Jim Simons proved the textbooks wrong, almost
bloomberg.com
bloomberg.com
Two big takeaways for his success -
1) He was pretty early, and quite contrarian, in betting on computer and quant strategies and thus took the “low hanging fruit” early on (def wasn’t low hanging back then when no one knew or believed in computer trades strategies)
2) From the book, Rentech’s main strategy was based on “reversion to the mean” - I.e “We make money from the reactions people have to price moves”. Trading on how you think OTHERS will trade and systemizing it (ex vol and momentum) is powerful but clearly doesn’t scale when you become the market yourself
And a bonus one - despite being a math genius, he basically was failing till he brought on others. He hired the right people (ie those interested in math not finance), created the right environment, took care of logistics, and pushed on a key insight (model to trade). He couldn’t have done it by himself.
Even in the early 1990s, Simons had basically checked out of the fund and was mainly doing venture stuff. He clearly made some good hires pre-1990s (I can't remember but the data guy clearly seemed to give them a huge edge over the competition, they clearly had data that no-one had) but it was that sequence of hires after this point that really elevated things: Peter Brown, Nick Patterson, Robert Mercer, etc. Very humbling. Of course, everyone will continue to think the strategies are the secret sauce.
Also, I think it highlights that quant investing starts out being very scalable but stops scaling quite quickly (and most similar firms hire people that, on paper, are very smart and get nowhere...so RenTech is the best example of scalability). At the top end, fundamental investing is still more scalable (which is what common sense would indicate).
As an aside, the article is totally pointless. Finance professors are engaged in an argument with themselves. They know they believe things that make no sense, and so spend all their time grappling with facts to fit them into their model. Humans do not reason perfectly, when you put a trade on you move the market, effects can last for ages (you have pure arbs that take years to close)...the whole discussion is just non-sensical, and any academic examination of finance should start from reality, not what theories are fun to teach. It is kind of tragic to see intelligent people do this to themselves...but some people just prefer Haskell to Python.
But I also disagree from the point that if physics did what you suggested we'd be no where at all. If they had to start with reality before producing useful models then we would of skipped pretty much all of modern physics today.
All models are wrong, but some are useful as they say.
These models aren't useful. Also, the saying is wrong. The reason why is that close to 100% of finance professors will quote that saying (srs, I think I have heard this 20-30 times now) because they use models that are wrong and not useful but this model seems to give them an intellectual reason for doing so: any "wrong" model could actually be good, according to this idea. But wrongness is neither nor there because wrongness for a model is utility, they are identical. The only point is utility. And the reason why these models aren't useful, as I have said already, is that they aren't used outside of academia. Their only utility is giving finance professors something fun to teach. And again, the solution is to build models from the way the world actually is (and btw, these are numerous...almost every successful investor, fundamental or quant, has a systematic process...but these models aren't fun to teach).
I'm not sure which models you are talking about - but models such as Modern Portfolio Theory, or Black Scholes, while inherently flawed have been massively useful in the real world. Claiming they aren't useful is simply not true. But again, you don't mention any specific models so it's hard to even know what you are talking about.
I mean all of them. Black-Scholes was used in industry before academia, and is only used in a heavily adjusted form (for example, option MMs have never used it as the only pricing input). MPT isn't useful: volatility doesn't describe risk to any degree (possibly as you move to the limit of retirement age...but then, not really), the empirical relationship is actually the inverse of that predicted by MPT (i.e. the model is not only wrong, it is misleading and will cause you to lose money), and it is easy to construct superior models that beat MPT models in every way (and even those aren't very good because they often use the same theoretical underpinning...again, most of these models exist because the subject needs to be taught in universities and needs to build on stuff learned earlier...the practical use is zero, which is why no-one really uses these theories...the only place I have seen them used at scale is in investment consultancies, and most of these places are clueless).
Many finance professors strike me as the kind of people who critique the design of a hammer without having ever built anything themselves. Every tool has perks and limitations, and the challenge of using that tool is to figure out what those things are and get them to bend to your favor. BSM is the lingua franca of the options market and can be tweaked in practice to accommodate many limitations (skew, event volatility, etc).
