Deep Learning Tools for Predicting Stock Market Movements.

Format:

Book

Author/Creator:

Sharma, Renuka.

Contributor:

Mehta, Kiran.

Language:

English

Subjects (All):

Stock price forecasting.

Deep learning (Machine learning).

Physical Description:

1 online resource (489 pages)

Edition:

1st ed.

Place of Publication:

John Wiley & Sons, Inc. 2024

Newark : John Wiley & Sons, Incorporated, 2024.

Summary:

This book, edited by Renuka Sharma and Kiran Mehta, explores the application of deep learning tools for predicting stock market movements. It provides a comprehensive overview of various methodologies and models, including LSTM, ARIMA, and sentiment analysis, used for stock market prediction. The book discusses the integration of artificial intelligence, quantum computing, and machine learning techniques to enhance predictive accuracy in stock market forecasting. It also addresses the challenges and future research directions in this field, making it a valuable resource for researchers, practitioners, and students in finance and technology. Generated by AI.

Contents:

Cover

Title Page

Copyright Page

Dedication Page

Contents

Preface

Acknowledgments

Chapter 1 Design and Development of an Ensemble Model for Stock Market Prediction Using LSTM, ARIMA, and Sentiment Analysis

1.1 Introduction

1.2 Significance of the Study

1.3 Problem Statement

1.4 Research Objectives

1.5 Expected Outcome

1.6 Chapter Summary

1.7 Theoretical Foundation

1.7.1 Sentiment Analysis

1.7.1.1 Subjectivity

1.7.1.2 Polarity

1.7.2 Stock Market

1.7.3 Sentiment Analysis of Twitter in Stock Market Prediction

1.7.4 Machine Learning Algorithms in Stock Market Prediction

1.8 Research Methodology

1.8.1 Stock Sentiment Data Fetching Through API

1.8.1.1 Stock Market Data Fetching

1.8.1.2 Sentiment Data Preprocessing

1.8.1.3 Stock Data Preprocessing

1.8.2 Project Plan

1.8.3 Use Case Diagram

1.8.4 Data Collection

1.8.5 Dataset Description

1.8.5.1 Tweets Precautions

1.8.5.2 Consolidation of Sentiment and Stock Price Data

1.8.6 Algorithm Description

1.8.6.1 ARIMA

1.8.6.2 LSTM

1.8.6.3 TextBlob

1.9 Analysis and Results

1.10 Conclusion

1.10.1 Limitation

1.10.2 Future Work

References

Chapter 2 Unraveling Quantum Complexity: A Fuzzy AHP Approach to Understanding Software Industry Challenges

2.1 Introduction

2.2 Introduction to Quantum Computing

2.3 Literature Review

2.4 Research Methodology

2.5 Research Questions

2.6 Designing Research Instrument/Questionnaire

2.7 Results and Analysis

2.8 Result of Fuzzy AHP

2.9 Findings, Conclusion, and Implication

Chapter 3 Analyzing Open Interest: A Vibrant Approach to Predict Stock Market Operator's Movement

3.1 Introduction

3.2 Methodology

3.3 Concept of OI

3.4 OI in Future Contracts

3.4.1 Interpreting OI &amp

Price Movement.

3.4.2 Open Interest and Cumulative Open Interest

3.4.3 Validation

3.4.4 Case Study with Live Market Data

3.5 OI in Option Contracts

3.5.1 Decoding Buyer or Seller in Option Chain

3.5.2 Put-Call Ratio (PCR)

3.5.3 Detection of Anomaly in Stock Price

3.6 Conclusion

Chapter 4 Stock Market Predictions Using Deep Learning: Developments and Future Research Directions

4.1 Background and Introduction

4.1.1 Machine Learning

4.1.2 About Deep Learning

4.2 Studies Related to the Current Work, i.e., Literature Review

4.3 Objective of Research and Research Methodology

4.4 Results and Analysis of the Selected Papers

4.5 Overview of Data Used in the Earlier Studies Selected for the Current Research

4.6 Data Source

4.7 Technical Indicators

4.7.1 Other (Advanced Technical Indicators)

4.8 Stock Market Prediction: Need and Methods

4.9 Process of Stock Market Prediction

4.10 Reviewing Methods for Stock Market Predictions

4.11 Analysis and Prediction Techniques

4.12 Classification Techniques (Also Called Clustering Techniques)

4.13 Future Direction

4.13.1 Cross-Market Evaluation or Analysis

4.13.2 Various Data Inputs

4.13.3 Unexplored Frameworks

4.13.4 Trading Strategies Based on Algorithm

4.14 Conclusion

Chapter 5 Artificial Intelligence and Quantum Computing Techniques for Stock Market Predictions

5.1 Introduction

5.2 Literature Survey

5.3 Analysis of Popular Deep Learning Techniques for Stock Market Prediction

5.3.1 Blind Quantum Computing (BQC) in Stock Market Prediction

5.3.2 Quantum Neural Networks (QNNs) for Time Series Forecasting

5.3.3 Artificial Intelligence-Based Algorithms

5.3.3.1 Deep Learning Models

5.3.3.2 Support Vector Machines (SVM)

5.3.3.3 Reinforcement Learning (RL)

5.3.4 Quantum Computing-Based Algorithms.

5.3.4.1 Quantum Machine Learning (QML)

5.3.4.2 Quantum Optimization

5.4 Data Sources and Methodology

5.5 Result and Analysis

5.6 Challenges and Future Scope

5.6.1 Challenges

5.6.2 Future Scope

5.7 Conclusion

Chapter 6 Various Model Applications for Causality, Volatility, and Co-Integration in Stock Market

