I am not sure I understand this argument. The article states the "laborers" in question are given a choice as follows: submit to unpaid forced labor else go to a CCP reeducation camp. Nowhere is it stated those subjected to this choice have the opportunity to take advantage of rising living standards.
Because lulu is judged on earnings and mirror is effectively a saas company that needs to achieve breakaway scale to own its market. My hunch is lulu doesn't have the stomach for the cost of acquisition required to get mirror there.
- Rubicon by Tom Holland
- SPQR by Mary Beard
- Dynasty by Tom Holland
- Caesar by Adrian Goldsworthy (more of a bio on Julius C, a bit drier than the above, has section on Alesia)
Thank you for the recommendation of Rubicon. I enjoyed reading through this thread so I thought I'd take you up on your recommendation. It was this first history book I've read in probably a decade, so I wasn't sure if I was going to enjoy it but I absolutely loved it.
I remember reading the opening few pages and chuckling out loud:
“Why is the Deliverator so equipped? Because people rely on him. He is a roll model. This is America. People do whatever the fuck they feel like doing, you got a problem with that? Because they have a right to. And because they have guns and no one can fucking stop them. As a result, this country has one of the worst economies in the world. When it gets down to it—talking trade balances here—once we've brain-drained all our technology into other countries, once things have evened out, they're making cars in Bolivia and microwave ovens in Tadzhikistan and selling them here—once our edge in natural resources has been made irrelevant by giant Hong Kong ships and dirigibles that can ship North Dakota all the way to New Zealand for a nickel—once the Invisible Hand has taken all those historical inequities and smeared them out into a broad global layer of what a Pakistani brickmaker would consider to be prosperity—y'know what? There's only four things we do better than anyone else:
I wonder if anyone has insight into how they have been able to do this consistently in the modern era of quantitative trading (this article had scant detail)? His returns are such an outlier and strategies such a closely guarded secret that they leave people on Wall Street in awe.
I have anecdotal insight. Many years ago I met with Jim Simons a few times. He had taken an interest in some of my theoretical computer science research (by referral, it was never published). I don't know any details of their strategies but one could infer it from their specific theoretical interests in those conversations.
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.
It is also impressive how many people there have intelligence agencies connections. And Medallion , their employees fund, underpaying 7B of taxes by misrepresenting short term as long term gains is very impressive too.
That's right. A lot of people are aware that RenTech scoops up talent from math and theoretical CS departments. But it's less well known that many of Simons' old colleagues from the NSA also contribute math and CS talent by referring them to Simons.
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.
To my knowledge, all he has ever said on the subject is: "I think people would be quite surprised if they knew how simple our methods are". You probably won't ever hear more information than that, until their strategies stop working.
Flip side to this - I remember studying a series of papers in statistics/optimisation/machine learning with highly non-trivial content published between 1998 and 2008 by the Della Pietra brothers. I had assumed they were mathematicians/statisticians at some major research university. At the bottom of one of these papers it had some personal blurbs which stated they had both been at RenTech since 1995 working on "statistical methods to model the stock market", which surprised me greatly given the amount of research they output. I assume the content of those papers were applied to their strategies, in which case there would also be a good amount of people who would be surprised if they knew how advanced their methods are. I say this especially in comparison to the "quant" strategies I know many other trading firms use/have used, which are actually often quite simple. Indeed, at some places they seem to think any systematic/automated strategy is "quant"...
You can find a bunch of papers published by people at RenTec. Search MathSciNet for "Renaissance Technologies" as the corporate affiliation for the author.
Likewise, search Google Scholar for "@rentec.com", or "Renaissance Technologies."
As throwawaymath mentioned, it's not too hard to find papers written by them, but I'll highlight some papers that I found particularly interesting.
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].
--------------------------------------
[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.
[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.
No, that wouldn't work. The options basket strategy you refer to did have nontrivial tax advantages, but
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."
...
"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?'"
...
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.
...
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?
"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."
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.
I work at the intersection of quant finance and fundamental analysis, it can absolutely be automated. The question of what can be determined from raw data like credit card transactions and mobile phone locations is a whole topic in itself, but thinking that you need manual intervention to trade on those signals is completely misguided and its a waste of time for me to argue with you
I didn't say you need manual intervention. I said you cannot do automatic inference generation. What you're referring to does not provide automatic inference generation, i.e. you cannot brute force hypotheses. That's why you still employ researchers.
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.
"you cannot brute force hypotheses", this isn't really true. Credit card data has notorious gaps and bias, but that doesn't mean that an algorithm cannot determine and make decisions about certain situations within that data. For example, if I receive a daily feed file of walmart transactions and the data is increasing in some kind of confidence measure that walmart will beat earnings, I stand to make a good sum by jumping into the market before competitors. It's common that all competitors are aware of the situation, aware of the possible alpha, and competing on speed/accuracy for it. So the superior ability of my model to take a calculated risk from incomplete data (as well as combine other data sources) is one way for me to make money. I may build a model of the common structure of the transactions, ensuring that any signal coming from the data is a real signal, and not one coming from one of the many data quality issues. In the case that my data quality classifier is pushing out high confidence, the result is saying earnings will beat, and other data sources are saying the same, then my model buys. Completing this kind of analysis by hand is too cumbersome (for my usual length of holding period), the money is in who gets there first. There are many ways, some more conservative.
In what timescale though? There are huge differences between the timescales of "realtime" (say HFT), a second later, a minute later, an hour later, a week later and so on. Do they operate at all of them?
I have no specialist knowledge, btw, I'd sincerely like to know!
If you're interested in this, you'll likely enjoy Gregory's interview on Masters in Business (a Bloomberg podcast) from last Wednesday. Also, Gregory's book will be out in a few days.
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.
From what I recall, their approach is mostly what you might call "special situations." That is, their analysis looks for significantly incorrectly priced items, and purchases/shorts them.
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.
>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.
Anecdotally I have to agree. Having lived in Manhattan for the past decade, I have seen more restaurants close in the last two years than the previous eight. Most of lower Manhattan's retail footprint is filled with chains (Starbucks etc) rather than neighborhood restaurants.
"Players in the crypto ecosystem will be shocked, shocked to know it got this bad, this quickly, but this has been an open secret for over a year."
----
I'm not entirely in agreement the crypto world will be shocked tether is insolvent. As long the the fiat gateways exist to exit the system with gains intact, most could care less about tether. It's musical chairs.
See Redoubt's comment. The use of "shocked, shocked" is meant ironically, in reference to a scene from Casablanca. In general when you see the word "shocked" doubled like that it basically always means this.
Yeah, people have been saying that the Tether stuff is super shady and just waiting for the bubble to pop for the better part of 3 years. It's very much been an open secret.
This is pretty revisionist. My own experience lurking in crypto communities (and reading the replies to @Bitfinixed), was to insist that everything was fine and furiously deny that Tether was a scam, and accuse me of being a dirty fiat shill for pointing out that Tether was a scam, right up until Tether starting admitting under oath in court that they were never backed by reserves, at which point the conversation switched to "we knew it was shady all along".
This sort of thing is pretty common in Bitcoin land. Mt. Gox was also totally fine, nothing wrong, how dare you accuse them of wrongdoing, right up until they declared bankruptcy and then suddenly everyone had known all along they were insolvent and only a fool would've put their money there.
Both viewpoints exist, loudly. You'll find plenty of people today that say that the Tether stuff is fine and completely ordinary, and if it hasn't popped by now it probably won't. (Hell, you could read my original comment in this thread that way.)
What changes is which side you look at. Think of it like a Rorschach test: there is no truth, but there's a smattering of contradictory evidence that can be interpreted both ways. Your brain can't deal with the contradiction, and then picks a side based on the preponderance of evidence it sees. It can flip sides if strong new evidence comes out, but in general the brain discounts evidence that contradicts its initial hypothesis and amplifies evidence that confirms it.
Still missing the context on that, because AFAICT the reports seem to show that it was at one point 100% backed.
It's very possible that I'm missing something, since this isn't something I've paid a lot of attention to (as it always seemed obvious that it would eventually end up in this state).
Suppose that you became a bank. I create an account, and deposit $100 into you.
You take that $100, keep $20, and then lend the other $80 to a heroin-addled homeless person. He swears he'll give it back next year, plus interest. And gives you a receipt and everything.
