Название: Deep Learning Tools for Predicting Stock Market Movements
Автор: Renuka Sharma, Kiran Mehta
Издательство: Wiley-Scrivener
Год: 2024
Страниц: 489
Язык: английский
Формат: pdf (true), epub
Размер: 14,5 MB

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 present study gives a comprehensive overview of the advancements and potential avenues in the realm of utilizing Deep Learning techniques for forecasting stock market trends. This study surveys the evolving landscape of Deep Learning methodologies employed in predicting stock price movements and offers insights into their effectiveness across various time frames and market conditions. The research delves into the multifaceted aspects of this field, encompassing architectures such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and more recent transformer-based models. The analysis underscores the influence of data preprocessing, feature engineering, and model complexity. Additionally, this chapter shows a roadmap for future research directions, emphasizing the need for hybrid models that integrate traditional financial indicators with Deep Learning approaches, the exploration of alternative data sources like social media sentiment, and the imperative of addressing ethical concerns pertaining to market manipulation. By synthesizing the current landscape and illuminating potential trajectories, this chapter serves as a valuable guide for researchers and practitioners seeking to navigate the evolving landscape of stock market prediction through Deep Learning.

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.

Contents: