play a crucial role in maintaining the integrity and transparency of financial markets. This thesis aims to provide a comprehensive analysis of existing insider trading detection algorithms, their effectiveness, and limitations. The study will focus on evaluating these algorithms using real-world financial data and comparing their performance. By exploring the relationship between insider trading, market efficiency, and regulatory frameworks, this thesis seeks to provide valuable insights for market participants, regulators, and researchers. The research methodology will involve data collection, preprocessing, feature extraction, and evaluation of algorithm performance. The findings from this study will contribute to the ongoing discussions on insider trading detection and provide recommendations for improving current practices.

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