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The Book of Alternative Data
A Guide for Investors, Traders and Risk Managers
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Gewicht:
798 g
Format:
238x157x27 mm
Beschreibung:
ALEXANDER DENEV is Head of AI, Financial Services - Risk Advisory at Deloitte LLP. Prior to that he led Quantitative Research & Advanced Analytics at IHS Markit. Previously, he held roles at the Royal Bank of Scotland, Societe Generale, and European Investment Bank. Denev is a visiting lecturer at the University of Oxford where he graduated with a degree in Mathematical Finance. He is author of numerous papers and books on novel methods of financial modeling with applications ranging from stress testing to asset allocation.
SAEED AMEN is the founder of Cuemacro, where he consults on systematic trading. For 15 years, he has developed systematic trading strategies and quantitative indices including at major investment banks, Lehman Brothers and Nomura. He is also a visiting lecturer at Queen Mary University of London and a co-founder of the Thalesians, a quant think tank.
SAEED AMEN is the founder of Cuemacro, where he consults on systematic trading. For 15 years, he has developed systematic trading strategies and quantitative indices including at major investment banks, Lehman Brothers and Nomura. He is also a visiting lecturer at Queen Mary University of London and a co-founder of the Thalesians, a quant think tank.
Preface xv
Acknowledgments xvii
Part 1 Introduction and Theory 1
1 Alternative Data: The Lay of the Land 3
1.1 Introduction 3
1.2 What is "Alternative Data"? 5
1.3 Segmentation of Alternative Data 7
1.4 The Many Vs of Big Data 9
1.5 Why Alternative Data? 11
1.6 Who is Using Alternative Data? 15
1.7 Capacity of a Strategy and Alternative Data 16
1.8 Alternative Data Dimensions 19
1.9 Who Are the Alternative Data Vendors? 23
1.10 Usage of Alternative Datasets on the Buy Side 24
1.11 Conclusion 26
2 The Value of Alternative Data 27
2.1 Introduction 27
2.2 The Decay of Investment Value 27
2.3 Data Markets 29
2.4 The Monetary Value of Data (Part I) 31
2.4.1 Cost Value 34
2.4.2 Market Value 34
2.4.3 Economic Value 35
2.5 Evaluating (Alternative) Data Strategies with and without Backtesting 35
2.5.1 Systematic Investors 36
2.5.2 Discretionary Investors 38
2.5.3 Risk Managers 39
2.6 The Monetary Value of Data (Part II) 39
2.6.1 The Buyer's Perspective 40
2.6.2 The Seller's Perspective 41
2.7 The Advantages of Maturing Alternative Datasets 45
2.8 Summary 46
3 Alternative Data Risks and Challenges 47
3.1 Legal Aspects of Data 47
3.2 Risks of Using Alternative Data 50
3.3 Challenges of Using Alternative Data 51
3.3.1 Entity Matching 52
3.3.2 Missing Data 54
3.3.3 Structuring the Data 55
3.3.4 Treatment of Outliers 56
3.4 Aggregating the Data 57
3.5 Summary 58
4 Machine Learning Techniques 59
4.1 Introduction 59
4.2 Machine Learning: Definitions and Techniques 60
4.2.1 Bias, Variance, and Noise 60
4.2.2 Cross-Validation 61
4.2.3 Introducing Machine Learning 62
4.2.4 Popular Supervised Machine Learning Techniques 64
4.2.5 Clustering-Based Unsupervised Machine Learning Techniques 70
4.2.6 Other Unsupervised Machine Learning Techniques 71
4.2.7 Machine Learning Libraries 71
4.2.8 Neutral Networks and Deep Learning 72
4.2.9 Gaussian Processes 80
4.3 Which Technique to Choose? 82
4.4 Assumptions and Limitations of the Machine Learning Techniques 84
4.4.1 Causality 84
4.4.2 Non-stationarity 85
4.4.3 Restricted Information Set 86
4.4.4 The Algorithm Choice 86
4.5 Structuring Images 87
4.5.1 Features and Feature Detection Algorithms 87
4.5.2 Deep Learning and CNNs for Image Classification 89
4.5.3 Augmenting Satellite Image Data with Other Datasets 90
4.5.4 Imaging Tools 91
4.6 Natural Language Processing (NLP) 91
4.6.1 What is Natural Language Processing (NLP)? 91
4.6.2 Normalization 93
4.6.3 Creating Word Embeddings: Bag-of-Words 94
