I’ve found RenTech to be fascinating over the years and highly recommend the book on it “Man who solved the market”
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.
I actually found the last part was my main takeaway.
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.
Not sure what Haskell has to do with it. I've written algorithms that take in 2D images and produce depth maps with live visualization in OpenGL using Haskell. It's incredibly practical once you learn it.
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.
"once you learn it"...yes, everything is incredibly practical once you remove all the disadvantages. The point is: some people prefer complexity for complexity's sake, and this doesn't work well in a team environment (where the "once you learn it" part becomes quite relevant, one person who prefers complexity for complexity's sake will take down the whole group, not understanding when something should be simple is an indication of ignorance...finance professors rarely have any understanding of actual finance, their ignorance on this is total).
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).
Are you implying software isn't complex? Or that imperative languages have low complexity? Haskell takes the complexity of software and provides useful constructs to generalize and abstract some of these complexities. Does it take time to learn? Absolutely. Is it easy for newbies to understand? Definitely not, because it's hard to appreciate their value until you have encountered these issues time and again in software. But it's most definitely not complexity for complexity sake. It can vastly simplify software in practice by restricting the domain in which you are working with a very powerful type system. That is the entire point of it all after all.
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).
> 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.
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.
Let’s say that an opportunity arose where you could buy a warehouse full of copper at a very low price. Also, you find that copper futures for delivery in three years are currently trading at a very high price. You calculate that the cost of purchasing the copper, maintaining the warehouse for three years, and then delivering the copper, is far less than the amount you would receive from selling an equivalent amount of copper futures contracts. You then buy the warehouse and immediately sell one futures contract for every 25,000 lbs of copper.
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.
This is a nice concrete example with tangible goods.
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.
I have no idea what some of the replies are about here. Lots of pure arbs don't close because they are driven by regulation or liquidity. The most well-known example is long bonds in the UK in the late 90s but linkers in 2008 were another, there are lots of examples (a lot of the current examples are related to linkers due to QE).
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.
Mercer had an extremely impressive resume pre-RenTech. Don’t think Simons would have known he’d finance the far right - Simons himself is a leading progressive donor.
>Don’t think Simons would have known he’d finance the far right - Simons himself is a leading progressive donor.
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.
> 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)
Also the data back then was much harder to acquire. Bloomberg didn't even exist at the time.
You could subscribe to a service and get pricing data and news. My father did it (with reverse effect) and I remember him using his fancy 9600baud modem to get his portfolio prices for the day (last 24h summaries, 5m and 1m candles).
There was no Bloomberg but there was compuserve and AOL and usenet and various other forms of financial forums.
His hiring strategy was fascinating. He specifically avoided people from traditional finance backgrounds. He'd target people with doctorate degrees in math, or electrical engineering, or other non-traditional backgrounds and assume (clearly correctly) that they could pick up any necessary knowledge on finance as needed.
That's not necessarily true, it depends. For example risk parity is common knowledge but it still beats the market. You don't really need any secret sauce to use it effectively. You could do it, personally, and you would probably do well.
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.
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.
It seems very disingenuous to say "you don't even have to open a textbook" and then link to a quant finance blog doing partial derivatives. That's well beyond the level of math that the average person would consider self-evident.
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?
One issue I've run into when I looked into strategies like this is that bonds have been an incredible investment over the last ~40 years. Sure, they haven't beaten the S&P500 straight up, but their volatility and max drawdown has been so good that you could have used leverage with them and gotten a portfolio that easily beats the S&P500 with as good or better volatility.
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.
Fair, it's not explicit. I misrecalled the control strategy. But the point still stands for the example in that article: over longer timespans SPY tends to return 7 - 10% or so. It has a beta of 1 (basically by definition). Levering up SPY will give you a better return, but at the cost of exposing you more to market volatility. In comparison the given risk parity strategy has a beta of about 0.5, and a natural return of about 10% (i.e. before leverage). You can safely lever the risk parity strategy to a higher total return than the historical market return without getting your beta beyond 1.
If that worked, everyone would do it, and so it would no longer work. There's something wrong with it, even if I can't identify what that something is.
Are there any mutual funds or ETFs which follow it?
> If that worked, everyone would do it, and so it would no longer work.
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.
People copy other successful businesses all the time. Granted, it takes some time, but innovative business ideas become mainstream if they're successful.
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.
Sure, but in finance it's not clean. It's not like one fund is getting 10% every single year and the other 7% every single year - there's high variability. The fund that'd underperforming may expect that the other fund's strategy is likely to blow up once every 20 years and thus not be worth it. It takes decades to get statistically significant results, and by then the world has changed.
PSLDX does not dynamically adjust the leverage between stocks/bonds as a typical risk parity strategy would. Not that this is necessarily bad, this fund has a consistent exposure to a duration trade, buying long term bonds and paying short-term borrowing rate. over the last 40 years or so this has been a fantastic trade, as interest rates dropping both raises the price of bonds, propels higher equity values, and lowers the cost of leverage.
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)
It does have an impressive record. 12 years is a good start, but not a long enough track record to prove much. I've been investing for nearly 40 years, and have had many with 12 good years go sour.
Not everyone does everything the best possible way.
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.
You are conflating beta with risk. Beta is just the correlation to the SPY return. There might be an asset (e.g. Oil, dunno but using it here for illustration) which have low SPY correlation but still high volatility. Levering it up 2x will bring you portfolio beta wrt to SPY to 1 but give you drawdowns far greater than SPY.
Pretty good article. It explains almost everything that is known about Medallion fund_
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.
