Book description
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
- 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
- CHAPTER 4: Implementing Alternative Data in an Investment Process
-
CHAPTER 5: Using Alternative and Big Data to Trade Macro Assets
- 5.1 INTRODUCTION
- 5.2 UNDERSTANDING GENERAL CONCEPTS WITHIN BIG DATA AND ALTERNATIVE DATA
- 5.3 TRADITIONAL MODEL BUILDING APPROACHES AND MACHINE LEARNING
- 5.4 BIG DATA AND ALTERNATIVE DATA: BROAD‐BASED USAGE IN MACRO‐BASED TRADING
- 5.5 CASE STUDIES: DIGGING DEEPER INTO MACRO TRADING WITH BIG DATA AND ALTERNATIVE DATA
- 5.6 CONCLUSION
- REFERENCES
- CHAPTER 6: Big Is Beautiful: How Email Receipt Data Can Help Predict Company Sales
- CHAPTER 7: Ensemble Learning Applied to Quant Equity: Gradient Boosting in a Multifactor Framework
- CHAPTER 8: A Social Media Analysis of Corporate Culture
- CHAPTER 9: Machine Learning and Event Detection for Trading Energy Futures
- CHAPTER 10: Natural Language Processing of Financial News
- CHAPTER 11: Support Vector Machine‐Based Global Tactical Asset Allocation
- CHAPTER 12: Reinforcement Learning in Finance
- CHAPTER 13: Deep Learning in Finance: Prediction of Stock Returns with Long Short‐Term Memory Networks
- Biography
- End User License Agreement
Product information
- Title: Big Data and Machine Learning in Quantitative Investment
- Author(s):
- Release date: March 2019
- Publisher(s): Wiley
- ISBN: 9781119522195
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