The point is to make money. If the tool helps you do that, then it's a good tool.
During the next three years, you keep evaluating the opportunities to reverse your transactions, but always calculate that you will make more by continuing to hold the short futures contracts and the copper. You thus end up in the arb for three years.
Commodities futures contracts are a very tangible example, but my understanding is that the pattern is much more general. Most futures arbitrage trades made by large multinationals are fundamentally these sorts of storage cost arbitrage and/or funding cost arbitrage. (Funding cost can be thought of as a storage cost for money/debt.)
For instance, my understanding is that trading stock index futures vs. a replicating basket of single-stock futures is usually a matter of finding ways to secure funding more cheaply than your competitors. In this case, your competitive advantage is fundamentally linked to time, and exiting early reduces your competitive advantage.
Not a pure ARB bc of dividend and interest rate risk, but it's close.
Nothing humbling about hiring a deceitful guy like Mercer. Of course, this being a technical website and everyone needing something to believe in there are people who say that this company's success is mostly based on its technical achievements (and on the people that helped implement those technical achievements), but looking at the character of people like Mercer that success is probably most likely based on stuff like insider trading.
It could be that smart businesspeople realize that employees can have diverse political views, and those views don't have to be at the centre of every discussion.
Any market-beating strategy will no longer work when the market adopts it. I.e. if you have such a strategy, keep it to yourself as long as practical.
However if your strategies are well known people typically won't pay you much (if anything) to manage their money, because a bunch of shops will be offering comparable results with the same thing.
Continuing with risk parity: there are walkthroughs of how this works with code and math available online: https://cryptm.org/posts/2020/08/01/parity.html
Note the alpha, beta, volatility and Sharpe measures comparing a straightforward risk parity strategy to SPY.
It's not controversial to anyone in the actual industry that you can beat the market on a risk-adjusted basis. Very often the techniques for doing that are well known and can be levered up to safely beat SPY on a total basis with less overall risk. What's truly difficult (and secret) is beating the market by several standard deviations.
Are there any mutual funds or ETFs which follow it?
https://www.portfoliovisualizer.com/backtest-portfolio?s=y&t...
(Disclaimer: this is not an endorsement)
the downside to this particular fund is the extreme turnover in the fixed income component (only suitable for tax-free accounts) and the interest rate risk; the fund could underperform SPY in a world with increasing interest rates (which is where many traders believe we are now)
Almost all people will immediately balk at the idea of using leverage in investing, despite the higher backward-looking risk adjusted returns. This is especially true when it might be statistically better, but in various stretches (eg. Last March) it does worse.
This simply isn't true. Sometimes there are dollar bills on the ground. It takes a lot of years for everyone to pick them all up.
Keep in mind that the efficient market hypothesis disproves(TM) starting a successful business just as well as it disproves finding a successful trading strategy. ie: if that were a good startup idea, someone would have already started it, so it can't be an opportunity any longer. EMH is a useful tool but in reality it takes a long time after a fundamental shift creates an opportunity for it to be arbitraged away, and sometimes the opportunity ends due to another fundamental shift, not due to people arbitraging it away.
Keep in mind that changing an investment strategy is simply changing the algorithm used. If I was running a fund returning 7% yoy, and yours was returning 10% yoy, you bet I'd be telling my staff to try out your algorithm.
Magellan was the biggest fund in the world until other funds adopted their innovations and it pretty much reverted to the mean.
I don't know anything about this blog^[0] , but I wanted to find some charts comparing a well known risk parity fund to more general portfiolios. Trusting that they're accurate, it looks like risk parity performed great in 2008, but hasn't beat the market over longer periods of time. Even measuring from 2007 to late 2020, it appears a 60/40 bond fund has beat it substantially.
Thus I'm not really sure what grounds there is to say risk parity beats the market. Certainly not by all measurements. I'm not a huge financial guy though, maybe I'm misunderstanding something?
[0] https://www.evidenceinvestor.com/the-all-weather-portfolio-e...
