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.
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.