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 speak with Giuliana Bordigoni, Director of Specialist Strategies at Man AHL.

In her role, Giuliana oversees the firm’s strategies that require specialist knowledge.  This includes, for example, alternative markets, options trading, credit, and machine learning.

We spend a good deal of time discussing alternative markets, a focus of Giuliana’s in both her current role and her prior as the Head of Alternative Markets.  We discuss the potential benefits and challenges of introducing alternative markets to existing CTA programs, unexpected roadblocks in doing so, and the opportunities that Giuliana is most excited about today.

We also discuss machine learning, which is treated as its own unique class of strategy rather than as a technique, and why Giuliana is so excited about systematic credit today.

I hope you enjoy my conversation with Giuliana Bordigoni.  

In this episode I speak with Adam Butler, co-founder and CIO of ReSolve Asset Management.  For full disclosure, at the time of recording I am personally an investor in one of ReSolve’s private funds.

Adam last joined the show in Season 1, where we discussed his background and philosophy of diversification.  This episode begins with a discussion of how Adam’s thinking and process has evolved over the last four-plus years, much of which is centered around the idea of experimental design.  Adam discusses the adoption of machine learning techniques, the spectrum of complexity between zero- and strong-prior signals, and how proper experiment design allows for greater process diversification.

The back half of the conversation dances across a few subjects.  We discuss topics such as seasonality, carry, the operational burdens of introducing a full-stack machine learning process, and the difficulties allocators face in introducing multi-strategy alternatives into their portfolios.

I hope you enjoy this episode with Adam Butler.