Return Predictability

Research on forecasting returns, choosing prediction targets, and testing whether signals survive implementation.

Curated by Signal & Evidence. Read our research approach for how we distinguish author findings from house tests.

2026-09-09021

Predictable rebalancing does not guarantee a profitable front-run.

A plausible institutional flow meets weak futures returns and a stronger, imperfect bond proxy.

Study: The Unintended Consequences of Rebalancing

2026-09-09019

Feature structure reduces one model’s parameter burden, but the stronger portfolio claim depends on weighting and costs.

Study: Dependence-Informed Sparse Neural Architecture for Stock Return Prediction

2026-09-09018

A study of earnings announcements finds delayed macro information in subsequent stock returns. Trading it requires precise timing.

Study: Firm–speciﬁc information processing and the delayed discovery of macroeconomic news: evidence from earnings announcement returns

2026-09-09012

Rank dynamics offer a parsimonious portfolio design, but universe selection and uncertainty qualify the headline.

Study: Are Three Matrices All You Need To Beat the Market? Observable Matrix Dynamics for Portfolio Optimization

2026-09-08008

A useful distinction between the shape of returns and the source of expected profit.

Study: The Science and Practice of Trend-Following Systems

2026-09-08005

We changed one training choice and kept the rest of the experiment fixed.

Study: Getting the Target Right in Return Prediction

2026-09-08003

The disagreement measure is elegant. Substituting the event feed changed the research problem.

Study: News-based investor disagreement and stock returns

2026-09-08002

A classic classification rule met a much weaker result in our later US sample.

Study: Decoding Inside Information