The Unrules: Man, Machines and the Quest to Master Markets

It looks good on the outside. Emigre quant from former USSR, rags to riches, quant-algorithmic finance, even a forward by Michael Milken. But let's analyze. If we go by evidence or numbers, this is threadbare. There are no metrics to tell this fellow's success here. What we get mostly is self-laudatory ads for his organization (with no financial info), plus a stack of motivational cliches. How cliched? for nine-tenths of this, you could sub in any coach of any sport. Let me summarize hours of this: Persist. Work hard. Realize risk exists and things won't stand still for you. Seize opportunities. How many versions of these phrases did I hear? Hundreds. To use the author's fave language, that's called very low signal, high noise. High redundancy. He might be a great quant, but it is impossible to verify here, so maybe he is just a salesman who has jumped the shark (if he ever had something going pre-shark anyway) and is flacking books and website. The impossibility of differentiating, based on what is here, means precisely: low information. He has an exaggerated sense of specialness of his experience (and his potency as an inspirational figure, philosopher and writer, in each of which he is profoundly mediocre) and personal inspirations, but what we get are toss-offs, dregs, low-content generalities. This is no Carl Sagan as an artistic presenter. Any curious adult who has followed the quant-computing world knows every story heard here, about the verifiable path-breakers, from Von Neumann to Turing to Mandelbrot and so on. Likewise the (I guessed supposed) insights are low-grade and common, on probability, pairs trades, power laws and so on. I wanted to like this book, and bought the print edition before (as happens SO much) audible, without any preview warning, later released is own version. Who is the right audience? I would say a middle-schooler of reasonable (but not stellar) brightness and curiosity who might want to go into quant finance. I am bored, disappointed and put off. It isn't the worst, but it is light years from greatness.

The UnRules by Igor Tulchinsky is quite an entertaining read: Part memoir, part history of quantitative finance and applied mathematics, part promotion of WorldQuant (Igor’s investment firm), in less than 140 pages. This is the sort of book that could be considered ‘‘motivational’’ and of the ‘‘personal philosophy’’ genre considering the chapter subtitles: “Take aggressive risks, but manage losses.” “All theories and all methods have flaws.” “Events don’t unfold as anticipated, so there are limits to what can be planned.” “Persistence compounds your ability.” “Take action. Nothing else counts.” “Without specific, quantifiable goals, movement through life is Brownian motion – random.” “Knowledge grows from knowledge, and good ideas constitute new knowledge, altering reality in the process of growth.” “Make everyone benefit.” “Think big – it’s easier.” “Quantity is quality.”Not a big fan of the genre. But this book is more than the usual clickbaits one can find on one’s (facebook) newsfeed about personal development and the paths to the riches.In between arcane scientific examples (the mathematics of waves) and the description of his seemingly random career choices (playing the odds, like in quant investing), Igor Tulchinsky (CEO of WorldQuant) exposes his vision of quant investing, which I find is partly shared with the one exposed at the start of Lopez de Prado’s book Advances in Financial Machine Learning: Build an alpha factory aiming at extracting a huge number of small and eventually short-lived alphas instead of focusing on a small number of very strong (and crowded) ones; Igor’s “quantity is quality”. Specializing functions: Lopez de Prado’s suggest that workers in his alpha factory are becoming the best at their respective tasks (data engineering, feature engineering, model building based on the features (strategists), backtesting (working in total isolation, reporting only to the management, to avoid overfitting using multiple tests); Igor splits researchers who create a library of millions of alphas (more or less crowdsourcing the research function), and portfolio strategists who help themselves in this library of signals. To quote the book: “Today a typical portfolio may contain tens of thousands of alphas; the largest may contain 100,000. To our portfolio strategists, individual alphas, which may have vectors of hundreds or thousands of securities, remain black boxes. The algorithms, logic, and intellectual property remain with the researchers; the strategists know individual alphas only as mathematical expressions of a market signal. As a result, portfolios are not shaped by taking a macroeconomic perspective or exploiting some notion of value. Instead, a portfolio is all math: How does the combination of its alphas perform in the market? What are its characteristics? Can it be improved?The automation of scientific discoveries is a common theme in Lopez de Prado’s and Igor’s book. WorldQuant seems to have reach this stage for alpha discovery: 13,000 alphas by the end of 2010; 26,000 in 2011; 60,000 in 2012; 425,000 in 2013; 1,700,000 in 2015; 4,000,000 in 2016. An exponential growth.As a side note, a chapter digresses on the exponential progress of quant biology, and on the cross-fertilizing collaboration between WorldQuant researchers and computational biologists.This book will sound particulary appealing to quants with a computer science background, maybe less so to economists…As a takeaway, automate, automate, and automate even more!

This book is more about the author's life and his way of thinking than about investment. Philosophy is important for investment, but that was not was I was looking for when I brought the book.Most of the authors insight does not come as a surprise, but the points where one could disagree is not covered in depth, things are only states as something the author have learned. One example is that the author recommend to 'manage losses', that is fine but simple mechanism like 'stop loss' does not always work.... Another example is the large number of rules that the authors fund have found (>millions), is this something done automatic and if so what about overfitting?The last chapter in the book is not really about investment, but seems to be aimed at securing the author an place in history by sponsoring some interesting research involving machine learning.

The book is just a lifeless set of biographical facts interspersed with some shallow introductions to basic quantitative concepts that don't even match the depth of a Wikipedia article.The author seems excited by his philosophical grandiosity, but only tiptoes around ever saying something of substance about his life or profession - oddly defending his terseness at times. And at only 136 pages, the content is somehow maddeningly repetitive and resorts to overselling truisms and cliches. Consider yourself done after reading the cover flap.

The author weaves many insightful observations into the storytelling of his life and career. All parts are fascinating. Though there's no concrete hint on any specific alpha, perhaps disappointing some of the reviewers, the overarching approach is clear. In time, I think, this book will become one of the classic finance books. Bought a few extra copies to hand out to our junior analysts.