2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale
2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale__right

Description

Product Description

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance.

Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers'' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher''s perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

Review

“This volume aims to present a broad yet technical treatment of (ML) algorithms used by financial practitioners and scholars alike. The book fills a large void. This encourages reproducibility as well as learning by doing, which is highly appreciated.” (Guillaume Coqueret, Quantitative Finance, October 15, 2020)

From the Back Cover

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance.

Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers'' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher''s perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

About the Author

Paul Bilokon, Ph.D., is CEO and Founder of Thalesians Ltd. Paul has made contributions to mathematical logic, domain theory, and stochastic filtering theory, and, with Abbas Edalat, has published a prestigious LICS paper. He is a member of the British Computer Society, the Institution of Engineering and the European Complex Systems Society.

Matthew Dixon, FRM, Ph.D., is an Assistant Professor of Applied Math at the Illinois Institute of Technology and an Affiliate of the Stuart School of Business. He has published over 20 peer reviewed publications on machine learning and quant finance and has been cited in Bloomberg Markets and the Financial Times as an AI in fintech expert. He is Deputy Editor of the Journal of Machine Learning in Finance, Associate Editor of the AIMS Journal on Dynamics and Games, and is a member of the Advisory Board of the CFA Quantitative Investing Group.

Igor Halperin, Ph.D., is a Research Professor in Financial Engineering at NYU, and an AI Research associate at Fidelity Investments. Igor has published more than 50 scientific articles in machine learning, quantitative finance and theoretic physics. Prior to joining the financial industry, he held postdoctoral positions in theoretical physics at the Technion and the University of British Columbia.

Product information

Brief content visible, double tap to read full content.
Full content visible, double tap to read brief content.

Videos

Help others learn more about this product by uploading a video!
Upload video
Brief content visible, double tap to read full content.
Full content visible, double tap to read brief content.

Customers who bought this item also bought

Related posts

Customer reviews

4.5 out of 54.5 out of 5
76 global ratings

Reviews with images

Top reviews from the United States

Female Finance Professional
5.0 out of 5 starsVerified Purchase
Great book. Congratulations to the authors!
Reviewed in the United States on July 26, 2020
An amazing and comprehensive presentation of many different relevant and useful concepts. The finance industry -- trading, asset management, risk management, banking, etc -- is most likely going to look much different in the not too distant future and much of this change... See more
An amazing and comprehensive presentation of many different relevant and useful concepts. The finance industry -- trading, asset management, risk management, banking, etc -- is most likely going to look much different in the not too distant future and much of this change is going to come from applications of this book''s concepts. The authors also do a great job of demonstrating that these "black boxes" are actually not mysterious and overly complicated but rather fairly intuitive and implementable. If anyone has ever seen the movie "AlphaGO" and was wondering how that type of paradigm shift would apply to finance, the next step is to buy this book.
5 people found this helpful
Helpful
Report
Amazon Customer
5.0 out of 5 starsVerified Purchase
The (new) standard texbook on machine learning in finance
Reviewed in the United States on July 28, 2020
Brand new but I anticipate this will become THE textbook on the subject that many instructors will use to teach around the world. The book nicely builds up throughout the chapters. I find it great to include multiple choice questions, exercises... See more
Brand new but I anticipate this will become
THE textbook on the subject that many instructors will use to teach around the world.

The book nicely builds up throughout the chapters.

I find it great to include multiple choice questions, exercises and an extra instructor booklet available to assist in the classroom.

And what a great idea to share Python code to make it all that more practical!

I find the insights on inverse reinforcement learning particularly interesting.
5 people found this helpful
Helpful
Report
Alan J. King
5.0 out of 5 starsVerified Purchase
Best technical book on machine learning
Reviewed in the United States on January 7, 2021
The authors cut through the hype and rebranding that litters the field of machine learning. They discuss models that are relevant to finance. Most importantly for professionals, they ground their discussion in concepts that will be familiar to statisticians and numerical... See more
The authors cut through the hype and rebranding that litters the field of machine learning. They discuss models that are relevant to finance. Most importantly for professionals, they ground their discussion in concepts that will be familiar to statisticians and numerical analysts (quants, in other words).

