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.
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