The Art and Science of Financial Prediction: A Reading List


Contents: Book reviews. Useful ideas for prospective traders on making predictions and data analysis. A little bit of politics but no horse racing.

While I enjoy the idea of reading a little more than the practice, I am a big reader. My first two articles here on were on the subject of horse racing and, while I've read many books in that field, I thought I'd devote this article to the theme of books on finance, particularly data analytics and a couple of related themes. Science-y, tech stuff...

The History of Finance

Niall Ferguson's The Ascent of Money: A Financial History of the World (2008) is a good account of the development of merchant banking in Europe, mostly, and its role in some of the awful crimes committed in the name of empire for the benefit of colonial trade. We can move on and cherish greater human ideals like equality between people of all races but we can also be wary of capital as it pertains to power and bear in mind that financial systems have long been associated with inequality and enslavement. Ferguson’s The Ascent of Money is not told like this but it probably should be.

1493: How Europe's Discovery of the Americas Revolutionized Trade, Ecology and Life on Earth (2011)

by Charles C. Mann is no radical polemic but it adopts a very different approach to history, focusing crucially on the interaction of two ecosystems. The Spanish conquest of the Americas initiated by Columbus brought widespread disease to the New World. The impact of these diseases was so devastating on local populations that the colonial invaders imported slaves from the African continent to take on the workload of farming and mining. Mann is a fascinating writer with a fresh yet holistic take on history.

Against the Gods: The Remarkable Story of Risk (1996)

by Peter L. Bernstein receives my highest recommendation in this category. Bernstein focuses on the crucial role that uncertainty regarding the future has played in the development of modern finance. Some security in trade can be purchased by the buyer of a price-guaranteed contract (hence insurance, hedging and derivatives trading) while traders taking big risks on low prices can make large profits. Bernstein's focus on risk helps elucidate much that is confusing about the modern trading system. Highly informative.

Michael Lewis is a very different sort of writer but another who sees the fundamentals at work in the financial system and where they might be lacking. He discovered his writing voice in 1989 with Liar's Poker having voluntarily quit work as a bond salesman on Wall Street. Most noteworthy about this funny book, written in Lewis's trader slang, is how it foreshadows his later work, The Big Short (2010). Having read Liar's Poker again recently, I was left with the strong impression that the disaster of the US housing bubble was near unfolding in 1989 - Lewis completely identifies the ill-measured economic risks being taken - and that it just took a further twenty years for a few big banks to fall.

Data Analytics: The Art and Science of Prediction

Between these two works Lewis writes Moneyball (2003), a nicely told tale of baseball manager Billy Beane ploughing his own furrow and applying data analytics to a sport strongly prone to traditional hearsay when scouting for talent. Moneyball is a great read, perhaps less successful on the screen than The Big Short (which is an excellent movie). It probably works best as a book if you know something about baseball and sabermetrics (baseball stats), which I don't.

Still, it was an easier read than Thinking, Fast and Slow by Daniel Kahneman. This summary of many years' academic work by Kahneman and fellow researcher Amos Tversky has been a consistent bestseller in the popular science field since its publication in 2011. It's an important work, especially for people focused on prediction markets in their work, though clearly it has much of interest to a casual reader, especially one looking to improve their methodical and analytical mindset. I found it a bit too dry and academic but it's a worthy read, necessary for bettors and prospective traders alike.

enjoyed Nate Silver's The Signal And The Noise: Why Some Predictions Fail But Some Don't (2012) more. In some ways it's more of an applied text as it draws many of its examples from the author's website FiveThirtyEight. The original sales line was something to the tune of 'this is how FiveThirtyEight forecasted the correct election result from every US state in the 2012 presidential election'. Which was great, only next time up (at Trump's surprise electoral win in 2016), FiveThirtyEight predicted an overall win for Hillary Clinton. The subtitle of the book perhaps says it all.

Nassim Taleb's Fooled By Randomness: The Hidden Role of Chance in Life and in the Markets (2001) and The Black Swan (2007) are worth mentioning here. I'm not sure if Trump's electoral win could quite be construed as a black swan event (defined by Wikipedia as a "high-profile, hard-to-predict, and rare event... beyond the realm of normal expectations in history, science, finance, and technology") but it certainly came as a surprise to many. Even and especially with AI systems deep learning our every move, our ability to make the wrong predictions and fool ourselves when making quite basic forecasts (which way will the US nation vote, for example) is sometimes pretty staggering. With Taleb I preferred the earlier work, Fooled By Randomness. The author is quite the intellectual of the markets. This book was more oriented towards technical analysis with less exposition on Ancient Greek philosophy.

This article is now deep into predictive data analytics theory. I think our ability to misjudge how events will turn out has a lot to do with our tendency to adjust our perceptions based on what other people think. Daniel Kahneman probably has a name for this heuristic (he talks of the 'priming effect' of media). This sheep-like mentality (the tendency to herd together when forming opinions) is not a bad thing per se.

