Advances in financial machine learning /
"Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests f...
| Main Authors: | , |
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| Format: | Book |
| Language: | English |
| Published: |
Hoboken, New Jersey :
Wiley,
[2018]
New Jersey : [2018] |
| Subjects: |
Table of Contents:
- Financial machine learning as a distinct subject
- Part 1: Data analysis. Financial data structures
- Labeling
- Sample weights
- Fractionally differentiated features
- Part 2: Modelling. Ensemble methods
- Cross-validation in finance
- Feature importance
- Hyper-parameter tuning with cross-validation
- Part 3: Backtesting. Bet sizing
- The dangers of backtesting
- Backtesting through cross-validation
- Backtesting on synthetic data
- Backtest statistics
- Understanding strategy risk
- Machine learning asset allocation
- Part 4: Useful financial features. Structural breaks
- Entropy features
- Microstructural features
- Part 5: High-performance computing recipes
- Multiprocessing and vectorization
- Brute force and quantum computers
- High-performance computational intelligence and forecasting technologies/ Kesheng Wu and Horst D. Simon
- Preamble, Financial Machine Learning as a Distinct Subject
- Part 1, Data Analysis. Financial Data Structures ; Labeling ; Sample Weights ; Fractionally Differentiated Features
- Part 2, Modelling. Ensemble Methods ; Cross-validation in Finance ; Feature Importance ; Hyper-parameter Tuning with Cross-Validation
- Part 3, Backtesting. Bet Sizing ; The Dangers of Backtesting ; Backtesting through Cross-Validation ; Backtesting on Synthetic Data ; Backtest Statistics ; Understanding Strategy Risk ; Machine Learning Asset Allocation
- Part 4, Useful Financial Features. Structural Breaks ; Entropy Features ; Microstructural Features
- Part 5, High-Performance Computing Recipes. Multiprocessing and Vectorization ; Brute Force and Quantum Computers ; High-Performance Computational Intelligence and Forecasting Technologies / Kesheng Wu and Horst Simon
- Preamble, Financial Machine Learning as a Distinct Subject
- Part 1, Data Analysis. Financial Data Structures ; Labeling ; Sample Weights ; Fractionally Differentiated Features
- Part 2, Modelling. Ensemble Methods ; Cross-validation in Finance ; Feature Importance ; Hyper-parameter Tuning with Cross-Validation
- Part 3, Backtesting. Bet Sizing ; The Dangers of Backtesting ; Backtesting through Cross-Validation ; Backtesting on Synthetic Data ; Backtest Statistics ; Understanding Strategy Risk ; Machine Learning Asset Allocation
- Part 4, Useful Financial Features. Structural Breaks ; Entropy Features ; Microstructural Features
- Part 5, High-Performance Computing Recipes. Multiprocessing and Vectorization ; Brute Force and Quantum Computers ; High-Performance Computational Intelligence and Forecasting Technologies / Kesheng Wu and Horst Simon