Flirting with Models

My guest in this episode is Kai Wu, CEO and founder of Sparkline Capital. Kai is a pioneer in the measurement of intangible value.  Using machine learning, he tackles unstructured data sources like patent filings, earnings transcripts, LinkedIn network connections, and GitHub code repositories to try to measure value across the four key pillars of Brand, Intellectual Property, Network, and Human Capital. We discuss why intangibles are important, how they differ from the traditional factor zoo, the opportunities and risks of unstructured data, and how even big data can have small data problems within it. Finally, we discuss Kai’s most recent applications of his research to the world of crypto. Please enjoy my conversation with Kai Wu.

Today I speak with David Sun, a retail trader who started his own hedge fund. Coming from a non-traditional background, David takes a non-traditional approach in his investment mandates.  Focused on selling options to capture the volatility risk premium, David believes that markets are ultimately efficient and therefore foregoes using any sort of active signal.  Instead, he focuses on explicitly controlling his win size relative to his loss size, and then choosing a strategy with a win rate that bumps him into positive expectancy.  By then maximizing the number of “at bats,” he lets the Central Limit Theorem take care of the rest.  It’s an approach he calls “expectancy hacking.” We discuss this approach in both theory and practice, addressing issues such as trading costs and slippage drag, as well as both sequence and event risk.  David’s approach is certainly non-traditional, but highlights some unique concepts of how traders may be able to architect a payoff profile around a risk premium. Please enjoy my episode with David Sun.

In this episode I talk with Aneet Chachra, fund manager at Janus Henderson. In his role, Aneet runs flow-driven strategies.  These are strategies that seek to find an edge in market events where trading volume creates a predictable pressure on price, such as index additions or deletions, corporate buybacks or issuance, or even the rebalancing of target date funds.  Our conversation is wide ranging, from the basics of how Aneet categorizes these types of trades, to views on how changing market structure has affected the opportunity set, to the impact of social leverage on risk management. While the approach may be highly niche, Aneet is bursting with broadly applicable wisdom. I hope you enjoy this episode with Aneet Chachra.

In this episode, I speak with the Twoquants: Moritz Seibert and Moritz Heiden. There are really two halves to this episode.  In the first, we discuss trend following strategies at length.  We cover the usual topics of signals, speeds, and portfolio construction before diving into some niche views, such as synthetic assets, spread trades, and alternative roll schedules. In the second half, we pivot to discuss crypto markets, as the Moritzes now serve as CIO and CTO for the Exponential Age Digital Asset Fund.  We discuss their journey into crypto, their explorations of the NFT space, considerations that make crypto unique from traditional markets from an allocators perspective, and advice for emerging managers in the space. So kick back, relax, and please enjoy my conversation with Moritz Seibert and Moritz Heiden.  

In a first for Flirting with Models, my guest this episode is anonymous, going only by the handle LightSpringFox on Twitter.   Mr. Fox is a quantitative trader who works in crypto market making at MGNR. Mr. Fox did not begin his career in crypto, nor even in market making.  Rather, his background is in traditional equity factor investing, and so we spend a good deal of comparing and contrasting the low- and high-frequency domains.  We also discuss the nature of market making edges, the unique risks of high frequency, how crypto and traditional finance market making deviate, and what Mr. Fox considers the “hardest problem in HFT.” Without further ado, please enjoy my conversation with LightSpringFox.

In this episode I speak with Michael Green, Chief Strategist as Simplify ETFs.  In a first for the Flirting with Models podcast, we recorded this episode live at the ETF Exchange in Miami in early April 2022.   Given Michael’s eclectic background, our conversation is wide ranging.  He has traded everything from small-cap value to commodities to housing derivatives to long volatility, and so we try to find the common elements and themes across his career.  One that sticks out is his quote that “it’s not enough to do the analysis: there needs to be a trade there as well.” Michael has become well known for his view that passive investing may now represent a systemic risk to markets.  We discuss the origins for this view, how it has evolved, counter-points, and the trade that pairs with the analysis.  Finally, we discuss the Simplify High Yield PLUS Credit Hedge ETF, the first strategy from Simplify that really has Michael’s fingerprints all over it. I hope you enjoy my conversation with Michael Green.

In this episode I speak with David Berns, co-founder and CIO of Simplify ETFs and author of the book Modern Asset Allocation for Wealth Management. Our conversation centers around the idea of what it means to build a portfolio for a human being. This concept arises both technically and philosophically in David’s work, where he emphasizes the importance of higher return moments in portfolio optimization, but goes about achieving this end through more holistic risk preference analysis. David expands upon the ideas of risk aversion, loss aversion, reflection, and how both our personal balance sheets and our standard of living expectations impact the portfolio choices we should be making. While there is no straight forward prescription, David emphasizes that simply being aware of these different factors can help advisors select more appropriate portfolios. And, hopefully, as the toolkit of investment options expand, adopt exposures that can better shape investor return distributions. I hope you enjoy my conversation with David Berns.

Today I am speaking with Russell Korgaonkar, CIO of Man AHL. In his role, Russell oversees a large research organization and so we spend a large part of our conversation talking about research management. Russell provides his thoughts on topics such as determining which projects to take on, quantifying investments in technology, data, and people, how to avoid group think, and how to incentivize both researchers and reviewers. There is tremendous organizational alpha to be gleaned here. In the back half of the conversation we discuss some of the research that Russell has published on dynamic risk controls. He explains how risk management signals are akin to alpha signals and how the practice of managing risk through 2020 differed from the theory of doing it. We conclude with Russell’s opinion as the most important due diligence question he could ask, either of another manager or of his own researchers. Please enjoy my conversation with Russell Korgaonkar.

Andrew Lapthorne is the Head of Quantitative Equity Research at SocGen, a role he’s held for nearly 14 years. Given the breadth of topics covered by bank research, it should be no surprise that this conversation takes some wide swings as well. We discuss everything from thematic baskets to style premia and machine learning to ESG. One of my favorite parts of the conversation is when Andrew discusses his research into strong balance sheet names in U.S. small-cap equities. For all the depth in discussion of how index composition rules affect small caps, why Merton’s distance-to-default correlates to credit cycles, and how this trade can potentially be a positive carry hedge, I love that the inception for the idea came from just updating spread sheets. While this podcast goes wider than it goes deep, Andrew’s experience allows him to sprinkle a bit of wisdom in every topic we hit. I hope you enjoy this episode with Andrew Lapthorne.

In this episode I chat with Greg Obenshain, Partner and Director of Credit at Verdad Capital. Prior to joining Verdad, Greg worked as the high-yield portfolio manager at Apollo Global Management and Stone Tower Capital. Despite his background as a fundamental analyst, Greg is a quant convert. His ideas are still grounded in a strong fundamental understanding of what it means to invest in credit, but in a sector where even just acquiring data may be an edge, he lets the data speak for itself. Greg argues that within credit, excess return comes from identifying improving and declining credit conditions. And, much like quantitative equity investing, there are certain characteristics that can provide insight into how those conditions might change. We discuss the counter-intuitive findings the data has brought to light, what Greg thinks most credit investors get wrong, and how to grapple with the dimensionality problem of fixed income. I hope you enjoy my conversation with Greg Obenshain.