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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 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.

Big Data and Machine Learning in Quantitative Investment

• Cover


• 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


• NOTES


• CHAPTER 2: Taming Big Data


• 2.1 INTRODUCTION: ALTERNATIVE DATA ? AN OVERVIEW


• 2.2 DRIVERS OF ADOPTION


• 2.3 ALTERNATIVE DATA TYPES, FORMATS AND UNIVERSE


• 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


• 2.7 THE BIGGEST ALTERNATIVE DATA TRENDS


• 2.8 CONCLUSION


• REFERENCE


• NOTES


• 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


• 3.4 INTERCONNECTEDNESS OF INDUSTRIES AND ENABLERS OF ARTIFICIAL INTELLIGENCE


• 3.5 SCENARIOS FOR INDUSTRY DEVELOPMENTS


• 3.6 FOR THE FUTURE


• 3.7 CONCLUSION


• REFERENCES


• NOTES


• CHAPTER 4: Implementing Alternative Data in an Investment Process


• 4.1 INTRODUCTION


• 4.2 THE QUAKE: MOTIVATING THE SEARCH FOR ALTERNATIVE DATA


• 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


• 4.8 CONCLUSIONS


• REFERENCES


• CHAPTER 5: Using Alternative and Big Data to Trad