Deep Learning Tools for Predicting Stock Market Movements (Record no. 51216)

MARC details
000 -LEADER
fixed length control field 02154 a2200205 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250906144648.0
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781394214303
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 332.63 SHA
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Sharma, Renuka
245 ## - TITLE STATEMENT
Title Deep Learning Tools for Predicting Stock Market Movements
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc Wiley
Date of publication, distribution, etc 2024
Place of publication, distribution, etc Singapore
300 ## - PHYSICAL DESCRIPTION
Extent 460
520 ## - SUMMARY, ETC.
Summary, etc DEEP LEARNING TOOLS for PREDICTING STOCK MARKET MOVEMENTS<br/>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.<br/><br/>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.<br/><br/>The book:<br/><br/>details the development of an ensemble model for stock market prediction, combining long short-term memory and autoregressive integrated moving average;<br/>explains the rapid expansion of quantum computing technologies in financial systems;<br/>provides an overview of deep learning techniques for forecasting stock market trends and examines their effectiveness across different time frames and market conditions;<br/>explores applications and implications of various models for causality, volatility, and co-integration in stock markets, offering insights to investors and policymakers.<br/>Audience<br/><br/>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
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Social sciences
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Economics
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Financial economics
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Personal Investing
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Mehta, Kiran
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Source of classification or shelving scheme Dewey Decimal Classification

No items available.