Ulf Holmberg

This study presents a preregistered out-of-sample replication of Holmberg (2024), where a variable derived from the Global Consciousness Project (GCP) appeared to improve stock-market forecasting models. Two frozen specifications were compared: Model A, a benchmark using conventional market and volatility variables, and Model B, which adds lagged Max[Z] values and related interaction terms. The preregistered hypothesis was that Model B would outperform Model A in total return, directional hit rate, and Sharpe ratio. The evaluation covers 198 trading days from late June 2025 to March 31, 2026, when a disruption of the original GCP data stream ended the prospective simulation earlier than planned. In the shutdown-excluded specification, Model B outperformed Model A on all three preregistered metrics. Total return increased by 7.60 percentage points, the Sharpe ratio rose by 0.07, and the hit rate improved marginally from 65.25% to 65.96%. Including the shutdown period, Model B retained a smaller return advantage of 3.06 percentage points and a higher Sharpe ratio, although hit-rate performance turned negative relative to the benchmark. Overall, the results support a narrower, twice- observed pattern: GCP-derived attention-related variables may contain incremental forecasting information under particular market conditions, mainly expressed through simulated return performance rather than broad directional accuracy.

Ulf Holmberg holds a PhD in Economics and an MSc in Statistics. He works as a Senior Analyst and Functional Lead at a large Nordic financial institution, where he leads macroeconomic scenario design in a risk management context. He has also served in international expert groups and provided technical assistance to foreign central banks through Sweden’s foreign aid program.

In parallel, he runs research focused on a simple but unusual question: whether GCP-derived coherence measures contain empirical information about financial markets, collective attention, and probabilistic physical systems.

Since 2020, this work has produced five peer-reviewed publications in Journal of Consciousness Studies, Journal of Consciousness Exploration & Research, Explore, Journal of Economic Studies, and Journal of Scientific Exploration.

To account for the observed patterns, he developed the Cognitive Entropy Shift Model (CESM), a Bayesian-informational framework that distinguishes passive emotional attention from goal-directed intention, each hypothesized to produce measurable entropy reductions in probabilistic physical systems. CESM was tested in a two-year controlled RNG experiment and published as Can Consciousness Nudge Randomness? in Journal of Scientific Exploration. Coherence Analytics is the platform built to move this research from retrospective analysis toward prospective, public, and reproducible testing.