Flirting with Models

My guest in this episode is Sandrine Ungari, Head of Cross-Asset Quantitative Research at SocGen.

Sandrine cut her teeth in the industry as a fixed-income pricing quant, but made her way over to sell-side, investment quant research in 2006. Her early research focused on credit and macro, but since 2012 has been heavily focused on equity and alternative risk premia.

Our conversation begins with equity factors and Sandrine provides insight both into how factor construction has evolved over the last decade as well as her thoughts into where the field is headed. We broaden our discussion to include alternative risk premia, and Sandrine provides a useful mental map for categorizing this broad range of strategies. We discuss the risks of crowding, latent beta risk in levered factors, and the influence of macro economic factors.

More recently, Sandrine has focused her research in the application of machine learning in strategy construction. We discuss one particular example – the application of a recurrent neural network in trend following – and Sandrine shares her views as to how machine learning might affect factor investing going forward.

Sandrine also shares some interesting ideas about where future risk premia might emerge from – but you’ll have to tune in to hear!

Please enjoy my conversation with Sandrine Ungari.

In this episode I speak with Dr. Michael Hunstad, Head of Quantitative Strategies at Northern Trust.

Our conversation centers around the four key trends Michael is seeing among institutional allocators in the factor space today. These trends are (1) the adoption of factors to manage concentration risk in market-cap weighted benchmarks, (2) a move from single- to multi-factor implementations, (3) using factors to de-risk equity exposure, and (4) a tactical tilt towards value.

But Michael isn’t afraid to get in the weeds. He discusses the risks of unintended exposures at length and at one point even explains the importance of matching decay speeds of different factor signals within multi-factor implementations.

For those interested both in the macro trends and the micro details of factor investing, this is not one to miss.

I hope you enjoy my conversation with Dr. Michael Hunstad.

In this episode I chat with Mads Ingwar and Martin Oberhuber, co-founders of Kvasir Technologies, a systematic hedge fund powered by a full-stack application of machine learning.

By full-stack I mean every layer of the process, including data ingestion, signal generation, portfolio construction, and execution, which gives us a lot to talk about.

Our conversation covers topics ranging from the limitations of machine learning and hard lessons learned to how to keep up in a rapidly evolving field and thoughts about managing model risk.

Given the niche knowledge in a field like machine learning, some of my favorite answers came when I asked how they might perform due diligence upon themselves or where they think other adopters of machine learning go wrong. For allocators, I think these answers are priceless.

I hope you enjoy my conversation with Mads and Martin.

My guest is Eric Crittenden, founder and Chief Investment Officer of Standpoint Funds.

Eric has spent his career with trend following strategies, first at BlackStar where he managed a fund-of-funds, then at Longboard, and now at Standpoint Funds. This background makes him not only a fountain of knowledge on trend following theory, but also the operational logistics and practical considerations.

In this episode our conversation ranges from the source of the trend-following premium to novel concepts for stress-testing managed futures programs. We discuss the struggles the space has faced, the evolution of CTAs, how to think about dispersion among managers, and how Eric thinks about solving for client behavior.

I hope you enjoy my conversation with Eric Crittenden.

In this episode I speak with Jeffrey Baird, managing partner at Merritt Point Partners.

Merritt Point Partners seeks to build diversified portfolios of convexity exposure through the commodities market. With that in mind, we talk about what makes the commodities market unique, who the players are, and the types of trades that Jeff looks for.

Stepping somewhat outside of the theme for this podcast, Jeff actually employs a heavily fundamentals-driven process. But what fundamental means in the commodity space is different than what it traditional means in the equity space, so Jeff walks us through how this concept applies in markets such as gold and natural gas.

With so many markets and corresponding derivatives to trade, the opportunity set seems overwhelming. And so does the risk of managing a portfolio. Jeff talks us through his framework for managing risk and the seemingly backwards idea that being profitable in a position can actually introduce more risk for portfolios seeking convexity.

I hope you enjoy my conversation with Jeff Baird.

My guest in this episode is Dr. Ernest Chan, founder of QTS Capital Management.

