Top Innovation in Finance Books to Read this Christmas

Top Innovation in Finance Books to Read this Christmas

by December 2, 2019
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I can’t believe how fast this year went by. 2020 is knocking on our doors shortly. As we are about to enter the last month of 2019 we want to share the Lightbulb Capital reading list based on books Derek and I read over the past few months. All of them have some angle to Innovation in Finance, as usual, even if it is not immediately obvious.

The list below does not come in any particular order.

We hope you can use some of the time off work in December to pick up one or two of the books that appeal to you most. To clarify:

  • Nothing ends on this list without us owning the book and at least one of us having read it
  • Nobody pays us to create this list. It is purely a summary of what influences our thinking and we hope you can also benefit from

So here we go:

The Deep Learning Revolution by Terrence Sejnowski

If you are keen to learn more about the history of Deep Learning and how it works this is actually a great book to pick up. Some history, Yann LeCun’s paper and of course what can done in the future is lined out well in this book

“Rebooting AI: Building Artificial Intelligence We Can Trust” by Gary Marcus and Ernest Davis

You’ll see that this reading list is quite AI heavy. And that is ok we think. This book outlines all the weaknesses of AI today to add a more realist angle of how fast things actually develop. It also highlights well that Machine Learning is just one part of AI. Knowledge representation and robotics are other important fields.

“Narrative Economics: How Stories Go Viral & Drive Major Economic Events” by Robert Shiller

There are no rankings in this list but if you can only pick up one book then it should probably be this one. Shiller books are always must reads. With Narrative Economics he explores the phenomenon of vitality and how it impacts economics events. He even uses the example of Bitcoin to illustrate some of his points.

“The Man Who Solved the Market” by Gregory Zuckerman

The story about one of the most successful quantitative trading firms in the markets: Renaissance Capital and their legendary Medallion Fund. If you are remotely interested in quantitative investments and electronic trading this is a must read – on the same level or perhaps even a notch up from Scott Patterson’s legendary “Dark Pools” book. What I took away from it as well: Entrepreneurship is hard… even if you are a PhD in Math and figured out how to consistently beat the market for a long time. Because in the end it is all about people.

“The Formula: The Five Laws behind Why People Succeed” by Albert-Laszlo Barabasi

No, we’re not suggesting you read a self help book. In fact, if you have not come across Prof Barabasi yet, he is Mr. Network Science and a highly accomplished academic. In this book he scientifically uncovers 5 laws of what seem to be the main contributors to success. Fascinating read.

“Good Economics for Hard Times” by Abhijit Banerjee and Esther Duflo

The academic couple receiving this year’s Nobel price in economics are focused on how to go about inequality and other challenges like technology disruption. I am very proud to say that my school, Erasmus University awarded a Dr. h.c. to Esther Duflo and she was on campus earlier this year. For the nerds amongst us: She is the master of randomized control trials. This book cannot not be on our list.

“Data Science for Business” by Foster Provost, Tom Fawcett

Ok – yes, I have reading this and the following two books because they are part of the readings for the “Financial Data Professional” certification. Something I’d highly recommend to anyone who is interested in Data Science, Machine Learning and alternative data in the investment world. This book is the easiest to read and very non-mathematical – mainly to introduce concepts in plain english.

“Big Data and Machine Learning in Quantitative Investment” by Tony Guida

This is the second book highlighted by the FDP institute. It contains more specific applications of BigData in Finance. It is a collection of chapters touching on different topics and use cases and their impact on the investment world.

“An Introduction to Statistical Learning” by Garreth James, Daniel Witten, Trevor Hastie and Robert Tibshirani

This is, even though it is called “Introduction”, by far the most technical book of the three required readings for the FDP Certification. It adds substance to Provost & Facett’s book and discusses the details of methods and models of statistical learning. It also describes key concepts well… but this is a book to work with – not a sunday afternoon read.

“Advances in Financial Machine Learning” by Marcos Lopez de Prado

This book is not part of the curriculum of the FDP Certification. But some of the papers Marcos wrote are on the list. This book is really written for folks who already have a good understanding of both, financial markets and Machine Learning. I do not know how Marcos does it but the amount of concepts he churns out is just impressive. It is not an easy read but a pleasure to see how someone has such deep understanding of both, markets and the technology.

The Life-Changing Manga of Tidying Up: A Magical Story by Marie Kondo

The principles behind getting your (physical) room tidy are the same as the ones you might find useful to tidy up what’s in your head / mental space and eventually even what you have on your work desk and your business. A fascinating comic book supporting you with executing projects rather than just talking about them.

“A mind for numbers” by Barbara Oakley

So… yes… all that ML and AI stuff we have been recommending… well… with my Math capability it is hard to understand. So I had to do some brushing up. And this book taught me how. I hope it is also useful for you all.


Yeah – so that was it.

We hope you enjoyed the list and encourage you to read more – regardless of how much you read today. Merry Christmas and happy new year… a bit ahead of time… talk to you more in the new year and best wishes. Please stay in touch.

 

This article first appeared on Linkedin.com

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