Tuesday, July 14, 2015
A finance industry insider on equilibrium asset-pricing models
A couple posts ago I wrote about equilibrium asset-pricing models, and how the finance industry has basically decided not to use them. I was emailed by a finance industry quant with 25 years of experience working on factor models (at some of the top firms), and we had a long discussion about modeling approaches. He agreed to let me post a redacted version of some of his comments, so here they are.
On the historical use of equilibrium asset pricing models in industry:
"Actually I think there has been historically quite a lot of interest in models based on macro factors that would lend themselves relatively naturally to interpretation in terms of equilibrium valuation models. The problem is that they don't tend to work very well empirically...Clients asked for it, and other firms were offering models with these types of factors, but those models just didn't do a good job of explaining a substantial fraction of asset returns...
The context I'm most familiar with is interest rates. I worked on these models in the late '80s, early 90s (figuring out how to do practical implementations of Heath-Jarrow-Morton, which is a pure factor model). As far as I know, there is absolutely no equilibrium framework that says anything useful about yield curves. Cox-Ingersoll-Ross dressed up the presentation of their model with some equilibrium argument, but its predictions of yield curve shapes were very far from realistic. People have done multi-factor versions that probably give more reasonable shapes, but AFAIK nobody even waves their hands towards an equilibrium justification any more. I think the view is that, sure, there's probably some fundamental explanation, but its far too complicated to be practical. A key point here is that if your chosen equilibrium said there were large mispricings in the market, you'd be much likelier to mistrust the model than you would to trade on it."
On why industry people are skeptical about equilibrium models:
"[W]hen you [Noah] talk about equilibrium models you're really focused on the link between consumers and asset values. But as an industry person, I think that link looks very weak, except maybe over intervals long compared to any timescale for decision making by market participants. Everyone in the finance food chain -- from the individual investing for retirement to investment advisor to investment management firm to pension consultant ... all the way to governments and corporations financing their spending -- have short term incentives that seem like they would dominate theoretical long term valuation considerations. Behavioral biases and agency effects are huge at every step of the way. Easier for an end user to forget about those kinds of models and just stick to empirics."
On why it's too risky to try to use equilibrium models for trading:
"I think the big picture here is just that relative value approaches based on factors leave you with little net risk (most of the time) by construction. They're in some sense "localized" to a small subset of factors and assets whose interrelationships can be modeled fairly reliably and with few assumptions...
[E]quilibrium models are much more ambitious [so] they're also much harder to get right enough to be useful on time scales not subject to large risks. Plus they usually seem to have unobservable or hard-to-observe components. For example, any model of interest rates is going to have to have some place for a market price of interest rate risk, or something that plays the same role as in the CCAPM. Even a small error in that estimate is going to produce a huge discrepancy to observed yields. Now how long can you wait to be right?...[Equilibrium asset-pricing models] are making something...like a cosmological grand claim about a necessary relationship that must be satisfied in the large, rather than merely an empirically observed relationship or one driven by relatively straightforward no-arbitrage condition among highly similar assets."
Basically, this shows that industry people didn't just take a look at equilibrium models and say "Hahaha no way!" They understood how the models work, they tried to use them, they thought deeply about the content of the models, and they also thought deeply about the risks of using them. Equilibrium models are not newfangled gadgets that haven't been tried yet; they are something that has been around for a good long while and - unlike their cousins, the factor models - have not yet passed the rigorous test of industrial applicability.