Sorry, I meant you don't need to find a book, you can find the info online.
The problem for me going forward is that these returns for the last 40 years have been do to falling interest rates. Can the rates keep falling? A little bit more. Will they go negative like some other countries? Maybe? But at some point I have to wonder if this strategy is still viable.
Also the data back then was much harder to acquire. Bloomberg didn't even exist at the time.
There was no Bloomberg but there was compuserve and AOL and usenet and various other forms of financial forums.
1. Very research oriented
2. Their strategy does not scale. Fund has limited size.
3. They use some kind of arbitrage. No high-frequency trading, but longer.
4. Their current strategy must be kept secret for it to make money and it changes over time.
This is why "I have discovered fool proof way to beat the market" sales pitch is always a hoax. If someone has it, they keep it secret and make money. If everyone has it, it has no value.
3. Every strategy can be viewed as a sort of 'arbitrage'.
4. Applies to every strategy.
No, it can’t. This is a bastardization resulting from the proliferation of “stat arb” to mean mispricing and is truly a misnomer.
Maybe a clearer comment would have been:
"If rentech's strategy can be considered an arb, then every strategy can be considered an arb".
Yep, agreed.
I think all of those points are the same for all Quant shops. All of them are at stagnant AUMs now for a reason. I mean this article seems like even GPT-3 could have written it.
The classic example being the arbitrage between on-the-run/off-the-run treasury spreads. Bonds in the current series ("on-the-run") tend to trade at a slight premium to equivalent bonds that were issued earlier ("off-the-run"). Theoretically this is a perfect arbitrage. Short sell the expensive bonds and use the cash to buy the cheap bonds. Sit back and collect the spread.
However, you have to understand why the OTR spread exists in the first place. Investors value the higher liquidity that comes with the current series. Consequently during liquidity crunches, the spread will blow out to multiple times higher losses. In these scenarios arbitrageurs face deep mark-to-market losses, margin calls and investors withdrawals. This very strategy was (one of) the "pure arbitrages" that blew up LTCM in 1998. The market can stay irrational longer than you can stay solvent.
So, even what looks like a simple mechanical science in practice is an art requiring experience and judgement. You have to know the right leverage to use and at what times. You have to keep your finger on the pulse of the market and have a sense of when spreads are too tight or loose given macro conditions. You have to secure good funding relationships. You have to be smart about keeping powder dry so you can buy at cheap prices during dislocations. And so on.
I have a feeling that the further you get from math, the more restrictive the intellectual atmosphere becomes. Math, as a tool, makes it much easier to successfully and productively stray from consensus since the opposition would require "better math" (to put it crudely) to defeat it, and math is math. There's no way around it.
One example: Myself and two colleagues took two weeks out of our studies to build an autonomous boat after we realised there was enough spare parts lying around the lab to do so. The idea originally came up over beers with the lab supervisor. I think he bet us £10 we couldn't do it in that amount of time.
I'm sure it varies a lot between labs and Universities, but it measures up to my experience.
However, the stock price of Zoom Technologies Inc (stock ticker ZTNO, but at the time ZOOM), a completely unrelated company based in China, went up by something like 1000%, due to what is generally believed to be uninformed day traders who saw "Zoom Technologies" and assumed it was the videoconferencing company.
Mostly likely based on what grounds? Rentech was very profitable for decades before they ever started their public funds.
> Nobody knows how Rentech makes money
There's nothing extraordinarily special about Rentech's returns, they just employ short-term stat arb type strategies that require relatively little capital to execute, so if you express their returns as a percentage of invested capital you get an eye-popping number. But it's not comparable to the returns that a traditional buy-and-hold fund makes (in particular because those returns don't compound). There are plenty of other quant shops and prop trading firms that would make huge (>Rentech) annual returns if they attempted to phrase their earnings in those terms, but they typically don't, because if you don't need a lot of capital then you don't need to raise money from the clients and outside investors (can just trade the partners' money) and you don't need to brag about your returns in public.