Not arbitrage in the literal sense. The article claims only a small edge over thousands of equal risk positions which means no trade, or pair of trades, is risk free.
I'll add that very few things, even those that are arbitrages in the theoretical sense, are truly risk-free.
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 don't know where the whole HFT thinking came from? Renaissance wasn't known for HFT, it was known for using erstwhile new technologies such as data analysis and some machine learning to find patterns between uncorrelated data (like the weather in Paris affecting LSE trades).
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 non-compounding aspect is critical: you can think of the Medallion fund as a business that, with a capital base of (say) 5 billion dollars, produces an annual profit of 2 billion dollars ... but cannot grow, and so distributes all of that profit every year. Kind of like a very profitable but geographically-isolated monopoly, telco, etc.
> "...with an open, freewheeling atmosphere more like a university department than a company." This gave me a pause. Which university department has "open, freewheeling atmosphere"?
That's my experience of every university math department I've ever been at. I always assumed that's also how non-math departments are but don't actually have any first-hand experience.
With a good PI, indeed it is, but similar to you, my experience is limited to neuroscience and biology departments.
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.
Mathematics departments are known for this. In my experience it is accurate. People take intellectual detours all the time to discuss interesting problems with colleagues, potentially unrelated to their main research focus.
The engineering lab I did my PhD in (the Ocean System's Lab at Heriot-Watt University in Edinburgh) fit this description.
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.
The stock price of Zoom Video Communications Inc (stock ticker ZM) spiked significantly, as you would expect given the Zoom hype due to the pandemic.
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.
According to Yahoo Finance, ZTNO has an average daily volume of about 10k shares ($2.5k) so it would be pretty easy to pump the price up. I could probably singlehandedly double the price if I wanted to. It's possible that the 1000% price increase was caused by only a handful (or even just one) trader(s).
This is most of it. EMH can't apply to low float stocks because the ability to borrow is scarce and so the market can't easily correct the price without the supply side taking on enormous asymmetric risk. All it takes is a few million dollars in buying power to keep a small stock up 100 percent and not even the biggest hedgefund can correct the inefficiency.
Nobody knows how Rentech makes money. The most likely explanation for the success of Medallion is that Rentech assigns ex post the best strategies to Medallion, which is run for the benefit of insiders. For instance the fund Bluecrest was charged and fined for doing exactly that. We also know, because it appeared in a Senate report, that Rentech is a massive tax fraud and owes over $5 billion in unpaid taxes. Is it so unreasonable that a massive tax cheat would also cheat his investors? The press is far too credulous towards Rentech. For instance Zuckerman in his book devotes just one paragraph to a discussion of the tax fraud and the Senate report. Noah Smith himself worked at SUNY Stony Brook, which is heavily funded by Simons.
Can't believe a comment this ignorant is so highly upvoted. Quant funds that do well are a real thing. Many people here work at them. Renaissance used a technicality to try to avoid taxes, and they are in a dispute over it with the IRS. That has absolutely nothing to do with the legitimacy of their primary fund. Medallion predates their public funds. They launched their public funds because Medallion was capacity constrained, and they thought they could cash in on its reputation. It's the public funds that are the afterthought, not Medallion. They are not moving the strategies around ex-post. You're just completely making things up here. Anyone with any knowledge of the history of Renaissance knows that that doesn't even make chronological sense.
No, that’s not the most likely explanation - it’s actually very unlikely.
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.
Virtu is a market-maker. Comparing their win record to RenTech makes no sense. They have a high win-rate but so did brokers in the 70s...Virtu is doing the same thing as them (they are also APs for ETFs...again, the innovation there has really been able to make markets at very low cost).
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.
Indeed, but it’s important to differentiate between model execution complexity, and model optimization complexity.
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)
Yeah in the Zuckerman book they mention an employee who worked on getting and cleaning data for decades, far before data science techniques were common in finance. I could see RenTech having good quality data going back decades being a serious advantage.
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.
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.)
What happens if your slow market has to coexist with other fast markets?
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).
It depends on where the slow market is relative to the bid-ask spread in the fast market.
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.
> So you're liquid in the direction that moves the price to where it needs to be and not liquid in the other direction.
"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 are solving the same problem in a very different way.
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.
"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."
I don't believe Medallion would be classified as a high frequency trading operation.
There are tons. I don't know which ones he's thinking of, but off the top of my head: PDT, Jump Trading, Domeyard, Two Sigma, DE Shaw, Jane Street, Citadel.
Rentech trades on extremely reliable (but constantly evolving) price movement patterns and levers them up to the hilt in order to generate their returns. This is one reason why they are capacity constrained and can't just compound their returns. When the coronavirus first knocked US markets out of orbit, because of this leverage, the medallion fund was actually close to losing all of their money due to many previously established patterns evaporating too quickly for their algorithms to adjust. I have heard this from someone with first hand familiarity with ren tech. As others have mentioned, they also understood at a very early stage the importance of solid data ingestion and infrastructure. They vacuum up anything that could plausibly be related to price movements.
"The most likely explanation for the success of Medallion is that Rentech assigns ex post the best strategies to Medallion."
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.
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.
If I'm interpreting your allegation correctly, that would be a serious crime. I could conceive of that happening in the early days, but Jim has tens of billions of dollars now. I don't know why he'd risk spending the rest of his life in prison for an extra 1-3b a year.
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.
Except Madoff had to conceal an accounting hole. If he ever stopped, his investors would ask for their $x back, and he would have to give them $0.5x. I know there's a lot of room for cynicism in the financial world, but you still need to know what is happening for each type of fraud or misdeed or good action.
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.