Some of the work presented in this book is new, particularly the sections on inverse reinforcement learning.

This will be an excellent resource for a graduate course. Those students who do not have the math background will likely be motivated to get it. There are well-designed Python notebooks that present examples of the analysis. Students with the ability to work with the concepts presented in this book would be welcome in any serious quant shop.
2 people found this helpful
Helpful
Report
H. S.
5.0 out of 5 starsVerified Purchase
Source codes
Reviewed in the United States on July 26, 2020
Great book
Where can I find the source codes?
8 people found this helpful
Helpful
Report
Derp
5.0 out of 5 starsVerified Purchase
Nothing else like this out there
Reviewed in the United States on December 21, 2020
As a first edition it is a bit less polished than I would like, but the topic coverage can''t be found anywhere else. Top 1 of 1 books covering deep learning in finance. Looking forward to picking up future iterations of this book.
2 people found this helpful
Helpful
Report
finance professionalTop Contributor: Fountain Pens
3.0 out of 5 stars
A compendium
Reviewed in the United States on July 30, 2021
[N.B.: this review is based on reading sections here and there, and on a careful reading of chapters 2, 3 and 10.] This book covers a large number of topics, with little theoretical or thematic overlap among them: bayesian methods, traditional time series methods, deep... See more
[N.B.: this review is based on reading sections here and there, and on a careful reading of chapters 2, 3 and 10.] This book covers a large number of topics, with little theoretical or thematic overlap among them: bayesian methods, traditional time series methods, deep learning, dynamic programming and reinforcement learning. To paraphrase the title of the songs by Simple Talk: “ML is what you make it”. The topic are presented clearly, concisely and accurately. Because of the topic breadth, I expect this to serve primarily as a reference book, even if several chapter groups could be used as course material.

While I think this book is above the average of its peers ("quant" books on Big Data/ML/AI in finance), I was still somewhat disappointed. As a far-fetched comparison, consider the classic "Elements of Statistical Learning" (ELS) by Friedman, Hastie and Tibshirani. Even when discussing well-trodden topics like OLS or ridge regression, the presentation felt new, engaging and original. I think this could be attributed to two factors. First, the life-long acquaintance of the authors with the subject matter; second, the topics in the book (statistical learning) were central to the book''s aim (teach how to learn from data). The book uses fairly advanced math (matrix decompositions, RKHS) but if the book had been about "Advanced Quantitative Methods for Statistical Learning", it would have been uninteresting and forgettable. Now, let’s go back to the book at hand. Here, the goal should be in the *application* of these methods to finance. But there is preciously little of that, and what is there (e.g., on multiperiod optimal execution, or on comparison of factor models to deep factor models) is so superficial as to be pointless. For example, Factor Model estimation (and deep estimation) is not quite explained, and the comparison metric is at best a secondary one in factor model evaluation; and LQR solutions are not discussed. The book feels very removed from the work of actual quantitative traders, who should be the target audience of this work.
Ultimately, whether the book succeeds or not is up to the reader. Probably for someone completely new to the topics of this book and in need of a reference, this is more than ok. My preference would still be to go for monographies, some of which freely available: the aforementioned ELS; "Deep Learning" by Goodfellow et al., "Reinforcement Learning" by Sutton and Barto (2nd ed); and the still-classic "Dynamic Programming" by Bertsekas. I am waiting for a book that a) selects methods that *work in financial investment* (I think I know a few) and that is organized around first-hand applications in Finance. Would it contain RL?
I don’t think so. Deep learning? Yes. Latest-generation graphical models. Yes, too. All of it with plenty of commentary relative to financial event details, flow and liquidity models, and test. The simple fact is that *theory is cheap and abundant*. There are hundreds of published papers on ML in finance.

