Advanced Financial Modeling for Stock Price Prediction: A Quantitative Methods Approach (Third Edition) continues the exploration of quantitative finance established in the earlier editions, Stock Price Predictions: An Introduction to Probabilistic Models and Forecasting Stock Prices: Mathematics of Probabilistic Models. This edition offers a deep dive into advanced financial models for stock price prediction. Beginning with Brownian Motion, the book discusses Geometric Brownian Motion, capturing the exponential growth of stock prices, followed by Mean Reversion Models that address the tendency of prices to revert to long-term averages. Volatility modeling is covered extensively with GARCH models, including their extensions, EGARCH and TGARCH, which analyze the asymmetric effects of news on volatility. The book also emphasizes Machine Learning techniques, such as Support Vector Machines and LSTMs, to identify complex patterns in financial data. Monte Carlo simulations are introduced as tools for assessing risk, while Copula Models provide insights into asset dependence, crucial for portfolio management. Each model is presented with clear mathematical formulations, estimation techniques, and practical applications, making it an essential resource for students, researchers, and practitioners in finance. Grounded in 95 research studies, this book supports its findings with transparency, enhancing its value in the field of stock price prediction.

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.

Whiteflies pose a significant threat to agricultural productivity worldwide, causing damage to crops and economic losses. In this study, we present an Android-based whiteflies detection system utilizing deep learning techniques, specifically leveraging the YOLOv5 algorithm. The objective is to create a robust and efficient solution capable of real-time whitefly detection in agricultural fields. The proposed system capitalizes on the powerful capabilities of YOLOv5, a state-of-the-art object detection algorithm, known for its accuracy and speed.

We study the forecasting power of financial variables for macroeconomic variables for 62 countries between 1980 and 2013. We find that financial variables such as credit growth, stock prices and house prices have considerable predictive power for macroeconomic variables at one to four quarters horizons. A forecasting model with financial variables outperforms the World Economic Outlook (WEO) forecasts in up to 85 percent of our sample countries at the four quarters horizon. We also find that cross-country panel models produce more accurate out-of-sample forecasts than individual country models.

Many fundamental technological and managerial issues surrounding the development and implementation of intelligent analytics within multi-industry applications remain unsolved. There are still questions surrounding the foundation of intelligent analytics, the elements, the big characteristics, and the effects on business, management, technology, and society. Research is devoted to answering these questions and understanding how intelligent analytics can improve healthcare, mobile commerce, web services, cloud services, blockchain, 5G development, digital transformation, and more. Intelligent Analytics With Advanced Multi-Industry Applications is a critical reference source that explores cutting-edge theories, technologies, and methodologies of intelligent analytics with multi-industry applications and emphasizes the integration of artificial intelligence, business intelligence, big data, and analytics from a perspective of computing, service, and management. This book also provides real-world applications of the proposed concept of intelligent analytics to e-SMACS (electronic, social, mobile, analytics, cloud, and service) commerce and services, healthcare, the internet of things, the sharing economy, cloud computing, blockchain, and Industry 4.0. This book is ideal for scientists, engineers, educators, university students, service and management professionals, policymakers, decision makers, practitioners, stakeholders, researchers, and others who have an interest in how intelligent analytics are being implemented and utilized in diverse industries.

The financial landscape is rapidly evolving, and professionals must keep pace with the complex relationship between traditional financial practices and cutting-edge technologies. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into finance presents a transformative shift that requires a deep understanding and strategic approach. Navigating the Future of Finance in the Age of AI offers a comprehensive exploration of AI's impact on the financial sector, from predictive analytics to algorithmic trading strategies. Each chapter is written by experts in the field, and they provide practical insights and real-world examples to make complex concepts accessible and actionable. The book also delves into regulatory challenges, ethical considerations, and case studies, equipping readers with the tools needed to harness AI's transformative power in finance. Whether you are a finance professional seeking to enhance decision-making, a data scientist aiming to apply ML techniques in finance, or an academic exploring AI's role in financial innovation, this book is an indispensable resource that offers a roadmap to navigate the complexities of AI-driven finance and seize the opportunities it presents.