4.6.4 Creating Word Embeddings: Word2vec and Beyond 94
4.6.5 Sentiment Analysis and NLP Tasks as Classification Problems 96
4.6.6 Topic Modeling 96
4.6.7 Various Challenges in NLP 97
4.6.8 Different Languages and Different Texts 98
4.6.9 Speech in NLP 99
4.6.10 NLP Tools 100
4.7 Summary 102
5 The Processes behind the Use of Alternative Data 105
5.1 Introduction 105
5.2 Steps in the Alternative Data Journey 106
5.2.1 Step 1. Set up a Vision and Strategy 106
5.2.2 Step 2. Identify the
Acknowledgments xvii
Part 1 Introduction and Theory 1
1 Alternative Data: The Lay of the Land 3
1.1 Introduction 3
1.2 What is "Alternative Data"? 5
1.3 Segmentation of Alternative Data 7
1.4 The Many Vs of Big Data 9
1.5 Why Alternative Data? 11
1.6 Who is Using Alternative Data? 15
1.7 Capacity of a Strategy and Alternative Data 16
1.8 Alternative Data Dimensions 19
1.9 Who Are the Alternative Data Vendors? 23
1.10 Usage of Alternative Datasets on the Buy Side 24
1.11 Conclusion 26
2 The Value of Alternative Data 27
2.1 Introduction 27
2.2 The Decay of Investment Value 27
2.3 Data Markets 29
2.4 The Monetary Value of Data (Part I) 31
2.4.1 Cost Value 34
2.4.2 Market Value 34
2.4.3 Economic Value 35
2.5 Evaluating (Alternative) Data Strategies with and without Backtesting 35
2.5.1 Systematic Investors 36
2.5.2 Discretionary Investors 38
2.5.3 Risk Managers 39
2.6 The Monetary Value of Data (Part II) 39
2.6.1 The Buyer's Perspective 40
2.6.2 The Seller's Perspective 41
2.7 The Advantages of Maturing Alternative Datasets 45
2.8 Summary 46
3 Alternative Data Risks and Challenges 47
3.1 Legal Aspects of Data 47
3.2 Risks of Using Alternative Data 50
3.3 Challenges of Using Alternative Data 51
3.3.1 Entity Matching 52
3.3.2 Missing Data 54
3.3.3 Structuring the Data 55
3.3.4 Treatment of Outliers 56
3.4 Aggregating the Data 57
3.5 Summary 58
4 Machine Learning Techniques 59
4.1 Introduction 59
4.2 Machine Learning: Definitions and Techniques 60
4.2.1 Bias, Variance, and Noise 60
4.2.2 Cross-Validation 61
4.2.3 Introducing Machine Learning 62
4.2.4 Popular Supervised Machine Learning Techniques 64
4.2.5 Clustering-Based Unsupervised Machine Learning Techniques 70
4.2.6 Other Unsupervised Machine Learning Techniques 71
4.2.7 Machine Learning Libraries 71
4.2.8 Neutral Networks and Deep Learning 72
4.2.9 Gaussian Processes 80
4.3 Which Technique to Choose? 82
4.4 Assumptions and Limitations of the Machine Learning Techniques 84
4.4.1 Causality 84
4.4.2 Non-stationarity 85
4.4.3 Restricted Information Set 86
4.4.4 The Algorithm Choice 86
4.5 Structuring Images 87
4.5.1 Features and Feature Detection Algorithms 87
4.5.2 Deep Learning and CNNs for Image Classification 89
4.5.3 Augmenting Satellite Image Data with Other Datasets 90
4.5.4 Imaging Tools 91
4.6 Natural Language Processing (NLP) 91
4.6.1 What is Natural Language Processing (NLP)? 91
4.6.2 Normalization 93
4.6.3 Creating Word Embeddings: Bag-of-Words 94
4.6.4 Creating Word Embeddings: Word2vec and Beyond 94
4.6.5 Sentiment Analysis and NLP Tasks as Classification Problems 96
4.6.6 Topic Modeling 96
4.6.7 Various Challenges in NLP 97
4.6.8 Different Languages and Different Texts 98
4.6.9 Speech in NLP 99
4.6.10 NLP Tools 100
4.7 Summary 102
5 The Processes behind the Use of Alternative Data 105
5.1 Introduction 105
5.2 Steps in the Alternative Data Journey 106
5.2.1 Step 1. Set up a Vision and Strategy 106
5.2.2 Step 2. Identify the
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The first and only book to systematically address methodologies and processes of leveraging non-traditional information sources in the context of investing and risk management
