Join the Neurah Project
📢 We invite scholars from diverse disciplines to submit proposals exploring fundamental questions across the interconnected domains of human knowledge.
🧠 The Neurah project seeks to foster collaboration and catalyze breakthroughs in theory and application through contributions in physics, biology, chemistry, mathematics, computer science, neuroscience, psychology, philosophy, economics, political science, sociology, art, and education.
🤝 By bringing together diverse perspectives, we aim to advance new ideas and frameworks with the potential to transform research and education across fields.
🔗 Learn more and apply at http://Neurah.com
📢 We invite scholars from diverse disciplines to submit proposals exploring fundamental questions across the interconnected domains of human knowledge.
🧠 The Neurah project seeks to foster collaboration and catalyze breakthroughs in theory and application through contributions in physics, biology, chemistry, mathematics, computer science, neuroscience, psychology, philosophy, economics, political science, sociology, art, and education.
🤝 By bringing together diverse perspectives, we aim to advance new ideas and frameworks with the potential to transform research and education across fields.
🔗 Learn more and apply at http://Neurah.com
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We are pleased to announce the launch of the NeuraStock project, a new research initiative by Artificial Neural Computing (ANC) dedicated to developing and testing theoretical models of financial market behavior. While ANC is not a financial services provider, this project represents a significant step toward building a scientific understanding of financial systems grounded in first principles.
The project began over a year ago with a foundational question: Can financial market behavior be modeled using tools from learning theory and neural physics? Through iterative cycles of theoretical development, numerical experimentation, and model refinement, we have constructed a predictive framework capable of generating consistent forecasts of market behavior. And no, it is not what is written in the books.
To test this framework, we developed the NeuraStock app (available on AppStore and Google Play), which currently implements the most basic version of our model — the local non-linear model. Despite its simplicity, this model predicts market direction correctly in approximately 52% of cases. While seemingly modest, this statistical edge translates into an annualized return of approximately 300%, as estimated through historical backtesting and early real-time trials. The model is available to the public free of charge for independent testing and verification.
Importantly, this is not a heuristic model; it is derived from theoretical principles that enable systematic generalization and extension. One such extension — the non-local non-linear model — is expected to yield annual returns exceeding 1000%. We are currently transitioning this model from simulation to real-time trading for empirical evaluation. If the model performs as expected, it will serve as a real-world confirmation of our theoretical framework. If not, the deviations will highlight important areas for further refinement and discovery. We plan to continue this empirical phase over the coming months.
Should the results align with theoretical predictions, we intend to publish the key findings — not only in the context of financial modeling but also within the broader effort to understand complex social systems. We believe the methods developed in this project may ultimately be applicable beyond finance, including in the study of social networks and political systems.
Stay tuned.
The project began over a year ago with a foundational question: Can financial market behavior be modeled using tools from learning theory and neural physics? Through iterative cycles of theoretical development, numerical experimentation, and model refinement, we have constructed a predictive framework capable of generating consistent forecasts of market behavior. And no, it is not what is written in the books.
To test this framework, we developed the NeuraStock app (available on AppStore and Google Play), which currently implements the most basic version of our model — the local non-linear model. Despite its simplicity, this model predicts market direction correctly in approximately 52% of cases. While seemingly modest, this statistical edge translates into an annualized return of approximately 300%, as estimated through historical backtesting and early real-time trials. The model is available to the public free of charge for independent testing and verification.
Importantly, this is not a heuristic model; it is derived from theoretical principles that enable systematic generalization and extension. One such extension — the non-local non-linear model — is expected to yield annual returns exceeding 1000%. We are currently transitioning this model from simulation to real-time trading for empirical evaluation. If the model performs as expected, it will serve as a real-world confirmation of our theoretical framework. If not, the deviations will highlight important areas for further refinement and discovery. We plan to continue this empirical phase over the coming months.
Should the results align with theoretical predictions, we intend to publish the key findings — not only in the context of financial modeling but also within the broader effort to understand complex social systems. We believe the methods developed in this project may ultimately be applicable beyond finance, including in the study of social networks and political systems.
Stay tuned.
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Forwarded from The World as a Neural Network
Not only curved space (which I already knew), but also curved space-time can emerge in learning systems governed by efficient learning algorithms.
https://www.researchgate.net/publication/396823864_On_the_emergence_of_spacetime_in_learning_systems
https://www.researchgate.net/publication/396823864_On_the_emergence_of_spacetime_in_learning_systems
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