James Surowiecki looks in detail at how this phenomenon can often achieve magically accurate predictions in The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations (2004). Maybe this is true so long as the individuals who make up the prediction market are not all being swayed by some (media?) bias. The Wisdom of Crowds is a fairly short, very readable text and another that's highly recommended.


It's been a long article but there's much to fit in. On cryptocurrency I preferred Dominic Frisby's Bitcoin: The Future of Money (2014) to Nathaniel Popper's Digital Gold (2015). Both are fairly basic introductions to readers seeking to understand blockchain finance. Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World (2016) by Don and Alex Tapscott is a strange thing. Go along with the Tapscotts for a bit and you can easily start to think 'Wow, this is going to change the world.' The altruistic take is laudable but the book lacks much stylistic colour, its prose quite formulaic, standard web 3.0 stuff.

The Secret Life: Three True Stories of the Digital Age (2017)

by Andrew O'Hagan is a collection of three gripping tales from real life. O'Hagan is concerned with the ambiguous zone where real life and virtual identities become blurred. In one tale he adopts the identity of a long-deceased man and builds an online identity anew. The other two pieces are close-up profiles of Craig Wright, making his claim to be Bitcoin founder Satoshi Nakamoto, and Julian Assange, who O'Hagan had been helping to write an autobiography. Suspense, intrigue, paranoia and USB sticks; this fairly short read has it all.

Tech Culture

The Code: Silicon Valley and the Remaking of America (2019)

by Margaret O'Mara is a history of Silicon Valley. Technical developments by companies located in the San Francisco Bay Area have, of course, had a huge impact on computers and online culture across the world. The book is comprehensive and good on the influence of politics, venture capital and military technology in the development of the tech industry in this region of Northern California. I haven't read the much earlier Hackers: Heroes of the Computer Revolution (1984) by Steven Levy but it covers similar themes and is regarded as a classic in this genre. Where Wizards Stay Up Late: The Origins of the Internet (2003) by Katie Hafner & Matthew Lyon is a little more animated than O'Mara's text. It focuses on ARPANET and the work of the early scientists behind the internet.

The book that deserves a push here is Paulina Borsook's fantastic Cyberselfish: A Critical Romp Through the Terribly Libertarian Culture of High Tech (2000). Wonderfully told in a deadpan tone, former Wired journalist Borsook looks back sadly on what might have been; or how money has sometimes kept ethics down in the not-all-that-modern-really white male dominated world of tech culture. Especially good on US taxation and its role in fostering, not hindering, the development of corporate tech America.

Big Data: A Revolution That Will Transform How We Live, Work and Think (2013)

by Viktor Mayer-Schonberger & Kenneth Cukier is a work that remains unread on my shelf. The Numerati (2009) by Stephen Baker was good enough, I think, on what was coming with big data and AI analytics. On the dangers not from AI so much as from big datasets in the hands of giant tech corporations, I did try The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power (2019) by Shoshana Zuboff but it was too dull for me (which is saying something actually!) and unfocused. I'd watch Netflix documentary The Social Dilemma (2020) instead.


I've never regretted choosing Python as my first programming language to learn. It is often cited as the most used coding language for quantitative analysis in finance. I started out learning Python with the website How To Think Like A Computer Scientist. The roots of this site stretch back to a 2002 Allen Downey work of the same title originally teaching Java code to students. Think Python: How to Think Like a Computer Scientist (2015) is Allen Downey's most recent version but there are many spin-offs of this guide now available for learning Python 3 (advised) in free PDF or book format.

One of those that never gets the credit I think it merits is Charles R. Severance's Python for Everybody: Exploring Data in Python 3 (2016). It’s an easy, readable and practical introductory text which I often find myself going back to. Dr. Chuck (as he’s monikered) has a Py4E website with a PDF copy of the book and relevant materials online.

Oh and I should add, if you're getting into the data analysis side of Python programming, Python for Data Analysis: Data Wrangling with Pandas, Numpy, and Ipython (2nd Ed, 2017) by Wes McKinney. McKinney is the author of Pandas, a very useful code library for Python. In actuality, if you're learning to code, the freely available online resources are often quite sufficient.

A Choice Selection

Against The Gods, 1493, The Secret Life

and Cyberselfish were very readable texts that stand out particularly. These books are all highly original and insightful. They do have an advantage on some of the more technical texts above in being free to approach their subjects from a more literary angle.

I'm not widely read in any of these study fields but my central focus has been on data analysis and prediction. For some insight into the techniques and theories of data forecasting, I'd recommend beginning with The Signal and the Noise and The Wisdom of Crowds.

Most of the above books come in a number of editions and all are widely available from internet retailers. I hope this article has been illuminating. Thank you for reading. What have I missed here and what should I read next?

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