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Amid The Volatility, Here’s The Latest From Marko Kolanovic, Goldman On Systematic Flows And Liquidity Provision

The phenomenon is not only real, but in fact pervasive. There are, of course, caveats...

Back on December 29, in “Trump, Machines And Markets: A Willing Suspension Of Disbelief“, I talked at length about the extent to which some in the quant community (i.e., some who employ quantitative strategies) were engaged in an increasingly desperate-sounding effort to convince the investing public that systematic flows don’t have a meaningful impact on markets.

That effort, I argued, stems at least in part from self-preservation concerns and an effort to stave off the inevitable in terms of heightened scrutiny from regulators.

We have of course written voluminously on the subject over the course of this site’s relatively short existence and one overarching goal of our coverage is to provide a take that’s some semblance of credible, a goal that permeates everything we write on markets. As mentioned in “Depth Charge, Gamma Gravity And A Dark, Twisted Fantasy Come True“, one of the other publicly-available portals that does a fairly decent job covering the impact of systematic flows long ago became a victim of its own success, succumbing to the allure of clickbait vis-à-vis their political commentary and general market coverage at the expense of veracity. An unfortunate side effect was (and is) that the two things that portal still does consistently well (economic coverage and analysis of systematic flows) are no longer taken seriously because at this point, separating the proverbial wheat from the chaff on a daily basis is impossible.

I try to provide an alternative – taking the baton, as it were. And while this site will probably never be a web traffic juggernaut, it will be a consistent source of high quality information, thoughtful commentary and, when it comes to politics, withering satire that’s unequivocally funny no matter what your party affiliation.

When it comes to systematic flows, our take has been consistent (if at times a bit too hyperbolic) and it can be summed up by the following quote excerpted from the first linked post above:

The idea that the proliferation of systematic/programmatic trading and the general robot-i-fication of market making/ trading is a development that is, at worst, free of consequences and side effects, and at best, a wholly positive revolution, is so wildly implausible that the only possible explanation for anyone who foists it on the investing public is that they have a vested interest in it.

One of the interesting things about this debate is that it’s virtually impossible to “get both sides of the story” – as it were. Quants (especially quants with Twitter accounts) are quick to lambast reporters and suggest (explicitly in some cases) that the sellside analysts who cover these dynamics should be fired and/or would never be hired on the buyside. If you e-mail me, I can cite several examples of that.

But the funny thing is, when you go out and you ask those quants to explain exactly why everyone is wrong about the impact of systematic flows, you get, at best, no response. At worst, you get admonished for being too stupid to understand.

Here’s the thing about people who tell you you’re too stupid to understand something: Sometimes it’s actually true. Sometimes, people ask questions about things they have absolutely no hope of grasping or otherwise coming to terms with and in those cases, the recognized authorities on the matter are wholly justified in politely suggesting that further discussion will be an exercise in futility. Allow me to trot out my “Gumby” example from a 2017 Dealbreaker post:

“You just don’t understand” is only an acceptable response to criticism in cases where the person doing the criticizing has absolutely no legitimate claim on an opinion. You can’t, for instance, criticize the design of the International Space Station by saying that it makes no sense for it not to resemble a giant, flying Gumby. There are technical considerations that override whatever joy humanity might derive from knowing that Gumby is perpetually in low-Earth orbit keeping an eye on things.

In that situation, the trotting out of technical considerations by rocket scientists isn’t a nefarious effort to keep the public from waking up to some self-evident design flaw (in this case that we’d all be better served if habitable satellites were fashioned to look like pieces of grinning, green Laffy Taffy). Rather, if the end we’re pursuing is a working space station, the means we have at our disposal for achieving that end dictate that it can’t look like Gumby. So to the extent you want to say it “makes no sense” that the design shouldn’t be aimed at maximizing nostalgia for a beloved claymation icon, well, “you just don’t understand.”

Spoiler alert: developing a basic understanding of the impact of systematic flows (and, even more critically, the impact of electronic market making) isn’t rocket science. And as such, defenders who trot out the “you’re too stupid to understand” excuse are everywhere and always just trying to avoid serious discussion with critics because, in most cases, they don’t have any good rejoinders. Obviously, there are exceptions, but what I’ve noticed over the years is that many quants are more than happy to pen lengthy defenses which they then publish on their firm’s website or on their own no-comments-allowed blogs, but don’t have a whole lot to say in an actual two-way discussion.

