Big data and machine learning in quantitative investment

Cover -- Title Page -- Copyright -- Contents -- 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 -- Chapter 2 Taming Big Data -- 2.1 Introduction: Alternative Data - an Overview -- 2.1.1 Definition: Why 'alternative'? Opposition with conventional -- 2.1.2 Alternative is not always big and big is not always alternative -- 2.2 Drivers of Adoption -- 2.2.1 Diffusion of innovations: Where are we now? -- 2.3 Alternative Data Types, Formats and Universe -- 2.3.1 Alternative data categorization and definitions -- 2.3.2 How many alternative datasets are there? -- 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 -- 2.6.1 US medical records -- 2.6.2 Indian power generation data -- 2.6.3 US earnings performance forecasts -- 2.6.4 China manufacturing data -- 2.6.5 Short position data -- 2.6.6 The collapse of carillion - a use case example for alt data -- 2.7 The Biggest Alternative Data Trends -- 2.7.1 Is alternative data for equities only? -- 2.7.2 Supply-Side: Dataset Launches -- 2.7.3 Most common queries -- 2.8 Conclusion -- Reference -- Chapter 3 State of Machine Learning Applications in Investment Management -- 3.1 Introduction -- 3.2 Data, Data, Data Everywhere -- 3.3 Spectrum of Artificial Intelligence Applications -- 3.3.1 AI applications classification -- 3.3.2 Financial analyst or competitive data scientist? -- 3.3.3 Investment process change: An 'Autonomous Trading' case