book

Big Data and Machine Learning in Quantitative Investment

by Tony Guida

March 2019

Beginner to intermediate

296 pages

10h 15m

English

Wiley

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1.1 INTRODUCTION1.2 REPLICATION OR REINVENTION1.3 REINVENTION WITH MACHINE LEARNING1.4 A MATTER OF TRUST1.5 ECONOMIC EXISTENTIALISM: A GRAND DESIGN OR AN ACCIDENT?1.6 WHAT IS THIS SYSTEM ANYWAY?1.7 DYNAMIC FORECASTING AND NEW METHODOLOGIES1.8 FUNDAMENTAL FACTORS, FORECASTING AND MACHINE LEARNING1.9 CONCLUSION: LOOKING FOR NAILSNOTES
2.1 INTRODUCTION: ALTERNATIVE DATA – AN OVERVIEW2.2 DRIVERS OF ADOPTION2.3 ALTERNATIVE DATA TYPES, FORMATS AND UNIVERSE2.4 HOW TO KNOW WHAT ALTERNATIVE DATA IS USEFUL (AND WHAT ISN'T)2.5 HOW MUCH DOES ALTERNATIVE DATA COST?2.6 CASE STUDIES2.7 THE BIGGEST ALTERNATIVE DATA TRENDS2.8 CONCLUSIONREFERENCENOTES
3.1 INTRODUCTION3.2 DATA, DATA, DATA EVERYWHERE3.3 SPECTRUM OF ARTIFICIAL INTELLIGENCE APPLICATIONS3.4 INTERCONNECTEDNESS OF INDUSTRIES AND ENABLERS OF ARTIFICIAL INTELLIGENCE3.5 SCENARIOS FOR INDUSTRY DEVELOPMENTS3.6 FOR THE FUTURE3.7 CONCLUSIONREFERENCESNOTES
4.1 INTRODUCTION4.2 THE QUAKE: MOTIVATING THE SEARCH FOR ALTERNATIVE DATA4.3 TAKING ADVANTAGE OF THE ALTERNATIVE DATA EXPLOSION4.4 SELECTING A DATA SOURCE FOR EVALUATION4.5 TECHNIQUES FOR EVALUATION4.6 ALTERNATIVE DATA FOR FUNDAMENTAL MANAGERS4.7 SOME EXAMPLES4.8 CONCLUSIONSREFERENCES
5.1 INTRODUCTION5.2 UNDERSTANDING GENERAL CONCEPTS WITHIN BIG DATA AND ALTERNATIVE DATA5.3 TRADITIONAL MODEL BUILDING APPROACHES AND MACHINE LEARNING5.4 BIG DATA AND ALTERNATIVE DATA: BROAD‐BASED USAGE IN MACRO‐BASED TRADING5.5 CASE STUDIES: DIGGING DEEPER INTO MACRO TRADING WITH BIG DATA AND ALTERNATIVE DATA5.6 CONCLUSIONREFERENCES
6.1 INTRODUCTION6.2 QUANDL'S EMAIL RECEIPTS DATABASE6.3 THE CHALLENGES OF WORKING WITH BIG DATA6.4 PREDICTING COMPANY SALES6.5 REAL‐TIME PREDICTIONS6.6 A CASE STUDY: http://amazon.com SALESREFERENCESNOTES
7.1 INTRODUCTION7.2 A PRIMER ON BOOSTED TREES7.3 DATA AND PROTOCOL7.4 BUILDING THE MODEL7.5 RESULTS AND DISCUSSION7.6 CONCLUSIONREFERENCESNOTES
8.1 INTRODUCTION8.2 LITERATURE REVIEW8.3 DATA AND SAMPLE CONSTRUCTION8.4 INFERRING CORPORATE CULTURE8.5 EMPIRICAL RESULTS8.6 CONCLUSIONREFERENCES
9.1 INTRODUCTION9.2 DATA DESCRIPTION9.3 MODEL FRAMEWORK9.4 PERFORMANCE9.5 CONCLUSIONREFERENCESNOTES

10.1 INTRODUCTION10.2 SOURCES OF NEWS DATA10.3 PRACTICAL APPLICATIONS10.4 NATURAL LANGUAGE PROCESSING10.5 DATA AND METHODOLOGY10.6 CONCLUSIONREFERENCES
11.1 INTRODUCTION11.2 FIFTY YEARS OF GLOBAL TACTICAL ASSET ALLOCATION11.3 SUPPORT VECTOR MACHINE IN THE ECONOMIC LITERATURE11.4 A SVR‐BASED GTAA11.5 CONCLUSIONREFERENCES
12.1 INTRODUCTION12.2 MARKOV DECISION PROCESSES: A GENERAL FRAMEWORK FOR DECISION MAKING12.3 RATIONALITY AND DECISION MAKING UNDER UNCERTAINTY12.4 MEAN‐VARIANCE EQUIVALENCE12.5 REWARDS12.6 PORTFOLIO VALUE VERSUS WEALTH12.7 A DETAILED EXAMPLE12.8 CONCLUSIONS AND FURTHER WORKREFERENCES
13.1 INTRODUCTION13.2 RELATED WORK13.3 TIME SERIES ANALYSIS IN FINANCE13.4 DEEP LEARNING13.5 RECURRENT NEURAL NETWORKS13.6 LONG SHORT‐TERM MEMORY NETWORKS13.7 FINANCIAL MODEL13.8 CONCLUSIONSAppendix AREFERENCES
CHAPTER 1CHAPTER 2CHAPTER 3CHAPTER 4CHAPTER 5CHAPTER 6CHAPTER 7CHAPTER 8CHAPTER 9CHAPTER 10CHAPTER 11CHAPTER 12CHAPTER 13