Title:
Deep learning tools for predicting stock market movements
Author:
Sharma, Renuka, editor.
ISBN:
9781394214334
9781394214327
9781394214310
Physical Description:
1 online resource
Contents:
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
Abstract:
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.
Local Note:
John Wiley and Sons
Genre:
Electronic Access:
https://onlinelibrary.wiley.com/doi/book/10.1002/9781394214334Copies:
Available:*
Library | Material Type | Item Barcode | Shelf Number | Status | Item Holds |
|---|---|---|---|---|---|
Searching... | E-Book | 599087-1001 | HG4661 .D44 2024 | Searching... | Searching... |