MIT Quant Guru Andrew Lo on Market's Meltdown/花街矿工

MIT Quant Guru Andrew Lo on Market's Meltdown: ‘August Sucks’
Michael P. Regan
Reganonymous
September 8, 2015 — 11:58 AM EDT

    Lo sees 'a number of different forces' coming to a head
    Event risk is something algo trading 'can't really manage yet'

Andrew Lo has spent a lot of time peering into Wall Street’s various black boxes and "modeling the endogenous risk among hedge fund strategies." The finance professor at Massachusetts Institute of Technology’s Sloan School of Management and chairman of AlphaSimplex Group LLC shared his thoughts on Friday about the recent spate of volatility in the stock market and what role strategies such as risk parity, trend-following commodity trading advisers and volatility targeting may have played.

Question: What does this volatility look like to you? Is this another quant meltdown?

Lo: I’m not sure I’d characterize it as just a quant meltdown. I think that makes it a little bit too cut and dried. Probably there are a number of different factors, including algorithmic trading, that plays into it. We have a number of different forces that are all coming to a head. And because of the automation of markets and the electronification of trading, we’re seeing much choppier markets than we otherwise would have five or 10 years ago. But it’s many forces operating at different time scales, all coming to a head.

Question: Is systematic trading exaggerating the moves?

Lo: I think it’s doing two things. One it can be exaggerating the moves if it lines up with what the market wants to do. So if the market is looking to sell because of an impending recession, then I think we’re going to see a lot of the algorithmic trading going in the same direction. And if the time horizon matches, you will see that kind of cascade effect. At the same time, I think algorithmic trading can play the opposite role. They can dampen some of the market swings if they’re going opposite to the general trend... The one thing that is true, though, is that algorithmic trading is speeding up the reaction times of these participants, so that’s the choppiness of the market. Everybody can move to the left side of the boat and the right side of the boat now within minutes as opposed to hours or days.
HFRX Macro/CTA Index
HFRX Macro/CTA Index

Question: When you talk about exaggerating the effect, is that mostly CTAs and momentum players or is it not that simple?

Lo: I think that over the course of the last few weeks, that’s actually a pretty decent bet: That there are trend followers that are unwinding because of some underperformance and concerns about the change in direction of the market. But, for example, what happened in August 2007 was equity market neutral strategies that unwound. So I think it really varies depending on the nature of the strategies that are getting hit and the money going into and out of those strategies, and how that’s affecting market dynamics.
Salient Risk Parity Fund
Salient Risk Parity Fund

Question: A lot of focus has fallen on risk parity strategies. The notion that, as volatility picked up, there was a lot of deleveraging going on, especially with futures and ETFs. Does that make sense to you from what we’ve seen?

Lo: Well, it certainly looks that way. Part of the challenge of risk parity is that it ignores anything about expected returns. The idea behind risk parity is not a bad one, which is to focus on risk and to manage your portfolio so as to try to stabilize that risk. But the problem with equalizing it across all asset classes or investments is that not all investments are created equal at all points in time. So there are certain strategies that end up doing worse than others during periods of times. And if you end up equalizing your volatility across those strategies, you might end up getting hit pretty hard as some of the equity risk parity strategies got hit over the course of the last few weeks.

Question: Is risk parity looking like a crowded trade?

Lo: I think there’s definitely a case in point of the idea of alpha becoming beta. The idea that once you start popularizing a particular investment approach, and it becomes so popular, that in and of itself creates these kinds of shock waves. So for example if the strategy itself underperforms, now we have a larger number of investors that are going to be unwinding that strategy and that will create a kind of cascade effect where the strategy will underperform even more as people start to take money out of the strategy. There are a number of examples. Risk parity, of course, is the most recent. But before that trend following, before that value investing, growth investing, earnings surprise, earnings momentum, any kind of a strategy can become a crowded trade. And when it does you have to just make sure that the risk premium associated with that trade is commensurate with the potential risks of getting hit with these unwinds.

Question: Are volatility targeting strategies part of the story? Have they become so popular that they’re exaggerating the moves?

Lo: Not only are they exaggerating the moves, but I think they are creating volatility of volatility. So it’s making the market quite a bit more complicated and the dynamics now are much more different and much more difficult to manage if you’re not aware of how these dynamics play out.

Question: What about when you get a big rebound? What do you suppose that is? Is that actually value-type of investors seeing the drops and coming in, or is it just another systematic trading function?

Lo: These rebounds are a confluence of a number of phenomena. One, you’re seeing that once selling pressure declines, investors will naturally become more optimistic and will come back into the market. That’s a common phenomenon. But I think that a rather newer phenomenon is the fact that these algorithms, because they operate at such high frequencies, when the price moves beyond a certain threshold, the algorithms will kick around and flip and go the other way. It’s happening at a rate that’s faster than it’s been anytime in the past because we haven’t had the technology to be able to do that.

And finally what we’re seeing is expectations shifting more rapidly because unlike five or 10 years ago we now have very big players in the financial markets, actively trying to move markets. In particular, I’m thinking about central banks and governments that are trying to manage economies by engaging in quantitative easing or other kinds of financial market transactions. When you have a small number of very big players that are going to be trying to move markets for political or long-term economic reasons, it becomes much, much harder to understand what’s happening. So people are all sort of trigger happy when small pieces of information hit the market, they tend to start moving money very quickly and in large size.

Question: Is that type government intervention something that algos can’t anticipate? Is that sort of an Achilles heel of algo strategies?

Lo: Absolutely. That event risk is something most algorithmic trading strategies really can’t manage yet. I say "yet" because in five or 10 years maybe natural language processing and artificial intelligence will have allowed them to read the news, interpret it and make judgments the way George Soros or Warren Buffett can. But I think we’re still a few years away from that

Question: Are a lot of momentum strategies able to turn on a dime that quickly? We’ll see this intraday drop of several hundred points, then it turns on a dime…

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Lo: I think that it’s hard for momentum strategies to be able to move that quickly. In fact, some of the strategies that do move that quickly end up getting whipsawed. The real challenge in operating in these markets is that risk management would have you cut risk in the face of losses. The problem is that if you cut risk too quickly and by too much, you may end up missing out on the rebound, in which case you’ve locked in your losses and you might be getting back in the market exactly at the worst time. So you’re getting hit on both ends. What this atmosphere creates is a much more complicated challenge to risk managers to figure out what is the right frequency with which they need to cut risk and put it back. And I think everybody is trying to figure out what that optimal frequency is. But until we get a sense of who’s involved in the markets and driving these frequencies, it’s going to be anybody’s guess. And as a result a lot of people are going to be surprised over the next few weeks and months.

Question: Any other observations you have from the last couple of weeks that you think people might be interested in?

Lo: Yeah. August sucks.

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