It''s the the successful method selection and the effort and intelligence in applying the method to a specific problem that are informative and valuable.
9 people found this helpful
Helpful
Report
Cristian Doloc
5.0 out of 5 stars
Comprehensive guide to ML in Finance for both students and practitioners
Reviewed in the United States on July 9, 2020
This book represents a very comprehensive guide to Machine Learning techniques in Finance and serves remarkably well both the students of quantitative and computational finance, as well as a large cross-section of industry’s practitioners. As someone who worked in this... See more
This book represents a very comprehensive guide to Machine Learning techniques in Finance and serves remarkably well both the students of quantitative and computational finance, as well as a large cross-section of industry’s practitioners. As someone who worked in this field for several decades, and wrote a book on this topic, I understand very well what it takes to put together a comprehensive guide on such a subject. Very rare are the books on this topic that address properly at the same time both the theoretical aspects of the problem at hand, as well as exemplifying these concepts with meaningful practical examples.

A book on application of ML to Finance is not necessary about providing tons of Py codes, and this one serves the reader very well. But it should be about setting up a strong foundation for the theories and concepts that underlie the ML machinery, as well as exemplifying these concepts with model reasoning that is applicable to real world problems. And the reader is served again extremely well from this perspective.

In my opinion this book introduces several novelties in a very crowded and sometimes over published field:
- A very elegant and well documented exposition on financial times series modelling, especially regarding the use of RNNs and Kalman filtering techniques;
- A mathematically sound and well exemplified section on the use of Reinforcement Learning, specifically promoting the use of Inverse Reinforcement Learning and Imitation Learning as modern tools for Optimal Control;
- Blending the ever-powerful set of Bayesian thinking into the world of financial ML, by developing an intuition for the role of functional regularization in the classical statistical setting.

I read this book and followed its Py code examples with great pleasure, and I strongly recommend it to anyone that is interested in applying these modern concepts, while having a solid understanding of the Mathematics that supports it. Enjoy it!
3 people found this helpful
Helpful
Report
B. Peterson
5.0 out of 5 stars
welcome and timely modern text
Reviewed in the United States on July 11, 2020
This book is a welcome and timely modern text on a very difficult topic. It covers the theoretical foundations for the use of machine learning models in finance, including supervised, unsupervised, and reinforcement learning approaches. The required math is presented after... See more
This book is a welcome and timely modern text on a very difficult topic. It covers the theoretical foundations for the use of machine learning models in finance, including supervised, unsupervised, and reinforcement learning approaches. The required math is presented after the intuition required for why the concepts are required, and does not overwhelm the presentation. References are copious and relevant, but are also likewise not a distraction to the main text. Most key concepts are accompanied by python notebooks so that concepts can be put into practice with working prototypes. I will be adding this book to the reading list for graduate courses that I teach on quantitative trading. Practitioners who are interested in the current state of machine learning models also have much to learn from this book.
One person found this helpful
Helpful
Report

