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.

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.

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)

The irresistible, ever-curious, and always bestselling Roach returns with a new adventure to the invisible realm that people carry around inside.

Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, and risk analysts will explore Python-based machine learning and deep learning models for assessing financial risk. You'll learn how to compare results from ML models with results obtained by traditional financial risk models. Author Abdullah Karasan helps you explore the theory behind financial risk assessment before diving into the differences between traditional and ML models. Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Revisit and improve market risk models (VaR and expected shortfall) using machine learning techniques Develop a credit risk based on a clustering technique for risk bucketing, then apply Bayesian estimation, Markov chain, and other ML models Capture different aspects of liquidity with a Gaussian mixture model Use machine learning models for fraud detection Identify corporate risk using the stock price crash metric Explore a synthetic data generation process to employ in financial risk.