Flirting with Models

My guest is Roni Israelov, CIO of NDVR.  Prior to NDVR, Roni was a principal at AQR Capital Management, where he worked on global risk models, high frequency factors, and lead the development and oversight of options-oriented strategies. Taking a page from Roni’s career and research, our conversation is far ranging.  We discuss topics from global asset risk models to the application of high frequency signals to tail risk hedging.  While there are insights to glean in each of these topics, I think the conversation helps paint an insightful picture about how Roni thinks about research in general. Towards the end of the conversation we talk about the new research Roni is tackling at NDVR, a financial advisory firm for high net worth individuals.  The role brings new challenges to consider, such as liability management and risk tolerance within the framework of portfolio optimization.  Even though the topics differ, I think you’ll hear a very common thread in how the research is performed. Please enjoy my conversation with Roni Israelov.  

Doug Colkitt is an ex-high frequency trader, ex-MEV bot searcher, and founder of the decentralized exchange CrocSwap. In this episode, we talk about all three.  We begin with high frequency trading, where Doug walks us through the differences between maker and taker strategies, why queue position is so critical for makers, and why volatility is a high frequency trader’s best friend. We then discuss Ethereum-based MEV strategies.  Doug explains what MEV is, how the architecture of the Ethereum block chain allows it to exist, and a high level topology of the different types of MEV strategies that exist.  He also explains how the game theory behind MEV changed dramatically with the launch of Flashbots. Finally, we talk about his new decentralized exchange CrocSwap and its primary innovations, including dynamic fee levels, identification of toxic flow, and vaults that enable KYC. I hope you enjoy my conversation with Doug Colkitt.

My guest this episode is Jeff Yan, founder of Chameleon Trading.   Jeff began his career in high frequency trading at Hudson River Trading but soon moved over to the world of crypto where he built one of the largest market making firms in the space. After Jeff gets me up to speed with the basics of high frequency market making, we dive into some of the more esoteric components, particularly with respect to centralized crypto exchanges.  These include infrastructure quirks, adversarial algorithms, and why HFT P&L might actually be predictive of medium-term price movement. In the back half of the conversation, Jeff explains the problems he sees with current decentralized exchanges and introduces Hyperliquid, a new decentralized trading platform built on its own blockchain to provide performant order book execution for perpetual futures. Please enjoy my conversation with Jeff Yan.

Jason Buck is the co-founder and CIO of Mutiny Funds and maybe one of the most interesting people I know. Jason made, and subsequently lost, a fortune in commercial real estate in the 2008 crash.  This “ego destroying event” was the catalyst for him to completely rethink the idea of resiliency, both in business and investments. Jason spent the better part of the 2010s developing the Cockroach portfolio, a modern take on Harry Brown’s permanent portfolio.  A quarter stocks, a quarter bonds, a quarter CTA, and a quarter long volatility, Jason has designed the portfolio to provide all weather returns, with the possibility of serving as an entrepreneurial hedge. We discuss the value of tail hedging, tail hedges versus long volatility trades, the limits of manager diversification, and managed futures/CTAs versus static commodity positions. As a final note, this episode was recorded live at the Exchange ETF event in Miami. Enjoy.

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.