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Big Data and Machine Learning in Quantitative Investment

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

  1. Cover
  2. CHAPTER 1: Do Algorithms Dream About Artificial Alphas?
    1. 1.1 INTRODUCTION
    2. 1.2 REPLICATION OR REINVENTION
    3. 1.3 REINVENTION WITH MACHINE LEARNING
    4. 1.4 A MATTER OF TRUST
    5. 1.5 ECONOMIC EXISTENTIALISM: A GRAND DESIGN OR AN ACCIDENT?
    6. 1.6 WHAT IS THIS SYSTEM ANYWAY?
    7. 1.7 DYNAMIC FORECASTING AND NEW METHODOLOGIES
    8. 1.8 FUNDAMENTAL FACTORS, FORECASTING AND MACHINE LEARNING
    9. 1.9 CONCLUSION: LOOKING FOR NAILS
    10. NOTES
  3. CHAPTER 2: Taming Big Data
    1. 2.1 INTRODUCTION: ALTERNATIVE DATA – AN OVERVIEW
    2. 2.2 DRIVERS OF ADOPTION
    3. 2.3 ALTERNATIVE DATA TYPES, FORMATS AND UNIVERSE
    4. 2.4 HOW TO KNOW WHAT ALTERNATIVE DATA IS USEFUL (AND WHAT ISN'T)
    5. 2.5 HOW MUCH DOES ALTERNATIVE DATA COST?
    6. 2.6 CASE STUDIES
    7. 2.7 THE BIGGEST ALTERNATIVE DATA TRENDS
    8. 2.8 CONCLUSION
    9. REFERENCE
    10. NOTES
  4. CHAPTER 3: State of Machine Learning Applications in Investment Management
    1. 3.1 INTRODUCTION
    2. 3.2 DATA, DATA, DATA EVERYWHERE
    3. 3.3 SPECTRUM OF ARTIFICIAL INTELLIGENCE APPLICATIONS
    4. 3.4 INTERCONNECTEDNESS OF INDUSTRIES AND ENABLERS OF ARTIFICIAL INTELLIGENCE
    5. 3.5 SCENARIOS FOR INDUSTRY DEVELOPMENTS
    6. 3.6 FOR THE FUTURE
    7. 3.7 CONCLUSION
    8. REFERENCES
    9. NOTES
  5. CHAPTER 4: Implementing Alternative Data in an Investment Process
    1. 4.1 INTRODUCTION
    2. 4.2 THE QUAKE: MOTIVATING THE SEARCH FOR ALTERNATIVE DATA
    3. 4.3 TAKING ADVANTAGE OF THE ALTERNATIVE DATA EXPLOSION
    4. 4.4 SELECTING A DATA SOURCE FOR EVALUATION
    5. 4.5 TECHNIQUES FOR EVALUATION
    6. 4.6 ALTERNATIVE DATA FOR FUNDAMENTAL MANAGERS
    7. 4.7 SOME EXAMPLES
    8. 4.8 CONCLUSIONS
    9. REFERENCES
  6. CHAPTER 5: Using Alternative and Big Data to Trade Macro Assets
    1. 5.1 INTRODUCTION
    2. 5.2 UNDERSTANDING GENERAL CONCEPTS WITHIN BIG DATA AND ALTERNATIVE DATA
    3. 5.3 TRADITIONAL MODEL BUILDING APPROACHES AND MACHINE LEARNING
    4. 5.4 BIG DATA AND ALTERNATIVE DATA: BROAD‐BASED USAGE IN MACRO‐BASED TRADING
    5. 5.5 CASE STUDIES: DIGGING DEEPER INTO MACRO TRADING WITH BIG DATA AND ALTERNATIVE DATA
    6. 5.6 CONCLUSION
    7. REFERENCES
  7. CHAPTER 6: Big Is Beautiful: How Email Receipt Data Can Help Predict Company Sales
    1. 6.1 INTRODUCTION
    2. 6.2 QUANDL'S EMAIL RECEIPTS DATABASE
    3. 6.3 THE CHALLENGES OF WORKING WITH BIG DATA
