Deep Learning Tools for Predicting Stock Market Movements
1st Edition
Published on 19. April 2024
Book
Hardback
496 pages
978-1-394-21430-3 (ISBN)
Description
DEEP LEARNING TOOLS for PREDICTING STOCK MARKET MOVEMENTS 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.
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.
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 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.
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.
More details
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Weight
989 gr
ISBN-13
978-1-394-21430-3 (9781394214303)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Renuka Sharma | Kiran Mehta
Deep Learning Tools for Predicting Stock Market Movements
E-Book
04/2024
1st Edition
Wiley
€204.99
Available for download
Renuka Sharma | Kiran Mehta
Deep Learning Tools for Predicting Stock Market Movements
E-Book
04/2024
1st Edition
Wiley
€204.99
Available for download
Persons
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.
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.
Content
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
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