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 xvii

Acknowledgments xxv

1 Design and Development of an Ensemble Model for Stock Market Prediction Using LSTM, ARIMA, and Sentiment Analysis 1
Poorna Shankar, Kota Naga Rohith and Muthukumarasamy Karthikeyan

1.1 Introduction 2

1.2 Significance of the Study 3

1.3 Problem Statement 5

1.4 Research Objectives 6

1.5 Expected Outcome 6

1.6 Chapter Summary 7

1.7 Theoretical Foundation 8

1.8 Research Methodology 13

1.9 Analysis and Results 22

1.10 Conclusion 33

2 Unraveling Quantum Complexity: A Fuzzy AHP Approach to Understanding Software Industry Challenges 39
Kiran Mehta and Renuka Sharma

2.1 Introduction 39

2.2 Introduction to Quantum Computing 41

2.3 Literature Review 43

2.4 Research Methodology 45

2.5 Research Questions 46

2.6 Designing Research Instrument/Questionnaire 48

2.7 Results and Analysis 49

2.8 Result of Fuzzy AHP 50

2.9 Findings, Conclusion, and Implication 54

3 Analyzing Open Interest: A Vibrant Approach to Predict Stock Market Operator's Movement 61
Avijit Bakshi

3.1 Introduction 62

3.2 Methodology 64

3.3 Concept of OI 64

3.4 OI in Future Contracts 65

3.5 OI in Option Contracts 79

3.6 Conclusion 85

4 Stock Market Predictions Using Deep Learning: Developments and Future Research Directions 89
Renuka Sharma and Kiran Mehta

4.1 Background and Introduction 90

4.2 Studies Related to the Current Work, i.e., Literature Review 97

4.3 Objective of Research and Research Methodology 100

4.4 Results and Analysis of the Selected Papers 100

4.5 Overview of Data Used in the Earlier Studies Selected for the Current Research 102

4.6 Data Source 103

4.7 Technical Indicators 105

4.8 Stock Market Prediction: Need and Methods 106

4.9 Process of Stock Market Prediction 107

4.10 Reviewing Methods for Stock Market Predictions 110

4.11 Analysis and Prediction Techniques 111

4.12 Classification Techniques (Also Called Clustering Techniques) 111

4.13 Future Direction 112

4.14 Conclusion 114

5 Artificial Intelligence and Quantum Computing Techniques for Stock Market Predictions 123
Rajiv Iyer and Aarti Bakshi

5.1 Introduction 124

5.2 Literature Survey 125

5.3 Analysis of Popular Deep Learning Techniques for Stock Market Prediction 132

5.4 Data Sources and Methodology 139

5.5 Result and Analysis 141

5.6 Challenges and Future Scope 142

5.7 Conclusion 144

6 Various Model Applications for Causality, Volatility, and Co-Integration in Stock Market 147
Swaty Sharma

6.1 Introduction 147

6.2 Literature Review 149

6.3 Objectives of the Chapter 153

6.4 Methodology 153

6.5 Result and Discussion 154

6.6 Implications 155

6.7 Conclusion 156

7 Stock Market Prediction Techniques and Artificial Intelligence 161
Jeevesh Sharma

7.1 Introduction 162

7.2 Financial Market 163

7.3 Stock Market 164

7.4 Stock Market Prediction 166

7.5 Artificial Intelligence and Stock Prediction 170

7.6 Benefits of Using AI for Stock Prediction 173

7.7 Challenges of Using AI for Stock Prediction 175

7.8 Limitations of AI-Based Stock Prediction 176

7.9 Conclusion 178

8 Prediction of Stock Market Using Artificial Intelligence Application 185
Shaina Arora, Anand Pandey and Kamal Batta

8.1 Introduction 186

8.2 Objectives 189

8.3 Literature Review 190

8.4 Future Scope 195

8.5 Sources of Study and Importance 196

8.6 Case Study: Comparison of AI Techniques for Stock Market Prediction 197

8.7 Discussion and Conclusion 198

9 Stock Returns and Monetary Policy 203
Baki Cem Sahin

9.1 Introduction 204

9.2 Literature 205

9.3 Data and Methodology 209

9.4 Index-Based Analysis 211

9.5 Firm-Level Analysis 212

9.5.1 Sectoral Difference 213

9.6 The Impact of Financial Constraints 216

9.7 Discussion and Conclusion 219

10 Revolutionizing Stock Market Predictions: Exploring the Role of Artificial Intelligence 227
Rajani H. Pillai and Aatika Bi

10.1 Introduction 227

10.2 Review of Literature 229

10.3 Research Methods 234

10.4 Results and Discussion 236

10.5 Conclusion 241

10.6 Significance of the Study 242

10.7 Scope of Further Research 243

11 A Comparative Study of Stock Market Prediction Models: Deep Learning Approach and Machine Learning Approach 249
Swati Jain

11.1 Introduction 250

11.2 Stock Market Prediction 253

11.3 Models for Prediction in Stock Market 257

11.4 Conclusion 266

12 Machine Learning and its Role in Stock Market Prediction 271
Pawan Whig, Pavika Sharma, Ashima Bhatnagar Bhatia, Rahul Reddy Nadikattu and Bhupesh Bhatia

12.1 Introduction 272

12.2 Literature Review 274

12.3 Standard ML 277

12.4 DL 279

12.5 Implementation Recommendations for ML Algorithms 280

12.6 Overcoming Modeling and Training Challenges 281

12.7 Problems with Current Mechanisms 283

12.8 Case Study 284

12.9 Research Objective 284

12.10 Conclusion 294

12.11 Future Scope 294

13 Systematic Literature Review and Bibliometric Analysis on Fundamental Analysis and Stock Market Prediction 299
Renuka Sharma, Archana Goel and Kiran Mehta

13.1 Introduction 300

13.2 Fundamental Analysis 301

13.3 Machine Learning and Stock Price Prediction/Machine Learning Algorithms 302

13.4 Related Work 303

13.5 Research Methodology 303

13.6 Analysis and Findings 304

13.7 Discussion and Conclusion 336

14 Impact of Emotional Intelligence on Investment Decision 341
Pooja Chaturvedi Sharma

14.1 Introduction 342

14.2 Literature Review 343

14.3 Research Methodology 347

14.4 Data Analysis 348

14.5 Discussion, Implications, and Future Scope 357

14.6 Conclusion 358

15 Influence of Behavioral Biases on Investor Decision-Making in Delhi-NCR 363
Pooja Gahlot, Kanika Sachdeva, Shikha Agnihotri and Jagat Narayan Giri

15.1 Introduction 364

15.2 Literature Review 367

15.3 Research Hypothesis 373

15.4 Methodology 373

15.5 Discussion 379

16 Alternative Data in Investment Management 391
Rangapriya Saivasan and Madhavi Lokhande

16.1 Introduction 391

16.2 Literature Review 393

16.3 Research Methodology 395

16.4 Results and Discussion 396

16.5 Implications of This Study 403

16.6 Conclusion 404

17 Beyond Rationality: Uncovering the Impact of Investor Behavior on Financial Markets 409
Anu Krishnamurthy

17.1 Introduction 410

17.2 Statement of the Problem 418

17.3 Need for the Study 418

17.4 Significance of the Study 419

17.5 Discussions 422

17.6 Implications 424

17.7 Scope for Further Research 424

18 Volatility Transmission Role of Indian Equity and Commodity Markets 429
Harpreet Kaur and Amita Chaudhary

18.1 Introduction 430

18.2 Literature Review 431

18.3 Data and Methodology 434

18.4 Results and Discussions 435

18.5 Conclusion 438

References 439

Glossary 445

Index 457