Autorentext

DR. MARCOS LÓPEZ DE PRADO is a principal at AQR Capital Management, and its head of machine learning. Marcos is also a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). SSRN ranks him as one of the most-read authors in economics, and he has published dozens of scientific articles on machine learning and supercomputing in the leading academic journals. Marcos earned a PhD in financial economics (2003), a second PhD in mathematical finance (2011) from Universidad Complutense de Madrid, and is a recipient of Spain's National Award for Academic Excellence (1999). He completed his post-doctoral research at Harvard University and Cornell University, where he teaches a graduate course in financial machine learning at the School of Engineering. Marcos has an Erdös #2 and an Einstein #4 according to the American Mathematical Society.

Klappentext

Praise for ADVANCES in FINANCIAL MACHINE LEARNING "Dr. López de Prado has written the first comprehensive book describing the application of modern ML to financial modeling. The book blends the latest technological developments in ML with critical life lessons learned from the author's decades of financial experience in leading academic and industrial institutions. I highly recommend this exciting book to both prospective students of financial ML and the professors and supervisors who teach and guide them." ?PROF. PETER CARR, Chair of the Finance and Risk Engineering Department, NYU Tandon School of Engineering "Financial problems require very distinct machine learning solutions. Dr. López de Prado's book is the first one to characterize what makes standard machine learning tools fail when applied to the field of finance, and the first one to provide practical solutions to unique challenges faced by asset managers. Everyone who wants to understand the future of finance should read this book." ?PROF. FRANK FABOZZI, EDHEC Business School; Editor of The Journal of Portfolio Management "Marcos has assembled in one place an invaluable set of lessons and techniques for practitioners seeking to deploy machine learning methods in finance. Marcos's insightful book is laden with useful advice to help keep a curious practitioner from going down any number of blind alleys, or shooting oneself in the foot." ?ROSS GARON, Head of Cubist Systematic Strategies; Managing Director, Point72 Asset Management "The first wave of quantitative innovation in finance was led by Markowitz optimization. Machine learning is the second wave and it will touch every aspect of finance. López de Prado's Advances in Financial Machine Learning is essential for readers who want to be ahead of the technology rather than being replaced by it." ?PROF. CAMPBELL HARVEY, Duke University; Former President of the American Finance Association "The author's academic and professional first-rate credentials shine through the pages of this book? indeed, I could think of few, if any, authors better suited to explaining both the theoretical and the practical aspects of this new and (for most)unfamiliar subject. Destined to become a classic in this rapidly burgeoning field." ?PROF. RICCARDO REBONATO, EDHEC Business School; Former Global Head of Rates and FX Analytics at PIMCO

Zusammenfassung
Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations.