    4. 6.4 PREDICTING COMPANY SALES
    5. 6.5 REAL‐TIME PREDICTIONS
    6. 6.6 A CASE STUDY: http://amazon.com SALES
    7. REFERENCES
    8. NOTES
  8. CHAPTER 7: Ensemble Learning Applied to Quant Equity: Gradient Boosting in a Multifactor Framework
    1. 7.1 INTRODUCTION
    2. 7.2 A PRIMER ON BOOSTED TREES
    3. 7.3 DATA AND PROTOCOL
    4. 7.4 BUILDING THE MODEL
    5. 7.5 RESULTS AND DISCUSSION
    6. 7.6 CONCLUSION
    7. REFERENCES
    8. NOTES
  9. CHAPTER 8: A Social Media Analysis of Corporate Culture
    1. 8.1 INTRODUCTION
    2. 8.2 LITERATURE REVIEW
    3. 8.3 DATA AND SAMPLE CONSTRUCTION
    4. 8.4 INFERRING CORPORATE CULTURE
    5. 8.5 EMPIRICAL RESULTS
    6. 8.6 CONCLUSION
    7. REFERENCES
  10. CHAPTER 9: Machine Learning and Event Detection for Trading Energy Futures
    1. 9.1 INTRODUCTION
    2. 9.2 DATA DESCRIPTION
    3. 9.3 MODEL FRAMEWORK
    4. 9.4 PERFORMANCE
    5. 9.5 CONCLUSION
    6. REFERENCES
    7. NOTES
  11. CHAPTER 10: Natural Language Processing of Financial News
    1. 10.1 INTRODUCTION
    2. 10.2 SOURCES OF NEWS DATA
    3. 10.3 PRACTICAL APPLICATIONS
    4. 10.4 NATURAL LANGUAGE PROCESSING
    5. 10.5 DATA AND METHODOLOGY
    6. 10.6 CONCLUSION
    7. REFERENCES
  12. CHAPTER 11: Support Vector Machine‐Based Global Tactical Asset Allocation
    1. 11.1 INTRODUCTION
    2. 11.2 FIFTY YEARS OF GLOBAL TACTICAL ASSET ALLOCATION
    3. 11.3 SUPPORT VECTOR MACHINE IN THE ECONOMIC LITERATURE
    4. 11.4 A SVR‐BASED GTAA
    5. 11.5 CONCLUSION
    6. REFERENCES
  13. CHAPTER 12: Reinforcement Learning in Finance
    1. 12.1 INTRODUCTION
    2. 12.2 MARKOV DECISION PROCESSES: A GENERAL FRAMEWORK FOR DECISION MAKING
    3. 12.3 RATIONALITY AND DECISION MAKING UNDER UNCERTAINTY
    4. 12.4 MEAN‐VARIANCE EQUIVALENCE
    5. 12.5 REWARDS
    6. 12.6 PORTFOLIO VALUE VERSUS WEALTH
    7. 12.7 A DETAILED EXAMPLE
    8. 12.8 CONCLUSIONS AND FURTHER WORK
    9. REFERENCES
  14. CHAPTER 13: Deep Learning in Finance: Prediction of Stock Returns with Long Short‐Term Memory Networks
    1. 13.1 INTRODUCTION
    2. 13.2 RELATED WORK
    3. 13.3 TIME SERIES ANALYSIS IN FINANCE
    4. 13.4 DEEP LEARNING
    5. 13.5 RECURRENT NEURAL NETWORKS
    6. 13.6 LONG SHORT‐TERM MEMORY NETWORKS
    7. 13.7 FINANCIAL MODEL
    8. 13.8 CONCLUSIONS
    9. Appendix A
    10. REFERENCES
  15. Biography
    1. CHAPTER 1
    2. CHAPTER 2
    3. CHAPTER 3
    4. CHAPTER 4
    5. CHAPTER 5
    6. CHAPTER 6
    7. CHAPTER 7
    8. CHAPTER 8
    9. CHAPTER 9
    10. CHAPTER 10
    11. CHAPTER 11
    12. CHAPTER 12
    13. CHAPTER 13
  16. End User License Agreement