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For Authors
Submit a ProposalDeep Learning Tools for Predicting Stock Market Movements
 | Edited by Renuka Sharma and Kiran Mehta
Copyright: 2024 | Status: Published ISBN: 9781394214303 | Hardcover | 486 pages Price: $225 USD  |
One Line Description
The book provides a comprehensive overview of current research and developments in the field of deep learning models for stock market forecasting in the developed and developing worlds.
Audience
The book has a wide audience of researchers in financial technology, financial software engineering, artificial intelligence, professional market investors, investment institutions, and asset management companies.
The book has a wide audience of researchers in financial technology, financial software engineering, artificial intelligence, professional market investors, investment institutions, and asset management companies.
Description
The book delves into the realm of deep learning and embraces the challenges, opportunities, and transformation of stock market analysis. Deep learning helps foresee market trends with increased accuracy. With advancements in deep learning, new opportunities in styles, tools, and techniques evolve and embrace data-driven insights with theories and practical applications. Learn about designing, training, and applying predictive models with rigorous attention to detail. This book offers critical thinking skills and the cultivation of discerning approaches to market analysis.
The book:
• details the development of an ensemble model for stock market prediction, combining long short-term memory and autoregressive integrated moving average;
• explains the rapid expansion of quantum computing technologies in financial systems;
• provides an overview of deep learning techniques for forecasting stock market trends and examines their effectiveness across different time frames and market conditions;
• explores applications and implications of various models for causality, volatility, and co-integration in stock markets, offering insights to investors and policymakers.
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The book delves into the realm of deep learning and embraces the challenges, opportunities, and transformation of stock market analysis. Deep learning helps foresee market trends with increased accuracy. With advancements in deep learning, new opportunities in styles, tools, and techniques evolve and embrace data-driven insights with theories and practical applications. Learn about designing, training, and applying predictive models with rigorous attention to detail. This book offers critical thinking skills and the cultivation of discerning approaches to market analysis.
The book:
• details the development of an ensemble model for stock market prediction, combining long short-term memory and autoregressive integrated moving average;
• explains the rapid expansion of quantum computing technologies in financial systems;
• provides an overview of deep learning techniques for forecasting stock market trends and examines their effectiveness across different time frames and market conditions;
• explores applications and implications of various models for causality, volatility, and co-integration in stock markets, offering insights to investors and policymakers.
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Author / Editor Details
Renuka Sharma, PhD, is a professor of finance at the Chitkara Business School, Punjab, India. She has authored more than 70 research papers published in international and national journals as well as authoring books on financial services. She is a much sought-after speaker on the international circuit. Her current research concentrates on SMEs and innovation, responsible investment, corporate governance, behavioral biases, risk management, and portfolios.
Renuka Sharma, PhD, is a professor of finance at the Chitkara Business School, Punjab, India. She has authored more than 70 research papers published in international and national journals as well as authoring books on financial services. She is a much sought-after speaker on the international circuit. Her current research concentrates on SMEs and innovation, responsible investment, corporate governance, behavioral biases, risk management, and portfolios.
Kiran Mehta, PhD, is a professor and dean of finance at Chitkara Business School, Punjab, India. She has published one book on financial services. Currently, her research endeavors focus on sustainable business and entrepreneurship, cryptocurrency, ethical investments, and women’s entrepreneurship. Additionally, Dr. Kiran is the founder and director of a research and consultancy firm.