Also, you've misunderstood the charges on Bluecrest. Platt may also have been doing the scheme you described, but that is not what the SEC fined him for. He would be in prison had he been charged with what you allege.
Medallion is not unique, there are other firms with comparable win record (Virtu, a Czech one, an Israeli one and a couple of British ones at the very least) but only 5-10% of the size; of all these, only Virtu is public and verifiable, the others aren’t but you can find people who will confirm it off the record.
People were begging Simons to take money. He wouldn’t let them into medallion (why should he share?) but he did start a higher-risk, lower-reward business and let’s people into that.
As far as I can tell, the commonality among those always-winning firms is high frequency low latency algorithmic trading. These days it takes millions of dollars per month just to pay for the infrastructure you need to be able to be competitive - and then you also have to have some nontrivial edge, without which there isn’t all that much profit in having low latency.
What’s Virtu’s or RenTexh’s/Medallion edge? I don’t know. In the past, they seemed to like people with speech/hmm background. But that was before the DNN / differential computing revolution. I have no idea where there edge is now (and actually whether hmm was their edge in the past - but it did seem to be quite common background among their recruits)
That said, they may or may not be tax frauds as well - I have no idea. But I don’t see any reason to suspect they are doing retroactive allocation of successful trades.
There are hundreds of other quant firms with public records. RenTech has better numbers because they stayed smaller. It is difficult to generalise but firms either grow assets to a point where the market moves against them when they trade/returns drop or they go into strategies with lower returns at scale (btw, both things are common outside of quant too). They aren't doing HFT. Some quant strategies are tangential to HFT, for example front-running news was a big strategy in the early 2010s...it is somewhat latency-based but is still distinct from HFT, which tends to refer more to making markets.
The book says they tried hmm/speech stuff and it didn't work. It is likely they are doing more complex things now but Nick Patterson said they were using linear regression for most of the 90s. Generally speaking, this is a common misconception: people believe that because the results are good, the model must be more complex. This reflects how university courses are organised but the real world isn't like that (one big advantage that RenTech had was data, they had data that no-one else had for a very long time, another big factor is execution...these kind of practical edges are far more important than people think).
Also, they use a ton of leverage...their returns actually compare pretty well to what fundamental managers can achieve outside of a public fund. Having investors is a significant limitation because they will often force you to behave in a way that reduces returns (i.e. redeeming at the worst time, asking for risk reductions at the worst time). The structure is very kind to gross returns.
Retroactive reallocation of successful trades is very old. The SEC cracked down on this in the 80s, it is very easy to prove, and it is very unlikely that someone doing this would hire a bunch of scientists and then give them a bunch of equity in the fund...it doesn't make any sense.
A linear model, if the inputs are e.g. squares and variable products, is a quadratic equivalent.
A logistic regression yields a linear model; you could tell people it’s linear regression and they’ll likely believe you, but won’t be able to replicate.
There’s a huge issue with itrelevant inputs and how to identify them - Emanuel Candes has done a lot of work on that, as did Rob Tibshirani.
Saying “linear models” is saying little more than “using math”, even if that’s true, and even saying “linear regression” doesn’t give much information about what is actually being done.
The bottom line is that the decision boundaries are usually simple and often have linear form - but the variables in that linear form are not raw data, but rather nonlinear transformations of it (e.g. order imbalance)
And that is why I like the idea of a "trade arbitrarily slowly with limited price change" market versus the current approach of "trade fast with an arbitrary price change" market.
See https://news.ycombinator.com/item?id=24760841 for an explanation of how the trade arbitrarily slowly market could work. Under normal conditions, it would look a lot like the current market does. Except that you're paying less to the HFT folks.
What I didn't describe there is that you could even have a chain of slower and slower markets. With a maximum rate of price change varying from 1% per day to 1% per minute. With the idea that ordinary folks would trade on the 1% per minute market while large institutional orders would be likely to go in the 1% per day market. (And when the price of two markets cross, open orders on the one can match as open orders on the other.)