Harnessing non-traditional data sources to generate alpha, analyze markets, and forecast risk is a subject of intense interest for financial professionals. A growing number of regularly-held conferences on alternative data are being established, complemented by an upsurge in new papers on the subject. Alternative data is starting to be steadily incorporated by conventional institutional investors and risk managers throughout the financial world. Methodologies to analyze and extract value from alternative data, guidance on how to source data and integrate data flows within existing systems is currently not treated in literature. Filling this significant gap in knowledge, The Book of Alternative Data is the first and only book to offer a coherent, systematic treatment of the subject.
This groundbreaking volume provides readers with a roadmap for navigating the complexities of an array of alternative data sources, and delivers the appropriate techniques to analyze them. The authors--leading experts in financial modeling, machine learning, and quantitative research and analytics--employ a step-by-step approach to guide readers through the dense jungle of generated data. A first-of-its kind treatment of alternative data types, sources, and methodologies, this innovative book:
* Provides an integrated modeling approach to extract value from multiple types of datasets
* Treats the processes needed to make alternative data signals operational
* Helps investors and risk managers rethink how they engage with alternative datasets
* Features practical use case studies in many different financial markets and real-world techniques
* Describes how to avoid potential pitfalls and missteps in starting the alternative data journey
* Explains how to integrate information from different datasets to maximize informational value
The Book of Alternative Data is an indispensable resource for anyone wishing to analyze or monetize different non-traditional datasets, including Chief Investment Officers, Chief Risk Officers, risk professionals, investment professionals, traders, economists, and machine learning developers and users.
Harnessing non-traditional data sources to generate alpha, analyze markets, and forecast risk is a subject of intense interest for financial professionals. A growing number of regularly-held conferences on alternative data are being established, complemented by an upsurge in new papers on the subject. Alternative data is starting to be steadily incorporated by conventional institutional investors and risk managers throughout the financial world. Methodologies to analyze and extract value from alternative data, guidance on how to source data and integrate data flows within existing systems is currently not treated in literature. Filling this significant gap in knowledge, The Book of Alternative Data is the first and only book to offer a coherent, systematic treatment of the subject.
This groundbreaking volume provides readers with a roadmap for navigating the complexities of an array of alternative data sources, and delivers the appropriate techniques to analyze them. The authors--leading experts in financial modeling, machine learning, and quantitative research and analytics--employ a step-by-step approach to guide readers through the dense jungle of generated data. A first-of-its kind treatment of alternative data types, sources, and methodologies, this innovative book:
* Provides an integrated modeling approach to extract value from multiple types of datasets
* Treats the processes needed to make alternative data signals operational
* Helps investors and risk managers rethink how they engage with alternative datasets
* Features practical use case studies in many different financial markets and real-world techniques
* Describes how to avoid potential pitfalls and missteps in starting the alternative data journey
* Explains how to integrate information from different datasets to maximize informational value
The Book of Alternative Data is an indispensable resource for anyone wishing to analyze or monetize different non-traditional datasets, including Chief Investment Officers, Chief Risk Officers, risk professionals, investment professionals, traders, economists, and machine learning developers and users.