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Introduction

Generative adversarial networks (GANs) have been gaining increasing popularity in recent years for their ability to generate realistic data samples. In the financial industry, the use of GANs in modeling financial data has shown promising results in tasks such as fraud detection, risk assessment, and portfolio optimization. This thesis aims to explore the application of GANs in financial modeling and evaluate their effectiveness in improving predictive accuracy and decision-making in the financial domain.

Chapter 1: Introduction

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 2: Literature Review

2.1 Overview of generative adversarial networks
2.2 Applications of GANs in finance
2.3 Traditional financial modeling techniques
2.4 GANs vs. traditional modeling techniques
2.5 Challenges and limitations of using GANs in financial modeling
2.6 Advantages of using GANs in financial modeling
2.7 Existing research on GANs in financial modeling
2.8 Ethical considerations in using GANs in finance
2.9 Future directions for research in GANs and financial modeling

Chapter 3: Research Methodology

3.1 Data collection
3.2 Data preprocessing
3.3 GAN model selection
3.4 Training the GAN
3.5 Evaluation metrics
3.6 Comparison with traditional modeling techniques
3.7 Sensitivity analysis
3.8 Validation of results

Chapter 4: Discussion of Findings

4.1 Performance of GANs in financial modeling tasks
4.2 Comparison with traditional modeling techniques
4.3 Interpretation of results
4.4 Impact of GANs on decision-making in finance
4.5 Robustness and reliability of GAN models
4.6 Practical implications for financial institutions
4.7 Challenges and limitations encountered in the study
4.8 Recommendations for future research

Chapter 5: Conclusion and Summary

5.1 Summary of findings
5.2 Contribution to the field of financial modeling
5.3 Implications for practitioners
5.4 Future research directions
5.5 Conclusion

Thesis Overview

Generative adversarial networks (GANs) have emerged as a powerful tool in the field of artificial intelligence, with applications across various domains such as image generation, natural language processing, and data synthesis. In the financial industry, GANs have shown potential in improving predictive accuracy and decision-making through the generation of realistic financial data samples. This thesis aims to investigate the application of GANs in financial modeling and evaluate their effectiveness in addressing key challenges in the financial domain.

The thesis begins with an introduction to GANs and their applications in finance, providing a background on the topic and identifying the research problem and objectives. The literature review explores existing research on GANs in financial modeling, compares GANs with traditional modeling techniques, and discusses the challenges and advantages of using GANs in finance. The research methodology outlines the data collection, preprocessing, and model training processes, as well as the evaluation metrics and validation methods used in the study.

The discussion of findings section analyzes the performance of GANs in financial modeling tasks, compares GANs with traditional techniques, and interprets the results in the context of decision-making in finance. The chapter also addresses the robustness and reliability of GAN models, practical implications for financial institutions, and recommendations for future research. Finally, the conclusion and summary chapter summarizes the key findings of the study, highlights contributions to the field, suggests implications for practitioners, and outlines potential avenues for future research in the intersection of GANs and financial modeling.

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