Advanced Financial Modeling for Stock Price Prediction
By Azhar ul Haque Sario
Length4h 28m
About this audiobook
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.
Audiobook details
GenreBusiness and Economics, Politics and Government
Length4 hrs 28 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
Publish dateNov 27, 2024
LanguageEnglish
Table of contents
1Abstract
42Feature Engineering and Selection
2Brownian Motion
43Model Evaluation and Validation
3Introduction to Brownian Motion
44Support Vector Machines (SVM)
4Mathematical Formulation of Brownian Motion
45Introduction to Support Vector Machines
5Geometric Brownian Motion
46Mathematical Formulation of SVM
Show all chaptersShow less
6Simulating Brownian Motion
47Kernel Methods in SVM
7Applications of Brownian Motion in Finance
48Parameter Tuning and Optimization
8Geometric Brownian Motion
49Applications of SVM in Finance
9Introduction to Geometric Brownian Motion
50Neural Networks
10Mathematical Formulation of Geometric Brownian Motion
51Introduction to Neural Networks
11Simulating Geometric Brownian Motion
52Architecture of Neural Networks
12Parameter Estimation in Geometric Brownian Motion
53Training Neural Networks
13Applications of Geometric Brownian Motion in Finance
54Regularization Techniques
14Mean Reversion Models
55Applications of Neural Networks in Finance
15Introduction to Mean Reversion Models
56Recurrent Neural Networks (RNN)
16Mathematical Formulation of Mean Reversion Models
57Introduction to Recurrent Neural Networks
17Ornstein-Uhlenbeck Process
58Architecture of RNN
18Parameter Estimation in Mean Reversion Models
59Training RNN
19Applications of Mean Reversion Models in Finance
60Long Short-Term Memory (LSTM) Networks
20Generalized Autoregressive Conditional Heteroskedasticity (GARCH)
61Applications of RNN in Finance
21Introduction to GARCH Models
62Long Short-Term Memory (LSTM)
22Mathematical Formulation of GARCH Models
63Introduction to Long Short-Term Memory Networks
23Parameter Estimation in GARCH Models
64Architecture of LSTM Networks
24Extensions of GARCH Models
65Training LSTM Networks
25Applications of GARCH Models in Finance
66Advanced LSTM Techniques
26EGARCH Models
67Applications of LSTM in Finance
27Introduction to EGARCH Models
68Monte Carlo Simulations
28Mathematical Formulation of EGARCH Models
69Introduction to Monte Carlo Simulations
29Parameter Estimation in EGARCH Models
70Basics of Monte Carlo Methods
30Model Diagnostics and Validation
71Simulating Stock Prices Using Monte Carlo Methods
31Applications of EGARCH Models in Finance
72Risk Management and Monte Carlo Simulations
32TGARCH Models
73Applications of Monte Carlo Simulations in Finance
33Introduction to TGARCH Models
74Copula Models
34Mathematical Formulation of TGARCH Models
75Introduction to Copula Models
35Parameter Estimation in TGARCH Models
76Mathematical Formulation of Copula Models
36Model Diagnostics and Validation
77Parameter Estimation in Copula Models
37Applications of TGARCH Models in Finance
78Dependence Structure and Copula Models
38Machine Learning Models
79Applications of Copula Models in Finance
39Introduction to Machine Learning Models
80Supplementary Data
40Supervised Learning Techniques
81About Author
41Unsupervised Learning Techniques
Series
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