But you want to know something really funny? Even if understanding the impact of systematic flows were rocket science, the sellside analyst best known for covering these flows is – wait for it – a rocket scientist. Or at least some semblance of a rocket scientist.

As most of you are probably aware, JPMorgan’s Marko Kolanovic has a Ph.D. in theoretical physics, and while I’ve never read his dissertation, I’m reasonably confident that his academic background is sufficient when it comes to whether he’s capable of understanding momentum-chasing, options hedging dynamics and vol-control funds.

[And please, spare me your "but he missed the December rally” criticism – I’m not even sure the combined intellectual prowess of Einstein and Wittgenstein would be capable of accurately predicting a single month’s worth of performance on the S&P in the era of Twitter, Donald Trump and Donald Trump on Twitter]

Well, in his latest note, Kolanovic appears to address the same article (or, if that wasn’t actually the intent, then it’s just a happy coincidence) that I addressed in the first linked post above. To recap, in that post I mentioned a Bloomberg piece called “Humans, Machines and Markets: Stocks Going Crazy Is Nothing New“.

That article is just a retrospective on historical market crashes and as the title suggests, the overarching point seems to be to deflect blame from quants. Cliff Asness loved it, which pretty much tells you everything you need to know.

Marko doesn’t mention it by name, but he does address the idea that because crashes happened in the past, modern crashes can’t be blamed on systematic flows and/or electronic market making. Suffice to say he’s more diplomatic about it than I was.

Kolanovic starts by reiterating something Nomura’s Charlie McElligott wrote last month after Charlie’s infamous “CTA selling is now live” call received some backlash – namely that a lot gets lost in translation between what the sellside writes and how fund managers react to press reports. Charlie described it as a “game of telephone” (that’s a reference to a children’s game where the original message gets distorted as the intermediaries proliferate) and Marko reiterates that.

“Many discussions on how important systematic flows are for the market involve some level of misunderstanding (telephone game) between analysts, the press, and quantitative and fundamental managers”, Kolanovic writes, on the way to clarifying how he approaches his analysis.

The first thing Marko makes clear is that while “systematic flows” is the catch-all term, what you find under that umbrella is a multifarious universe that includes “the insurance industry, derivatives hedging and market making programs, quantitative asset managers, passive indexers, liquidity provision strategies, etc.”

In addition to that, he reminds you that asset managers of the quant variety run strategies that vary widely, comprising everything from “equity long-short quant, futures-based and trend following, risk budgeting/portfolio construction, and big data and machine learning.” And then there’s HFT strats focused on “liquidity provision, short-term arbitrage of indices, co-movement of stocks”, to name a few. The point, Marko notes, is this:

It would be wrong to classify all these vastly different parts of the industry and related flows under one term such as “quants,” “algos,” “machines,” etc., and collectively blame them for certain positive or negative market effects.

I would argue that reporters don’t generally do that (i.e., lump everything together in the course of saying “the machines did it”). In the same vein, I would argue that critics more generally (myself included) don’t do that either. Of course reporters and anyone who writes for public consumption needs to figure out how to slap a title on something and while I suppose the media could theoretically employ lengthy headlines in the interest of accuracy, I, for one, can’t. And I mean that literally – my platform won’t let me write titles that go beyond a certain number of words, so sometimes, using catch-all terms is the only thing that’s possible.

Marko goes on to address the idea that because there are myriad historical examples of harrowing crashes that played out before the advent of modern market structure, systematic flows and electronic market making can’t be blamed. Again, I don’t know whether he was specifically addressing that Bloomberg article or not, but his commentary certainly answers a lot of the questions implicitly posed in Bloomberg’s piece.

Essentially, Kolanovic addresses the extent to which computerized trading and systematic strategies mimic things humans would have done long ago.

“We find that correlation between volatility and mean reversion, which is today largely driven by option hedging, was present a long time before options were invented (e.g., in the 1930s)”, Marko writes, adding that “at that time it was a result of stop-loss trading; the invention of options just provided a product that enabled a more flexible stop-loss strategy.”

He goes on to note that when you think about it, a lot of systematic strats are just optimized/automated versions of old market adages. To wit:

Many other systematic strategies do what discretionary investors have done for decades. For instance, the century old saying ‘cut your losses short and let your profits run’ has found application in CTAs, equity long-short factors, and even risk-based portfolio approaches such as volatility targeting. ‘Don’t put all your eggs in one basket’ found its application in risk parity, multi-factor models, etc.

