Deep learning tools for predicting stock market movements

• Titel: Deep learning tools for predicting stock market movements
• Mitwirkende: Sharma, Renuka [HerausgeberIn]
• Mitwirkende: Mehta, Kiran [HerausgeberIn]
• Verf.angabe: edited by Renuka Sharma and Kiran Mehta
• Verlagsort: Hoboken, NJ ; Beverly, MA
• Verlagsort: Hoboken, NJ ; Beverly, MA
• Verlag: John Wiley & Sons, Inc.
• Verlag: Scrivener Publishing LLC
• Jahr: 2024
• Jahr: 2024
• Umfang: 1 online resource
• Fussnoten: Description based on online resource; title from digital title page (viewed on April 24, 2024)
• ISBN: 978-1-394-21433-4
• ISBN: 1-394-21433-2
• ISBN: 978-1-394-21432-7
• ISBN: 1-394-21432-4
• ISBN: 978-1-394-21430-3
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Sach-SW: Actions (Titres de société) ; Modèles mathématiques
• K10plus-PPN: 1892761556
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4544453917
• Lokale URL Inst.: Zum Volltext

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.