{"product_id":"the-book-of-alternative-data-9781119601791","title":"The Book of Alternative Data","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAcknowledgments xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1 Introduction and Theory 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Alternative Data: The Lay of the Land 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 What is “Alternative Data”? 5\u003c\/p\u003e \u003cp\u003e1.3 Segmentation of Alternative Data 7\u003c\/p\u003e \u003cp\u003e1.4 The Many Vs of Big Data 9\u003c\/p\u003e \u003cp\u003e1.5 Why Alternative Data? 11\u003c\/p\u003e \u003cp\u003e1.6 Who is Using Alternative Data? 15\u003c\/p\u003e \u003cp\u003e1.7 Capacity of a Strategy and Alternative Data 16\u003c\/p\u003e \u003cp\u003e1.8 Alternative Data Dimensions 19\u003c\/p\u003e \u003cp\u003e1.9 Who Are the Alternative Data Vendors? 23\u003c\/p\u003e \u003cp\u003e1.10 Usage of Alternative Datasets on the Buy Side 24\u003c\/p\u003e \u003cp\u003e1.11 Conclusion 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The Value of Alternative Data 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 27\u003c\/p\u003e \u003cp\u003e2.2 The Decay of Investment Value 27\u003c\/p\u003e \u003cp\u003e2.3 Data Markets 29\u003c\/p\u003e \u003cp\u003e2.4 The Monetary Value of Data (Part I) 31\u003c\/p\u003e \u003cp\u003e2.4.1 Cost Value 34\u003c\/p\u003e \u003cp\u003e2.4.2 Market Value 34\u003c\/p\u003e \u003cp\u003e2.4.3 Economic Value 35\u003c\/p\u003e \u003cp\u003e2.5 Evaluating (Alternative) Data Strategies with and without Backtesting 35\u003c\/p\u003e \u003cp\u003e2.5.1 Systematic Investors 36\u003c\/p\u003e \u003cp\u003e2.5.2 Discretionary Investors 38\u003c\/p\u003e \u003cp\u003e2.5.3 Risk Managers 39\u003c\/p\u003e \u003cp\u003e2.6 The Monetary Value of Data (Part II) 39\u003c\/p\u003e \u003cp\u003e2.6.1 The Buyer’s Perspective 40\u003c\/p\u003e \u003cp\u003e2.6.2 The Seller’s Perspective 41\u003c\/p\u003e \u003cp\u003e2.7 The Advantages of Maturing Alternative Datasets 45\u003c\/p\u003e \u003cp\u003e2.8 Summary 46\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Alternative Data Risks and Challenges 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Legal Aspects of Data 47\u003c\/p\u003e \u003cp\u003e3.2 Risks of Using Alternative Data 50\u003c\/p\u003e \u003cp\u003e3.3 Challenges of Using Alternative Data 51\u003c\/p\u003e \u003cp\u003e3.3.1 Entity Matching 52\u003c\/p\u003e \u003cp\u003e3.3.2 Missing Data 54\u003c\/p\u003e \u003cp\u003e3.3.3 Structuring the Data 55\u003c\/p\u003e \u003cp\u003e3.3.4 Treatment of Outliers 56\u003c\/p\u003e \u003cp\u003e3.4 Aggregating the Data 57\u003c\/p\u003e \u003cp\u003e3.5 Summary 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Machine Learning Techniques 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 59\u003c\/p\u003e \u003cp\u003e4.2 Machine Learning: Definitions and Techniques 60\u003c\/p\u003e \u003cp\u003e4.2.1 Bias, Variance, and Noise 60\u003c\/p\u003e \u003cp\u003e4.2.2 Cross-Validation 61\u003c\/p\u003e \u003cp\u003e4.2.3 Introducing Machine Learning 62\u003c\/p\u003e \u003cp\u003e4.2.4 Popular Supervised Machine Learning Techniques 64\u003c\/p\u003e \u003cp\u003e4.2.5 Clustering-Based Unsupervised Machine Learning Techniques 70\u003c\/p\u003e \u003cp\u003e4.2.6 Other Unsupervised Machine Learning Techniques 71\u003c\/p\u003e \u003cp\u003e4.2.7 Machine Learning Libraries 71\u003c\/p\u003e \u003cp\u003e4.2.8 Neutral Networks and Deep Learning 72\u003c\/p\u003e \u003cp\u003e4.2.9 Gaussian Processes 80\u003c\/p\u003e \u003cp\u003e4.3 Which Technique to Choose? 