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Item #:
18852341
Advances in Financial Machine Learning
88% of respondents would recommend this to a friend
Item #:
18852341
BSD 39
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Advances in Financial Machine Learning is a book that equips investment professionals with the groundbreaking tools needed to succeed in modern finance.
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What Stands Out
Cutting-edge Techniques
Explore modern algorithms and methods revolutionizing finance through machine learning, enabling users to gain insights that traditional models can't achieve, addressing complex financial challenges effectively.
Practical Applications
Focus on real-world applications with practical examples, helping readers seamlessly integrate machine learning into their financial strategies, overcoming barriers faced in traditional financial analysis.
Expert Insights
Authored by leading experts in the field, this edition provides authoritative guidance, ensuring comprehensive understanding of sophisticated concepts in financial machine learning, a valuable resource for both practitioners and scholars.
Product Details
- Learn to understand and implement the latest machine learning innovations to improve investment performance
- Explore structuring big data for ML algorithms and conducting ML research on big data
- Learn to use supercomputing methods and back test discoveries while avoiding false positives
- Address real-life problems faced by practitioners in finance and find scientifically sound solutions
- Written by a recognized expert and portfolio manager, offering groundbreaking tools for success in modern finance
| Publisher | Wiley |
| Publication date | February 21, 2018 |
| Edition | 1st |
| Language | English |
| Print length | 400 pages |
| ISBN-10 | 1119482089 |
| ISBN-13 | 978-1119482086 |
| Item Weight | 1.46 pounds (660 grams) |
| Dimensions | 6.3 x 1.2 x 9.1 inches (16 x 3 x 23.1 cm) |
Who Should Buy?
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Finance Professionals
Ideal for practitioners seeking to integrate machine learning techniques into quantitative finance strategies and trading algorithms.
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Data Scientists
Beneficial for data scientists focusing on applications of machine learning in financial markets and risk management approaches.
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Academic Researchers
Suitable for researchers exploring advanced financial models and machine learning methodologies for empirical studies and publications.
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Beginners
Not suitable for novices as it assumes prior knowledge in finance, statistics, and machine learning concepts.
Product Description
Advances in Financial Machine Learning
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Machine Theory Editorial Review
Advances in Financial Machine Learning is an insightful resource for those looking to integrate machine learning into finance, emphasizing the need for a strong foundation rather than quick profit methods. Dr. Lopez de Prado expertly combines diverse fields such as mathematics and information theory to challenge common misconceptions in financial modeling, specifically emphasizing that financial data often defy IID assumptions. The book serves both beginners and seasoned professionals, offering rigorous analysis and practical methodologies, making it a must-read for anyone serious about the intersection of finance and machine learning. Readers appreciate the depth of knowledge provided, which goes beyond surface-level topics and encourages exploration of advanced concepts.
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Pros
- Expertly combines multiple disciplines for comprehensive understanding
- Challenges common misconceptions in financial modeling
- Provides practical methodologies for algorithmic investing
- Dense content with rigorous analysis and references
- Suitable for both beginners and experienced professionals
Cons
- Dense information may require careful reading to grasp fully
Product Price History
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Features & Benefits
- ML is changing virtually every aspect of our lives.
- Finance is ripe for disruptive innovations that will transform how the following generations understand money and invest.
- The book teaches how to structure big data in a way that is amenable to ML algorithms.
- It explains scientifically sound solutions using math, supported by code and examples.
- Readers become active users who can test the proposed solutions in their individual setting.
- Written by a recognized expert and portfolio manager.
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