The Illusion of the Carbon Premium

Carbon that has not yet been emitted should not be used to predict stock returns. While this sounds obvious, prior research papers have done exactly that. This critical observation forms the basis for the Robeco Institutional Asset Management research team’s re-examination of the relationship between climate risk and asset pricing. Investors and academics alike have sought to understand how environmental factors influence stock returns, often assuming that higher emitters command a risk premium. However, the timing of data availability is crucial in quantitative strategy formation, and misalignments here can lead to spurious conclusions about the pricing of carbon emissions.

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Quantpedia’s Research Workflow: From Idea Discovery to Portfolio Construction

Quantitative strategy research is rarely about discovering a single “perfect” trading rule. In practice, robust portfolios emerge from a structured research process that filters ideas, evaluates evidence, and combines complementary strategies.

In this article, we demonstrate how such a workflow can be implemented using the tools available in Quantpedia Pro. Rather than focusing on maximizing the performance of a single strategy, we walk through the research process step by step—from thematic filtering to portfolio-level evaluation.

To make the process concrete, we use value-based equity strategies as our working example. However, the goal of the article is not to identify the ultimate value strategy, but to illustrate how a systematic research workflow can be used to build a diversified portfolio of strategies around any investment hypothesis.

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Timing Value vs. Growth: Evidence from 100 Years of Small Value–Large Growth Spread

The goal of our article is to examine the long-term relationship between small value and large growth stocks using more than 100 years of data and test whether the spread between small value and large growth portfolios shows trends that could help investors switch between the two styles. Using the Fama and French 2×3 and 5×5 size and book-to-market portfolios, we construct the small value minus large growth (SV–LG) spread and apply simple trend-following signals based on moving averages and momentum with horizons ranging from 3 to 12 months. Our results show that trend-following strategies are able to capture part of the value outperformance on the long side. Timing periods when growth stocks dominate is much more difficult.

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Anomaly-Based Trading Strategies in the Real Estate Sector. Can the Market Be Beaten?

This study examines the effectiveness of several anomaly-based trading strategies applied to the real estate sector represented by the RlEst index from the Fama–French 48 industry portfolios. Using monthly data from July 1, 1926, to December 1, 2025, we analyze whether selected strategies are capable of generating superior risk-adjusted returns compared to both the standalone RlEst index and the broader market represented by the Fama–French 12-industry portfolios. The tested approaches include trend-following strategies based on moving averages, momentum strategies based on the rate of change of the index, and seasonality-based strategies utilizing different look-back periods.

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Quantpedia in February 2026

Hello all,

What have we accomplished in the last month?

– Revisited and significantly expanded the Trend/Reversal Analysis Report
– 8th episode of our YouTube video series QuantBeats, this time with Jiri Mrkva
– Invitation to Future Alpha conference
– Quantpedia Awards 2026 reminder
– 11 new Quantpedia Premium strategies
– 5 new related research papers
– 7 new backtests written in QuantConnect code
– and finally, 5 new blog posts on our Quantpedia blog

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