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- Herausgeber: tredition
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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. Delve into stock price forecasting, familiarize yourself with probabilistic mathematical models and broaden your understanding of stock intricacies.
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Seitenzahl: 159
Veröffentlichungsjahr: 2023
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Forecasting Stock Prices:
Mathematics of Probabilistic Models
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© 2023 Azhar ul Haque Sario
Druck und Distribution im Auftrag des Autors: tredition GmbH, Heinz-Beusen-Stieg 5, 22926 Ahrensburg, Germany
Das Werk, einschließlich seiner Teile, ist urheberrechtlich geschützt. Für die Inhalte ist der Autor verantwortlich. Jede Verwertung ist ohne seine Zustimmung unzulässig. Die Publikation und Verbreitung erfolgen im Auftrag des Autors, zu erreichen unter: tredition GmbH, Abteilung "Impressumservice", Heinz-Beusen-Stieg 5, 22926 Ahrensburg, Deutschland.
Table of Contents
Cover
Title Page
Copyright
Foreword
Generalized Autoregressive Conditional Heteroskedasticity (Garch)
Vector Autoregression (Var)
Multivariate Garch (Mgarch)
Stochastic Volatility (Sv) Models
Hidden Markov Models (Hmm)
Bayesian Network Models
Monte Carlo Simulation
Autoregressive Moving Average (Arma)
Random Walk Models
Mean Reversion Models
Exponential Smoothing Models
Logistic Regression Models
Naive Bayes Classifier
Gaussian Processes
Markov Chain Monte Carlo (Mcmc) Methods
Kalman Filter Models
Long Short-Term Memory (Lstm) Models
Extreme Value Theory (Evt)
Time Series Forecasting Models
Probit Models
Latent Dirichlet Allocation (Lda)
Gaussian Mixture Models (Gmm)
Neural Network Models
Decision Tree Models
Support Vector Machine Models
Reinforcement Learning Models
Convolutional Neural Network (Cnn) Models
Black-Scholes Model
Monte Carlo Simulations
Brownian Motion Model
Autoregressive Integrated Moving Average (Arima)
Garch Model
Lstm (Long Short-Term Memory) Models
Facebook’S Prophet
Forecasting Stock Prices
Cover
Title Page
Copyright
Foreword
Facebook’S Prophet
Forecasting Stock Prices
Cover
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Foreword
For all the complexity that comes with the world of stock price prediction, this book, “Forecasting Stock Prices: Mathematics of Probabilistic Models” is crafted with the goal of simplifying as many of these mathematical intricacies as possible. Delving into the mathematical models about stock price prediction is not a route that is as complex as it is painted to be. Therefore, this immensely detailed resource makes it possible and feasible. The book, faced with the daunting task of encapsulating an array of predictive analytic models, sets off with general models such as Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Vector Autoregression (VAR). Journeying carefully through all of them, we weave past theories such as Monte Carlo Simulation and Markov Chain Monte Carlo Methods (MCMC), all the way to financial approaches founded on principles such as the Black-Scholes Model.
Every model is treated with detailed numerical explanation, favouring clarity and accessibility over jargon cluttered detail. Illustrative references, practical examples, application potential, and their workings are added for each model. Ultimately, the purpose is to segregate each model facilitating clarity for readers across all related field.
This book does not claim or aim to make you an expert, but guarantee that readers will close the book on the other side being a lot more knowledgeable. So, whether you’re a student, a stock market practitioner, a financial analyst, or a curious mind, this go-to resource could pack for you all the understanding you need in the provocative (and yet uncertain) world of stock price forecasting and its corresponding mathematical models.
Here’s to our journey through “Forecasting Stock Prices: Mathematics of Probabilistic Models”, hoping that it ushers in a greater and clearer understanding of the relationship between predicative analytics and stock markets.
