Applications of Computational Intelligence in Data-Driven Trading - Hardcover

Doloc, Cris

Hardcover

ISBN 10: 1119550505 ISBN 13: 9781119550501

Publisher: Wiley, 2019

Synopsis

“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.

About this title

About the Author

CRIS DOLOC is a leading computational scientist with more than 25 years of experience in quantitative finance. He holds a PhD in Computational Physics and is currently teaching at the University of Chicago in the Financial Mathematics program. Cris is also the founder of FintelligeX, a technology platform designed to promote data-driven education, and he is very passionate about the opportunities that recent developments in Cognitive Computing and Computational Intelligence could bring to the field of Quant education.

From the Back Cover

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

From the Inside Flap

Traders have always been among the first to see and take advantage of the true potential of new technologies. From the telephone and the telegraph to computers and the Internet, trading has provided a fertile ground for the growth and development of tech innovations. Now, with the rise of data, machine learning, and artificial intelligence, quantitative analysts and trading professionals are again at the vanguard. However, unlike in times past, there is now a dizzying procession of new tools and products, and traders may have difficulty keeping up.

In Applications of Computational Intelligence in Data-Driven Trading, leading computational scientist Cris Doloc outlines a paradigm through which quantitative analysts can become astute problem solvers. Readers will learn how to effectively access, process, and interpret data, regardless of which tools they use. With this book's clear and detailed explanation of the principles behind computational finance including time series analysis, forecasting, dynamic programming, and neural networks readers will develop a lasting understanding that will enable them to solve financial problems in a way that transcends the latest fads.

In service of the goal of equipping students and practitioners alike with useful knowledge, Doloc has dedicated the second half of his book to in-depth case studies demonstrating how computational intelligence and data-driven trading intersect for powerful market insights. These case studies are scientifically structured, clearly demonstrating how machine learning methods are applied to specific problems, outlining the empirical results, and drawing conclusions that will help readers apply these cutting-edge techniques in their own work.

Rare is the book that actively aims to de-hype hot topics like machine learning and artificial intelligence. Applications of Computational Intelligence in Data-Driven Trading takes a refreshingly grounded approach, assessing in a balanced way how quantitative practitioners can extract the signal from the noise around artificial intelligence and permanently add valuable, data-driven problem-solving techniques to their skillsets.

Edition details

• Publisher: Wiley
• Publication date: 2019
• Language: English
• ISBN 10: 1119550505
• ISBN 13: 9781119550501
• Binding: Hardcover
• Edition number: 1
• Number of pages: 304