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Big Data and Machine Learning in Quantitative Investment
Get to know the 'why' and 'how' of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it's a book by practitioners for practitioners, covering the...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Newark :
John Wiley & Sons, Incorporated,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Guida, Tony. | |
| 245 | 1 | 0 | |a Big Data and Machine Learning in Quantitative Investment |
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| 300 | |a 1 online resource (299 pages) | ||
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| 505 | 0 | |a Cover; Title Page; Copyright; Contents; Chapter 1 Do Algorithms Dream About Artificial Alphas?; 1.1 Introduction; 1.2 Replication or Reinvention; 1.3 Reinvention with Machine Learning; 1.4 A Matter of Trust; 1.5 Economic Existentialism: A Grand Design or an Accident?; 1.6 What is this System Anyway?; 1.7 Dynamic Forecasting and New Methodologies; 1.8 Fundamental Factors, Forecasting and Machine Learning; 1.9 Conclusion: Looking for Nails; Chapter 2 Taming Big Data; 2.1 Introduction: Alternative Data -- an Overview; 2.1.1 Definition: Why 'alternative'? Opposition with conventional | |
| 505 | 8 | |a 2.1.2 Alternative is not always big and big is not always alternative2.2 Drivers of Adoption; 2.2.1 Diffusion of innovations: Where are we now?; 2.3 Alternative Data Types, Formats and Universe; 2.3.1 Alternative data categorization and definitions; 2.3.2 How many alternative datasets are there?; 2.4 How to Know What Alternative Data is Useful (And What isn't); 2.5 How Much Does Alternative Data Cost?; 2.6 Case Studies; 2.6.1 US medical records; 2.6.2 Indian power generation data; 2.6.3 US earnings performance forecasts; 2.6.4 China manufacturing data; 2.6.5 Short position data | |
| 505 | 8 | |a 2.6.6 The collapse of carillion -- a use case example for alt data2.7 The Biggest Alternative Data Trends; 2.7.1 Is alternative data for equities only?; 2.7.2 Supply-Side: Dataset Launches; 2.7.3 Most common queries; 2.8 Conclusion; Reference; Chapter 3 State of Machine Learning Applications in Investment Management; 3.1 Introduction; 3.2 Data, Data, Data Everywhere; 3.3 Spectrum of Artificial Intelligence Applications; 3.3.1 AI applications classification; 3.3.2 Financial analyst or competitive data scientist?; 3.3.3 Investment process change: An 'Autonomous Trading' case | |
| 505 | 8 | |a 3.3.4 Artificial intelligence and strategies development3.4 Interconnectedness of Industries and Enablers of Artificial Intelligence; 3.4.1 Investments in development of AI; 3.4.2 Hardware and software development; 3.4.3 Regulation; 3.4.4 Internet of things; 3.4.5 Drones; 3.4.6 Digital transformation in steps -- case study; 3.5 Scenarios for Industry Developments; 3.5.1 Lessons from autonomous driving technology; 3.5.2 New technologies -- new threats; 3.5.3 Place for discretionary management; 3.6 For the Future; 3.6.1 Changing economic relationships; 3.6.2 Future education focus; 3.7 Conclusion | |
| 504 | |a ReferencesChapter 4 Implementing Alternative Data in an Investment Process; 4.1 Introduction; 4.2 The Quake: Motivating the Search for Alternative Data; 4.2.1 What happened?; 4.2.2 The next quake?; 4.3 Taking Advantage of the Alternative Data Explosion; 4.4 Selecting A Data Source for evaluation; 4.5 Techniques for Evaluation; 4.6 Alternative Data for Fundamental Managers; 4.7 Some Examples; 4.7.1 Example 1: Blogger sentiment; 4.7.2 Example 2: Online consumer demand; 4.7.3 Example 3: Transactional data; 4.7.4 Example 4: ESG; 4.8 Conclusions; References | ||
| 500 | |a Chapter 5 Using Alternative and Big Data to Trade Macro Assets | ||
| 520 | |a Get to know the 'why' and 'how' of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it's a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. - Gain a solid reason to use machine learning - Frame your question using financial markets laws - Know your data- Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment - and this book shows you how. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Investments |x Study and teaching. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Big data. | |
| 650 | 6 | |a Investissements |x Étude et enseignement. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Données volumineuses. | |
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| 650 | 7 | |a Big data |2 fast | |
| 650 | 7 | |a Investments |x Study and teaching |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
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| 776 | 0 | 8 | |i Print version: |a Guida, Tony. |t Big Data and Machine Learning in Quantitative Investment. |d Newark : John Wiley & Sons, Incorporated, ©2018 |z 9781119522195 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5614243 |z Texto completo |
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