Adventures in Financial Data Science

Download or read book Adventures in Financial Data Science written by Graham L Giller and published by Giller Investments (New Jersey), LLC. This book was released on 2020-11-17 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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."