“Life on earth is filled with many mysteries, but perhaps the most challenging of these is the nature of Intelligence.”
– Prof. Terrence J. Sejnowski, Computational Neurobiologist
The main objective of this book is to create awareness about both the promises and the formidable challenges that the era of Data-Driven Decision-Making and Machine Learning are confronted with, and especially about how these new developments may influence the future of the financial industry.
The subject of Financial Machine Learning has attracted a lot of interest recently, specifically because it represents one of the most challenging problem spaces for the applicability of Machine Learning. The author has used a novel approach to introduce the reader to this topic:
- The first half of the book is a readable and coherent introduction to two modern topics that are not generally considered together: the data-driven paradigm and Computational Intelligence.
- The second half of the book illustrates a set of Case Studies that are contemporarily relevant to quantitative trading practitioners who are dealing with problems such as trade execution optimization, price dynamics forecast, portfolio management, market making, derivatives valuation, risk, and compliance.
The main purpose of this book is pedagogical in nature, and it is specifically aimed at defining an adequate level of engineering and scientific clarity when it comes to the usage of the term “Artificial Intelligence,” especially as it relates to the financial industry.
The message conveyed by this book is one of confidence in the possibilities offered by this new era of Data-Intensive Computation. This message is not grounded on the current hype surrounding the latest technologies, but on a deep analysis of their effectiveness and also on the author’s two decades of professional experience as a technologist, quant and academic.
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PRAISE FOR
APPLICATIONS OF COMPUTATIONAL INTELLIGENCE IN DATA-DRIVEN TRADING
"Cris Doloc has a rare combination of expertise??in technology, trading, and modeling. The resulting synergies shine here, informing and illuminating this admirable introduction to the frontiers of computational learning in data-driven finance. I highly recommend this book."
—Prof. Roger Lee, Director of the Financial Mathematics Program, University of Chicago
"Cris Doloc successfully tackles a pair of difficult problems: bringing economic and mathematical rigor to Machine Learning in financial markets, while keeping the discussion relevant and accessible to practitioners and theorists alike. He brings a much-needed critical yet objective eye to the 'myth and reality' of AI and Machine Learning in quantitative finance, cutting through the AI hype with intellectual honesty. His book deserves a prominent space in every financial engineer's bookshelf."
—Gerald A. Hanweck, Jr., PhD, CEO and Founder, Hanweck Associates, LLC
"Applications of Computational Intelligence in Data-Driven Trading, is an essential addition to the library of any existing or endeavoring quantitative professional. This book is unique. It's an important read for those who want to be at the forefront of the data revolution in trading."
—Prof. Dan Nicolae, Chair of Statistics, University of Chicago
"Doloc addresses a wide range of applications of Machine Learning in trading and portfolio management. What is unique about this book is the case study approach. It's a must read for students in AI and ML as it goes far beyond the techniques of ML: it analyzes how to best implement these techniques given the specifics of each problem. Well documented with an interesting historical perspective, it is food for thought regarding the future of finance."
—Linda Kreitzman, Executive Director, Master of Financial Engineering Program, UC Berkeley
