Deep Learning Tools for Predicting Stock Market Movements

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.

• Preface xviiAcknowledgments xxv1 Design and Development of an Ensemble Model for Stock Market Prediction Using LSTM, ARIMA, and Sentiment Analysis 1Poorna Shankar, Kota Naga Rohith and Muthukumarasamy Karthikeyan1.1 Introduction 21.2 Significance of the Study 31.3 Problem Statement 51.4 Research Objectives 61.5 Expected Outcome 61.6 Chapter Summary 71.7 Theoretical Foundation 81.8 Research Methodology 131.9 Analysis and Results 221.10 Conclusion 332 Unraveling Quantum Complexity: A Fuzzy AHP Approach to Understanding Software Industry Challenges 39Kiran Mehta and Renuka Sharma2.1 Introduction 392.2 Introduction to Quantum Computing 412.3 Literature Review 432.4 Research Methodology 452.5 Research Questions 462.6 Designing Research Instrument/Questionnaire 482.7 Results and Analysis 492.8 Result of Fuzzy AHP 502.9 Findings, Conclusion, and Implication 543 Analyzing Open Interest: A Vibrant Approach to Predict Stock Market Operator's Movement 61Avijit Bakshi3.1 Introduction 623.2 Methodology 643.3 Concept of OI 643.4 OI in Future Contracts 653.5 OI in Option Contracts 793.6 Conclusion 854 Stock Market Predictions Using Deep Learning: Developments and Future Research Directions 89Renuka Sharma and Kiran Mehta4.1 Background and Introduction 904.2 Studies Related to the Current Work, i.e., Literature Review 974.3 Objective of Research and Research Methodology 1004.4 Results and Analysis of the Selected Papers 1004.5 Overview of Data Used in the Earlier Studies Selected for the Current Research 1024.6 Data Source 1034.7 Technical Indicators 1054.8 Stock Market Prediction: Need and Methods 1064.9 Process of Stock Market Prediction 1074.10 Reviewing Methods for Stock Market Predictions 1104.11 Analysis and Prediction Techniques 1114.12 Classification Techniques (Also Called Clustering Techniques) 1114.13 Future Direction 1124.14 Conclusion 1145 Artificial Intelligence and Quantum Computing Techniques for Stock Market Predictions 123Rajiv Iyer and Aarti Bakshi5.1 Introduction 1245.2 Literature Survey 1255.3 Analysis of Popular Deep Learning Techniques for Stock Market Prediction 1325.4 Data Sources and Methodology 1395.5 Result and Analysis 1415.6 Challenges and Future Scope 1425.7 Conclusion 1446 Various Model Applications for Causality, Volatility, and Co-Integration in Stock Market 147Swaty Sharma6.1 Introduction 1476.2 Literature Review 1496.3 Objectives of the Chapter 1536.4 Methodology 1536.5 Result and Discussion 1546.6 Implications 1556.7 Conclusion 1567 Stock Market Prediction Techniques and Artificial Intelligence 161Jeevesh Sharma7.1 Introduction 1627.2 Financial Market 1637.3 Stock Market 1647.4 Stock Market Prediction 1667.5 Artificial Intelligence and Stock Prediction 1707.6 Benefits of Using AI for Stock Prediction 1737.7 Challenges of Using AI for Stock Prediction 1757.8 Limitations of AI-Based Stock Prediction 1767.9 Conclusion 1788 Prediction of Stock Market Using Artificial Intelligence Application 185Shaina Arora, Anand Pandey and Kamal Batta8.1 Introduction 1868.2 Objectives 1898.3 Literature Review 1908.4 Future Scope 1958.5 Sources of Study and Importance 1968.6 Case Study: Comparison of AI Techniques for Stock Market Prediction 1978.7 Discussion and Conclusion 1989 Stock Returns and Monetary Policy 203Baki Cem Sahin9.1 Introduction 2049.2 Literature 2059.3 Data and Methodology 2099.4 Index-Based Analysis 2119.5 Firm-Level Analysis 2129.5.1 Sectoral Difference 2139.6 The Impact of Financial Constraints 2169.7 Discussion and Conclusion 21910 Revolutionizing Stock Market Predictions: Exploring the Role of Artificial Intelligence 227Rajani H. Pillai and Aatika Bi10.1 Introduction 22710.2 Review of Literature 22910.3 Research Methods 23410.4 Results and Discussion 23610.5 Conclusion 24110.6 Significance of the Study 24210.7 Scope of Further Research 24311 A Comparative Study of Stock Market Prediction Models: Deep Learning Approach and Machine Learning Approach 249Swati Jain11.1 Introduction 25011.2 Stock Market Prediction 25311.3 Models for Prediction in Stock Market 25711.4 Conclusion 26612 Machine Learning and its Role in Stock Market Prediction 271Pawan Whig, Pavika Sharma, Ashima Bhatnagar Bhatia, Rahul Reddy Nadikattu and Bhupesh Bhatia12.1 Introduction 27212.2 Literature Review 27412.3 Standard ML 27712.4 DL 27912.5 Implementation Recommendations for ML Algorithms 28012.6 Overcoming Modeling and Training Challenges 28112.7 Problems with Current Mechanisms 28312.8 Case Study 28412.9 Research Objective 28412.10 Conclusion 29412.11 Future Scope 29413 Systematic Literature Review and Bibliometric Analysis on Fundamental Analysis and Stock Market Prediction 299Renuka Sharma, Archana Goel and Kiran Mehta13.1 Introduction 30013.2 Fundamental Analysis 30113.3 Machine Learning and Stock Price Prediction/Machine Learning Algorithms 30213.4 Related Work 30313.5 Research Methodology 30313.6 Analysis and Findings 30413.7 Discussion and Conclusion 33614 Impact of Emotional Intelligence on Investment Decision 341Pooja Chaturvedi Sharma14.1 Introduction 34214.2 Literature Review 34314.3 Research Methodology 34714.4 Data Analysis 34814.5 Discussion, Implications, and Future Scope 35714.6 Conclusion 35815 Influence of Behavioral Biases on Investor Decision-Making in Delhi-NCR 363Pooja Gahlot, Kanika Sachdeva, Shikha Agnihotri and Jagat Narayan Giri15.1 Introduction 36415.2 Literature Review 36715.3 Research Hypothesis 37315.4 Methodology 37315.5 Discussion 37916 Alternative Data in Investment Management 391Rangapriya Saivasan and Madhavi Lokhande16.1 Introduction 39116.2 Literature Review 39316.3 Research Methodology 39516.4 Results and Discussion 39616.5 Implications of This Study 40316.6 Conclusion 40417 Beyond Rationality: Uncovering the Impact of Investor Behavior on Financial Markets 409Anu Krishnamurthy17.1 Introduction 41017.2 Statement of the Problem 41817.3 Need for the Study 41817.4 Significance of the Study 41917.5 Discussions 42217.6 Implications 42417.7 Scope for Further Research 42418 Volatility Transmission Role of Indian Equity and Commodity Markets 429Harpreet Kaur and Amita Chaudhary18.1 Introduction 43018.2 Literature Review 43118.3 Data and Methodology 43418.4 Results and Discussions 43518.5 Conclusion 438References 439Glossary 445Index 457