Inhalt

About the Author xxi

PREAMBLE 1

1 Financial Machine Learning as a Distinct Subject 3

1.1 Motivation, 3

1.2 The Main Reason Financial Machine Learning Projects Usually Fail, 4

1.2.1 The Sisyphus Paradigm, 4

1.2.2 The Meta-Strategy Paradigm, 5

1.3 Book Structure, 6

1.3.1 Structure by Production Chain, 6

1.3.2 Structure by Strategy Component, 9

1.3.3 Structure by Common Pitfall, 12

1.4 Target Audience, 12

1.5 Requisites, 13

1.6 FAQs, 14

1.7 Acknowledgments, 18

Exercises, 19

References, 20

Bibliography, 20

Part 1 Data Analysis 21

2 Financial Data Structures 23

2.1 Motivation, 23

2.2 Essential Types of Financial Data, 23

2.2.1 Fundamental Data, 23

2.2.2 Market Data, 24

2.2.3 Analytics, 25

2.2.4 Alternative Data, 25

2.3 Bars, 25

2.3.1 Standard Bars, 26

2.3.2 Information-Driven Bars, 29

2.4 Dealing with Multi-Product Series, 32

2.4.1 The ETF Trick, 33

2.4.2 PCA Weights, 35

2.4.3 Single Future Roll, 36

2.5 Sampling Features, 38

2.5.1 Sampling for Reduction, 38

2.5.2 Event-Based Sampling, 38

Exercises, 40

References, 41

3 Labeling 43

3.1 Motivation, 43

3.2 The Fixed-Time Horizon Method, 43

3.3 Computing Dynamic Thresholds, 44

3.4 The Triple-Barrier Method, 45

3.5 Learning Side and Size, 48

3.6 Meta-Labeling, 50

3.7 How to Use Meta-Labeling, 51

3.8 The Quantamental Way, 53

3.9 Dropping Unnecessary Labels, 54

Exercises, 55

Bibliography, 56

4 Sample Weights 59

4.1 Motivation, 59

4.2 Overlapping Outcomes, 59

4.3 Number of Concurrent Labels, 60

4.4 Average Uniqueness of a Label, 61

4.5 Bagging Classifiers and Uniqueness, 62

4.5.1 Sequential Bootstrap, 63

4.5.2 Implementation of Sequential Bootstrap, 64

4.5.3 A Numerical Example, 65

4.5.4 Monte Carlo Experiments, 66

4.6 Return Attribution, 68

4.7 Time Decay, 70

4.8 Class Weights, 71

Exercises, 72

References, 73

Bibliography, 73

5 Fractionally Differentiated Features 75

5.1 Motivation, 75

5.2 The Stationarity vs. Memory Dilemma, 75

5.3 Literature Review, 76

5.4 The Method, 77

5.4.1 Long Memory, 77

5.4.2 Iterative Estimation, 78

5.4.3 Convergence, 80

5.5 Implementation, 80

5.5.1 Expanding Window, 80

5.5.2 Fixed-Width Window Fracdiff, 82

5.6 Stationarity with Maximum Memory Preservation, 84

5.7 Conclusion, 88

Exercises, 88

References, 89

Bibliography, 89

Part 2 Modelling 91

6 Ensemble Methods 93

6.1 Motivation, 93

6.2 The Three Sources of Errors, 93

6.3 Bootstrap Aggregation, 94

6.3.1 Variance Reduction, 94

6.3.2 Improved Accuracy, 96

6.3.3 Observation Redundancy, 97

6.4 Random Forest, 98

6.5 Boosting, 99

6.6 Bagging vs. Boosting in Finance, 100

6.7 Bagging for Scalability, 101

Exercises, 101

References, 102

Bibliography, 102

7 Cross-Validation in Finance 103

7.1 Motivation, 103

7.2 The Goal of Cross-Validation, 103

7.3 Why K-Fold CV Fails in Finance, 104

7.4 A Solution: Purged K-Fold CV, 105

7.4.1 Purging the Training Set, 105

7.4.2 Embargo, 107

7.4.3 The Purged K-Fold Class, 108

7.5 Bugs in Sklearn's Cross-Validation, 109

Exercises, 110

Bibliography, 111

8 Feature Importance 113

8.1 Motivation, 113

8.2 The Importance of Feature Importance, 113

8.3 Feature Importance with Substitution Effects, 114

8.3.1 Mean Decrease Impurity, 114

8.3.2 Mean Decrease Accuracy, 116

8.4 Feature Importance without Substitution Effects, 117

8.4.1 Single Feature Importance, 117

8.4.2 Orthogonal Features, 118

8.5 Parallelized vs. Stacked Feature Importance, 121

8.6 Experiments with Synthetic Data, 122

Exercises, 127

References, 127

9 Hyper-Parameter Tuning with Cross-Validation 129

9.1 Motivation, 129

9.2 Grid Search Cross-Validation, 129

9.3 Randomized Search Cross-Validation, 131

9.3.1 Log-Uniform Distribution, 132

9.4 Scoring and Hyper-parameter Tuning, 134

Exercises, 135

References, 136

Bibliography, 137

Part 3 Backtesting 139

10 Bet Sizing 141

10.1 Motivation, 141

10.2 Strategy-Independent Bet Sizing Approaches, 141

10.3 Bet Sizing from Predicted Probabilities, 142

10.4 Averaging Active Bets, 144

10.5 Size Discretization, 144

10.6 Dynamic Bet Sizes and Limit Prices, 145

Exercises, 148

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