Adapt Or Die: New Age Trading Strategies Are Changing How We Trade

5 years ago, it would have been hard to fathom hundred-point market moves in a span of hours, or even minutes. Today, we see them happen all the time. But why? Such changes don't just happen with a flick of a finger.

The aftermath of 2015's "Black Monday" was packed with statistically impossible price swings, moves which would mathematically happen a few times every year.

But it's not just in the wake of such market crashes such as the one we just saw. There have been many episodes in 2015 alone where we'd see intraday crashes, those so called flash crashes that happen with alarming frequency. Intraday cycles have also been becoming more pronounced, and therefore observable even to the naked eye.

For us retail traders (or actually any plain vanilla trader on the street with zero or limited access to privy information and advanced technology for that matter) it's becoming unarguably more difficult to generate consistent returns across undulating market conditions, which themselves have become much less predictable.

Technology: Boon Or Bane?

In the era of the 80s and 90s, when most of trading was still done physically or through telephone, the collective market was able to function in a much more organized fashion. It might have been the golden age for traders, mainly because the factors that contributed to effective price discovery were still 'clean'.

Fast forwarding 25 years, technology has meant that almost all trading is conducted electronically. More than 70% of trading volume on U.S. stock exchanges come from robots, or in proper parlance, algorithmic trading systems. This figure has been on the constant rise since 2010. Everyone who has a smartphone and an Internet connection would be able to execute a trade wirelessly with a few key strokes.

Access to the financial markets has never been more widespread, but that means nothing for actually being able to generate justifiable risk-adjusted returns over time. It seems that real alpha is increasingly becoming rare, and that the ability to manage one's leverage while riding on beta has actually overtaken the former in importance.

However, things don't happen without reasons. Especially shifts that are this secular in nature. The changing tide shifts the entire fleet of boats. The ability to understand, adapt, and make necessary changes will determine if traders (retail and professional alike) survive. Adapt or die!

Bold New Age Strategies

To give readers some insight into what may be possibly behind these massive changes in the landscape, we'll provide our brief prognosis and then leave readers with a rather interesting interview with a quant towards the end.

As we've highlighted in several of our pieces this year, traditional methods of measuring the degree of choppiness in the markets aren't working as they used to. Metrics like implied volatility (mainly the VIX for equities) haven't been as good as pricing risk premium as they should.

The "Black Monday" crash of August 2015 was the fiercest (according to equity volatility) in almost 5 years since the U.S. fiscal scare event in 2010/2011. But other than these occasional flares in volatility, the VIX has comfortably traded under 15 almost 80% of the time. What is not observable here is volatility of volatility, which has been even more chaotic. Chart by Business Of Finance

The "Black Monday" crash of August 2015 was the fiercest (according to equity volatility) in almost 5 years since the U.S. fiscal scare event in 2010/2011. But other than these occasional flares in volatility, the VIX has comfortably traded under 15 almost 80% of the time. What is not observable here is volatility of volatility, which has been even more chaotic.

Chart by Business Of Finance

Active traders will be able to attest to the markets not feeling they way it should when the VIX consistently prints at sub-12 levels. Obviously, variance is skewed to the downside, and will naturally be biased to selling pressure. As the interviewed quant says, volatility of volatility is rising (something we've talked about before). That's the choppiness of volatility itself.

Price swings have also become very micro. Intraday reversals can happen on a drop of a penny. Whatever is moving the markets these days is able to do so with tons of force and finesse. Imagine a big bulldozer moving with the agility of an F1 racing car. The big players have so much top tier technology and capital in their hands, that both the markets and regulators are forced to follow them, not the other way round.

Systematic trading relies heavily on technology and mathematics. It includes algorithmic trading strategies, and high frequency trading strategies (HFTs). Lesser known strategies which have recently gained mainstream attention are risk parity strategiesvolatility targeting strategies which are both heavily employed by CTAs (commodity trading advisors).

An Achilles Heel For The Markets

The information sphere has obviously changed massively compared to 20 years ago. However, the way major market participants react to information has also changed radically. As said before, much of trading is automated. Either completely by computers or with slight human input.

The models that these sophisticated market players employ share more similarities than differences. This means that when presented with a specific set of inputs (such as a market crash or rapid decline in prices), they mostly react in the same predictable manner. We aren't making this stuff up because it has been proven time and again.

This is precisely why regulators are so concerned about the long term stability of their financial markets. But it is besides the question when so many of these regulatory bodies are miles behind the curve. It's both tragic and scary. Mathematicians are running the richest alleys of Wall Street, London, Zurich, and Tokyo. Gone are they days where having a degree in economics and finance gives you an edge over others.

For almost a year now, the basket of strategies CTAs (commodity trading advisors) employ have been outperforming risk parity strategies by a huge margin. It is apparent that the largest divergence on record (since risk parity returns were widely tracked) started around the period when the Fed's QE3 ended in 4Q14. That was a major market event that risk parity strategies struggled with, perhaps because their fundamental focus is towards managing drawdown rather than chasing momentum. Chart courtesy of Zero Hedge

For almost a year now, the basket of strategies CTAs (commodity trading advisors) employ have been outperforming risk parity strategies by a huge margin. It is apparent that the largest divergence on record (since risk parity returns were widely tracked) started around the period when the Fed's QE3 ended in 4Q14. That was a major market event that risk parity strategies struggled with, perhaps because their fundamental focus is towards managing drawdown rather than chasing momentum.

Chart courtesy of Zero Hedge

There's so much more we want to talk about because it has affected us personally. Thankfully though, we stumbled across this immensely insightful Bloomberg interview with a quant (short for quantitative analyst) from MIT, who also happens to chair a company that has its skin in the game. The interview goes about to discuss (in Q&A style) how the game has changed so drastically.

Here's Bloomberg's enlightening interview:

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

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?

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.

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

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.

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?

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?

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?

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?

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?

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…

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?

Yeah. August sucks.