Of course none of that means that systematic flows (and especially electronic market making) aren’t in some way involved in exacerbating volatility. Indeed, it stands to reason that when you automate or otherwise optimize old market wisdom/adages, things move faster and thus have the potential to exacerbate moves on the downside and upside.

On that point, Marko basically reminds you (again, in more polite terms than I employed last weekend) that contrary to what some in the quant community will tell you, this isn’t really up for debate. To wit, from Kolanovic:

While it is incorrect to say that systematic flows are the sole driver of recent market moves, it would be equally incorrect to say that systematic flows don’t have a meaningful impact. This has now been broadly accepted, and many analysts are forecasting various systematic flows (with various degrees of accuracy). Collectively, flows from strategies such as CTAs, volatility targeting, option hedging, passive rebalances, CPPI structures, etc. can represent a large part of market flows and may appear as an unknown force to some fundamental investors.

As I put it on December 29, this “just is what it is”. You don’t have to couch it in nefarious terms if you don’t want to and you don’t have to paint “the robots” with the Terminator/Skynet brush, but arguing that these flows aren’t material or otherwise don’t play a role in exacerbating price action is an increasingly untenable position.

Finally, Marko notes that the impact of electronic market making is perhaps even more consequential than systematic strats. He’s been over this on too many occasions to count and the overarching point is that liquidity provision is inextricably bound up with volatility, which means things have a tendency to snowball both on the downside and on the upside.

“Liquidity has become to a large extent driven by market volatility, thus reinforcing a negative feedback loop between volatility and liquidity”, he writes, adding that “the most recent examples include an unprecedented drop in futures market depth (alongside an increase of the VIX), currency flash crash on Jan 2, or the equity market ‘upside crash’ on December 26.”

Read more about those episodes

This Flash Crash Matters – Here’s Why

Strategist Trump Nails ‘Buy The Dip’ Call As Stocks Explode Higher After President Trump Leaves Country

Obviously, this argues for a broad “rethink” (to quote Kolanovic again) of liquidity provision with an eye towards ameliorating the disappearance of countervailing forces by somehow making human value investing “great again” (sorry) and/or unshackling the Street by revisiting the deleterious side effects of the post-crisis regulatory regime.

Meanwhile, in a cross-asset strategy note, Goldman documents the role of systematic flows and diminished liquidity in the context of 2018’s various crashes and vol. spikes.

After reiterating that in their view, “the key reason for the pressure on risky assets for most of the year has been a de-risking from investors broadly that started at the beginning of the year, in response to the weaker macro backdrop and the ongoing liquidity drain from central banks”, Goldman flags an unwind in bullish CTA and risk parity positions in explaining the February vol. spike before noting that factor crowding and the dynamics the bank described in their infamous “Is FANG mispriced?” note (from June of 2017) may well have contributed to the October unwind. Somewhere, Howard Marks is smiling.

Moving to address the structural points flagged by Kolanovic, Goldman notes that “while changes in positioning and flows could have been exacerbated by technical factors, there have also been increased concerns about market liquidity with less liquidity provision from banks, more from high frequency trading, and an increased negative link between liquidity, e.g., in S&P 500 futures, and volatility” (left pane below).

GSLiquidity

(Goldman)

The bank adds that “there are [a[also]oncerns about levered or liquidity-constrained exchange-traded products, e.g., on high yield credit or commodities” (right pane above).

The bottom line is that while the investment community may hold some misconceptions about how exactly it is that systematic flows, electronic market making and modern market structure more generally contribute to volatility and otherwise exacerbate price action, the phenomenon is not only real, but in fact pervasive.

As with anything else, there’s a ton of nuance (as documented by Kolanovic). But one cannot simply explain away recent volatility and/or absolve systematic strats and modern market structure of their culpability by pointing to historical examples of market crashes that, by virtue of playing out before the advent of recent innovations, couldn’t possibly be attributable to the impact of quants, computers, robots, algos or whatever catch-all you care to use.


 

 

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14 comments on “Amid The Volatility, Here’s The Latest From Marko Kolanovic, Goldman On Systematic Flows And Liquidity Provision

  1. Anonymous

    I first read your analysis on Seeking Alpha…..figured a guy that used Breaking Bad as a pseudonym might have a bit different take on the Markets than the gang trying to sell you their expertise.
    It has been an education……I am just a plumbing contractor who wanted to take a more hands on approach in investing my retirement savings. I’m also a total political junky who enjoys good writing and a sense of humor…..which you have in spades.
    I’m the guy who would have been told……”you just won’t understand” a year ago…..and would have agreed. After reading you for over a year……I’ve gotten quite the education…..and can understand far more than before.
    Thanks for that, for being an amazing news feed, for providing a laugh a day, for brilliant political and economic analysis.
    I really appreciate your hard work…….not sure why you do work this hard, but it is appreciated.