Top reviews from other countries

msh
2.0 out of 5 starsVerified Purchase
Collection of ML topics treated with advanced mathematical exposition
Reviewed in the United Kingdom on September 6, 2020
There is nothing interesting in the book about finance. The book essentially covers some ML approaches with advanced mathematical exposition with little practical examples. Limited in scope and mostly good as an academic reference point for certain ML approaches. The book...See more
There is nothing interesting in the book about finance. The book essentially covers some ML approaches with advanced mathematical exposition with little practical examples. Limited in scope and mostly good as an academic reference point for certain ML approaches. The book takes some ML approaches to a more advanced mathematical treatment --- that''s it !!! Nothing else nothing more. Barely of any help in practice. They fail to go from theory to practice, in the sense of providing coding examples for each ML algorithm. There are a few examples as supplementary material, but this is just to lure purchases of the book than are of any real value. In many pages, there are distracting multiple-choice questions with little pedagogical value as well. Again, there more interesting books out there -- and the word "finance" in the title is a gimmick. Using examples from Finance does not mean that the book is *for finance* applications. Don''t buy unless you want to have a book in your library that has some good references to the literature.
10 people found this helpful
Report
Velu Mani
5.0 out of 5 starsVerified Purchase
Excellent intersection of Machine Learning, Finance and their various foundational disciplines
Reviewed in the United Kingdom on August 16, 2020
I have a decent understanding of Machine Learning, and wanted to know more about its applications in Finance. It has been a very useful book, as it is rare to find books covering applications of ML in Finance. The best part about this book is that, it also covers various...See more
I have a decent understanding of Machine Learning, and wanted to know more about its applications in Finance. It has been a very useful book, as it is rare to find books covering applications of ML in Finance. The best part about this book is that, it also covers various foundational disciplines like Maths & Statistics wherever I felt there was a need for it. I like the fact that it comes with exercises at the end of chapters, and quite a lot of code samples that can be readily executed to understand the concepts. Colour images are a big bonus too. There was a minor issue, 4 or 5 of the colour images, have black text on very dark backgrounds, hence making them unreadable, luckily many of these can be read by executing the code samples, so, it was not a big issue for me. I would have deducted about 0.25 stars for the image issue, but I can''t do that, and it is otherwise an excellent addition to my learning. I believe that it will also be equally good for Finance professionals who want to know about Machine Learning, while I belong to the ML group wanting to learn it''s applications in Finance.
One person found this helpful
Report
Quant
5.0 out of 5 starsVerified Purchase
Machine Learning for Quants
Reviewed in Canada on August 1, 2020
I just started to read the book and I have found it to be very informative for people with interests and background in quantitative finance. Machine Learning, Artificial Intelligence and specially Reinforcement Learning is currently a focus point of research as there has...See more
I just started to read the book and I have found it to be very informative for people with interests and background in quantitative finance. Machine Learning, Artificial Intelligence and specially Reinforcement Learning is currently a focus point of research as there has been interesting breakthroughs, e.g. DeepMind''s AlphaGo. Financial industry is also benefiting from the machine learning advancements, specially when non-traditional alternative data are available, e.g. sentiment-based trading or natural language processing. The book authors have extensive experience and background in quantitative finance. The book aims to presents the machine learning subject for quantitative finance professionals and graduate students in quantitative disciplines, e.g. Mathematics, Physics, Statistics. The book is divided to three parts: Machine Learning with Cross-Sectional Data, Sequential Learning, and Sequential Data with Decision-Making. Each part encompasses relevant topics presented in a few chapters where each chapters is accompanied by corresponding reference aiding interested readers to dive into the chapter''s material. The book is also accompanied with a collection of Python codes to further facilitate the learning process. For readers with knowledge of option pricing, optimal hedging the reinforcement learning part of the book provides the dynamic programming approach toward relevant classical option pricing problems through reinforcement learning closely resembling the celebrated Black-Scholes-Merton model. Overall, the book is valuable resource for Quants to become acquainted with the emerging Machine Learning Applications in Finance. The book should be helpful to the whole Machine Learning, and Artificial Intelligence community, and in particular to quants community in financial industry.
One person found this helpful
Report
msuzen
5.0 out of 5 starsVerified Purchase
A unique book
Reviewed in Germany on August 9, 2020
Traditionally finance industry uses mathematical approaches on so-called from "quantitative finance" perspective. Dixon-Halperin-Bilokon''s refreshing book does not only capture specialised usage of machine learning in finance but it also serves as a machine learning...See more
Traditionally finance industry uses mathematical approaches on so-called from "quantitative finance" perspective. Dixon-Halperin-Bilokon''s refreshing book does not only capture specialised usage of machine learning in finance but it also serves as a machine learning reference book. They treat chapters in great substance with carefully covering basic concepts in a non-superficial manner.
Report
David Chen
5.0 out of 5 starsVerified Purchase
Great book!
Reviewed in Canada on December 23, 2020
A classical reference for quantitative financial practitioners!
Report
See all reviews
Brief content visible, double tap to read full content.
Full content visible, double tap to read brief content.

Customers who viewed this item also viewed

Brief content visible, double tap to read full content.
Full content visible, double tap to read brief content.

What other items do customers buy after viewing this item?

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale

2021 Machine Learning in sale Finance: From outlet sale Theory to Practice online sale