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Big data and machine learning in quantitative investment

By: Guida, Tony.
Material type: materialTypeLabelBookSeries: Wiley finance series. Publisher: London John Wiley and Sons, Ltd 2019Description: vi, 285p.ISBN: 9781119522195.Subject(s): Investments -- study and teaching | Machine learning | Big dataDDC classification: 332.60285631 Summary: 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. https://www.wiley.com/en-ae/Big+Data+and+Machine+Learning+in+Quantitative+Investment-p-9781119522195
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Slot 819 (0 Floor, East Wing) Non-fiction 332.60285631 G8B4 (Browse shelf) Available 199247

TABLE OF CONTENTS

CHAPTER 1 Do Algorithms Dream About Artificial Alphas?
By Michael Kollo

CHAPTER 2 Taming Big Data
By Rado Lipuš and Daryl Smith

CHAPTER 3 State of Machine Learning Applications in Investment Management
By Ekaterina Sirotyuk

CHAPTER 4 Implementing Alternative Data in an Investment Process
By Vinesh Jha

CHAPTER 5 Using Alternative and Big Data to Trade Macro Assets
By Saeed Amen and Iain Clark

CHAPTER 6 Big Is Beautiful: How Email Receipt Data Can Help Predict Company Sales
By Giuliano De Rossi, Jakub Kolodziej and Gurvinder Brar

CHAPTER 7 Ensemble Learning Applied to Quant Equity: Gradient Boosting in a Multifactor Framework
By Tony Guida and Guillaume Coqueret

CHAPTER 8 A Social Media Analysis of Corporate Culture
By Andy Moniz

CHAPTER 9 Machine Learning and Event Detection for Trading Energy Futures
By Peter Hafez and Francesco Lautizi

CHAPTER 10 Natural Language Processing of Financial News
By M. Berkan Sesen, Yazann Romahi and Victor Li

CHAPTER 11 Support Vector Machine-Based Global Tactical Asset Allocation
By Joel Guglietta

CHAPTER 12 Reinforcement Learning in Finance
By Gordon Ritter

CHAPTER 13 Deep Learning in Finance: Prediction of Stock Returns with Long Short-Term Memory Networks
By Miquel N. Alonso, Gilberto Batres-Estrada and Aymeric Moulin

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.

https://www.wiley.com/en-ae/Big+Data+and+Machine+Learning+in+Quantitative+Investment-p-9781119522195

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