Details

Deep Learning Tools for Predicting Stock Market Movements

1. Aufl.

von: Renuka Sharma, Kiran Mehta
194,99 €
Verlag:	Wiley
Format:	EPUB
Veröffentl.:	10.04.2024
ISBN/EAN:	9781394214310
Sprache:	englisch
Anzahl Seiten:	496

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Beschreibungen

Titelbeschreibung

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: <ul><li>details the development of an ensemble model for stock market prediction, combining long short-term memory and autoregressive integrated moving average;</li> <li>explains the rapid expansion of quantum computing technologies in financial systems;</li> <li>provides an overview of deep learning techniques for forecasting stock market trends and examines their effectiveness across different time frames and market conditions;</li> <li>explores applications and implications of various models for causality, volatility, and co-integration in stock markets, offering insights to investors and policymakers.</li></ul> 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.

Inhaltsverzeichnis

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

Autorenportrait

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.

Back cover copy

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: <ul><li>details the development of an ensemble model for stock market prediction, combining long short-term memory and autoregressive integrated moving average;</li> <li>explains the rapid expansion of quantum computing technologies in financial systems;</li> <li>provides an overview of deep learning techniques for forecasting stock market trends and examines their effectiveness across different time frames and market conditions;</li> <li>explores applications and implications of various models for causality, volatility, and co-integration in stock markets, offering insights to investors and policymakers.</li></ul> 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.