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Introduction

The stock market is a complex and dynamic system that is influenced by a multitude of factors, making it notoriously difficult to predict. Traditional financial models often fall short in accurately forecasting stock market movements due to their limited scope and inability to capture the interconnectedness and interdependence of various market entities. Social network analysis (SNA) offers a promising alternative approach to stock market prediction by leveraging the power of network theory to uncover hidden patterns and relationships within financial markets.

This thesis aims to explore the potential of using social network analysis for stock market prediction. By analyzing the relationships between different market entities such as stocks, investors, and market indexes, we can gain valuable insights into the underlying dynamics of the stock market and improve our ability to forecast future market movements. This research has the potential to revolutionize the field of stock market prediction and provide investors with more accurate and reliable forecasting tools.

Table of Content

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 Traditional methods of stock market prediction
2.2 Social network analysis in finance
2.3 Network theory and its application in stock market prediction
2.4 Previous studies on social network analysis for stock market prediction
2.5 The role of social media in stock market prediction
2.6 Machine learning techniques in stock market prediction
2.7 Sentiment analysis in stock market prediction
2.8 Network centrality measures in stock market prediction
2.9 Network visualization techniques in stock market analysis
2.10 Limitations of existing research in social network analysis for stock market prediction

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection
3.3 Data preprocessing
3.4 Network construction
3.5 Network analysis techniques
3.6 Statistical modeling
3.7 Evaluation metrics
3.8 Ethical considerations

Chapter 4: Discussion of Findings
4.1 Analysis of network structures
4.2 Identification of key network entities
4.3 Network-based prediction models
4.4 Comparison with traditional prediction methods
4.5 Interpretation of results
4.6 Implications for stock market prediction
4.7 Future research directions

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

Thesis Overview

Social network analysis has emerged as a powerful tool for studying complex systems, including financial markets. This thesis investigates the application of social network analysis in predicting stock market movements. By analyzing the relationships between different market entities using network theory, we aim to improve the accuracy and reliability of stock market predictions.

Chapter 1 provides an introduction to the research topic, outlining the background, problem statement, objectives, and scope of the study. The significance of the study and the structure of the thesis are also discussed, along with key definitions of terms used throughout the thesis.

Chapter 2 reviews the existing literature on stock market prediction, social network analysis, and related topics. This chapter sets the stage for our research by examining the strengths and limitations of current prediction methods and highlighting the potential of social network analysis in improving prediction accuracy.

Chapter 3 details the research methodology, including data collection, preprocessing, network construction, analysis techniques, and modeling approaches. Ethical considerations related to data privacy and use of sensitive information are also discussed.

Chapter 4 presents a comprehensive analysis of the findings, including network structures, key entities, prediction models, comparison with traditional methods, and implications for stock market prediction. The results are interpreted and discussed in the context of existing literature, with suggestions for future research directions.

Chapter 5 concludes the thesis by summarizing the key findings, discussing the contributions to the field, outlining practical implications for investors and policymakers, acknowledging the study’s limitations, and providing recommendations for further research in the area of social network analysis for stock market prediction.

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