My understanding is that
a) either there is a huge price lag to the fast market and say you offer some good cheaper that the fast market. Then the HFT would come and buy your stuff and sell it more expensive on the fast market. Until
b) your market becomes illiquid.
In both cases there is little incentive to use your market. It would only make sense for huge trades (similar to take over offers, etc).
If the slow market is outside of the bid-ask spread, then HFT will be happy to move the price to the bid-ask spread. So you're liquid in the direction that moves the price to where it needs to be and not liquid in the other direction.
If the slow market is inside of the bid-ask spread, then HFT is likely to be willing to buy/sell on the fast market and complete the other half of the trade on the slow market. That is, they don't snap up the slow order right away, but they will snap it up to complete trades. Getting a guaranteed trade is better than holding the stock and not trading. This gives liquidity in both directions.
The incentive to use this market for smaller orders is that you are likely to get a price somewhere between the bid-ask spread. I don't have recent data, but a decade ago the bid-ask spread for small stocks was around 2%, lowering to 0.6% for the top 20% of stocks. (As you go to the behemoths, the spread drops farther.)
I'm sure it is smaller today, but if you are a day trader, that spread is a hidden tax that is going to kill you over time.
Even if nobody used the slow market, HFT would guarantee that you get no worse than the spread on a market order. But when traders use the slow market directly, they bypass the HFT middleman and save themselves money.
"liquid in one direction" is a nice way of saying that no trades are happening, which is to say illiquid.
> If the slow market is inside of the bid-ask spread, then HFT is likely to be willing to buy/sell on the fast market and complete the other half of the trade on the slow market.
This seems to hinge on an unrealistic model of HFT as perfect-arbitrage machines that need to immediately close positions. In fact, it would be quite surprising if they were willing to do this, based on how they currently behave. If they were willing to close the loop like that, they would equivalently be willing to do it on existing exchanges, which they could do by posting an order inside the spread (which would, of course, shrink the spread). The fact that that is described as "inside the spread" is a pretty clear indicator that they are not doing this.
> The incentive to use this market for smaller orders is that you are likely to get a price somewhere between the bid-ask spread.
The reason that you don't currently get filled "between the bid-ask spread" is that the whole point of the spread is that it is the region inside which no one is currently willing to trade. If they were, the spread would be smaller. By what magic are they willing to trade inside the spread on your exchange, but not on traditional ones?
Similar principle... IIRC it delays execution to try and prevent HFT. A big part of Flash Boys by Michael Lewis was chronicling the history of what led to this exchange being created.
They take away a lot of the tools that HFT uses to increase their edge, simplify the ordering structure, and try to make as many trades as possible to be between traders instead of the HFT market makers. But they have not fundamentally redone the structure of orders such that prices are guaranteed to move slowly at the cost of indefinite delays in execution.
I don't believe Medallion would be classified as a high frequency trading operation.
The $5 billion in unpaid taxes is a small fraction of their total returns. So again, not a great explanation.
Everything I've read (and you obviously have to take it with a grain of salt) doesn't agree with this assessment. The core fund trades commodities and stocks/options in a pair format with short holding times. To make their public fund, they needed to adopt more scalable strategies which meant longer holding periods.
So it's not a matter of choosing/assigning ex post - they are fundamentally different approaches. They said up front that the public fund wouldn't replicate the internal fund. Whether people listened to them or not is another question.
I get that we should be skeptical, but by the same token I don't think you can says fraud is happening without any evidence. Yes, they had a tax case - but that was related to their derivatives contracts and the tax handling of them. Clearly they were wrong on that - and they've stopped using them - and still been up huge after that.
I'm just not clear why we should assume fraud just because they are successful. To me, it looks like tiny profits magnified by enormous leverage - but with holding periods and market neutral positioning to reduce risk.
The larger "public" funds are for strategies that can't be used at Medallion but still provide value for others.
> Noah Smith himself worked at SUNY Stony Brook, which is heavily funded by Simons.
Simons is very clear that his money is to go to the Math and some science departments. Noah is out of their Finance department.
Even if you take the senate report at face value...it only explains 1/5th % of their yearly returns.