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Good one, thanks. In truth the industry people who "just took a look at equilibrium models and said 'Hahaha no way!'" outnumbered those who "understood how the models work, tried to use them, thought deeply about the content of the models, and also thought deeply about the risks of using them." But the industry does hire no small number of people steeped in econ and/or finance theory and those people do try to make as much use out of it in finance as they can.ReplyDelete
When you're working with a complex system, any model that is good in some respects is going to be bad in others. Two modelers who wish to put their models to different uses will weigh those plusses and minuses differently. A simple conjecture is that academic economists want models that generate understanding, while industry economists want models that predict the future. Other things being equal, both would like to do better on each of these dimensions (and countless other dimensions). But when push comes to shove, a rough and ready regression often beats a theoretical model when it comes to making predictions, and a model built on optimizing agents with defined constraints often beats a regression when it comes to generating understanding.ReplyDelete
Sad. The finance economists don't even succeed within their core field, but that doesn't stop them from infringing on macroeconomics.ReplyDelete
It's really the other way around. You might be thinking of Cochrane, but he's the exception.Delete
On the equity side, my finance went unused. In fact, algebra went unused, except for a couple two variable systems that showed up. Logs were useful for a while, but they devolved to rules of thumb. PV calculations have been consistently useful.ReplyDelete
These fixed income guys, though, they use terrific leverage and hedging. Both force a short orientation, as does the compensation cycle. It does seem that there should exist long oriented fixed income shops working equilibrium models, no leverage, sufficient net worth to not worry about compensation.
I think it was 05-06 when Warren Buffett basically said, okay all you hotshots, I'll beat you with one stock, and it was an industrial REIT, close to a bond. The equilibrium analysis behind that was along the lines of "these valuations cannot go on forever, therefore they will stop", a la Herb Stein. That's a long term insight, but does not require much math.
Very nice. Its even inspired me to write a blog post of my own. http://qoppac.blogspot.co.uk/2015/07/equilibrium-asset-pricing-models.htmlReplyDelete
A couple posts ago I wrote about equilibrium asset-pricing models,
you may not know it, but there is this technology called hypertext markup that lets you put a link to the old column in..
You may not know it, but I am a dumbass and I don't always remember to do stuff... ;-)Delete
A few years ago I had a conversation with a trader who had a physics background. He argued that there are three relevant time horizons for traders. The shortest term is high frequency trading, really at its peak within a day and really far shorter than that, which essentially uses econophysics sorts of methods or variations thereof. The intermediate time period, which is at its best at maybe a half year interval or so, but clearly dominates from maybe more than a day out to several years, is behaviorally based. The fancy version now used by lots of algorithmic traders is to use rolling and declining abnormalities identified by massive data crunching. Put them in and then let them gradualy slide out with declining weights. Finally, in the long run, many years, neoclassical equilibrium holds, basically random walk.ReplyDelete
BTW, of course that long run does not have much influence on any active trading, so the basic practical irrelevance of equilibrium analysis in financial asset markets is pretty much correct.Delete
As always, the situation is much simpler. Any model should have somen useful information to contribute. Some might do it only on very large time scales, which makes them useless for any decisions. However, if there's any info present, there's a trade to be had. If there's no edge you can produce from those models, that implies that they do not have any useful ( incremental) info.ReplyDelete
If any academic thinks that the academic models are unfairly maligned, the solution is very simple. Show us the money.
Your post shows you really have no useful thoughts. In the future you should keep them to yourself.Delete
All models are only one point of view of realityReplyDelete
"Equilibrium models are not newfangled gadgets that haven't been tried yet; they are something that has been around for a good long while and - unlike their cousins, the factor models - have not yet passed the rigorous test of industrial applicability."ReplyDelete
How many more decades will it take before academics understand that?
I guess in their tenured positions they don't have to worry.
Equilibrium models are designed to explain why markets move with the economy; why stocks fell in the 2008 recession, and why risk premiums rose at that time, for example. They are not designed to help you trade. They are designed to explain what markets look like after all the trading opportunities have vanished. They are designed to be useless for trading, so it is no surprise that they are useless for trading.ReplyDelete
Shouldn't they be useful for hedging risks, though?Delete
Factor models are also supposed to be useless for trading, other than helping you hedge out risks.
I don't even think factor models help you hedge much.Factor models are of help in performance attribution ex-post, resulting in a better understanding of positioning and allocation at large. Trading is a tough business exposed to a very complex marketDelete