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Table of Contents
Preface
Acknowledgments
1. Design and Development of an Ensemble Model for Stock Market Prediction Using LSTM, ARIMA, and Sentiment Analysis
Poorna Shankar, Kota Naga Rohith and Muthukumarasamy Karthikeyan
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
2. Unraveling Quantum Complexity: A Fuzzy AHP Approach to Understanding Software Industry Challenges
Kiran Mehta and Renuka Sharma
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
References
3. Analyzing Open Interest: A Vibrant Approach to Predict Stock Market Operator’s Movement
Avijit Bakshi
3.1 Introduction
3.2 Methodology
3.3 Concept of OI
3.4 OI in Future Contracts
3.4.1 Interpreting OI & 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
References
4. Stock Market Predictions Using Deep Learning: Developments and Future Research Directions
Renuka Sharma and Kiran Mehta
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
References
5. Artificial Intelligence and Quantum Computing Techniques for Stock Market Predictions
Rajiv Iyer and Aarti Bakshi
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
References
6. Various Model Applications for Causality, Volatility, and Co-Integration in Stock Market
Swaty Sharma
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
References
7. Stock Market Prediction Techniques and Artificial Intelligence
Jeevesh Sharma
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
References
8. Prediction of Stock Market Using Artificial Intelligence Application
Shaina Arora, Anand Pandey and Kamal Batta
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
References
9. Stock Returns and Monetary Policy
Baki Cem Şahin
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
References
Appendix 1
Appendix 2
10. Revolutionizing Stock Market Predictions: Exploring the Role of Artificial Intelligence
Rajani H. Pillai and Aatika Bi
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
References
11. A Comparative Study of Stock Market Prediction Models: Deep Learning Approach and Machine Learning Approach
Swati Jain
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
References
12. Machine Learning and its Role in Stock Market Prediction
Pawan Whig, Pavika Sharma, Ashima Bhatnagar Bhatia, Rahul Reddy Nadikattu and Bhupesh Bhatia
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
References
13. Systematic Literature Review and Bibliometric Analysis on Fundamental Analysis and Stock Market Prediction
Renuka Sharma, Archana Goel and Kiran Mehta
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
References
14. Impact of Emotional Intelligence on Investment Decision
Pooja Chaturvedi Sharma
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
References
15. Influence of Behavioral Biases on Investor Decision-Making in Delhi-NCR
Pooja Gahlot, Kanika Sachdeva, Shikha Agnihotri and Jagat Narayan Giri
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
15.5.1 Conclusion and Implications
15.5.2 Limitations and Future Directions
15.5.3 Notes
References
16. Alternative Data in Investment Management
Rangapriya Saivasan and Madhavi Lokhande
16.1 Introduction
16.2 Literature Review
16.3 Research Methodology
16.4 Results and Discussion
16.4.1 Understanding the Current Landscape
16.4.2 Challenges in the Adoption of Alternative Data
16.4.3 The Way Forward
16.5 Implications of This Study
16.6 Conclusion
References
17. Beyond Rationality: Uncovering the Impact of Investor Behavior on Financial Markets
Anu Krishnamurthy
17.1 Introduction
17.1.1 Traditional Finance
17.1.2 Behavioral Factors Affecting Investment Decisions
17.1.3 Investor Behavior
17.1.4 Emergence of Behavioral Finance
17.1.5 Traditional Finance vs. Behavioral Finance
17.2 Statement of the Problem
17.3 Need for the Study
17.4 Significance of the Study
17.4.1 Types of Investors
17.4.1.1 Risk-Averse
17.4.1.2 Risk Seeker
17.4.1.3 Greedy
17.4.1.4 Hopeful
17.5 Discussions
17.6 Implications
17.7 Scope for Further Research
References
18. Volatility Transmission Role of Indian Equity and Commodity Markets
Harpreet Kaur and Amita Chaudhary
18.1 Introduction
18.2 Literature Review
18.3 Data and Methodology
18.3.1 Conversion of Price Series into Volatility Series
18.3.2 Test of Stationary
18.3.3 Diebold-Yilmaz Index—A Vector Autoregressions-Based Approach
18.4 Results and Discussions
18.4.1 Descriptive Analysis
18.4.2 Stationary Test
18.4.3 Volatility Transmission
18.5 Conclusion
References
Glossary
