Big Data and Machine Learning in Quantitative Investment

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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Guida, Tony
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2018.
Temas:
Investments > Study and teaching.
Machine learning.
Big data.
Investissements > Étude et enseignement.
Apprentissage automatique.
Données volumineuses.
BUSINESS & ECONOMICS > Finance > General.
Big data
Investments > Study and teaching
Machine learning
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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
  • 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
  • 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
  • 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

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