82\u003c\/p\u003e \u003cp\u003e4.4 Assumptions and Limitations of the Machine Learning Techniques 84\u003c\/p\u003e \u003cp\u003e4.4.1 Causality 84\u003c\/p\u003e \u003cp\u003e4.4.2 Non-stationarity 85\u003c\/p\u003e \u003cp\u003e4.4.3 Restricted Information Set 86\u003c\/p\u003e \u003cp\u003e4.4.4 The Algorithm Choice 86\u003c\/p\u003e \u003cp\u003e4.5 Structuring Images 87\u003c\/p\u003e \u003cp\u003e4.5.1 Features and Feature Detection Algorithms 87\u003c\/p\u003e \u003cp\u003e4.5.2 Deep Learning and CNNs for Image Classification 89\u003c\/p\u003e \u003cp\u003e4.5.3 Augmenting Satellite Image Data with Other Datasets 90\u003c\/p\u003e \u003cp\u003e4.5.4 Imaging Tools 91\u003c\/p\u003e \u003cp\u003e4.6 Natural Language Processing (NLP) 91\u003c\/p\u003e \u003cp\u003e4.6.1 What is Natural Language Processing (NLP)? 91\u003c\/p\u003e \u003cp\u003e4.6.2 Normalization 93\u003c\/p\u003e \u003cp\u003e4.6.3 Creating Word Embeddings: Bag-of-Words 94\u003c\/p\u003e \u003cp\u003e4.6.4 Creating Word Embeddings: Word2vec and Beyond 94\u003c\/p\u003e \u003cp\u003e4.6.5 Sentiment Analysis and NLP Tasks as Classification Problems 96\u003c\/p\u003e \u003cp\u003e4.6.6 Topic Modeling 96\u003c\/p\u003e \u003cp\u003e4.6.7 Various Challenges in NLP 97\u003c\/p\u003e \u003cp\u003e4.6.8 Different Languages and Different Texts 98\u003c\/p\u003e \u003cp\u003e4.6.9 Speech in NLP 99\u003c\/p\u003e \u003cp\u003e4.6.10 NLP Tools 100\u003c\/p\u003e \u003cp\u003e4.7 Summary 102\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 The Processes behind the Use of Alternative Data 105\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 105\u003c\/p\u003e \u003cp\u003e5.2 Steps in the Alternative Data Journey 106\u003c\/p\u003e \u003cp\u003e5.2.1 Step 1. Set up a Vision and Strategy 106\u003c\/p\u003e \u003cp\u003e5.2.2 Step 2. Identify the Appropriate Datasets 107\u003c\/p\u003e \u003cp\u003e5.2.3 Step 3. Perform Due Diligence on Vendors 108\u003c\/p\u003e \u003cp\u003e5.2.4 Step 4. Pre-assess Risks 109\u003c\/p\u003e \u003cp\u003e5.2.5 Step 5. Pre-assess the Existence of Signals 109\u003c\/p\u003e \u003cp\u003e5.2.6 Step 6. Data Onboarding 110\u003c\/p\u003e \u003cp\u003e5.2.7 Step 7. Data Preprocessing 110\u003c\/p\u003e \u003cp\u003e5.2.8 Step 8. Signal Extraction 111\u003c\/p\u003e \u003cp\u003e5.2.9 Step 9. Implementation (or Deployment in Production) 112\u003c\/p\u003e \u003cp\u003e5.2.10 Maintenance Process 113\u003c\/p\u003e \u003cp\u003e5.3 Structuring Teams to Use Alternative Data 114\u003c\/p\u003e \u003cp\u003e5.4 Data Vendors 116\u003c\/p\u003e \u003cp\u003e5.5 Summary 118\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Factor Investing 119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 119\u003c\/p\u003e \u003cp\u003e6.1.1 The CAPM 119\u003c\/p\u003e \u003cp\u003e6.2 Factor Models 120\u003c\/p\u003e \u003cp\u003e6.2.1 The Arbitrage Pricing Theory 122\u003c\/p\u003e \u003cp\u003e6.2.2 The Fama-French 3-Factor Model 123\u003c\/p\u003e \u003cp\u003e6.2.3 The Carhart Model 124\u003c\/p\u003e \u003cp\u003e6.2.4 Other Approaches (Data Mining) 125\u003c\/p\u003e \u003cp\u003e6.3 The Difference between Cross-Sectional and Time Series Trading Approaches 126\u003c\/p\u003e \u003cp\u003e6.4 Why Factor Investing? 