User Reviews
Rating: really liked it
The Philosophy of Financial Markets There are essentially two ways, two visions, two philosophies, of conducting inquiry in the social sciences. In one, rational behaviour is defined by some plausible propositions; behavioural data are then analysed; and people are shown to often act irrationally. In the other philosophy, the observed patterns of human behaviour are used to define an implicit standard of rationality which may be hidden and even unconscious. These patterns (or ‘signals’) are then used to predict future states. The first is an example of the philosophy of Rationalism, which holds that laws precede and produce facts. The second is an example of Empiricism, which claims that facts precede and produce laws. The intellectual battle about which of these views is better is ancient and still hasn’t been resolved - not just in the social sciences, but also in all scientific inquiry.
Within the social sciences, financial economists are generally Rationalists. They create models of economic choice which they then use to judge the rationality (which they call efficiency) of markets, and sometimes to exploit what they find to be irrational behaviour by buying or selling to correct the situation (making the market more efficient makes money, a win/win for the individual and society, they believe). Financial chartists (or technical analysts if one prefers) are Empiricists. They look for patterns (‘structure’ in the jargon) in the movements of markets prices from which they attempt to predict future prices (chartists don’t apologise; they are in it for the money). Financial economists and chartists view each other as fools and hucksters. Economists point to the absence of chartists’ theory as proof of their irrationality. Chartists claim the lack of theory as a virtue and deride the economists ignorance of the real world. They don’t want to second-guess the market, only to understand its inherent rationality.
Historically, Rationalist financial economists had the upper hand in academic circles and among the big names in financial trading.* Beginning in the early 1950’s, its influence grew rapidly as it was taught to generations of MBA’s who spread it like an infection throughout the world. The bias toward rationalism was so pervasive that it was the primary cause of the 2007 financial crisis, which demonstrated just how irrational rationality could be. In the way of these things, fashions changed in the perennial attempt to beat the market. Empiricism was in; Rationalism was out. Old-fashioned chartism entered the realm of Artificial Intelligence and became respectable (hence the euphemism of ‘technical analysis’).
And the new chartism works. No one knows why it works. It just does, as Jimmy Simons and Robert Mercer discovered to their enormous personal benefit. Neither knows all that much about financial markets, but they know about data, raw information from a staggering array of sources, within which are hidden patterns like the traces of gold at Sutters Creek or like intelligible messages buried within the gobbledygook of an enemy code. Markets didn’t need a theory; they provide their own theory if one pays enough attention to the detail. And computer technology was just the tool that was needed to sift, sort, and correlate all the detailed data one might collect in the search for the El Dorado of financial trading.
Financial economics worked, while it worked, largely because big investors felt compelled by academic theory to act rationally. Fund managers, banks, and other fiduciaries had a duty to act rationally on behalf of their clients. In the absence of any plausible alternative, professional ethics demanded adoption of the theory. The theory, therefore, became a self-fulfilling prophecy - and the prophecies came true until the world discovered that its rationality was no more than a conventional fiction. By avoiding the intellectual arrogance of presuming it knows better than the market, the new chartists can claim to be grounded in reality not economic fantasy.
The problem of course is that no one knows why the various correlations, connections, and intersections of data work (when they do). Empiricists don’t usually look for reasons. And when they do, it is typically to rationalise the conclusions their algorithms have already produced.** The algorithms which manipulate the data may contain an implicit theory but that doesn’t really bother the Empiricist. Nor does the lack of reasons for the various correlations. Coincidence or cause, the empiricist isn’t worried. What he does worry about is someone stealing his proprietary algorithms. The Rationalist benefits by the widespread use of his theory; the Empiricist by the strict secrecy of his programmes.***
Therein lies the Empiricist’s Achilles Heel. There is literally no reason to believe his correlations are stable. There is no way to test or verify hypotheses. Technically, there are no hypotheses. And no one aside from the proprietor is checking the validity of the findings of inquiry (thus violating a fundamental principle of true scientific inquiry). Correlations may change randomly and without warning. The enemy code, if there is one, might be altered entirely from day to day. Investors who employ the chartist strategy will never know if they are, quite literally, entering uncharted territory. On the other hand, society is considerably safer in the hands of chartists, as long as they act independently of each other based on their own algorithms (something the old-fashioned chartists did not do). Some may win while others loose; but they’re unlikely to all win or lose together, thus provoking systematic misery. That, however, until enough big investors discover similar correlations and interpret them as signals rather than noise.
Ideas have life cycles just like ladies’ fashion and gentlemen’s fascination with machines. When ideas become widely adopted, they are more accurately described as fads. If you miss one, don’t worry; they be another along shortly. The publication of this book probably announces the entry of high-tech chartism into fad-dom. no doubt there will be more and more success stories reported which will generate more and more interest, and produce more and more demand for data-mining and other empirical techniques. The failures, of course, will go largely unreported. At least until one big enough occurs that is worthy of newsprint, airtime, or blog space. I am eagerly awaiting first reports.
*I am not entirely unbiased on this subject. My great uncle was Fischer Black who devised the options pricing model which is arguably a central concept of financial economics in theory and in practice.
** Simons’s mathematical background seems to make him unaware of this as a fatal flaw. Numbers, after all, have stable relationships with each other. Once discovered, these relationships never vary.
***This is not strictly true in that Goldman Sachs, for example, has an interest in keeping its proprietary pricing models confidential. However, it is essential that Goldman’s also can convince its clients that the proprietary model conforms to a responsible financial theory. The general acceptability of the theory is what matters. The rest is a matter of client faith... or gullibility.
Rating: really liked it
If you, like me, have already scoured the interwebs for tidbits on Jim Simons and his Long Island quant shop, then there is not
too much new stuff here in terms of the history of the company, but the story is still nice to revisit, and there are insights not presented anywhere, in particular some viewpoints from Magerman that elucidate his position and why he acted like he did. The tragic story of James Ax is also interesting, albeit unfortunately very lopsided as he wasn't around anymore to present his side, and the psychological problems underlying them are gushed over.
However, there are some weird, yet possibly not incorrect, discrepancies that seem to linger throughout the work. The most glaring is probably that Jim Simons seem to be a wholly peripheral figure in the development of RenTec; he appears occasionally in shorts and sandals wielding an ever-present Merit cigarette, but other than that it seems like he really spent most of his time running his venture capital business (which is never really explored; only very few companies are even mentioned to be related to Simons and they're all trading related, and its pretty much impossible to google anything about it) and did other things, such as the occasional math paper or founding the odd scientific laboratory - anything other than being part of building the trading business, really. Did he, for example, just show up at the office one day in the late-90s, told the employees to improve their equity trading, and then vanished into a cloud of tobacco smoke?
Another is the tough-acting Russian researcher Alexander Belopolsky that suddenly appears, is described as a bit of a problem child, apparently changes the whole atmosphere of the company, and then leaves for Izzy Englander's Millenium Capital. We, the readers, definitely missed out on the actual story which, most likely I think, included something close to a coup. A lone parenthesis mentions that people close to him disagree with the portrayal given in the book, but this is never explored, nor is the clearly significant changes he caused to the culture and development of the firm. Sure, some time is spent on poor awkward Magerman feeling a bit stressed out, but that is pretty much it.
Finally, the writing is often not very smooth, and there are numerous small mistakes in the text, such as repeated words and awkward sentences. As a more humorous example, one paragraph mentions a wife of a researcher who is a professor of speech pathology at Stony Brook, the very same paragraph ends up describing that she ends up with a PhD - in speech pathology. I'd imagine Stony Brook being a good enough university that they make people professors after they receive their PhD, not the other way around, but of course I can't be sure. The author also occasionally inserts a weird aside in a parenthesis, they most often fall flat and appear totally out of place - any decent editor would either have cut them, or at least sharpened them up.
In a sense the book is just a rehash of what everyone with a serious interest in RenTec already pretty much knew, except for a few details, but it is nice to have it all in one place and presented chronologically. However, what we already knew about RenTec, unfortunately, is pretty close to nothing - a few haphazard facts, the names of the most important people, that they run statistical arbitrage on steroids. Of course, I don't think anyone would ever have expected any book on Jim Simons or RenTec to actually tell us all about what anyone inside the vault think.
Overall, if you don't know anything about RenTec the book is probably 3.5-4 stars. If you already wasted your time digging for irrelevant trivia about the Mount Olympus of quantitative finance, then this is mostly just SparkNotes.
PS: As a relevant aside, I should point out that the Medallion returns up until 2005 are actually available, and have been for a while, in a critically under-read book (3 ratings currently on GR), Scenarios for Risk Management and Global Investment Strategies, by Rachel and Thomas Ziemba - they even include an additional significant digit (geeks rejoice!), and it all fits with the numbers Zuckerman provides, which is reassuring. The latter author was a consultant for RenTec and has some insightful views on the fund as well, plus many others and the industry, in the book. A much more recommended read than this one - unfortunately, the price appears to have shifted a bit since I picked up a used copy for around $15 shipped, the cheapest I can find is just under $1000.
Rating: really liked it
Every few months, I get a LinkedIn message from a headhunter regarding a discreet search by a secretive firm in the New York area. The message will reference a team of leading computer scientists and mathematicians. Some will use adjectives like "renowned" and "legendary" and phrases like "total compensation in excess of $500k."
My trader friends and I, a technologist with no academic credentials aside from being the first person at my college to turn a B+ into a teaching assistant role for a C++ class, always hope it is RenTec. However, we stopped responding to the messages once we realized that they were written so as to fool us into thinking the firm was RenTec, which Jim Simons founded and is the subject of this book. Not to say that any of the other companies fitting the descriptions aren't impressive or that I don't want to make $500k+, it's just that there is special prestige afforded to the place.
The book is well-written in a style you'd expect from the Journal. The author pays homage to many significant points in the history of quantitative trading while providing a clear idea of Renaissance Technologies' place in it all. It's easy to lose sight of the likelihood of this book never having been written at all. But here we are.
I did not find the read particularly rewarding or worth skipping my morning run for. Particularly distasteful was the attitude toward Bob Mercer's role in helping Donald Trump get elected. I get it, you can't ignore that Mercer, co-CEO of RenTec, was instrumental in getting Trump elected. I didn't know this! But there is snark insinuating Trump == Bad for more pages than I'd care to read on a Sunday morning and cancel culture assured the guy had to step down from his role at RenTec anyway.
I also took issue with the implication that Thomas Sowell,
who is black (omitted), wrote economic and social theory books that were being used to further agendas of white supremacy. I read one of his lesser-known books,
Race and Economics, and referenced it for a final paper in a class about slavery and got an A. But that was over three years ago. It's unfortunate that public discourse, especially that being directed by my dear WSJ, doesn't allow this type of thing to be discussed in a healthy way.
Anyway, the most common criticism is going to be that it isn't clear at all what innovations were actually made through the 2000s and that Jim himself took a largely managerial role early on. Much of the book is about the supporting cast, who thankfully were all weirdos. I was still kind of pissed about the Trump stuff but the end of the book is prescient regarding the current state of the industry and the current angles data people at trading firms are exploring. It's a must-read because it's RenTec (and because it highlights a quote from Gary Shteyngart who wrote the satirical book
Lake Success, my favorite of 2018) but if this can't be it, I doubt we'll ever get the modern-day trading classic we've been waiting for.
Rating: really liked it
It's not a bad book. If you like biographies focused on year by year events you will enjoy it.
I was expecting something else. Some market knowledge, some mathematical formulas.
The whole book is like listening to grandpa's story, where he is talking about himself, missing on every interesting fact and plot.
Also, in the end, we get into politics, making this even worse.
Rating: really liked it
I've always wondered what Jim Simons, the liberal leaning head of Renaissance Technologies, thought of the co-head of his firm, Robert Mercer. I hope Simons lives long enough to see the consequences of helping Mercer to his billions.
This book, reviewed in the NYT - How to Beat the Market, may provide some insight, to quote:
You can certainly argue, as one former Renaissance executive does, that hedge funds are “a game in which rich people play around with each other, and it doesn’t do the world much good.” You could also argue, as another former executive guiltily put it, that working for Renaissance “helped provide Mercer with the resources to put Trump in office.”
The interview I recall Simons once did with vice.com seems to have been taken down and I regret not keeping a copy. FT article here as well: The Man Who Solved the Market — how Jim Simons built a moneymaking machine
Rating: really liked it
Very disappointing. I read it in 10 hours or so, at least 8 were a waste of time.
The title is surely chosen by the publisher as a marketing gimmick. It's not *that* much about Jim Simons, in fact past certain years (the 80s?) it's about Brown and Mercer, who came from IBM where they did natural language processing.
Basically the algorithm needs to "understand" what you're saying by trying to guess what are you *going* to say:
e.g. if you say "apple" the algo will apply a high probablity that the following word will be "pie", under specific circumstances.
So, one "principle" behind the modus operandi of some algorithms employed by RenTech is this one.
It doesn't dig deep into the math, the importance of discoveries by Simons and his PhD friends.
The author seems concerned with how much tall the guys are, what food did they brought to work, the brand of the cigarettes smoked by Simons, how much money did they play in poker, the back story about the sons and daughters of the employees.
Very little is said about the guys who stole the code and brought elsewhere.
There's an introduction of a non-collaborative new hire who raises near the top, some old guys aren't happy with his lacking team-play, he said the bad mouthed Mercer and Brown and the same Brown wants to keep him because he adds value. In the same chapter he introduces the two that stole the code
(but the theft happened earlier, and then on the following chapter he vaguely explain what happened to the two thieves, but not much is said about how they came to the fund, how they left, etc... - at least, I hope I didn't get distracted - ).
There's one or one and a half chapter talking about Mercer and his political donations and about the election. Who gives a fu**. From 2008 he went to one episode of 2010 (market flash crash?) to another one where Jim didn't believe the algos and wanted to painc sell in '12(?) and then straight to Mercer's political exploits in '15-16-18.
The author constantly talks about how bad things were going in basically every year, it makes the company (because really early in the book Simons seems to not be the relevant person running the operations that make money to the company) look like they were always on the verge of going badly, while the hard data that he himself provides show the other way.
He wants to led us to think that they struggled or that they did badly because minor episode costed millions on a tuesday (or whatever), then you go check and the fund was up 20 or 30% for the year, and the least paid employee probably took a 7 figures salary.
I understand why he needs to be overly dramatic, but it pi**es me off.
Overall, that's exactly the book I expected about someone/some entity covered with lifetime NDAs by anyone: mostly fluff/useless trivia
Skip it, don't fall for the peer pressure to having to read it because someone said he did it as well.
Rating: really liked it
I thoroughly enjoyed this book. It was exactly what I was looking forward to reading- the growth of quant-based trading in finance through the lens of arguably the most successful firm in the field.
It's important to note what this book is NOT about. Firstly, it certainly doesn't just trace the life of Jim Simons- only about 25% of the book is about him. This book is about The *Men* Who Solved the Market and about the people who make/made RenTech. Secondly, it's definitely not about trading strategies and doesn't talk about any "quant"/math that you wouldn't know already.
It's a book about mathematicians, about scientists, about their decisions, passions and motivations - and how a few unrelenting men with great intelligence came up with a good idea and worked on it for decades to create a new field, whose by-product was large sums of money.
It seems impossible to me that anyone who is purely motivated by money could pull this off, which is what I really enjoyed about these personalities who made RenTech. (Even though its eluded to many times in the book that Simons was business minded, which was supposed to reassure the reader that Simon's priority was just making money- his actions and decisions just didn't match up to that).
I really didn't care for the politics in the ending. Having to read about what Jim/Mercer did/supported politically honestly was probably the reason why I'm giving this book 4 stars and not 5. Another reason would be that I really wanted to know more- not just about the people but also their professional challenges and their ideas. I'll have to go look for that in another book(if such a book exists) but I'm satisfied with this read for now!
Rating: really liked it
I had always believed in the efficient market hypothesis. This book convinced me that I was wrong: it's not that there aren't inefficiencies to be exploited in financial markets, it's just that humans suck at seeing them. The same cognitive biases that create those inefficiencies in the first place also prevent us from exploiting them. We see signal where there is only noise, we anchor our expectations, we become emotionally invested in our choices. But the machine is immune to all that.
Zuckerman gets into a lot more detail about Renaissance's models than I expected him to. I guess by now there are enough ex-employees willing to share company secrets. Or maybe the company secrets they are willing to share are not that big anymore: using Markov chains to model price movements, looking for price ratios instead of absolute prices, etc. Whatever is happening at quant funds right now is probably way beyond any of that (convolutional neural networks that count cars in Walmart's parking lots, that sort of thing).
I was ready to roll up my sleeves and start modelling stuff, but fortunately I got to this point in the book first: "In the five years leading up to spring of 2019, quant-focused hedge funds gained about 4.2 percent a year on average, compared with a gain of 3.3 percent for the average hedge fund in the same period." Well, the S&P500 yields on average 9.8% a year (6% after inflation). For Simons to get his average 66% yearly return he had to hire a team of geniuses. I'm no genius, and I'm not in a position to hire any geniuses to work for me, so I guess I'm staying with index funds (except maybe for some "fun money").
Overall this is a well written, well researched book, and I got a lot out of it.
Rating: really liked it
Zuckerman is a superb spinner of complex stories, his latest book is no exception. Quantitative investing developed over the last 40 years as a result of increased use of mathematical formulas and large data sets. The theory assumed that the new methods would eliminate human errors.
The book concentrates on James Simons who began life as a distinguished mathematician and evolved into a very successful investment firm. The results of building these new models was phenomenal.
But Zuckerman also does a great job of explaining that even with taking out the human errors in investing does not eliminate the human conflict in firms. As the firm developed the egos of the math professionals did not stop rivalries.
Where I would fault the book when he gets into the politics of one of the key players and the tax policies of how to treat the kinds of hedge investments that are critical to the Simons style of investing. Robert Mercer, who became co-CEO of Renaissance Technologies when Simons retired became controversial because of his beliefs on a limited government and the effects of various policies including the Civil Rights Act of 1964. He was also a key funder of the Trump 2016 campaign (after Ted Cruz dropped out) and of the move in the UK for Brexit. But Zuckerman's descriptions of his actions lack nuance. I would have also liked a bit more discussion of the errors of Long Term Capital Management - which was another quant approach which failed horribly.
There is one other issue which some of the reviewers raise - Simons assumption is that there are patterns in markets which can be discovered. And his firm has spent several decades continuing to refine their models based on the increasing availability of data. The problem with the basic theory is that the patterns may be more apparent than real; some market behavior may be genuinely irrational - take a look at the last ten days of market performance at the time this review is being written. Warren Buffet's notion of Mr. Market (the irrational guy who decides market direction) may actually be truer than Simons and other rationalists would believe.
Even with those limits this book is a good discussion of how quant theory developed and who some of the major players in developing the theory were.
Rating: really liked it
An Excellent Biography, I enjoyed reading political factions within a company. It seems that it can be applied everywhere.
I would recommend this to people who are interested in Biographies, Investment, Wall-Street.
Deus Vult,
Gottfried
Rating: really liked it
A nice telling of the people behind Renaissance Technologies, although I would've liked more math and equations
Rating: really liked it
The story of the genius who is able to make huge profits through some foolproof formula or algorithm--except when he is wrong--is now so common that it feels like I've read this one before.
Rating: really liked it
Show me the money! Prove it, in other words, a major mantra in my financial services world and prove it they do. Renaissance Technology, despite the highest fees among major hedge funds has managed to return, net, over 39% per year after fees since 1988. How, you might ask, as you dive into this book to discover the secret. Yeah, good luck with that! With all the non-disclosure agreements in place you aren't likely to find out. The author has done a good job taking some bare facts and critical details and weaving a narrative around them. Basic thrust is that Simon, a mathematician hired other accomplished mathematicians to determine patterns that could predict stock market returns. Interestingly, despite the rapid trading, sophisticated models and wealthy drivers, the system he developed is right only 51% of the time. But that is all it takes to produce billionaires by the bushel. Some interesting side trips on the political side as Simon became a supporter of liberal causes and his partner Mercer virtually put Trump in office. So, as much for the general interest market as it is for professionals. No need to fear any math, those details tend to not be available beyond returns and assets under management and the discussion of IRS battles. (They used bank contracts to convert ordinary income into capital gains at much lower rates. IRS lost that battle, not sure why.) Anyway, well written by an experienced financial journalist so gets the 4 star rating.
Rating: really liked it
It's amazing to think that the biggest/baddest hedge fund of all time was not founded by Wall Street types but a math professor from MIT/Stony Brook who relentlessly pursued wealth, and assembled a team of math wizards and computer scientists from Cornell, Stony Brook and IBM.
From the book, it sounds like many of the models were Hidden Markov Models, and stochastic differential equations - which I never got to in my schooling, but maybe one day if I have the chance and discipline?
One guy Strauss spent his efforts gathering and cleaning data - and it was amazing that for a long time people only used the opening and closing prices as the data points for their models, until Strauss obtained the TIC data (or much more granular trading price/volume data). As a data engineer myself, I can relate to both the tedium as well as the pride one gets in wrangling messy data to make it usable for others.
I've read Zuckerman's earlier work The Greatest Trade ever on Paulson and Pellegrini's short of the housing bubble, which was great - and I'm considering picking up his book on the fracker oil barons.
Rating: really liked it
A story about numbers, markets, causes, money and ultimately humanityGiven the secrecy of Renaissance Technologies, this must have been a very difficult book to research and write, and shines light on a most successful investment firm, if not the most successful. What Zuckerman achieved however is to both explain how Renaissance went about creating its algorithms and training systems, but also the motivations and lives of the characters in the story.
[Spoiler alert]
What is fascinating however is the impact of money on the characters. How life kept happening with both random tragedies and own goals. How people grate each other in spite of having no material shortcomings. Regardless of how smart people are mathematically, it does not impart wisdom or even just kindness.
It is a challenging book. Challenging in that it questions pursuits. It is hard to argue that Renaissance Technologies has any social purpose, other than to make its employees rich. It must be highly frustrating for a bulk of the team that their monetary successes contributed to history changing events such as Trump and Brexit.
A book worth reading.
