What Marko Kolanovic Is Looking at Now
(Bloomberg Markets) -- People pay attention to Marko Kolanovic. The global head of macro quantitative and derivatives research at JPMorgan Chase & Co. has gained a following for his forecasts about whether systematic investing approaches such as risk-parity strategies, volatility-targeting funds, and commodity-trading advisers will be buying or selling.
Kolanovic, 43, is widely credited with market-moving pronouncements. On Oct. 12, for instance, he reported that systematic selling was 70 percent done after a big U.S. stock drawdown. A few minutes after that note landed, the S&P 500 rallied. His most famous moment might have been in the August-September 2015 tumult induced by China. Several of his market-direction predictions came to pass, both upward and downward. His star, in fact, has reached such heights that CNBC sometimes identifies him in its chyron as “Half-Man, Half-God.”
A native of Croatia, the bassoon-playing Kolanovic earned a degree in classical music. He’d like to take up the instrument again at some point, he says, but doesn’t have the time these days. In the late 1990s, he came to the U.S. to study at New York University. He received a Ph.D. in theoretical physics in 2003, then joined Merrill Lynch as a derivatives research quant. “At that time, most market participants were fundamentally driven, so having this quant angle was pretty powerful,” he says. After two years, Kolanovic moved to Bear Stearns to head up equity-linked strategy—and moved to JPMorgan when it saved Bear from collapse. “The trading floor was hectic, with employees afraid for their careers, the firm, and the financial system. There was conflicting information, and until Thursday everyone was thinking the firm would be fine,” Kolanovic recalls of that week in mid-March 2008. “Only on Thursday we realized the end may be near, and on Friday it was all over. I was immediately contacted by then-head of JPMorgan research Tom Schmidt and the head of derivative sales, both of whom knew about my work.”
Kolanovic says that through the 2000s, quantitative trading grew to become a bigger market force than the fundamental investors. The shift to quant, he says, made him start to grapple with the purely quantitative approach’s blind spots. “If you’re running a strategy just based on the formulas, you’ll miss a lot of bigger-picture issues, whether it’s macro developments around central banks or politicians, or geopolitical risks, or a lot of behavioral biases,” he says. “There are limitations as to how far a quantitative approach can get you.”
Kolanovic started developing his ideas about systematic strategies in 2015, recognizing that when market levels or dynamics change dramatically, funds may have to alter their positions to restore their preferred balance of risk, assets, and volatility exposure. The idea caught on, and numerous firms now make their own estimates of such imbalances.
Tracking systematic strategies isn’t Kolanovic’s only responsibility. He leads a global team of about 50 people who research everything from fundamental strategy to equity quantitative approaches, derivatives, passive indexes, big data, and asset allocation. Kolanovic talked with Bloomberg News’s Joanna Ossinger about markets, his career, and his latest research.
Joanna Ossinger: What’s helped you stand out in a crowded research field?
Marko Kolanovic: I do tend to be a more contrarian person who looks at things that people aren’t looking at right now—which is good and bad. It’s good because you can uncover things that nobody thought of, and they become very relevant. The bad is that you may be sometimes looking too far out, and then it’s not relevant. If most people don’t look at something, chances are it isn’t going to be relevant very soon. “Too early” sometimes also means “wrong” in finance. If you’re just going to be stating consensus, and a trend follower, you’re not adding much value. The proposition of being a bit more out-of-the-box contrarian is more risky, but it also differentiates.
JO: What bigger-picture market trends are you watching these days?
MK: There’s this fragility in the marketplace that came with the new structure of liquidity, with electronic market-making, computers, and growth in passive. Passive assets and quant assets will grow, and computers and AI will have a bigger role in market-making. At some point that’s going to end up badly—most likely when the next recession hits. Some of the problems around computerized liquidity are going to be fully exposed, and it may really deal a blow to investors and markets overall. Not that we are forecasting it with a certain timeline, but more that investors should have it in the back of their minds.
Is there a way to hedge yourself for some type of catastrophic event where liquidity collapses and this whole microstructure potentially fails? Can you design strategies that are going to be resilient to this type of fragility? Can you run some effective hedges that won’t cost you a lot of money? We are thinking about a number of things like timing: market-timing and timing of risk premia. It’s almost a holy grail—can you time these risk premia? So recently we’ve been testing machine-learning algorithms in the context of timing.
JO: So you’re worried about the rise of electronic trading?
MK: There’s more algorithmic trading, where algos are going through headlines or sorting through earnings statements or going through social media in real time and trading. What are the consequences for investors?
We’re seeing reaction time get shorter and shorter for releases, which can also incur costs or take advantage of slower human investors. There are signs of potential abuses with social media posts and headlines. That’s going to get worse and worse and be more of an impediment for human investors to make money. It’s going to cause more confusion in the marketplace.
If you run a certain strategy, how do you insulate yourself against these things? If these algos are taking advantage of you on the liquidity side, maybe they are also taking advantage of you with social media and news headlines. Can you have some sort of countermeasures, if you have a strategy that is vulnerable to that type of thing? What are the limitations? What are the regulatory angles as well? If someone is creating fake tweets to hurt your strategy, are you allowed to defend yourself by throwing off that algorithm? Where’s the limit of market manipulation vs. defense?
JO: How do you use data and the advances in computer science?
MK: We use big alternative data and AI to gauge trends that people aren’t picking up with a conventional way of thinking and try to see how that may impact the markets. We look at the sentiment indicators—we would do textual analysis, how much something is mentioned in the news, how often corporates are mentioning it. Looking at these alternative data sets and some of these newer methods of machine learning and applying them to specific questions we have about the markets. Last year we spent a lot of time on big data and AI. This year is more macro-driven, with interest rates, the fears about trade wars, and then quantitative strategies.
Ossinger is a markets editor at Bloomberg News in New York.
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