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Biblioteca

MARC Lista (tabellare)
Decentralized Finance and Blockchain : A Game Changing Duo
Decentralized Finance and Blockchain : A Game Changing Duo
Autore Sharma Renuka
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2025
Descrizione fisica 1 online resource (321 pages)
Disciplina 332.02857588
Altri autori (Persone) MehtaKiran
VyasVishal
Kumar ShuklaVinod
Soggetto topico Finance - Technological innovations
Blockchains (Databases)
Cryptocurrencies
ISBN 1-394-24268-9
1-394-24267-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9911022471403321
Sharma Renuka  
Newark : , : John Wiley & Sons, Incorporated, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Deep Learning Tools for Predicting Stock Market Movements
Deep Learning Tools for Predicting Stock Market Movements
Autore Sharma Renuka
Edizione [1st ed.]
Pubbl/distr/stampa John Wiley & Sons, Inc, 2024
Descrizione fisica 1 online resource (489 pages)
Disciplina 332.63222028563
Altri autori (Persone) MehtaKiran
Soggetto topico Stock price forecasting
Deep learning (Machine learning)
ISBN 9781394214327
1394214324
9781394214334
1394214332
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Preface -- Acknowledgments -- Chapter 1 Design and Development of an Ensemble Model for Stock Market Prediction Using LSTM, ARIMA, and Sentiment Analysis -- 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 -- Chapter 2 Unraveling Quantum Complexity: A Fuzzy AHP Approach to Understanding Software Industry Challenges -- 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 -- Chapter 3 Analyzing Open Interest: A Vibrant Approach to Predict Stock Market Operator's Movement -- 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 -- Chapter 4 Stock Market Predictions Using Deep Learning: Developments and Future Research Directions -- 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 -- Chapter 5 Artificial Intelligence and Quantum Computing Techniques for Stock Market Predictions -- 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 -- Chapter 6 Various Model Applications for Causality, Volatility, and Co-Integration in Stock Market -- 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 -- Chapter 7 Stock Market Prediction Techniques and Artificial Intelligence -- 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 -- Chapter 8 Prediction of Stock Market Using Artificial Intelligence Application -- 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 -- Chapter 9 Stock Returns and Monetary Policy -- 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 -- Chapter 10 Revolutionizing Stock Market Predictions: Exploring the Role of Artificial Intelligence -- 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 -- Chapter 11 A Comparative Study of Stock Market Prediction Models: Deep Learning Approach and Machine Learning Approach -- 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 -- Chapter 12 Machine Learning and its Role in Stock Market Prediction -- 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 -- Chapter 13 Systematic Literature Review and Bibliometric Analysis on Fundamental Analysis and Stock Market Prediction -- 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 -- Chapter 14 Impact of Emotional Intelligence on Investment Decision -- 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 -- Chapter 15 Influence of Behavioral Biases on Investor Decision-Making in Delhi-NCR -- 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.
Record Nr. UNINA-9911020222603321
Sharma Renuka  
John Wiley & Sons, Inc, 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui