My impression was that they were doing sophisticated sparse signal reconstruction and then applying some pattern induction algorithms against those signals. The former was, to the best I could discern, absolute state-of-the-art; the latter was merely competent (I've never talked to anyone that was exceptional at this bit). It has been a while but my impression was that a real strength was that this process was highly automated and general, so it could be thrown against almost arbitrary data sources. It is not difficult to imagine how one could build a sustainable and significant edge with this capability. I could be wrong but I don't think I am that far off. Very good math brains, even compared to many of their peers, based on my limited exposure.
I find their performance believable, given the above.
EDIT: I'm not saying that's everything, but automated signal extraction is a major part of their secret.
Of the people I know who work at (or used to work at) RenTech, one actually joined after working at the NSA. His PhD thesis was a joint collaboration between Harvard's physics department and the NSA.
Likewise, search Google Scholar for "@rentec.com", or "Renaissance Technologies."
Many optimization problems that arise in machine learning can be viewed as minimizing a particular Bregman distance subject to affine constraints (I'll call these "Bregman distance optimization problems").
In [1], the authors develop some quite general and widely applicable convex analysis type results for Bregman distances and use those results to give the technique of "Auxiliary Functions" which can be used to derive and prove the convergence of algorithms for particular Bregman distance optimization problems.
In [2], the authors show that finding the optimal parameters of two quite different approaches of binary classification, AdaBoost and Logistic Regression, can both be simultaneously viewed as the same Bregman distance optimization problem (with slightly different initial parameters). They then present several algorithms for solving this unified problem (and thus, for optimizing both AdaBoost and Logistic Regression), and prove the convergence of their algorithms with the method of Auxiliary functions developed in [1]. This paper was the first general proof of convergence for AdaBoost which was proposed by Yoav and Schapire (one of the authors of [2]) and earned them the Gödel prize in 2003.
If you don't mind me plugging in some expository work of mine, I wrote an essay ([3]) giving an introduction to Bregman distances and is pitched at a lower level than [1] and [2]. The end of the first chapter discusses in particular the general Bregman distance optimization problem, setting it up in the same framework as [1] and [2], and the final chapter presents the algorithm and proof of convergence originally given in [2] but hopefully with a slightly simplified presentation due to focusing only on the Logistic Regression case of the algorithm.
In case you want to play around with it, I have implemented the algorithm from [2], as well as a related algorithm from [4] which incorporates L1 (Ivanov) regularization into that algorithm, both being available at [5]. The middle chapters contain brief discussions on the relation of Bregman distances with Exponential Families, and on Generalized Linear Models, which are relevant to the overall purpose of the essay but not so closely related to the content of [1] and [2] and may safely be skipped.
If you generally enjoy the types of problems discussed in [1] and [2], you might also enjoy some of these papers: [6] [7] [8] [9].
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[1] - "Duality and Auxiliary Functions for Bregman Distances". Stephen Della Pietra, Vincent Della Pietra, and John Lafferty, 2001.
[2] - "Logistic Regression, AdaBoost and Bregman Distances". Robert Schapire, Michael Collins, Yoram Singer, 2001.
[3] - "Optimisation of Bregman Divergences". Ragib Zaman, 2018. https://github.com/RagibZaman/mathematical-optimisation/blob...
[4] - "Bregman distance to L1 regularized logistic regression". T. Huang and M. Gupta, International Conference on Pattern Recognition, 2008.
[5] - Notebook containing implementations of Bregman divergence based algorithms for Logistic Regression from [2] and [4]. https://github.com/RagibZaman/mathematical-optimisation/tree...
[6] - "Legendre functions and the method of random Bregman projections." H. Bauschke and J. Borwein, Journal of Convex Analysis, 1997.
[7] - "Inducing features of random fields". Stephen Della Pietra, Vincent Della Pietra, and John Lafferty, 1997.
[8] - "Statistical learning algorithms based on Bregman distances". Stephen Della Pietra, Vincent Della Pietra, and John Lafferty, 1997.
[9] - " A maximum entropy approach to natural language processing". Adam L. Berger, Stephen Della Pietra and Vincent Della Pietra. Computational Linguistics, 1996.
In a world where all their statistical arbitrage has ceased to be viable, financial professionals believe that the outsize returns of the Medallion fund in recent history are siphoned from their institutional funds via shell games.
Then, their famous ability to make money even in down markets is attributed to how they smooth out an extremely profitable short term trade, that may have occurred years ago, over the course of many years. This sort of surfaced in their tax avoidance lawsuit too.
You too can make a "Medallion Fund." First, make a venture investment in something that turns out to be Facebook. Keep that equity secret, even when Facebook goes public. All that time, say that your fund has gained 15% year over year, even during a recession. Do this for years until you have taken your 2% management fee to your liking. You've turned your 200x return that happened all at once into something that looks like the world's greatest hedge fund. Lawfully of course.
1) Those tax advantages can only improve returns which are already fundamentally strong, and
2) There is no "smoothing" effect achieved; the options baskets do not defer returns for years at a time.
