In this special episode of Flirting with Models, I’m joined by two guests: Andrew Beer of DBi and Adam Butler of ReSolve Asset Management.Rather than my usual interview format, I wanted to foster a conversation about the replication of managed futures strategies. Specifically, I wanted to bring on two practitioners who both share the same high level beliefs – namely that more investors should allocate to managed futures, that managed futures are well suited for replication, and that replication can help dramatically reduce fees – but differ on the implementation details.And it is in that disagreement that I hoped to highlight the different pros and cons as well as any embedded assumption in any of these replication approaches.We discuss return-based replication, process-based replication, determining the number of markets to trade, expectations for tracking error, and more.I hope you enjoy this episode with Andrew Beer and Adam Butler.

Today, August 28th, 2023, my company Newfound Research turns 15.  It feels kind of absurd saying that.  I know I’ve told this story before, but I never actually expected this company to turn into anything.  I started the company while I was still in undergrad and I named it Newfound Research after a lake my family used to visit in New Hampshire.  I fully expected the company to be shut down within a year and just go on to a career on Wall Street.But here we are, 15 years later.  I’m not sure why, but this milestone feels larger than any recent birthday I can remember.  I’m so incredibly grateful for what this company has given me.  I’m grateful to my business partner, Tom.  I’m grateful to employees – both past and present – who dedicated part of their lives and careers to work here.  I’m grateful to our clients who supported this business.  I’m grateful for all the friends in the industry that I’ve made.  And I’m grateful to people like you who have given me a bit of a platform to explore the ideas I’m passionate about.Coming up on this anniversary, I reflected quite a bit on my career.  And one of the things I thought about was all the lessons I’ve learned over the years.  And I thought that a fun way to celebrate would be to take the time and write down some of those ideas and lessons that have come to influence my thinking.So, without further ado, here are 15 lessons, ideas, and frameworks from 15 years.

In this episode, Corey Hoffstein, CIO of Newfound Research, Rodrigo Gordillo, President of ReSolve Global* and Adam Butler, CIO of ReSolve Global, delve into the concept of return stacking and introduce the innovative RSBT Return Stacked™ Bonds & Managed Futures ETF.This podcast is essential for investors, financial advisors, and anyone interested in learning more about return stacking, the RSBT ETF, and the potential benefits of combining bonds and managed futures for portfolio diversification and risk management. Don't miss out on this insightful conversation to deepen your understanding of these innovative investment strategies and their potential impact on today's complex financial markets.They cover a wide range of topics, including: • The motivation behind the return stacking concept and its relevance in today's market environment • The history of institutional leverage and diversification in retail portfolios • The advantages of using return stacked strategies for portfolio construction and risk management • The role of bonds and managed futures in building a robust, diversified investment portfolio • The importance of low correlation between asset classes for effective diversification • The mechanics of combining bond exposure with a managed futures overlay in the RSBT ETF, including the use of cash collateral and Treasury Futures • The benefits of using ETFs as capital-efficient building blocks for return stacking • The potential for a family of return stacked ETF products to address various investor needs and preferences • The significance of managed futures as a "third leg of the stool" for managing inflation and mitigating market risks • The challenges and opportunities related to implementing managed futures strategies and managing leverage in retail portfolios • The goal of matching the RSBT ETF's bond strategy to core US fixed income, such as the Bloomberg US Core Aggregate Bond Index, and adjusting duration accordingly

There is no doubt that the tools of machine learning and the promise of artificial intelligence has captured the imagination of quantitative researchers everywhere.  But I am aware of few fund managers who have wholesale adopted the ideas into their investment stack as thoroughly as Angus Cameron. In this dive back into the archives, we return to Season 4, Episode 6 where I spoke with Angus about his background as a discretionary macro trader and his evolution into a fully systematic, machine-learning driven investment stack.  Not just in how signal is identified, but in how trades are structured and managed. If the idea of a swarm of AI trading bots doesn’t get you excited, this might not be the episode… or the podcast… for you!

In our industry, we’re all too often guilty of asking, “what is your alpha,” rather than, “what is your process for finding alpha?”  Yet, in the long run, it is the process that is important.   I’m equally guilty of this.  In the history of this podcast, I’ve probably overemphasized the outcome of research versus the process of research. There are a few exceptions, though.  And in this dive into the archives, I wanted to return to Season 2, when I spoke with Chris Meredith, Co-Chief Investment Officer at O’Shaughnessy Asset Management. There are a lot of nuggets in this episode, ranging from ingesting data to working with research partners to a discussion of hardware setup.  But the part that has always stuck with me the most was Chris’s process for prioritizing research proposals based upon an AUM-scaled information ratio.   I’ll let Chris explain.  Enjoy.

