Personal Introduction


Thank you for the invitation to join this group. I’m thrilled and wanted to take a moment to introduce myself.

I’m Ulf Holmberg, a Ph.D. economist based in Stockholm, working at one of Sweden’s largest banks, specializing in macroeconomic tail risk assessment. My professional journey has been a blend of economics and a unique research interest.

Since 2020, I’ve been delving into research using data from the Global Consciousness Project. I’ve uncovered correlations by analyzing daily aggregates from this data alongside stock market returns and Google search trends. These findings have opened up possibilities for practical applications and in my most recent research project, I’ve demonstrated that GCP data, when combined with market sentiment measures, can enhance trading models. This was shown through a year-long out-of-sample simulation study.

I’m looking forward to sharing insights and to engage in thoughtful discussions.

Best regards,

Possibly, you’ve done more than GCP with your research to analyze data.
Because despite of all incredible amount of observations, GCP has bad methodology. They failed to provide distinction between significant events and insignificant ones. Correlations between stock market, google trends and GCP data is much more firm evidence.

Could you share your results?


Holmberg, U. (2020). “Stock Returns and the Mind: An Unlikely Result that Could Change Our Understanding of Consciousness”, Journal of Consciousness Studies, 27(7-8), pp. 31-49.

Holmberg, U. (2021). “Revisiting Stock Returns and the Mind: Digging Deeper into the Data”, Journal of Consciousness Exploration & Research, 12(3), pp. 207-223.

Holmberg, U. (2023). “Validating the GCP Data Hypothesis Using Internet Search Data”, Explore (NY), 19(2), 228-237.

And finally, my most recent but yet-to-be-published work:

Holmberg, U. (2023). “Market Sentiment and the Global Consciousness Project’s Data: Exploring a Surprising Link and Demonstrating Its Usability”, unpublished manuscript.

You can download them all from my website:

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Greetings Ulf.
I am pleased to welcome you to our group. I consider the most important application of MMI-type systems is obtaining information that is otherwise not computable (non-inferable). I previously had a system that could be used to predict some select indices, such as QQQ, in real time. To do that I also had to develop an algorithm that can calculate the volatility of stocks or indices effectively in real time so I could use their probability distribution functions to calculate the probability of a particular sized change during a specified time period. That system is no longer operational since the infrastructure is complex and expensive, including a real-time (tick-by-tick) index and stock quote with local error checking that delivered the actual trade prices in a small fraction of a second.

Any way I can help with your endeavors or related interests, please let me know. Best wishes, Scott

Hi Ulf, I’m really happy that you’ve joined the forum. I came across Ulf’s papers when I signed up to the new GCP 2.0 project that is being run by the HeartMath institute a few months ago and invited him here. I purchased one of their QRNGs to participate in the project.
As far as I can tell they haven’t made any of their raw data available like the original GCP v1 project run by Roger Nelson et all but I’ve been in touch with some of them and we can expect some amazing things in the future. Their chief data scientist is also a member of this forum. That’s how I found out about GCP 2.0.
Looking forward to one day analyzing gcp 2 data with Ulf’s theories.

Fascinating stuff, took a brief look at your papers and will dive in more this week. Welcome!

Your results are outstanding. That research might be first scientifically valid proof of GCP point.
However, if I understand correctly, higher MaxZ precedes fall of indexes statistically. So, statistical anomalies in GCP Data can predict market shocks?

Also, google search analysis is interesting. But I’d question your methodology with word weight values. I’d advise you to look at semantic vector embeddings. That approach allow to catch meaning of sentences numerically.

Thanks, Scott. Looking forward to interesting discussions going forward!

Thank you all for the warm welcome. Looking forward to interesting discussions going forward!

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