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Graham Giller is one of Wall Street's original data scientists. Starting his career at Morgan Stanley in the UK, he was an early member of Peter Muller's famous PDT group and went on to run his own investment firm. He was Bloomberg LP's original data science hire and set up the data science team in the Global Data division there. He them moved to J.P. Morgan to take the role of Chief Data Scientist, New Product Development, and was subsequently Head of Data Science Research at J.P. Morgan and Head of Primary Research at Deutsche Bank. This book is briefly a biography but mostly a narrative of Graham's research in the fields of financial, economic, and alternative data. It contains extensive analysis of the true empirical properties of financial data and a detailed exploration of topics including Stock Market Prices, Treasury Bill Rates, LIBOR and Eurodollar Futures, Volatility and Options Prices, Sentiment Analysis on Social Media, Demographics and Survey Research, Time-Series Analysis of the Climate, and work on Language, Politics and Health Care data. 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." It has entertaining tales of a life in quantitative finance and data science including trading UK Government Bonds from 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 brief time spent with Jim Simons, and giving Michael Bloomberg a tutorial on Granger Causality. When an ex-Morgan Stanley colleague was shown this book his response was: "I might pay you quite a lot to not publish – that's a lot of insight into what works and what doesn't."
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 directly focuses on finding optimal trading strategies in the real world and supports that with a well-defined theoretical foundation that allows trading strategy problems to be solved. Critically, it also delivers a menu of actual solutions that can be applied by traders with various risk profiles and objectives in markets that exhibit substantial tail risk. It shows how the Markowitz approach leads to excessive risk taking, and trader underperformance, in the real world. It summarizes the key features of Utility Theory, the deficiencies of the Sharpe Ratio as a statistic, and develops an optimal decision theory with fully developed examples for both 'Normal' and leptokurtotic distributions.
This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)
This book tells the story of how financial markets have evolved over time and became increasingly more complex. The author, a successful and experienced trader, who among other things won the 2015 battle of the quants futures contest held in New York, shares how one can navigate today's dangerous financial markets and be successful. Readers at all levels will benefit from his analysis and many real life examples and experiences. The coverage is broad and there is considerable discussion on ways to stay out of trouble, protect oneself and grow one's assets. The author was the first one to do turn of the year January effect trades in the futures markets starting in the beginning of S&P 500 futures trading in 1982. That has been successful and the author explains his ideas and experiences from the beginning in simple markets to the current, very complex markets we have in 2017.The author discusses the various ways that traders and investors lose money in the financial markets. Many examples are provided, including Long Term Capital Management, ENRON, Amarath, Neiderhoffer's funds and many major companies such as Lehman Brothers, Society Generale, Saloman Brothers. This is invaluable to understanding ways to avoid such losses.The author discusses great investors, their methods and evaluation and the authors' work with several of them. Risk arbitrage and mean reversion strategies are described through actual use. Asset-liability models for pension funds, insurance companies and other financial institutions devised by the author are described. The author uses racetrack bias ideas in behavorial finance in trading index futures and options. Large stock market crashes that can be predicted are discussed with several models of the author and others. Many mini crashes including the January-February 2016, Brexit, Trump and French elections that are plausible but largely unpredictable are described and how they were dealt with successfully.Along with ways to deal with them, investment in top quality racehorses, oriental carpets, real estate and other interesting investments are covered. The author was instrumental in viewing racing as a stock market. The ideas are used by the top racing syndicates as well as hedge funds.The book proceeds by weaving these aspects of the financial markets in the modern era into a story of the author's academic, professional and personal life. This is told through the people he met and worked with and the academic and personal travel he had all over the world this past half century. The text is simply written with details, sources and references in the notes of each chapter. Details of various important events and how they evolved are described. There are numerous color and black and white photos in the text plus graphs, tables etc. in the notes to tell the story. The teaching and research into various financial and gambling markets takes the reader to interesting places around the world. These include the US and its many stock market ups and downs, Japan when they were ruling the financial world and then they collapsed, the UK visits with lectures, teaching and research work at their great Universities including Cambridge and Oxford, Europe with many activities in France, Italy, Germany and other places, to Asia including discussions about travels to Persia, Turkey, Singapore, Korea, China, Afghanistan, Russia and other countries. Also discussed are visits to U.S. universities including Chicago, MIT, Berkeley, UCLA and Washington. His work with horse racing syndicates took him to Australia and Hong Kong. Crises like those in Greece, US housing and internet and the flash crash are discussed.