126\u003c\/p\u003e \u003cp\u003e6.5 Smart Beta Indices Using Alternative Data Inputs 127\u003c\/p\u003e \u003cp\u003e6.6 ESG Factors 128\u003c\/p\u003e \u003cp\u003e6.7 Direct and Indirect Prediction 129\u003c\/p\u003e \u003cp\u003e6.8 Summary 132\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2 Practical Applications 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Missing Data: Background 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 135\u003c\/p\u003e \u003cp\u003e7.2 Missing Data Classification 136\u003c\/p\u003e \u003cp\u003e7.2.1 Missing Data Treatments 137\u003c\/p\u003e \u003cp\u003e7.3 Literature Overview of Missing Data Treatments 139\u003c\/p\u003e \u003cp\u003e7.3.1 Luengo et al. (2012) 139\u003c\/p\u003e \u003cp\u003e7.3.2 Garcia-Laencina et al. (2010) 143\u003c\/p\u003e \u003cp\u003e7.3.3 Grzymala-Busse et al. (2000) 146\u003c\/p\u003e \u003cp\u003e7.3.4 Zou et al. (2005) 147\u003c\/p\u003e \u003cp\u003e7.3.5 Jerez et al. (2010) 147\u003c\/p\u003e \u003cp\u003e7.3.6 Farhangfar et al. (2008) 148\u003c\/p\u003e \u003cp\u003e7.3.7 Kang et al. (2013) 149\u003c\/p\u003e \u003cp\u003e7.4 Summary 149\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Missing Data: Case Studies 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 151\u003c\/p\u003e \u003cp\u003e8.2 Case Study: Imputing Missing Values in Multivariate Credit Default Swap Time Series 152\u003c\/p\u003e \u003cp\u003e8.2.1 Missing Data Classification 153\u003c\/p\u003e \u003cp\u003e8.2.2 Imputation Metrics 154\u003c\/p\u003e \u003cp\u003e8.2.3 CDS Data and Test Data Generation 154\u003c\/p\u003e \u003cp\u003e8.2.4 Multiple Imputation Methods 157\u003c\/p\u003e \u003cp\u003e8.2.5 Deterministic and EOF-Based Techniques 160\u003c\/p\u003e \u003cp\u003e8.2.6 Results 164\u003c\/p\u003e \u003cp\u003e8.3 Case Study: Satellite Images 173\u003c\/p\u003e \u003cp\u003e8.4 Summary 176\u003c\/p\u003e \u003cp\u003e8.5 Appendix: General Description of the MICE Procedure 178\u003c\/p\u003e \u003cp\u003e8.6 Appendix: Software Libraries Used in This Chapter 179\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Outliers (Anomalies) 181\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 181\u003c\/p\u003e \u003cp\u003e9.2 Outliers Definition, Classification, and Approaches to Detection 182\u003c\/p\u003e \u003cp\u003e9.3 Temporal Structure 183\u003c\/p\u003e \u003cp\u003e9.4 Global Versus Local Outliers, Point Anomalies, and Micro-Clusters 184\u003c\/p\u003e \u003cp\u003e9.5 Outlier Detection Problem Setup 184\u003c\/p\u003e \u003cp\u003e9.6 Comparative Evaluation of Outlier Detection Algorithms 185\u003c\/p\u003e \u003cp\u003e9.7 Approaches to Outlier Explanation 189\u003c\/p\u003e \u003cp\u003e9.7.1 Micenkova et al. 189\u003c\/p\u003e \u003cp\u003e9.7.2 Duan et al. 191\u003c\/p\u003e \u003cp\u003e9.7.3 Angiulli et al. 192\u003c\/p\u003e \u003cp\u003e9.8 Case Study: Outlier Detection on Fed Communications Index 194\u003c\/p\u003e \u003cp\u003e9.9 Summary 201\u003c\/p\u003e \u003cp\u003e9.10 Appendix 202\u003c\/p\u003e \u003cp\u003e9.10.1 Model-Based Techniques 202\u003c\/p\u003e \u003cp\u003e9.10.2 Distance-Based Techniques 202\u003c\/p\u003e \u003cp\u003e9.10.3 Density-Based Techniques 203\u003c\/p\u003e \u003cp\u003e9.10.4 Heuristics-Based Approaches 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Automotive Fundamental Data 205\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 205\u003c\/p\u003e \u003cp\u003e10.2 Data 206\u003c\/p\u003e \u003cp\u003e10.3 Approach 1: Indirect Approach 211\u003c\/p\u003e \u003cp\u003e10.3.1 The Steps Followed 212\u003c\/p\u003e \u003cp\u003e10.3.2 Stage 1 213\u003c\/p\u003e \u003cp\u003e10.4 Approach 2: Direct Approach 223\u003c\/p\u003e \u003cp\u003e10.4.1 The Data 223\u003c\/p\u003e \u003cp\u003e10.4.2 Factor Generation 224\u003c\/p\u003e \u003cp\u003e10.4.3 Factor Performance 225\u003c\/p\u003e \u003cp\u003e10.4.4 Detailed Factor Results 229\u003c\/p\u003e \u003cp\u003e10.5 Gaussian Processes Example 238\u003c\/p\u003e \u003cp\u003e10.6 Summary 239\u003c\/p\u003e \u003cp\u003e10.7 Appendix 240\u003c\/p\u003e \u003cp\u003e10.7.1 List of Companies 240\u003c\/p\u003e \u003cp\u003e10.7.2 Description of Financial Statement Items 241\u003c\/p\u003e \u003cp\u003e10.7.3 Ratios Used 242\u003c\/p\u003e \u003cp\u003e10.7.4 IHS Markit Data Features 243\u003c\/p\u003e \u003cp\u003e10.7.5 Reporting Delays by Country 244\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Surveys and Crowdsourced Data 245\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 245\u003c\/p\u003e \u003cp\u003e11.2 Survey Data as Alternative Data 245\u003c\/p\u003e \u003cp\u003e11.3 The Data 247\u003c\/p\u003e \u003cp\u003e11.4 The Product 247\u003c\/p\u003e \u003cp\u003e11.5 Case Studies 249\u003c\/p\u003e \u003cp\u003e11.5.1 Case Study: Company Event Study (Pooled Survey) 249\u003c\/p\u003e \u003cp\u003e11.5.2 Case Study: Oil and Gas Production (Q\u0026amp;A Survey) 252\u003c\/p\u003e \u003cp\u003e11.6 Some Technical Considerations on Surveys 254\u003c\/p\u003e \u003cp\u003e11.7 Crowdsourcing Analyst Estimates Survey 255\u003c\/p\u003e \u003cp\u003e11.8 Alpha Capture Data 256\u003c\/p\u003e \u003cp\u003e11.9 Summary 256\u003c\/p\u003e \u003cp\u003e11.10 Appendix 256\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Purchasing Managers’ Index 259\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 259\u003c\/p\u003e \u003cp\u003e12.2 PMI Performance 261\u003c\/p\u003e \u003cp\u003e12.3 Nowcasting GDP Growth 262\u003c\/p\u003e \u003cp\u003e12.4 Impacts on Financial Markets 263\u003c\/p\u003e \u003cp\u003e12.5 Summary 266\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Satellite Imagery and Aerial Photography 267\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 267\u003c\/p\u003e \u003cp\u003e13.2 Forecasting US Export Growth 269\u003c\/p\u003e \u003cp\u003e13.3 Car Counts and Earnings Per Share for Retailers 271\u003c\/p\u003e \u003cp\u003e13.4 Measuring Chinese PMI Manufacturing with Satellite Data 277\u003c\/p\u003e \u003cp\u003e13.5 Summary 280\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Location Data 283\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 283\u003c\/p\u003e \u003cp\u003e14.2 Shipping Data to Track Crude Oil Supplies 283\u003c\/p\u003e \u003cp\u003e14.3 Mobile Phone Location Data to Understand Retail Activity 287\u003c\/p\u003e \u003cp\u003e14.3.1 Trading REIT ETF Using Mobile Phone Location Data 288\u003c\/p\u003e \u003cp\u003e14.3.2 Estimating Earnings per Share with Mobile Phone Location Data 291\u003c\/p\u003e \u003cp\u003e14.4 Taxi Ride Data and New York Fed Meetings 295\u003c\/p\u003e \u003cp\u003e14.5 Corporate Jet Location Data and M\u0026amp;A 296\u003c\/p\u003e \u003cp\u003e14.6 Summary 298\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Text Web Social Media and News 299\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 299\u003c\/p\u003e \u003cp\u003e15.2 Collecting Web Data 299\u003c\/p\u003e \u003cp\u003e15.3 Social Media 300\u003c\/p\u003e \u003cp\u003e15.3.1 Hedonometer Index 302\u003c\/p\u003e \u003cp\u003e15.3.2 Using Twitter Data to Help Forecast US Change in Nonfarm Payrolls 305\u003c\/p\u003e \u003cp\u003e15.3.3 Twitter Data to Forecast Stock Market Reaction to FOMC 308\u003c\/p\u003e \u003cp\u003e15.3.4 Liquidity and Sentiment from Social Media 309\u003c\/p\u003e \u003cp\u003e15.4 News 309\u003c\/p\u003e \u003cp\u003e15.4.1 Machine-Readable News to Trade FX and Understand FX Volatility 310\u003c\/p\u003e \u003cp\u003e15.4.2 Federal Reserve Communications and US Treasury Yields 316\u003c\/p\u003e \u003cp\u003e15.5 Other Web Sources 320\u003c\/p\u003e \u003cp\u003e15.5.1 Measuring Consumer Price Inflation 321\u003c\/p\u003e \u003cp\u003e15.6 Summary 322\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Investor Attention 323\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 323\u003c\/p\u003e \u003cp\u003e16.2 Readership of Payrolls to Measure Investor Attention 323\u003c\/p\u003e \u003cp\u003e16.3 Google Trends Data to Measure Market Themes 325\u003c\/p\u003e \u003cp\u003e16.4 Investopedia Search Data to Measure Investor Anxiety 328\u003c\/p\u003e \u003cp\u003e16.5 Using Wikipedia to Understand Price Action in Cryptocurrencies 330\u003c\/p\u003e \u003cp\u003e16.6 Online Attention for Countries to Inform EMFX Trading 330\u003c\/p\u003e \u003cp\u003e16.7 Summary 333\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Consumer Transactions 335\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 335\u003c\/p\u003e \u003cp\u003e17.2 Credit and Debit Card Transaction Data 336\u003c\/p\u003e \u003cp\u003e17.3 Consumer Receipts 337\u003c\/p\u003e \u003cp\u003e17.4 Summary 340\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Government, Industrial, and Corporate Data 341\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 341\u003c\/p\u003e \u003cp\u003e18.2 Using Innovation Measures to Trade Equities 342\u003c\/p\u003e \u003cp\u003e18.3 Quantifying Currency Crisis Risk 344\u003c\/p\u003e \u003cp\u003e18.4 Modeling Central Bank Intervention in Currency Markets 346\u003c\/p\u003e \u003cp\u003e18.5 Summary 348\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Market Data 351\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 351\u003c\/p\u003e \u003cp\u003e19.2 Relationship between Institutional FX Flow Data and FX Spot 351\u003c\/p\u003e \u003cp\u003e19.3 Understanding Liquidity Using High-Frequency FX Data 355\u003c\/p\u003e \u003cp\u003e19.4 Summary 357\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Alternative Data in Private Markets 359\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 359\u003c\/p\u003e \u003cp\u003e20.2 Defining Private Equity and Venture Capital Firms 360\u003c\/p\u003e \u003cp\u003e20.3 Private Equity Datasets 362\u003c\/p\u003e \u003cp\u003e20.4 Understanding the Performance of Private Firms 363\u003c\/p\u003e \u003cp\u003e20.5 Summary 364\u003c\/p\u003e \u003cp\u003e\u003cb\u003eConclusions 365\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSome Last Words 365\u003c\/p\u003e \u003cp\u003eReferences 367\u003c\/p\u003e \u003cp\u003eAbout the Authors 373\u003c\/p\u003e \u003cp\u003eIndex 375\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407096750423,"sku":"9781119601791","price":30.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119601791.jpg?v=1730498165","url":"https:\/\/bookcurl.com\/products\/the-book-of-alternative-data-9781119601791","provider":"Book Curl","version":"1.0","type":"link"}