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Introduction

Stock price prediction has always been of great interest to investors, financial analysts, and researchers, as the ability to accurately forecast the movement of stock prices can lead to significant financial gains. Deep learning, a subset of artificial intelligence, has shown promising results in various fields, including finance. However, one of the main challenges of deep learning models is their lack of explainability, which makes it difficult for users to understand why a particular prediction was made. In this thesis, we aim to explore the use of explainable deep learning techniques for stock price prediction, with the goal of improving the transparency and interpretability of the models.

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 Two: Literature Review
– Overview of stock price prediction
– Deep learning techniques in finance
– Explainable AI in finance
– Stock market anomalies
– Feature selection for stock price prediction
– Model interpretability in finance
– Previous research on explainable deep learning for stock price prediction
– Evaluation metrics for stock price prediction models
– Limitations of existing approaches
– Opportunities for future research

Chapter Three: Research Methodology
– Data collection and preprocessing
– Feature engineering for stock price prediction
– Model selection and architecture
– Explainable deep learning techniques
– Evaluation criteria
– Performance metrics
– Validation and testing procedures
– Ethical considerations

Chapter Four: Discussion of Findings
– Performance comparison of different models
– Interpretability of model predictions
– Impact of feature selection on prediction accuracy
– Analysis of key findings
– Limitations and challenges
– Recommendations for future research

Chapter Five: Conclusion and Summary
– Summary of key findings
– Contributions of the study
– Implications for investors and financial analysts
– Future research directions
– Conclusion

Thesis Overview: Explainable deep learning for stock price prediction

Stock price prediction is a complex and challenging task that has long been of interest to investors, financial analysts, and researchers. Traditional forecasting methods often rely on statistical models and technical analysis, but recent advancements in deep learning have shown promise in improving prediction accuracy. However, one of the main limitations of deep learning models is their lack of explainability, which can hinder their adoption in real-world financial applications.

In this thesis, we aim to address this limitation by exploring the use of explainable deep learning techniques for stock price prediction. By incorporating interpretability into the model, we hope to provide users with insights into why a particular prediction was made, which can help build trust and confidence in the model’s decisions. The research methodology will involve data collection and preprocessing, feature engineering, model selection, and evaluation using appropriate performance metrics.

The literature review will provide an overview of stock price prediction, deep learning techniques in finance, explainable AI in finance, and previous research on explainable deep learning for stock price prediction. The discussion of findings will compare the performance of different models, analyze the interpretability of predictions, and discuss the impact of feature selection on prediction accuracy. The conclusion will summarize key findings, implications for investors, and future research directions.

Overall, this thesis aims to contribute to the growing body of research on explainable deep learning for stock price prediction and provide valuable insights for investors and financial analysts seeking to improve their decision-making process.

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