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 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.

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TONY GUIDA is a senior investment manager in quantitative equity at the investment manager of a major UK pension fund in London, where he manages multifactor systematic equity portfolios. During his career, he held such positions as senior consultant for smart beta and risk allocation at EDHEC RISK Scientific Beta and senior research analyst at UNIGESTION. He is a former member of the research and investment committee for Minimum Variance Strategies, where he led the factor investing research group for institutional clients, and a regular speaker at quant conferences. Tony is chair of machineByte ThinkTank EMEA.

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Praise for Big Data and Machine Learning in Quantitative Investment "Alternative data and machine learning are about to become essential components of the modern investment process. This excellent book offers practitioners a rich collection of case studies written by some of the most capable quants in the world today. It will be on our shelves here at Quandl for sure." Tammer Kamel, CEO and founder, Quandl, Toronto "Tony Guida has managed to cover an impressive list of recent topics in Financial Machine Learning and Big Data, such as deep learning, reinforcement learning or natural language processing, in this book. It is accessible and rich with real-world applications, written in readable style. It will appeal to quants, students and regulators at all levels, and will undoubtedly become a reference textbook, one of the few not to be missed by anybody interested in Machine Learning and Big Data applications." Ahcene Gareche, Head of Quantitative Strategies, AXA IM Chorus, Hong Kong "Artificial intelligence and machine learning, big and alternative data, are unequivocally buzz words of our times and quantitative finance is not exempt from that. However, not all datasets are necessarily useful for financial applications and not all ML techniques can be applied on a "plug-and-play" basis. Importantly, the industry needs specialised guidance on how different datasets and ML techniques should be used for quantitative investments. The new book, edited by Tony Guida, is here to address this need by providing a diverse collection of 13 self-contained chapters written by practitioners who offer different perspectives and use cases of big data and ML techniques in finance and investments. Some chapters are more philosophical, providing guidance and perspective. Others are more practical focusing either on the manipulation of big data or on the specifics of particular ML approaches when employed for financial applications. All in all, for the investment professional who is either experienced or new entrant in the ML/big data in quantitative investing space, Tony Guida has made a remarkable attempt to provide a holistic view of the landscape. It is worth a read." Nick Baltas, Head of R&D - Systematic Trading Strategies, Goldman Sachs, London

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

CHAPTER 2 Taming Big Data 13
By Rado Lipu 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