  2. Machine-learning algos are trained on historical data that was generated by human (emotional monkeys) carbon-based traders. They, therefore, trade like their teachers did, only faster. This makes it look like the machines are wildly emotional. At some point, however, the training data is going to contain some threshold of machine-generated trading data that will produce a new type of result…machine creativity; new moves that have never been seen by human.

    • AI

      Your last conclusion is very interesting and a point I have not seen publicly up to now. This is the way machines will gradually wield power, whether we like it or not. We will never know what part of their learning is based on what they do for themselves and so we will be powerless to stop it. We want the tool and this is what we will get as a Secret Santa present along with it. Ain’t we got fun?

      • In professional chess-playing, any creative move that has never been seen before will disqualify you because it implies AI was used. Why a machine-learning algo does what it does is a black box. Even the guy that programs the neural network doesn’t know why is does what it does. You can’t know the patterns it has seen.

        I can see a time when instead of betting on stocks or ETFs, we are betting on specific algos that in turn bet on something we have no idea of. That has already started, in a clumsy way. Impossible to know what it all means for the future. All we can be sure of is that it WILL happen.

        Look up AlphaGo.

    • Anonymous

      Exactly, well said

  3. Thank you. As always, thoughtful heisenberg report.

  4. I second the comments of Anonymous above. I tend to fall into the rut of reading news and opinion from the same sources all the time. And that is rarely a good idea. I absolutely love hearing a divergent voice from someone who is looking at facts I may not be thinking about, and who lays out all the information supporting his opinion. Thank you.
    As to all the quant trading, as a small investor, I can’t do much about it. But I need to understand what is happening and where we are going. A couple weeks ago, there was a great article in The NY Times about the progress that computers made in chess, go and other games: https://www.nytimes.com/2018/12/26/science/chess-artificial-intelligence.html
    The computers started with human instructions on how to play the game. But over the years, they switched to a mode where they just figured it out on their own. That works in a zero sum game, where, no matter how complicated, there are finite variables and finite possibilities. When I saw that article, I thought about the use of computers in trading. In trading, there are infinite variables and infinite possibilities. Chess and go are easy by comparison. Still, they should be able to process much more than any human. And what seems like volatility to a human may be much less so to a machine processing information basically at the speed of light. In the coming years, I have to believe we will see tremendous progress on the use of machines in making decisions where possibilities are infinite, and trading would have to be one of the profitable immediate uses.

  5. No offense to second-hand Kolanovic/Gandalf (thanks, love it), but now waiting on Powell&Co. to signal a drop in FFR after dragging Yellen and Bernanke into the fire pit yesterday (i.e., explicitly acknowledge policy mistake).

  6. Kind of like explaining away anthropogenic climate change; because the climate has changed throughout the history of the planet. A deception strategy that works for the groups that need to say it, and the people who want to hear/believe it.

  7. Anonymous

    2017 I think showed the impact of passive and quants/momo. Markets do not act like that (even with the data/policy etc) when humans are involved. Humans de-risk, look for mean reversion, look for what can go wrong etc.

    AAPL in 2018 is another good example. A real company, real earnings, big market cap. Did fundamentals really change enough for investors to rally it (almost straight up) 50% from March to Sept (the smart money was not long a lot on the last of the move) then realizing over charging for the same thing they sell it off 40+% from the highs (did the services business really change $300-450bn or did the hardware business?).

    Yes humans get caught up in fear and greed but computers do much better in straight up and straight down momo trading.

    The last couple of years show to me tge impact of the computers and how much trend following they are relative to professional investors/traders in the past.

  8. Anonymous

    Imagine robot systems being fed knowledge about each other, and capital market microstructure liquidity vulnerabilities. Then imagine a human with high frequency technology that is ready for all that. ETF liquidation scenarios can also be fed in to all this. Eventually you might want to avoid using margin, especially if you are piling on to a crowded trade like we just saw with short 10 yr.

  9. HBensmiller

    There will be “winners” in the early algo days (now) due to differing quality / performance, but will there be when there are 1×10^9 comprehensive yet different algo platforms using AI/ML trading? I think not… it will be a stabilizing, mean reversion exercise due to the balance created by pulls in different directions as algos search for infinitesimal advantage over one another. Rest easy my friends.

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