In this episode, the Concretum Research Team breaks down a hidden but powerful force in systematic investing: Rebalance Timing Luck—the idea that two identical factor strategies can produce dramatically different long-term returns simply because they rebalance on different days. Drawing from a new study by Carlo Zarattini and Alberto Pagani, the discussion reveals how a concentrated U.S. equity momentum strategy showed nearly 350 bps of annualized performance dispersion across monthly schedules, and why traditional fixes like tranching—splitting a portfolio into multiple staggered sub-portfolios—are not the free lunch they appear to be. While tranching reduces timing noise, it also increases trading costs, creating what the authors call The Tranching Dilemma, where the optimal number of tranches depends heavily on portfolio size: too few magnifies luck, too many erodes returns. The episode offers a practical, cost-aware framework for determining the right balance, making it essential listening for systematic traders, quant PMs, and anyone curious about why “identical” smart-beta products often behave nothing alike.

In this episode, the Concretum Research Team discusses a new study at the intersection of artificial intelligence and systematic investing. Conducted with Finreon Asset Management, the research asks a simple but ambitious question: can a large language model like ChatGPT read corporate news and use that information to refine momentum-based investment strategies?

Using daily data on S&P 500 stocks, the study combines traditional price-based signals with ChatGPT’s interpretation of firm-specific news. The results show that this AI-assisted approach improves both stock selection and portfolio performance, delivering higher risk-adjusted returns than a standard momentum strategy — even in out-of-sample tests.

This Episode shows how individual investors can profit from the volatility risk premium using VIX-linked ETNs. A dynamic strategy tested from 2008–2025 yields strong returns with low equity correlation. With proper tools, volatility trading is now accessible—but must be approached cautiously.

We explore how a classic trend-following strategy using Donchian channels performs in crypto markets. Tested on Bitcoin and a broad altcoin dataset since 2015, this approach delivers strong returns and risk-adjusted performance. We also cover transaction cost management and real-world execution tips.

This paper explores the performance of trend-following strategies in U.S. stock markets over the past 70 years, addressing practical implementation challenges and techniques to reduce transaction costs.

In this episode, we share an approved Deep Dive by NotebookLM discussing the key insights from our paper, "Does Trend Following Still Work on Stocks?"

This AI-generated NotebookLM-approved episode shows how individual investors can profit from the volatility risk premium using VIX-linked ETNs. A dynamic strategy tested from 2008–2025 yields strong returns with low equity correlation. With proper tools, volatility trading is now accessible—but must be approached cautiously.

In this episode, we share an approved Deep Dive by Notebook LM, highlighting a crypto trend-following strategy that blends classic technical tools with digital asset data, as explored in a paper on Donchian channels and volatility-based rotation.