Real Talk With Michal Dziegielewski Of FQS Capital Management

Aug 25 2017 | 3:06pm ET

Today, we’re sitting down with Michal Dziegielewski, the senior member of the research team and a member of the Investment Committee of New York-based quantitative fund of hedge funds FQS Capital Partners. The firm offers multi-strategy, long/short and quantitative fund of hedge fund portfolios.

Prior to FQS, Dziegielewski was the strategy head for Global Macro, fixed income and credit strategies at FIM. Before moving into that role, he was the strategy head for U.S. Equity Long/ Short, and prior to working at FIM, was an investment analyst at Ivy Asset Management. Michal obtained a BA in Political Science from Grinnell College and an MA in Economics from the University of Delaware. He is a CFA charterholder.

FQS Capital Partners is an alternative investment specialist led by Dr. Robert Frey, a former managing director of Renaissance Technologies. He is well known for his research and experience in quantitative asset management. FQS seeks to exploit its unique quantitative heritage to research, develop and utilize innovative tools and techniques to better understand, rank and optimize hedge fund risks and returns. 

FINalternatives: We were at SALT in May, and quantitative strategies really generated a lot of buzz. What is your view on how this space is evolving? 

Dziegielewski: It has been amazing to see the increasing amount of focus on quant over the last couple of years, but it’s not surprising. We think we are still in the early innings of several powerful long-term trends coming together and changing the game for quantitative investing. These include increased computing power, cheaper technology, and more and more data becoming available, and have led to an impressive advancement in the depth and sophistication of several types of quant strategies. It has also benefitted the development of factor-driven investment approaches, like smart beta and risk premium investing. From my perspective, it’s hard to see what stops these trends. 

How are you seeing investors utilize quantitative strategies? How do they best fit into a broader portfolio?

There are a few ways. While quants tend to all get lumped in together, there are really several distinct styles. CTA trend followers have long been utilized for their tendency to perform well during periods of rising volatility. Longer-term smart beta equity strategies and risk premium strategies can serve a useful role in a portfolio of long-only or passive investments as a source of diversification. And then there are the advanced stat arb shops that are aiming to be pure alpha generators. In today’s environment, this seems to be one of the only areas where it’s still possible to get attractive returns with low correlations while remaining in liquid markets. That’s not to say it’s easy, of course – the competition is significant. 

Modern Trader has done many stories about the future of machine learning and artificial intelligence. This is an evolving industry. But what is your candid opinion... is this a place where there is real opportunity to make money in the next few years, or are the headlines just hype?

The promise is definitely exciting. Can A.I. unlock a huge amount of alpha that is hiding in these massive new datasets? Or for that matter, can it find alpha that has been hiding in plain sight for years in regular price data? I can see why there’s excitement, and we’ve certainly see a few funds have some success in this area, but like with many things, the marketing spin has gotten a bit ahead of where we are today. Almost everyone we meet is using or experimenting with some elements of machine learning to solve certain types of problems, but this is not an automatic path to success – there are many potential pitfalls. While there are some notable entries in this space, very few are doing full end to end autonomous trading systems that you might call “A.I.” The potential is there, but the jury is out on whether the results will be able to match the potential. 

Given the secrecy and limited transparency around many quantitative strategies, how are you able to properly evaluate these funds?

We take a two-step approach. First, we look at the data. We want to understand a system by watching what it does and studying its behavior rather than necessarily cutting it open. We study its returns, exposures, attribution, and are often able to come up with a good sense of whether it is generating truly uncorrelated returns or taking on some kind of systematic risk. Second, while managers are understandably secretive around alpha models, and in some cases are even limited in their own ability to understand what is going on, when it comes to describing risk models, transaction cost models, and technology – most managers are willing to talk. We have PhD’s in mathematics and computer science, as well as our founder with his unparalleled experience, who can help evaluate whether managers are implementing best practices in these areas and thereby help determine whether managers are truly best in class.

A follow up to that: When you are doing quantitative analysis, what role does qualitative insight come in when one picks and chooses their positions?

That’s key too. Aside from trying to understand the quality of the team as it relates to specific skills, you also need to understand their mindset and approach. We want managers who are well aligned with investors and who are eating their own cooking. We also want them to be incredibly risk conscious – when difficult periods come, we don’t want them to be surprised, we’d like to see some plan in place. 

What makes a great quant manager? Does it require some advanced expertise? Are there common qualities you look for when analyzing managers?

More than anything else, we think that a great quant has to be a great scientist. They can’t take anything for granted and have to be indefatigable about testing hypotheses and questioning their assumptions. And to find truly new things, they have to be creative as well. Experience matters too – it is one thing to read about August 2007 and 2008, and it is quite another to have been there. 

What do you see as the biggest risks for this space going forward? What are the biggest opportunities?

Clearly a lot of money has moved into this space, and there is a question of how much capacity certain sub-strategies can handle. We are seeing some of the most well known signals deteriorate in performance. Part of that may be crowding, and a low volatility environment almost certainly contributes. To deal with this, we have placed an even greater emphasis on identifying managers we think are doing something truly uncorrelated and are not entirely dependent on a high volatility environment to succeed. 

What strategy outside of your area of focus do you think offers promise and opportunities for investors?

While quant is a big piece of our portfolio, we invest across a broad range of strategies. In the discretionary world, we like managers that we think are insulated in some way from the increasing computerization of markets and who are adding value in some tangible way. Distressed debt is a classic example, and despite a low default environment, we have seen some managers continue to generate alpha in this environment by stepping into the void left by the big banks, most of whom no longer have the capability to lend in more complex, less liquid situations. 

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