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This third edition in the "Stock Predictions" series builds upon its predecessors, offering a deep dive into the quantitative methods used in stock price prediction. It presents a comprehensive guide to advanced financial models, ranging from the foundational Brownian Motion to cutting-edge machine learning techniques. The book explores key concepts like Geometric Brownian Motion for modeling exponential growth, Mean Reversion Models for capturing price reversion tendencies, and GARCH models for understanding volatility. It also delves into the world of machine learning, showcasing how Support Vector Machines, Neural Networks, and LSTMs can enhance prediction accuracy. Monte Carlo simulations and Copula Models are further discussed for their roles in risk assessment and portfolio management. Throughout the book, mathematical formulations, parameter estimation techniques, and practical applications are presented with clarity. The strengths and limitations of each model are highlighted, enabling readers to make informed choices. This edition is an invaluable resource for anyone in finance and investments seeking to master the quantitative tools used in stock price prediction.