Now, suppose that you are dragged up into a courtroom, over your mishandling of customer funds. You point out that no funds were mishandled - you have $20 in cash... and an $80 IOU from a junkie. You are fully capitalized! Solid as houses!
Reality: You're not. Any independent auditor with two brain cells to rub together will not value that $80 IOU at face value (The chap is highly unlikely to pay you back.) Your bank is actually insolvent.
Tether Reality: Bitfinex/The Tether Corporation played a shell game, such that they are '100% capitalized' if you add up their cash reserves + IOUs. The IOUs aren't worth the paper they are printed on.
It's possible that at some point in the distant past, they were 100% capitalized, but given their resistance to external audits, it's highly unlikely that it has been the case.
The most amazing thing about this entire adventure is that USDT/USD still trades at par. It seems like BitFinex could host a press conference, with the entire leadership team wearing T-Shirts that say "WE STOLE ALL YOUR MONEY", and it wouldn't move the market (The 'bitcoin enthusiasts' would obviously interpret it to be a sarcastic dig at all the bad publicity surrounding them.)
I agree they weren't fully capitalized after they lent funds out, but the poster I was responding to was claiming that their filings prove they never were fully capitalized as they had long claimed.
It appears to me that instead, their filings are strong evidence against the poster prior claims-- suggesting instead that they were at one point fully capitalized (and are not any longer, of course).
The thing with a Schrodinger's scam is that it really doesn't matter whether or not the scam was fully capitalized.
When you issue a full reserve currency, without any of the necessary oversight required to run one (Like... An independent auditor...) you're committed to operating in a manner indistinguishable from a scammer.
It's possible that they were fully capitalized for the first twenty minutes of Tether's launch. It is possible they were fully capitalized for the first twenty days. Or the first twenty months. Without any kind of proper bookkeeping - which was a deliberate decision on their part[1], we have no idea. And it doesn't matter. [3]
[1] The most charitable interpretation of why they made that decision was that their long-term plan for keeping Tether running would be under-the-table money laundering through Crypto Capital, or its ilk.[2]
[2] [3] Mind you, this means that the entire implementation of Tether is unsustainable! It doesn't matter that they are sitting on a multi-billion-dollar, 100% capitalized reserve if their customers can't ever redeem Tether for USD! Such a situation is indistinguishable from having a 0% capitalized 'reserve'.
Crypto communities are a lot more credulous than financial communities. Every institution I ever talked to had approximately zero trust in tether. Some of them chose to do business with bitfinex anyway. I'm guessing they regret that decision enormously
You mean you've been swarmed by comments from a loud minority. Tether supporters employ large troll armies, to make it appear to be a popular opinion while it might not be.
> As long the the fiat gateways exist to exit the system with gains intact
Basically the entire article points out that this is in fact impossible, only the first ones to run will be able to do this. Much like a bank run, there is not enough cash to back a tether exodus.
It's like 2007/2008 again. Everyone savvy knew there was fraud in the mortgage market at the retail level but they didn't know how pervasive it was. To some extent the Chinese commercial real estate market is the same way: nobody trusts the numbers but no one is sure how bad it is. The author is right that there is this pressure to win the money back and that this is where things can get really out of control. Of course, there is a remote chance they can pull a rabbit out of a hat and make enough money legitimately to pay back all the people they owe, but I doubt it.
Myclobutanil is a fungicide that cannabis growers use to prevent mildew. Traces of it being found in oil indicate something needs to be done to prevent its usage at the growth, storage, and transport stage of the supply chain. This is probably more easily done if Marijiuana is legalized federally and heavily regulated. Given that would take an act of Congress, I fear that sweeping vaping regulation will kill the industry, robbing combustible cigarette users of a much safer alternative to nicotine delivery.
Myclobutanil is illegal for licensed grows and no one with a license would ever risk it, as it's 1) criminal and 2) would immediately invalidate their license. Unfortunately the crazy high taxes have created a robust black market where unlicensed extractors don't care about the bio-accumulation that occurs during CO2 extraction and/or cannot purge the byproducts correctly.
The distillation race was never going to end well and the next shoe to drop will certainly be on the quasi-legal CBD/hemp scams which -- if you think these tests are bad -- buckle up.
