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Introduction

Statistical arbitrage is a popular trading strategy that involves exploiting pricing inefficiencies in the market by using statistical models and quantitative techniques. Exchange-Traded Funds (ETFs) have gained significant popularity in recent years due to their low costs, diversification benefits, and easy tradability. This research aims to explore the concept of statistical arbitrage in ETF markets, focusing on identifying and exploiting mispricings in these investment vehicles.

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
1. Historical Development of Statistical Arbitrage
2. Efficient Market Hypothesis and Market Anomalies
3. ETF Market Structure and Characteristics
4. Statistical Arbitrage Strategies in ETF Markets
5. Empirical Studies on Statistical Arbitrage in ETFs
6. Risk Management in Statistical Arbitrage
7. Regulatory Environment for ETF Trading
8. Behavioral Finance and its Implications for Statistical Arbitrage
9. Machine Learning and Artificial Intelligence in Statistical Arbitrage
10. Challenges and Future Directions in Statistical Arbitrage Research

Chapter 3: Research Methodology
1. Research Design
2. Data Collection and Sources
3. Sample Selection
4. Variables and Hypotheses
5. Model Specification
6. Data Analysis Techniques
7. Software Tools
8. Ethical Considerations

Chapter 4: Discussion of Findings
1. Descriptive Analysis of ETF Market Data
2. Identification of Mispricings and Arbitrage Opportunities
3. Performance Evaluation of Statistical Arbitrage Strategies
4. Impact of External Factors on Arbitrage
5. Comparison of Different Statistical Arbitrage Models
6. Risk-Return Tradeoffs in ETF Arbitrage
7. Sensitivity Analysis and Robustness Checks
8. Case Studies of Successful and Failed Arbitrage Trades

Chapter 5: Conclusion and Summary
This chapter will summarize the key findings of the research, discuss implications for practitioners and policymakers, provide recommendations for future research, and conclude the thesis.

Thesis Overview on Statistical Arbitrage in ETF Markets

ETFs have become increasingly popular investment vehicles due to their low costs, liquidity, and diversification benefits. However, like any other financial instrument, ETFs are subject to pricing inefficiencies that can be exploited by savvy investors through statistical arbitrage strategies. This thesis explores the concept of statistical arbitrage in ETF markets, aiming to identify profitable trading opportunities and analyze the factors that influence the success of such strategies.

The literature review provides a comprehensive overview of historical developments in statistical arbitrage, market anomalies, ETF market structure, and statistical arbitrage strategies applied in the context of ETFs. It also discusses the empirical studies, risk management techniques, regulatory environment, behavioral finance implications, and the role of machine learning in statistical arbitrage.

The research methodology chapter details the research design, data collection process, sample selection criteria, variables, hypotheses, model specification, data analysis techniques, software tools, and ethical considerations involved in the study.

The discussion of findings chapter presents the results of the empirical analysis, including descriptive statistics of ETF market data, identification of mispricings and arbitrage opportunities, performance evaluation of statistical arbitrage strategies, impact of external factors, comparison of different models, risk-return tradeoffs, sensitivity analysis, and case studies.

In conclusion, this thesis contributes to the existing literature on statistical arbitrage by focusing specifically on ETF markets and provides valuable insights for investors, researchers, and policymakers. The findings of this study can help improve understanding of pricing inefficiencies in ETFs and inform the development of more effective arbitrage strategies in the future.

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