Applications of Computational Intelligence in Data-Driven Trading

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John Wiley & Sons, Oct 29, 2019 - Business & Economics - 304 pages

“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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The Evolution of Trading Paradigms
Artificial Intelligence Between Myth and Reality
2 5 MetaLearning An Exciting New Development
Computational Intelligence A Principled Approach for
How to Apply the Principles of Computational Intelligence
4 Conclusions References
The Dynamics of the Limit Order Book
4 Studying the Dynamics of the LOB with Reinforcement
k Policy 8 5 4 The CNN Implementation
Derivatives Valuation
Using Machine Learning for Risk Management
Conclusions and Future Directions
k Index

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About the author (2019)

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.

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