Financial Engineering with Machine Learning and Python. Or How to Build Your Own Quantitative Hedge Fund - Helion

Tytuł oryginału: Financial Engineering with Machine Learning and Python. Or How to Build Your Own Quantitative Hedge Fund
ISBN: 9781806109166
Format: ebook
Księgarnia: Helion
Cena książki: 139,00 zł
Książka będzie dostępna od sierpnia 2025
In a world where machine learning and AI are becoming increasingly prevalent, it is crucial not to be left behind. This book goes beyond the typical machine learning and Python books by incorporating in-depth finance content to provide readers with a unique understanding of how these technologies intersect in the financial realm.
Starting with a review of the basics of financial and econometric analyses and coding, readers will quickly and intuitively progress to more sophisticated techniques. The book equips readers with the necessary knowledge to successfully apply machine learning and AI to various finance-related problems. Instead of solely focusing on the intricacies of machine learning algorithms, this book emphasizes the strategic use of machine learning and python as enablers for solving real-world finance problems.
By the end of the book, readers will not only have a solid grasp of different types of advanced AI mechanisms, but they will also possess the ability to compare various machine learning techniques and select the most appropriate one for the specific problem at hand. Bridging the gap between theory and practice, readers will be able to build their own efficient and effective machine learning models to tackle the challenges and complexities of the finance industry.
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Spis treści
Financial Engineering with Machine Learning and Python. Or How to Build Your Own Quantitative Hedge Fund eBook -- spis treści
- 1. Machine Learning and Modern Financial Landscape
- 2. Prices and Returns
- 3. Investment Performance Measures
- 4. Data Cleaning
- 5. Risk-Return Tradeoffs and Efficient Frontier
- 6. Model Performance, Linear Regression and Factor Models
- 7. Linear Regression, Statistical Arbitrage and Market-Neutral Strategies
- 8. Penalized Regressions and Portfolio Optimization
- 9. K-Nearest Neighbors and Support Vector Machines
- 10. Bayesian Learning
- 11. Decision Trees
- 12. Random Forests
- 13. Semi-supervised Learning
- 14. Neural Networks
- 15. Transformers
- 16. Unsupervised Learning
- 17. Explaining returns
- 18. Advanced Portfolio Strategies
- 19. Microstructure Investing
- 20. Options Pricing
- 21. Build Your Own Hedge Fund





