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Introduction

Stock price prediction is a challenging and critical task in financial markets. Investors, traders, and researchers are always looking for accurate and reliable methods to predict the future movements of stock prices. Deep learning, a subset of artificial intelligence, has shown promising results in various domains, including finance. However, the lack of interpretability in deep learning models has limited their adoption in stock price prediction. Explainable deep learning aims to address this limitation by providing explanations for the predictions made by the model.

Background of Study

The stock market is known for its volatility and complexity, making it difficult to predict stock prices accurately. Traditional time series analysis methods such as ARIMA, GARCH, and machine learning algorithms have been widely used for stock price prediction. However, deep learning models, especially neural networks, have shown superior performance in capturing complex patterns and relationships in data. Despite their high predictive power, deep learning models are often referred to as black boxes due to their lack of transparency and interpretability.

Problem Statement

The lack of interpretability in deep learning models hinders their adoption in critical applications such as stock price prediction. Investors and financial analysts require explanations for the predictions made by the model to make informed decisions. Therefore, there is a need to develop explainable deep learning models for stock price prediction.

Objective of Study

The main objective of this study is to develop an explainable deep learning model for stock price prediction. The model should not only provide accurate predictions but also explain the rationale behind the predictions. Additionally, this study aims to compare the performance of the explainable deep learning model with traditional time series analysis and machine learning algorithms.

Limitation of Study

This study is limited to historical stock price data and does not consider other external factors that may influence stock prices, such as macroeconomic indicators, news sentiment, and market trends. The scope of this study is also limited to a specific time period and set of stocks.

Scope of Study

This study focuses on applying explainable deep learning techniques, such as attention mechanisms and feature visualization, to stock price prediction. The study will consider various deep learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The performance of the explainable deep learning model will be evaluated using historical stock price data.

Significance of Study

The development of an explainable deep learning model for stock price prediction has significant implications for investors, traders, and financial analysts. By providing transparent and interpretable predictions, the model can help users understand the factors influencing stock prices and make informed investment decisions.

Structure of the Thesis

Chapter One: 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 Two: Literature Review
2.1 Overview of Stock Price Prediction
2.2 Traditional Time Series Analysis Methods
2.3 Machine Learning Algorithms for Stock Price Prediction
2.4 Deep Learning Models for Stock Price Prediction
2.5 Explainable Deep Learning
2.6 Applications of Explainable Deep Learning in Finance
2.7 Challenges and Limitations of Explainable Deep Learning
2.8 Interpretability in Neural Networks
2.9 Attention Mechanisms in Deep Learning
2.10 Feature Visualization

Chapter Three: Research Methodology
3.1 Data Collection
3.2 Data Preprocessing
3.3 Model Architecture
3.4 Training and Evaluation
3.5 Explainability Techniques
3.6 Performance Metrics
3.7 Experimental Setup
3.8 Comparison with Traditional Methods

Chapter Four: Discussion of Findings
4.1 Performance of Explainable Deep Learning Model
4.2 Interpretability of Predictions
4.3 Comparison with Traditional Methods
4.4 Impact of Attention Mechanisms
4.5 Feature Importance Analysis
4.6 Case Studies
4.7 Sensitivity Analysis
4.8 Robustness Testing

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications for Future Research
5.4 Practical Applications
5.5 Limitations and Future Directions
5.6 Conclusion

Thesis Overview

Explainable deep learning has emerged as a promising approach to enhance the interpretability of deep learning models in various domains, including finance. In this thesis, we focus on the application of explainable deep learning for stock price prediction. The study aims to develop a transparent and interpretable deep learning model that can provide explanations for stock price predictions. The thesis is structured as follows:

Chapter one provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis.

Chapter two presents a comprehensive literature review on stock price prediction, traditional time series analysis methods, machine learning algorithms, deep learning models, explainable deep learning, applications in finance, challenges, interpretability, attention mechanisms, and feature visualization.

Chapter three details the research methodology, including data collection, preprocessing, model architecture, training, evaluation, explainability techniques, performance metrics, experimental setup, and comparison with traditional methods.

Chapter four discusses the findings of the study, focusing on the performance of the explainable deep learning model, interpretability of predictions, comparison with traditional methods, impact of attention mechanisms, feature importance analysis, case studies, sensitivity analysis, and robustness testing.

Chapter five concludes the thesis, summarizing the findings, highlighting contributions, discussing implications for future research, practical applications, limitations, and potential directions for further investigation.

Overall, this thesis aims to contribute to the advancement of stock price prediction techniques by developing an explainable deep learning model that can provide transparent and interpretable predictions, thereby assisting investors, traders, and financial analysts in making informed decisions in financial markets.

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