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.

Mads Ingwar and Martin Oberhuber - Full Stack Machine Learning (S3E8)

Flirting with Models is the show that aims to pull back the curtain and meet the investors who research, design, develop, and manage quantitative investment strategies.

Join Corey Hoffstein, Chief Investment Officer of Newfound Research, on a journey to explore systematic investment strategies, ranging from value to momentum and merger arbitrage to managed futures.

For more on Newfound Research, visit www.thinknewfound.com.

Business

Investing

Careers

Management & Marketing

Business News

Shopping

In this episode, I speak with Nicolas Mirjolet, CEO and Co-Head of Research at Quantica Capital. We begin with Nicolas’s experience operating a statistical arbitrage fund, where he provides his thoughts as to what makes a strategy easier or harder to scale a business on.  Nicolas also provides some context for his somewhat counter-intuitive view that the larger players had a bigger edge in this capital constrained space. We then transition to Quantica’s flagship managed futures program.  Nicolas explains that while Quantica is a price-based trend follower, they apply a multivariate approach to their signal analysis.  We discuss how the approach works and how it contrasts against a standard univariate approach.  Specifically, Nicolas shares his thoughts on how the multivariate approach impacts the portfolio return profile and why you may want more or fewer variables in your signal universe than your tradable market universe. We end the conversation with Quantica’s most recent quarterly research paper, which provides quantitative insight into the convexity versus robustness tradeoff trend managers make when they add more markets to their portfolio. Please enjoy my conversation with Nicolas Mirjolet.

Nicolas Mirjolet - Multivariate Trend Following (S7E8)

Welcome to the inaugural episode of the Get Stacked Investment Podcast. This episode brings together Corey Hoffstein, Rodrigo Gordillo, Mike Philbrick, and Adam Butler to dive deep into the concepts of Return Stacking, market efficiency, and investment strategies beyond traditional stock picking. Providing insights into Return Stacking's relevance in today's investment landscape, the importance of structured diversification to enhance portfolio sustainability and its potential to create excess returns with more confidence than traditional stock picking. This podcast episode serves as a comprehensive introduction to Return Stacking and provides valuable insights for investors looking to navigate the complexities of modern markets with innovative strategies.

[PREVIEW] Enter the New World of Return Stacking | Get Stacked Podcast

In this episode I chat with Markku Kurtti, author of the blog Outcast Beta. Markku is classically trained as an electrical engineer and works on receiver algorithms for mobile phones.  A passion for investing, however, lead him to pursue an MS in Finance and an interview with Ed Thorp compelled him to devote his time to better understanding compounding processes. This obsession has driven him to develop a number of analytical and numerical models that provide differentiated insights into topics such as “why do most individual stocks historically underperform cash,” “how many stocks should an active manager actually hold,” and “how does the uncertainty of uncertainty help explain the equity risk premium puzzle?” With Markku’s work, I’m reminded of the phrase: all models are wrong, but some models are useful.  His outsider’s take provides some unique insights into the benefits, and opportunity costs, of diversification. I hope you enjoy my conversation with Markku Kurtti.  

Markku Kurtti – Diversification is a Negatively Priced Lunch (S7E7)

In today’s episode I speak with Otto van Hemert, Director of Core Strategies at Man AHL. After briefly touching upon Otto’s background, we dive into one of his most popular papers: The Best Strategies for Inflationary Times.  Otto shares the inspiration for the research as well as some of what he feels were the less obvious results. Trend strategies, which were a standout winner in the inflation resilience horse race, serve as the bridge to a discussion on seasonality.  Interestingly, Otto’s research suggests that long-term trend signals are actually capturing seasonality effects! Otto shares his thoughts on different approaches to measuring seasonality, why he believes seasonality emerges in both commodities and financial markets, and how to think about combining trend and seasonality in a single portfolio. Please enjoy my conversation with Otto van Hemert.

Otto van Hemert - Seasonality Everywhere (S7E6)

My guest this episode is Clayton Gillespie, VP at Deutsche Bank where he works in quant equity research for the QIS team. Clayton began his career at Credit Suisse HOLT, where he got his hands dirty in extracting fundamental information.  This formative experience dramatically impacted how he views how fundamentals should be incorporated into quantitative equity strategies. Today, at DB, he strives to improve quantitative equity strategies by anchoring them with a strong fundamental understanding. We discuss how fundamental and statistical interpretations can be at odds, how a strong fundamental understanding can help with the identification of emergent risk factors during regime changes, and how best to incorporate fundamental insights while avoiding potential biases from the analysts who deliver them. Please enjoy my conversation with Clayton Gillespie.  

Mads Ingwar and Martin Oberhuber - Full Stack Machine Learning (S3E8)

Download our free app to listen on your phone