Deep Learning Tools for Predicting Stock Market Movements
eBook - ePub

Deep Learning Tools for Predicting Stock Market Movements

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Deep Learning Tools for Predicting Stock Market Movements

About this book

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.

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Yes, you can access Deep Learning Tools for Predicting Stock Market Movements by Renuka Sharma,Kiran Mehta in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Table of Contents
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Dedication Page
  7. Preface
  8. Acknowledgments
  9. 1 Design and Development of an Ensemble Model for Stock Market Prediction Using LSTM, ARIMA, and Sentiment Analysis
  10. 2 Unraveling Quantum Complexity: A Fuzzy AHP Approach to Understanding Software Industry Challenges
  11. 3 Analyzing Open Interest: A Vibrant Approach to Predict Stock Market Operator’s Movement
  12. 4 Stock Market Predictions Using Deep Learning: Developments and Future Research Directions
  13. 5 Artificial Intelligence and Quantum Computing Techniques for Stock Market Predictions
  14. 6 Various Model Applications for Causality, Volatility, and Co-Integration in Stock Market
  15. 7 Stock Market Prediction Techniques and Artificial Intelligence
  16. 8 Prediction of Stock Market Using Artificial Intelligence Application
  17. 9 Stock Returns and Monetary Policy
  18. 10 Revolutionizing Stock Market Predictions: Exploring the Role of Artificial Intelligence
  19. 11 A Comparative Study of Stock Market Prediction Models: Deep Learning Approach and Machine Learning Approach
  20. 12 Machine Learning and its Role in Stock Market Prediction
  21. 13 Systematic Literature Review and Bibliometric Analysis on Fundamental Analysis and Stock Market Prediction
  22. 14 Impact of Emotional Intelligence on Investment Decision
  23. 15 Influence of Behavioral Biases on Investor Decision-Making in Delhi-NCR
  24. 16 Alternative Data in Investment Management
  25. 17 Beyond Rationality: Uncovering the Impact of Investor Behavior on Financial Markets
  26. 18 Volatility Transmission Role of Indian Equity and Commodity Markets
  27. Glossary
  28. Index
  29. End User License Agreement