Concretum Research

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

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.

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.

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.

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.

In this episode, we share an approved Deep Dive by Notebook LM, discussing the experiment conducted by Carlo Zarattini and Marios Stamatoudis, as presented in their fascinating paper titled "The Power of Price Action Reading."

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 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.