Good graphs make complex problems clear. From the weather forecast to the Dow Jones average, graphs are so ubiquitous today that it is hard to imagine a world without them. Yet they are a modern invention. This book is the first to comprehensively plot humankind's fascinating efforts to visualize data, from a key seventeenth-century precursor--England's plague-driven initiative to register vital statistics--right up to the latest advances. In a highly readable, richly illustrated story of invention and inventor that mixes science and politics, intrigue and scandal, revolution and shopping, Howard Wainer validates Thoreau's observation that circumstantial evidence can be quite convincing, as when you find a trout in the milk. The story really begins with the eighteenth-century origins of the art, logic, and methods of data display, which emerged, full-grown, in William Playfair's landmark 1786 trade atlas of England and Wales. The remarkable Scot singlehandedly popularized the atheoretical plotting of data to reveal suggestive patterns--an achievement that foretold the graphic explosion of the nineteenth century, with atlases published across the observational sciences as the language of science moved from words to pictures. Next come succinct chapters illustrating the uses and abuses of this marvelous invention more recently, from a murder trial in Connecticut to the Vietnam War's effect on college admissions. Finally Wainer examines the great twentieth-century polymath John Wilder Tukey's vision of future graphic displays and the resultant methods--methods poised to help us make sense of the torrent of data in our information-laden world.
The irresistible, ever-curious, and always bestselling Roach returns with a new adventure to the invisible realm that people carry around inside.
Finally, a Simple Way to Understand Data Science Have you have heard about data science, big data, machine learning and AI, but not sure how to make sense of them? Have you thought of boosting your career with data skills but don't know where to start? Have you even attempted to learn data science, only to be frustrated by this seemingly complex and intimidating subject? In this engaging and informative book, Meor Amer invites you learn data science through a story, not complex maths and confusing terms. Nuts About Data will help you to.. Discover the power of using data to solve problems via easy, memorable examples. Finally get data and be able to confidently discuss about it. Find the path to transform your career to become skilled in data. This Story is About.. ..a squirrel country where a clan was suffering from a declining supply of nuts, their only source of food. They were smaller in number and inferior in physique compared to three other clans. They must beat the odds and fight for their share of nuts, mined in a very deep maze. Aly, the clan leader, sought the help of an unlikely source, Moe, to try and devise a plan using data science with their clan's survival at stake. What You Will Learn.. Why you need to know about data science even if you are not a technical person What is data science and how does it work What are big data, machine learning, and AI, and how are they related to data science What benefits do you get from data science, by understanding the kinds of questions that it can answer Why you have a unique role to play even if you don't plan to become a data scientist or analyst The five steps of a data science project The three levels of analytics What You Will NOT Find.. Mathematical equations and confusing graphs Complex statistics and programming languages Deep dive into algorithms Advanced subjects like deep learning and neural networks This Book is Perfect For You if You Are.. A professional (in any industry) who wants to be data-savvy A leader who wants to build a data-driven team A student who wants to start developing data skills Read this book and gain the perfect start in your journey to become data-savvy.
Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!
An engrossing examination of the science behind the little-known world of sleep. Like many of us, journalist David K. Randall never gave sleep much thought. That is, until he began sleepwalking. One midnight crash into a hallway wall sent him on an investigation into the strange science of sleep. In Dreamland, Randall explores the research that is investigating those dark hours that make up nearly a third of our lives. Taking readers from military battlefields to children’s bedrooms, Dreamland shows that sleep isn't as simple as it seems. Why did the results of one sleep study change the bookmakers’ odds for certain Monday Night Football games? Do women sleep differently than men? And if you happen to kill someone while you are sleepwalking, does that count as murder? This book is a tour of the often odd, sometimes disturbing, and always fascinating things that go on in the peculiar world of sleep. You’ll never look at your pillow the same way again.