In this section, we describe the approaches we used to investigate the existence of the stylized facts in the S&P 500 log return series.
- Title Page
- Copyright and Credits
- Dedication
- About Packt
- Contributors
- Preface
- Financial Data and Preprocessing
- Technical Analysis in Python
- Time Series Modeling
- Multi-Factor Models
- Modeling Volatility with GARCH Class Models
- Monte Carlo Simulations in Finance
- Asset Allocation in Python
- Identifying Credit Default with Machine Learning
- Loading data and managing data types
- Exploratory data analysis
- Splitting data into training and test sets
- Dealing with missing values
- Encoding categorical variables
- Fitting a decision tree classifier
- Implementing scikit-learn's pipelines
- Tuning hyperparameters using grid searches and cross-validation
- Advanced Machine Learning Models in Finance
- Deep Learning in Finance
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