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E-raamat: Adventures In Financial Data Science: The Empirical Properties Of Financial And Economic Data (Second Edition)

(Giller Inverstments, Usa)
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"This book provides insights into the true nature of financial and economic data, and is a practical guide on how to analyze a variety of data sources. The focus of the book is on finance and economics, but it also illustrates the use of quantitative analysis and data science in many different areas. Lastly, the book includes practical information on how to store and process data and provides a framework for data driven reasoning about the world. The book begins with entertaining tales from Graham Giller's career in finance, starting with speculating in UK government bonds at the Oxford Post Office, accidentally creating a global instant messaging system that went "viral" before anybody knew what that meant, on being the person who forgot to hit "enter" to run a hundred-million dollar statistical arbitrage system, what he decoded from his brief time spent with Jim Simons, and giving Michael Bloomberg a tutorial on Granger Causality. The majority of the content is a narrative of analytic work done on financial, economics, and alternative data, structured around both Dr Giller's professional career and some of the things that just interested him. The goal is to stimulate interest in predictive methods, to give accurate characterizations of the true properties of financial, economic and alternative data, and to share what Richard Feynman described as "The Pleasure of Finding Things Out.""--

This book provides insights into the true nature of financial and economic data, and is a practical guide on how to analyze a variety of data sources. The focus of the book is on finance and economics, but it also illustrates the use of quantitative analysis and data science in many different areas. Lastly, the book includes practical information on how to store and process data and provides a framework for data driven reasoning about the world. The book begins with entertaining tales from Graham Giller's career in finance, starting with speculating in UK government bonds at the Oxford Post Office, accidentally creating a global instant messaging system that went ""viral"" before anybody knew what that meant, on being the person who forgot to hit ""enter"" to run a hundred-million dollar statistical arbitrage system, what he decoded from his brief time spent with Jim Simons, and giving Michael Bloomberg a tutorial on Granger Causality. The majority of the content is a narrative of analytic work done on financial, economics, and alternative data, structured around both Dr Giller's professional career and some of the things that just interested him. The goal is to stimulate interest in predictive methods, to give accurate characterizations of the true properties of financial, economic and alternative data, and to share what Richard Feynman described as ""The Pleasure of Finding Things Out.""

Preface vii
About the Author ix
List of Figures
xv
List of Tables
xxxi
Chapter 1 Biography and Beginnings
1(30)
1.1 About this Book
1(1)
1.2 Family
2(8)
1.3 Oxford, Physics and Bond Trading
10(3)
1.4 Morgan Stanley and P.D.T.
13(10)
1.5 Self Employed
23(1)
1.6 Professional Data Science
23(8)
Chapter 2 Financial Data
31(110)
2.1 Modeling Asset Prices as Stochastic Processes
31(4)
2.2 Abnormality of Financial Distributions
35(13)
2.3 The US Stock Market Through Time
48(8)
2.4 Interest Rates
56(15)
2.5 LIBOR and Eurodollar Futures
71(17)
2.6 Asymmetric Response
88(25)
2.7 Equity Index Options
113(11)
2.8 The VTX Index
124(7)
2.9 Microwave Latency Arbitrage
131(7)
2.10 What I've Learned about Financial Data
138(3)
Chapter 3 Economic Data and Other Time-Series Analysis
141(86)
3.1 Non-Farm Payrolls
142(14)
3.2 Initial Claims
156(9)
3.3 Twitter
165(19)
3.4 Analysis of Climate Data
184(30)
3.5 Sunspots
214(13)
Chapter 4 Politics, Schools, Public Health, and Language
227(58)
4.1 Presidential Elections
227(13)
4.2 School Board Elections
240(9)
4.3 Analysis of Public Health Data
249(22)
4.4 Statistical Analysis of Language
271(13)
4.5 Learning from a Mixed Bag of Studies
284(1)
Chapter 5 Demographics and Survey Research
285(62)
5.1 Machine Learning Models for Gender Assignment
285(8)
5.2 Bayesian Estimation of Demographics
293(3)
5.3 Working with Patreon
296(9)
5.4 Survey and Opinion Research
305(16)
5.5 Working with China Beige Book
321(5)
5.6 Generalized Autoregressive Dirichlet Multinomial Models
326(13)
5.7 Presidential Approval Ratings
339(8)
Chapter 6 Coronavirus
347(48)
6.1 Discrete Stochastic Compartment Models
348(4)
6.2 Fitting Coronavirus in New Jersey
352(6)
6.3 Independent Models by State
358(7)
6.4 Geospatial and Topological Models
365(19)
6.5 Looking Back at this Work
384(4)
6.6 COVID Partisanship in the United States
388(5)
6.7 Final Conclusions
393(2)
Chapter 7 Theory
395(54)
7.1 Some Remarks on the PDT Trading Algorithm
395(1)
7.2 Cosine Similarity
396(6)
7.3 The Construction and Properties of Ellipsoidal Probability Density Functions
402(27)
7.4 The Generalized Error Distribution
429(9)
7.5 Frictionless Asset Allocation with Ellipsoidal Distributions
438(6)
7.6 Asset Allocation with Realistic Distributions of Returns
444(5)
Epilogue
449(4)
E.1 The Nature of Business
449(1)
E.2 The Analysis of Data
450(1)
E.3 Summing Things Up
451(2)
Appendix A How I Store and Process Data
453(4)
A.1 Databases
453(1)
A.2 Programming and Analytical Languages
454(1)
A.3 Analytical Workflows
454(1)
A.4 Hardware Choices
455(2)
Appendix B Some of the Data Sources I've Used for This Book
457(4)
B.1 Financial Data
457(1)
B.2 Economic Data
457(1)
B.3 Social Media and Internet Activity
458(1)
B.4 Physical Data
458(1)
B.5 Health and Demographics Data
458(1)
B.6 Political Data
458(3)
Bibliography 461(8)
Index 469