6.1 Introduction

6.2 Literature Review

6.3 Objectives of the Chapter

6.4 Methodology

6.5 Result and Discussion

6.6 Implications

6.7 Conclusion

Chapter 7 Stock Market Prediction Techniques and Artificial Intelligence

7.1 Introduction

7.2 Financial Market

7.3 Stock Market

7.4 Stock Market Prediction

7.4.1 Consideration of Analysis for Stock Prediction

7.4.2 The Necessity of Stock Prediction

7.5 Artificial Intelligence and Stock Prediction

7.5.1 Artificial Intelligence-Based Techniques for Predicting the Stock Market

7.6 Benefits of Using AI for Stock Prediction

7.7 Challenges of Using AI for Stock Prediction

7.8 Limitations of AI-Based Stock Prediction

7.9 Conclusion

Chapter 8 Prediction of Stock Market Using Artificial Intelligence Application

8.1 Introduction

8.1.1 Stock Market

8.1.2 Artificial Intelligence

8.2 Objectives

8.3 Literature Review

8.4 Future Scope

8.5 Sources of Study and Importance

8.5.1 Data Collection

8.5.2 Feature Selection

8.5.3 Implementation of AI Techniques

8.6 Case Study: Comparison of AI Techniques for Stock Market Prediction

8.7 Discussion and Conclusion

8.7.1 Overall Results

8.7.2 Challenges and Limitations

8.7.3 Insights and Recommendations

8.7.4 Conclusion

Chapter 9 Stock Returns and Monetary Policy

9.1 Introduction

9.2 Literature

9.3 Data and Methodology

9.4 Index-Based Analysis

9.5 Firm-Level Analysis.

9.5.1 Sectoral Difference

9.6 The Impact of Financial Constraints

9.7 Discussion and Conclusion

Appendix 1

Appendix 2

Chapter 10 Revolutionizing Stock Market Predictions: Exploring the Role of Artificial Intelligence

10.1 Introduction

10.2 Review of Literature

10.3 Research Methods

10.4 Results and Discussion

10.4.1 Discussion on the Literature on Artificial Intelligence

10.4.2 Discussion on Artificial Intelligence and Stock Prediction

10.5 Conclusion

10.6 Significance of the Study

10.7 Scope of Further Research

Chapter 11 A Comparative Study of Stock Market Prediction Models: Deep Learning Approach and Machine Learning Approach

11.1 Introduction

11.1.1 Stock Market

11.2 Stock Market Prediction

11.2.1 Data Types

11.3 Models for Prediction in Stock Market

11.3.1 Traditional Methods

11.3.2 Modern Techniques

11.3.2.1 Artificial Intelligence

11.3.2.2 Machine Learning

11.3.2.3 Deep Learning Approach

11.4 Conclusion

Chapter 12 Machine Learning and its Role in Stock Market Prediction

12.1 Introduction

12.2 Literature Review

12.2.1 How ML is Applied to Stock Prediction

12.2.2 Best Machine Learning Methods for Predicting Stock Prices

12.2.3 Approaches to Stock Price Prediction

12.3 Standard ML

12.4 DL

12.5 Implementation Recommendations for ML Algorithms

12.5.1 Fundamental and Technical Analysis Data Types

12.5.2 Selection of Data Sources

12.5.3 Using ML to Sentiment Analyses

12.6 Overcoming Modeling and Training Challenges

12.6.1 The Benefit of Machine Learning for Stock Prediction

12.6.2 Challenges with ML-Based Stock Prediction

12.7 Problems with Current Mechanisms

12.8 Case Study

12.9 Research Objective

12.9.1 Justification for Sample Size and Sample Selection Criteria.

12.10 Conclusion

12.11 Future Scope

Chapter 13 Systematic Literature Review and Bibliometric Analysis on Fundamental Analysis and Stock Market Prediction

13.1 Introduction

13.2 Fundamental Analysis

13.3 Machine Learning and Stock Price Prediction/Machine Learning Algorithms

13.4 Related Work

13.5 Research Methodology

13.6 Analysis and Findings

13.6.1 Publication Activity of Fundamental Analysis and Stock Price Prediction

13.6.2 Top Authors, Countries, and Institutions of Fundamental Analysis and Stock Market Prediction

13.6.3 Top Journals for Fundamental Analysis and Stock Market Prediction Research

13.6.4 Top Articles in Fundamental Analysis and Stock Market Prediction

13.6.5 Keyword Occurrence Analysis in Stock Price Prediction Research

13.6.6 Thematic Clusters of Stock Market Prediction Through Bibliographic Coupling

13.6.7 List of Machine Learning Algorithms Used

13.6.8 List of Training and Testing Dataset Criteria Used

13.6.9 List of Evaluation Metrics Used

13.6.10 List of Factors Used in Fundamental Analysis

13.6.11 List of Technical Indicators Used

13.6.12 List of Feature Selection Criteria

13.7 Discussion and Conclusion

Chapter 14 Impact of Emotional Intelligence on Investment Decision

14.1 Introduction

14.2 Literature Review

14.3 Research Methodology

14.4 Data Analysis

14.4.1 Reliability Analysis

14.4.2 Factors Naming

14.4.3 Multiple Regression Analysis

14.5 Discussion, Implications, and Future Scope

14.6 Conclusion

Chapter 15 Influence of Behavioral Biases on Investor Decision-Making in Delhi-NCR

15.1 Introduction

15.2 Literature Review

15.2.1 Overconfidence Bias

15.2.2 Illusion of Control Bias

15.2.3 Optimism Bias

15.3 Research Hypothesis

15.4 Methodology

15.4.1 Result.

15.5 Discussion.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

9781394214327

1394214324

9781394214334

1394214332

OCLC:

1428861464