Deep Learning Tools for Predicting Stock Market Movements, 1st Edition - Renuka Sharma, Kiran Mehta

This book, “Deep Learning Tools for Predicting Stock Market Movements, 1st Edition,” presents the more developed research and innovation on deep learning models propagated exclusively for predicting issues concerning stock market movement in both developed and emerging economies. Accepting the prospects of deep learning and its complexities, this book explains and demonstrates how these advanced methods can improve the accuracy of market trend predictions, which can be valuable for investors and policymakers.

The audience will find comprehensive information on designing, training, and applying predictive models based on deep learning approaches. Starting with the programming of the models based on long short-term memory associated with autoregressive integrated moving average techniques up to the presentation of an aggressive approach to quantum computing included in the financial platforms during recent years, this book, entitled “Deep Learning Tools for Predicting Stock Market Movements, 1st Edition”, provides a solid arsenal for mastering the modern market fluctuations. Engaging the readers with the comparison of deep learning techniques in different time horizons and market environments, the study enhances readers’ critical thinking abilities, which are vital for decision-making in the highly volatile finance sector.

In addition, the book “Deep Learning Tools for Predicting Stock Market Movements, 1st Edition” provides a learning of the functional deployment and significance in causality, volatility, and co-integration in the stock markets of deep learning models. By providing information regarding the interactions of complex models and markets, it assists the reader in the ways of the discerning methodologies required for making sense of huge amounts of data. 

Deep Learning Tools for Predicting Stock Market Movements, 1st Edition Table of Contents:

1. Chapter 1: Design and Development of an Ensemble Model for Stock Market Prediction Using LSTM, ARIMA, and Sentiment Analysis
2. Chapter 2: Unraveling Quantum Complexity: A Fuzzy AHP Approach to Understanding Software Industry Challenges
3. Chapter 3: Analyzing Open Interest: A Vibrant Approach to Predict Stock Market Operator's Movement
4. Chapter 4: Stock Market Predictions Using Deep Learning: Developments and Future Research Directions
5. Chapter 5: Artificial Intelligence and Quantum Computing Techniques for Stock Market Predictions
6. Chapter 6: Various Model Applications for Causality, Volatility, and Co-Integration in Stock Market
7. Chapter 7: Stock Market Prediction Techniques and Artificial Intelligence
8. Chapter 8: Prediction of Stock Market Using Artificial Intelligence Application
9. Chapter 9: Stock Returns and Monetary Policy
10. Chapter 10: Revolutionizing Stock Market Predictions: Exploring the Role of Artificial Intelligence
11. Chapter 11: A Comparative Study of Stock Market Prediction Models: Deep Learning Approach and Machine Learning Approach
12. Chapter 12: Machine Learning and its Role in Stock Market Prediction
13. Chapter 13: Systematic Literature Review and Bibliometric Analysis on Fundamental Analysis and Stock Market Prediction
14. Chapter 14: Impact of Emotional Intelligence on Investment Decision
15. Chapter 15: Influence of Behavioral Biases on Investor Decision-Making in Delhi-NCR
16. Chapter 16: Alternative Data in Investment Management
17. Chapter 17: Beyond Rationality: Uncovering the Impact of Investor Behavior on Financial Markets
18. Chapter 18: Volatility Transmission Role of Indian Equity and Commodity Markets

Who is this course for?

1. Scientists and analysts who are involved in the creation of financial applications and tools using artificial intelligence.
2. The specific target audience would be professionals in financial software engineering.
3. About market investors that are in need of credit risk analysis services involving more complex analysis techniques.
4. Various investment institutions are in search of better strategies for forecasting.
5. It is the desire of asset management companies to expand the precision of their predictive models.

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