Goodreads helps you keep track of books you want to read.
Start by marking “Advances in Financial Machine Learning” as Want to Read:
Advances in Financial Machine Learning
Enlarge cover
Rate this book
Clear rating
Open Preview

Advances in Financial Machine Learning

4.22  ·  Rating details ·  80 ratings  ·  9 reviews
Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is ame ...more
ebook, 400 pages
Published January 23rd 2018 by Wiley (first published 2018)
More Details... edit details

Friend Reviews

To see what your friends thought of this book, please sign up.
This book is not yet featured on Listopia. Add this book to your favorite list »

Community Reviews

Showing 1-30
4.22  · 
Rating details
 ·  80 ratings  ·  9 reviews

Sort order
Denis Vasilev
Oct 24, 2018 rated it it was amazing  ·  review of another edition
Практические советы по применению МЛ в торговле на фондовых рынках. Все по делу, очень интересно было глянуть на основные вопросы работы на одном из самых конкурентных рынков.
Aug 14, 2018 rated it really liked it
Machine Learning is about gaining confidence in your algorithm. Looking at a financial trading model, you only get a limited amount of data from, for example, Bloomberg services on which to formulate confidence. Drilling down you may approximate third party transactions on which you can only obtain partial viability. In this book we look at the various factors that obscure a supply data model and which therefore reduce the information that may be derived. Given a large and diverse supply populat ...more
Max Bolingbroke
Aug 05, 2018 rated it it was ok
Read his free paper on hierarchical risk parity (SSRN 2708678) instead.
Anthony Wittemann
If you're coming from a computer science and/or machine learning background, you will learn a lot about how to frame your algorithmic thinking in the domain of finance and will leave you hungry for more hardcore graph theory, parallelization, machine learning (beyond simple random forest ensembles and clustering), advanced algorithms, and gutty details of implementation, which are left for you to explore and enjoy.

The purpose of this book is not to explain how to apply Deep Learning to make mon
Terran M
May 04, 2018 rated it it was amazing
This book is for people who already understand machine learning or predictive modeling, and who already understand investment, and would like some guidance on applying the one to the other. It is an excellent book if and only if you meet these conditions.

The author has a hint of Taleb-style arrogance, wanting to be recognized for being the smartest person in the room, but not enough to impede enjoyment of the book, and it answers the question of why he published it at all in a field which is oth
Jul 25, 2018 rated it it was amazing
Knowledge like this is hard to come by because it is much more profitable to implement it than to write about it. Marcos must have had an urge to share his knowledge that overwhelmed the common wisdom in this industry - to not share or sell anything that works.
Jan 20, 2019 rated it it was amazing
Excellent book with practical example and issues in financial machine learning
Feb 04, 2019 rated it really liked it  ·  review of another edition
Application of ML algorithms to financial data is straightforward, at least in a technical sense.
Practically, God (or the devil) is in the details.
Dec 04, 2018 rated it it was amazing
This book contains an overview of tricks and techniques useful for time series analysis. I bet you do not know at least 10 of them even if you work with time series on a daily basis. Almost every mathematical description is accompanied by a code sample and this is a gem that gives this book real value. It would be great if other books in ML had same level of reproducibility AND mathematical rigor.
Davide Bulgarelli
rated it it was amazing
Nov 06, 2018
rated it it was amazing
Nov 08, 2018
Kyle Banks
rated it it was ok
Mar 02, 2019
Lei Hou
rated it it was amazing
Jul 05, 2018
rated it it was amazing
Nov 28, 2018
rated it liked it
Mar 08, 2018
Narin Dispat
rated it it was amazing
Jun 11, 2018
Benjamin Adams
rated it really liked it
Dec 26, 2018
Chris Donnan
rated it really liked it
Mar 25, 2018
Stephen Morrell
rated it it was amazing
Nov 12, 2018
Robert Martin
rated it it was amazing
Aug 04, 2018
rated it really liked it
Mar 26, 2018
rated it it was amazing
Nov 06, 2018
rated it it was amazing
Feb 26, 2019
Sean Nolan
rated it it was amazing
Jan 08, 2019
rated it really liked it
Apr 07, 2018
Michal Dziubinski
rated it it was amazing
Aug 24, 2018
rated it it was amazing
Sep 23, 2018
Isamu Watanabe
rated it really liked it
Oct 07, 2018
Mathieu Zaradzki
rated it it was amazing
Mar 30, 2018
« previous 1 3 next »
There are no discussion topics on this book yet. Be the first to start one »
  • The Futures: The Rise of the Speculator and the Origins of the World's Biggest Markets
  • The Kelly Capital Growth Investment Criterion: Theory and Practice
  • Building Machine Learning Systems with Python
  • Machine Learning: The Art and Science of Algorithms That Make Sense of Data
  • The Invisible Hands: Hedge Funds Off the Record - Rethinking Real Money
  • R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics
  • Contrarian Investment Strategies: The Classic Edition
  • Option Volatility & Pricing: Advanced Trading Strategies and Techniques
  • Hedgehogging
  • Python for Finance: Analyze Big Financial Data
  • The Bible of Options Strategies: The Definitive Guide for Practical Trading Strategies
  • Triumph of the Optimists: 101 Years of Global Investment Returns
  • Inside the Black Box: The Simple Truth about Quantitative Trading
  • The Deep Learning Revolution
  • Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined
  • Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
  • The New Lombard Street: How the Fed Became the Dealer of Last Resort
  • The Essential Buffett: Timeless Principles for the New Economy

Goodreads is hiring!

If you like books and love to build cool products, we may be looking for you.
Learn more »
“Dollar bars are formed by sampling an observation every time a pre-defined market value is exchanged. Of course, the reference to dollars is meant to apply to the currency in which the security is denominated, but nobody refers to euro bars, pound bars, or yen bars (although gold bars would make for a fun pun).” 0 likes
“Econometrics is the application of classical statistical methods to economic and financial series. The essential tool of econometrics is multivariate linear regression, an 18th-century technology that was already mastered by Gauss before 1794. Standard econometric models do not learn. It is hard to believe that something as complex as 21st-century finance could be grasped by something as simple as inverting a covariance matrix.” 0 likes
More quotes…