Advanced Financial Modeling for Stock Price Prediction
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Advanced Financial Modeling for Stock Price Prediction: A Quantitative Methods Approach (Third Edition) This third volume in the "Stock Predictions" series builds on the success of the first edition, "Stock Price Predictions: An Introduction to Probabilistic Models" (ISBN 979-8223912712), and the second edition, "Forecasting Stock Prices: Mathematics of Probabilistic Models" (ISBN 979-8223038993). This new edition delves deeper into the complex world of quantitative finance, providing readers with a comprehensive guide to advanced financial models used in stock price prediction. The book cover...
Advanced Financial Modeling for Stock Price Prediction: A Quantitative Methods Approach (Third Edition) This third volume in the "Stock Predictions" series builds on the success of the first edition, "Stock Price Predictions: An Introduction to Probabilistic Models" (ISBN 979-8223912712), and the second edition, "Forecasting Stock Prices: Mathematics of Probabilistic Models" (ISBN 979-8223038993). This new edition delves deeper into the complex world of quantitative finance, providing readers with a comprehensive guide to advanced financial models used in stock price prediction. The book covers a wide array of models, beginning with the foundational concept of Brownian Motion, which represents the random movement of stock prices and underpins many financial models. It then progresses to Geometric Brownian Motion, a model that accounts for the exponential growth often observed in stock prices. Mean Reversion Models are introduced to capture the tendency of stock prices to revert to their long-term average, offering a counterpoint to trend-following strategies. The book explores the world of volatility modeling with GARCH models, which capture the clustering and persistence of volatility in financial markets, crucial for risk management and option pricing. Extensions of GARCH, such as EGARCH and TGARCH, are examined to address the asymmetric impact of positive and negative news on volatility. In the latter part of the book, the focus shifts to Machine Learning, demonstrating how techniques like Support Vector Machines and Neural Networks can uncover complex patterns in financial data and enhance prediction accuracy. Recurrent Neural Networks, particularly LSTMs, are highlighted for their ability to model sequential data, making them ideal for capturing the temporal dynamics of stock prices. Monte Carlo simulations are discussed as a powerful tool for generating a range of possible future outcomes, enabling investors to assess risk and make informed decisions. Finally, Copula Models are introduced to model the dependence structure between multiple assets, critical for portfolio management and risk assessment. Throughout the book, each model is presented with a clear explanation of its mathematical formulation, parameter estimation techniques, and practical applications in stock price prediction. The book emphasizes the strengths and limitations of each model, equipping readers with the knowledge to select the most appropriate model for their specific needs. This book is an invaluable resource for students, researchers, and practitioners in finance and investments seeking to master the quantitative tools used in stock price prediction. With its rigorous yet accessible approach, this book empowers readers to leverage advanced financial models and make informed investment decisions in today's dynamic markets. The book is based on 95 research studies, which are listed on the references page and uploaded on Harvard University's Dataverse for transparency. As a published book, it has undergone review for originality.
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