Comment by hogFeast | Hacker News Reader

Comment by hogFeast | Hacker News Reader

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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.

You have demonstrated my argument.

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.

Can you give an example of a pure arb that takes years to close?

Grayscale Bitcoin Trust has traded at a 20%+ premium to its underlying crypto holdings for five years.

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.

Reversal/conversion on long dated options.

Not a pure ARB bc of dividend and interest rate risk, but it's close.

There's really no such thing as pure arb. All trades bear some sort of risk that isn't entirely considered.

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).

> Robert Mercer, etc. Very humbling

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.

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