In July 2020 I interviewed Cliff Asness, co-founder of AQR.  This was several months after he penned a perspective piece titled The Valuesburg Address, where he waxed poetic about the multi-year drawdown in the value factor. Nearly three years later, he recently wrote the perspective piece titled, The Bubble Has Not Popped.  I say wrote, but it is just a single image of the value spread between growth and value, adjusted for just about every possible noise factor you can imagine.  The spread still hovers near generational highs. This isn’t Cliff’s first value drawdown.  While never easy, I suspect his past experience at least makes it a bit easier. In this archive clip, I wanted to highlight the wisdom of experience.  To me, that entails understanding what you know, what you wish you could know, and what you believe. I hope you enjoy.

It’s no secret that high flying growth stocks were hammered in 2022, so I thought it would be fun to revisit my conversation with Jason Thomson back in Season 3.   Jason is a portfolio manager at O’Neil Global Advisors, where he manages highly concentrated portfolios of growth stocks. Now, Jason is a discretionary PM, which may seem like an unusual guest for a quant podcast.  But his approach is so data and process driven, it’s hard to tell the difference.   I selected a few questions about his take on growth investing in general, but I’d highly recommend you go back and listen to the original episode for his thoughts on portfolio construction and risk management as well. Enjoy!

After March 2020, a growing research interest of mine was the question, “how do strategies reflexively impact the markets they trade?”  Beyond crowding risk, can adoption of strategies fundamentally change market dynamics. In Season 3 Episode 11, I spoke with Omer Cedar, who argues that equity quants have done precisely that.  The mass adoption of factor models, whether for alpha or risk, fundamentally changed how baskets of stocks are bought and sold.  For a discretionary manager to ignore this sea change is to ignore a fundamental shift in the current of the water they swim in. In this clip from the episode, Omer discusses how quants have changed the market and how fundamental managers should use this information to sharpen their edge.

We’re back with another clip from the archives.  This time it’s Season 4 Episode 9 with Vivek Viswanathan. For three decades, equity quants have largely lived under the authoritative rule of the Fama-French 3 Factor Model and linear sorts.  In this episode, Vivek provides an cogent alternative to the orthodoxy.  Specifically, he explains why an unconstrained, characteristic-driven portfolio can more efficiently capture behavioral-based market anomalies.  I think this is a master class for alternative thinking in quant equity. It was really tough to clip this episode.  Vivek’s comments about Chinese markets provide a tremendous example about finding alpha in alternative markets.  But I’ll leave that for you to go back and dig out! Okay, let’s dive in.

We’re rewinding to Season 3, Episode 3 to chat with Benn Eifert, founder of QVR.   Benn was my first repeat guest and this is probably one of our more popular episodes. Instead of the usual interview format, I called this episode “Bad Ideas with Benn Eifert,” and basically just asked him a bunch of questions about naive option trades and whether they are a good idea or not. For anyone starting their journey with options or volatility, the whole episode is a must listen. The clips I chose here were selected because I thought they provided a really good cross-section of topics in the world of options while highlighting one important common thread: the risk of unintended bets.  I think this is one of the most universally important concepts in trading and investing, and Benn really drives the points home here as we cover topics ranging from writing options for income to why VIX minus realized doesn’t mean what you think it does.  The subtle through line is the reminder that it’s what we don’t know we don’t know that will eventually get us in trouble.

We’re trying something new here, folks.  I’ve got 5 seasons and 60 brilliant episodes and I thought it would be fun, in the off season, to go back to the archives and highlight past conversations. So using my trusty random number generator, I chose an episode at random.  So, we’re going back to 2018 to my conversation with John Alberg, co-founder of Euclidean Technologies, where machine learning is applied to the value investing problem.   The part I’m highlighting starts around minute 20 and is about the formulation of the machine learning problem and how the research question should be asked.  I like this section because I think it really highlights how we can think about the tradeoff of degrees of complexity versus accuracy and the problem of overfitting. Enjoy!

In this episode I am going to read Newfound’s latest research paper, LIQUIDITY CASCADES: The Coordinated Risk of Uncoordinated Market Participants. This reading will refer to a number of figures within the paper, so I urge you to go to our website, thinknewfound.com, and download the PDF so you get better follow along. This paper is unlike any research we've shared in the past. Within we dive into the circumstantial evidence surrounding the "weird" behavior many investors believe markets are exhibiting. We tackle narratives such as the impact of central bank intervention, the growing scale of passive / indexed investing, and asymmetric liquidity provisioning. Spoiler: Individually, the evidence for these narratives may be nothing more than circumstantial. In conjunction, however, they share pro-cyclical patterns that put pressure upon the same latent risk: liquidity. In the last part of the paper we discuss some ideas for how investors might try to build portfolios that can both seek to exploit these dynamics as well as remain resilient to them. I hope you enjoy.