I get that the cynical take is, as ever, the attractive one on Hacker News. But speaking frankly, what you're saying doesn't actually make sense. Among other problems with your explanation, there's a straightforward wrinkle. While it's not available to the general public, other institutions like Bloomberg and WSJ have had (and still have) access to audited attestations of Medallion's track record over a timespan of 25 years.
"But we look at anomalies that may be small in size and brief in time. We make our forecast. Then, shortly thereafter, we reevaluate the situation and revise our forecast and our portfolio. We do this all day long. We're always in and out and out and in. So we're dependent on activity to make money."
Renaissance essentially attempts to predict the future movement of financial instruments, within a specific time frame, using statistical models. The firm searches for something that might be producing anomalies in price movements that can be exploited. At Renaissance they're called "signals." The firm builds trading models that fit the data.
When the trading starts, the models run the show. Renaissance has 20 traders who execute at the lowest cost and without moving markets, crucial requirements for quant investors trading on narrow margins. But the models decide what to buy and sell. Only in cases of extreme volatility, or if the signals appear to be weakening, does the firm sometimes manually cut back. Says Simons, "We don't override the models."
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"We search through historical data looking for anomalous patterns that we would not expect to occur at random. Our scheme is to analyze data and markets to test for statistical significance and consistency over time," says Simons. "Once we find one, we test it for statistical significance and consistency over time. After we determine its validity, we ask, 'Does this correspond to some aspect of behavior that seems reasonable?'"
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Many of the anomalies we initially exploited are intact, though they have weakened some. What you need to do is pile them up. You need to build a system that is layered and layered. And with each new idea, you have to determine, Is this really new, or is this somehow embedded in what we've done already? So you use statistical tests to determine that, yes, a new discovery is really a new discovery. Okay, now how does it fit in? What's the right weighting to put in? And finally you make an improvement. Then you layer in another one. And another one.
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Everyone in the company read the book about LTCM. It makes you wary in a general sense. Our approach is very different. We don't start with models. We start with data. We don't have any preconceived notions. We look for things that can be replicated thousands of times. A trouble with convergence trading is that you don't have a time scale. You say that eventually things will come together. Well, when is eventually?
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https://www.institutionalinvestor.com/article/b151340bp779jn...
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"Have an open atmosphere. The best way to conduct research on a larger scale is to make sure everyone knows what everyone else is doing... The sooner the better - start talking to other people about what you're doing. Because that's what will stimulate things the fastest. No compartmentalization. We don't have any little groups that say. this is our system and we run it we get paid because of it. We meet once a week - all the researchers meet once a week, any new idea gets brought up, discussed, vetted, and hopefully put into production. And people get paid based on the profits of the entire firm. You don't get paid just on your work. You get paid based on the profits pf the firm. So everyone gets paid based on the firm's success."
In sum, the secret is:
"Great people. Great infrastructure. Open environment. Get everyone compensated roughly based on the overall performance... That made a lot of money."
I have no specialist knowledge, btw, I'd sincerely like to know!
The Medallion Fund is kept fairly small so it can capture these items without changing their prices substantially. That is, the fund owners have to take their 40% return each year out of the fund.
The Medallion is for the employees money. That reminds about salary payment schema in Russian banks in 199x (don't know for today) - employees got to open very special, employees only, accounts paying extremely high, many times beyond the market, interest. The bank account interest got beneficial taxation for the employees, and the bank didn't have to pay various taxes, like social security, etc., which an employer would normally pay on salary. Of course how much an employee could put into such an account had a limit specific for a given employee, and thus the employee did have to regularly take the money out of the account.
If memory serves, in the aforementioned podcast Gregory mentions that the RenTech generally holds most things for a few days (sometimes a few hours). However, they don't engage in HFT or HFT-like trading. This was surprising to me as I assumed it was all reasonably short holdings (relatively speaking), although I knew they weren't a pure HFT firm.
I also seem to recall Gregory mentioning there's some kind of running joke internally that their trading systems aren't nearly as good as they should be (or like what you would find at HFT firms). Given the intellectual and monetary heft within RenTech perhaps that's a bit of false modesty on their part.
I'll be interested in reading Gregory's book as he does seem to have put together a lot of novel information on RenTech. However, he does seem to suggest that very little of the day-to-day workings of the firm will be explored, which would obviously be immensely interesting.
EDIT: RenTech has several funds, it should be noted. Some of which still take outside capital. What I've said above may have only been applicable to the Medallion fund.
Renaissance has always put massive personnel and technology investment into its data processing and analysis pipeline. But there is no "automatic inference" generation. It's not so much brute forcing alpha as it is streamlining the process of hypothesis testing for research scientists so that strategies can be very rapidly generated and examined.
Automatic inferences would be susceptible to two major risks. First, you'd run into spurious correlations at the dimensionality of data we're talking about. Those spurious signals would have to be pruned, significantly reducing any advantage.
Second, you'd decouple the strategy generation from financial domain expertise. The strategies are not developed in a vacuum - contrary to popular belief, quant trading firms do apply financial acumen.
More to your specific example, I've also worked with the alternative data you're talking about and it doesn't offer automated inference generation. You implicitly have a hypothesis (or several) in mind when you're working with things like credit card transaction data from Yodlee or Second Measure.
Automation is a continuum. What you're talking about is automating time series analysis. I never said you can't do that.