Chapman and Hall/CRC Financial Mathemati Machine Learning for Factor Investing: Python Version, (Paperback)
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Machine Learning for Factor Investing: Python Version bridges the gap between complex ML concepts and practical investment strategies for both specialists and non-specialists.
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產品詳情
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Textbooks |
| Publication date | August, 2023 |
| Pages | 340 |
| Reading level | Professional & Vocational |
| Subgenre | Probability & Statistics |
| Series title | Chapman and Hall/CRC Financial Mathematics |
| Edition | 1 |
| Publisher | CRC Press |
| Original languages | English |
| Language | English |
| Edu focus | Mathematics |
| Is collectible | N |
| Editor | Tony Guida and Guillaume Coqueret |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 7.00 x 0.74 x 10.00 in (17.8 x 1.9 x 25.4 cm) |
| Assembled product weight | 1.37 lb (620 grams) |
| Bisac subject heading | Mathematics |
產品敘述
Chapman and Hall/CRC Financial Mathemati Machine Learning for Factor Investing: Python Version, (Paperback)
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Guillaume Coqueret All Books Editorial Review
Chapman and Hall/CRC Financial Mathemati Machine Learning for Factor Investing: Python Version is a compelling new addition to the realm of professional textbooks, focusing on the integration of machine learning with factor investing. With 340 pages of content, this paperback edition published in August 2023 is tailored for professionals and vocational readers in the field of Mathematics. It provides in-depth insights into probability and statistics, making it an essential resource for those looking to enhance their quantitative analysis skills in finance. The contributions from editors Tony Guida and Guillaume Coqueret ensure that the material is well-structured and accessible, making the complex subject matter engaging and practical.
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優點
- Comprehensive coverage of machine learning techniques
- Focus on factor investing strategies
- Written for professional and vocational readers
- High-quality content from expert editors
- Useful for enhancing quantitative analysis skills
缺點
- May be too advanced for beginners in finance
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特色和優勢
- Comprehensive guide to modern ML-based investment strategies.
- Covers a range of topics from economic rationales to portfolio back-testing.
- Explains supervised learning algorithms in style investing context.
- Includes practical Python code samples applied to real datasets.
- Accessible for both specialists and those with basic finance knowledge.
- Enhances financial and technical expertise through practical illustrations.
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