"Stock Price Predictions: An Introduction to Probabilistic Models" provides an all-inclusive guide to understanding the complex domain of predicting stock prices. Catering for absolute beginners as well as advanced investors, the book breaks down both customary and innovative techniques to forecast stock market trends.
Firstly, it presents common approaches to stock predictions, including Fundamental, Technical, and Quantitative Analysis. For each methodology, the book thoroughly delves into its process, how it works, and its respective advantages and shortcomings.
In the following section, the information becomes dense and specified towards probabilistic models which predict stock market trends. The section serves as a detailed introduction to various schematic models such as:
ARIMA, GARCH, VAR, MGARCH, and Stochastic Volatility Models among others. The book plows through the detailed mechanisms and pragmatic applications of these models, shaping it into a beneficial resource for those who want to further explore probability-based patterns for stock price forecasting.
Towards the end of the book, readers are provided with testimony based on instances of flourishing forecasts obtained by utilizing these advanced models as well as real-world examples that shed light on the practical aspect of theoretical teachings
In this final part, methods like the Black-Scholes Model, Monte Carlo Simulations, Brownian Motion Model are briefly touched on before diving intodepth into forecasting successes yielded by newer models like LSTM and Facebook's Prophet.
Here, each of these processes are validated through mentions of accurate real-time predictions.