Index
Back to Top
Preface
Acknowledgments
1. Design and Development of an Ensemble Model for Stock Market Prediction Using LSTM, ARIMA, and Sentiment Analysis
Poorna Shankar, Kota Naga Rohith and Muthukumarasamy Karthikeyan
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
2. Unraveling Quantum Complexity: A Fuzzy AHP Approach to Understanding Software Industry Challenges
Kiran Mehta and Renuka Sharma
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
References
3. Analyzing Open Interest: A Vibrant Approach to Predict Stock Market Operator’s Movement
Avijit Bakshi
3.1 Introduction
3.2 Methodology
3.3 Concept of OI
3.4 OI in Future Contracts
3.4.1 Interpreting OI & 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
References
4. Stock Market Predictions Using Deep Learning: Developments and Future Research Directions
Renuka Sharma and Kiran Mehta
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
References
5. Artificial Intelligence and Quantum Computing Techniques for Stock Market Predictions
Rajiv Iyer and Aarti Bakshi
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
References
6. Various Model Applications for Causality, Volatility, and Co-Integration in Stock Market
Swaty Sharma
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
References
7. Stock Market Prediction Techniques and Artificial Intelligence
Jeevesh Sharma
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
References
8. Prediction of Stock Market Using Artificial Intelligence Application
Shaina Arora, Anand Pandey and Kamal Batta
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
References
9. Stock Returns and Monetary Policy
Baki Cem Şahin
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
References
Appendix 1
Appendix 2
10. Revolutionizing Stock Market Predictions: Exploring the Role of Artificial Intelligence
Rajani H. Pillai and Aatika Bi
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
References
11. A Comparative Study of Stock Market Prediction Models: Deep Learning Approach and Machine Learning Approach
Swati Jain
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
References
12. Machine Learning and its Role in Stock Market Prediction
Pawan Whig, Pavika Sharma, Ashima Bhatnagar Bhatia, Rahul Reddy Nadikattu and Bhupesh Bhatia
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
References
13. Systematic Literature Review and Bibliometric Analysis on Fundamental Analysis and Stock Market Prediction
Renuka Sharma, Archana Goel and Kiran Mehta
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
References
14. Impact of Emotional Intelligence on Investment Decision
Pooja Chaturvedi Sharma
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
References
15. Influence of Behavioral Biases on Investor Decision-Making in Delhi-NCR
Pooja Gahlot, Kanika Sachdeva, Shikha Agnihotri and Jagat Narayan Giri
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
15.5.1 Conclusion and Implications
15.5.2 Limitations and Future Directions
15.5.3 Notes
References
16. Alternative Data in Investment Management
Rangapriya Saivasan and Madhavi Lokhande
16.1 Introduction
16.2 Literature Review
16.3 Research Methodology
16.4 Results and Discussion
16.4.1 Understanding the Current Landscape
16.4.2 Challenges in the Adoption of Alternative Data
16.4.3 The Way Forward
16.5 Implications of This Study
16.6 Conclusion
References
17. Beyond Rationality: Uncovering the Impact of Investor Behavior on Financial Markets
Anu Krishnamurthy
17.1 Introduction
17.1.1 Traditional Finance
17.1.2 Behavioral Factors Affecting Investment Decisions
17.1.3 Investor Behavior
17.1.4 Emergence of Behavioral Finance
17.1.5 Traditional Finance vs. Behavioral Finance
17.2 Statement of the Problem
17.3 Need for the Study
17.4 Significance of the Study
17.4.1 Types of Investors
17.4.1.1 Risk-Averse
17.4.1.2 Risk Seeker
17.4.1.3 Greedy
17.4.1.4 Hopeful
17.5 Discussions
17.6 Implications
17.7 Scope for Further Research
References
18. Volatility Transmission Role of Indian Equity and Commodity Markets
Harpreet Kaur and Amita Chaudhary
18.1 Introduction
18.2 Literature Review
18.3 Data and Methodology
18.3.1 Conversion of Price Series into Volatility Series
18.3.2 Test of Stationary
18.3.3 Diebold-Yilmaz Index—A Vector Autoregressions-Based Approach
18.4 Results and Discussions
18.4.1 Descriptive Analysis
18.4.2 Stationary Test
18.4.3 Volatility Transmission
18.5 Conclusion
References
Glossary
Index
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