Forecasting Stock Prices

Struggling with the complicated process of stock price predictions? Now introducing "Forecasting Stock Prices: Mathematics of Probabilistic Models", your key to unlocking various aspects of predictive analytic models in an elaborate yet easy-to-understand manner. It prides on clarity and interpretation of complex statistical terminologies into user-friendly frameworks that assist in your journey through the realm of probabilistic models. Explore an array of valued Probabilistic Models, from the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) to Vector Autoregression (VAR), in addition to advanced models like the known-everywhere Black-Scholes Model and Facebook's Prophet.In this competent dispatcher, all models are brimming with practical instances demonstrating useful functionalities, anticipated uncertainties, actionable insights, and the mathematics that underpin the central phases and their results. It is rich with various paths leading towards the enigma, difficulties, and enlightenment of predicting stock prices. The aim is not to highlight complex terms, but to emphasise on the real importance of these analytics. What's more, it offers a detailed study of topics ranging from rainfall to specific formats such as LSTM (Long Short-Term Memory) models, the tenets of Monte Carlo simulations, Markov Chain Monte Carlo methods, and other paramount models.Whether you're a beginner or an expert at forecasting stock prices, this book simplifies even the understood complex rules to make you skilled in every aspect of these systems. Regardless of whether you intend to enrich classroom content, utilize analytical software at work, or gain comprehensive insight into the application of analytics to predict stock prices - this serves as a valuable educational resource for all levels and different purposes in this field. Get to grips with the straightforward syntax combined with a detailed practical demonstration through every model presented in this book.