Investor, researcher, and educator, Ernie is well-known for his blog – which he has been publishing since 2006 – as well as the several books he has authored, including Quantitative Trading, Algorithmic Trading, and Machine Trading.

Our conversation meets at the intersection of tail risk hedging and machine learning. Ernie has a long history with machine learning, having first applied it on Wall Street in the late 1990s. After striking out on his own in 2006, he abandoned it due to the overfitting issues he believed it suffered.

In recent years, however, Ernie has re-adopted machine learning, believing that modern approaches help circumvent the overfitting problems and create robust, reliable models.

Specifically, Ernie applies machine learning as a risk-management layer on QTS’s Tail Reaper program, an intraday trend-following model designed to profit in periods of crisis. We discuss why such a program can be effective as a tail hedge and how the risk management layer can potentially help reduce the premium bleed typically associated with tail programs.

For listeners keen on understanding modern applications of machine learning, this is not one to miss.

In this episode I speak with Jim Masturzo, Head of Asset Allocation at Research Affiliates.

In his role, Jim oversees the research and publication of the firm’s capital market assumptions as well as the implementation of those views into a suite of tactical portfolios.

We begin our conversation discussing the foundational assumptions behind the capital market assumptions. Like most firms, Research Affiliates takes a long-term view on return and risk. In line with the firm’s guiding philosophy, they also introduce long-term mean reversionary effects.

Not surprisingly, these assumptions have been relatively bearish on U.S. equity returns for a large part of the last decade, and we discuss how to view the dispersion between these model forecasts and realized results.

We then shift our conversation to the application of tactical views. With capital market assumptions serving as the strategic backbone, Jim and his team develop a number of regime-based model portfolios that can be blended to express different tactical views.

But the team does not take a purely quantitative approach. Jim proactively acknowledges and seeks out model blindness. Rather than try to force idiosyncratic fixes into the models that might bias results, however, he and his team adopt qualitative trades to adapt the portfolios.

From strategic to tactical and quantitative to qualitative, this is a wide ranging conversation all about asset allocation. I hope you enjoy.

Today I am speaking with Benn Eifert, founder and CIO of QVR Advisors.

Benn is my first repeat guest on the podcast, making his first appearance in Season 2. When I asked listeners who they wanted on for Season 3, he was high on the list.

In this episode, we take things in a bit of a different direction. Rather than a normal interview, I use this opportunity to ask Benn about his opinion on a number of different trade ideas, from covered calls to shorting VIX ETPs. Benn walks me through the subtleties of each trade and why the PnL of what might look like a simple trade can be incredibly nuanced.

Towards the end of the conversation we turn to broader market topics and discuss the general impact of structured product desks and options dealers as well as Benn’s view as to whether March 2020 will create a lasting impact on volatility markets.

I hope you enjoy my conversation with Benn Eifert.

In this episode I am joined by Michael Krause, co-founder of Counterpoint Asset Management and Counterpoint Mutual Funds.

Our conversation covers two major topics. In the first half, we discuss some of the nuances of high yield bond timing and the subtleties of strategy construction.

In the second half, we discuss long/short equity strategies. For listeners more interested in the technical, this is where the meat and potatoes of the conversation lies.

We discuss Michael’s evolution from regression to machine learning techniques, the unintended consequences of accidental exposures, and managing risk through optimization while managing the risk of optimization.

I hope you enjoy my conversation with Michael Krause.

My guest today is K.C. Hamann, founder of AQIS LLC.

K.C. is a Warren Buffett disciple and spent his first decade in the industry working as an analyst at discretionary, deep value long/short equity hedge funds. Which probably makes him sound like an odd guest for a podcast all about quantitative investing.

K.C.’s experiences, however, lead him to identify a number of biases that he believes pollute the stock picking skills of discretionary analysts. And thinking of a hedge fund as a system whose first goal is survival, he believes that these biases are durable.

For K.C., 13F filings are prospect theory in action. By modeling both the universal and idiosyncratic biases of a manager, K.C. seeks to better identify cases of true conviction which often do not correspond to position size. And it is in these high conviction ideas that K.C. believes are the best opportunities to generate excess returns.

I hope you enjoy my conversation with K.C. Hamann.