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.
Table of Contents
CHAPTER 1 Do Algorithms Dream About Artificial Alphas? 1
By Michael Kollo
CHAPTER 2 Taming Big Data 13
By Rado Lipuš and Daryl Smith
CHAPTER 3 State of Machine Learning Applications in Investment Management 33
By Ekaterina Sirotyuk
CHAPTER 4 Implementing Alternative Data in an Investment Process 51
By Vinesh Jha
CHAPTER 5 Using Alternative and Big Data to Trade Macro Assets 75
By Saeed Amen and Iain Clark
CHAPTER 6 Big Is Beautiful: How Email Receipt Data Can Help Predict Company Sales 95
By Giuliano De Rossi, Jakub Kolodziej and Gurvinder Brar
CHAPTER 7 Ensemble Learning Applied to Quant Equity: Gradient Boosting in a Multifactor Framework 129
By Tony Guida and Guillaume Coqueret
CHAPTER 8 A Social Media Analysis of Corporate Culture 149
By Andy Moniz
CHAPTER 9 Machine Learning and Event Detection for Trading Energy Futures 169
By Peter Hafez and Francesco Lautizi
CHAPTER 10 Natural Language Processing of Financial News 185
By M. Berkan Sesen, Yazann Romahi and Victor Li
CHAPTER 11 Support Vector Machine-Based Global Tactical Asset Allocation 211
By Joel Guglietta
CHAPTER 12 Reinforcement Learning in Finance 225
By Gordon Ritter
CHAPTER 13 Deep Learning in Finance: Prediction of Stock Returns with Long Short-Term Memory Networks 251
By Miquel N. Alonso, Gilberto Batres-Estrada and Aymeric Moulin
Biography 279
