Show HN: RIMC – An Alpha-Drift Framework for Finite-Speed Learning Markets (github.com)

A new theoretical framework called RIMC (Recursive Intelligence Market Cycle) has been introduced, analyzing financial markets as dynamic learning systems that process information at finite speeds. Unlike traditional models that view markets as static equilibria, RIMC focuses on the interplay between technology and economic value through dynamic recursion, offering insights into how information is absorbed over time. It reinterprets the CAPM alpha (α) not as random noise, but as a product of delayed observations and learning biases, encapsulated in a structural drift term. RIMC's uniqueness lies in its dual-layer structure: a generative layer that captures the driving technological recursion ($R(t)$) and its economic value ($V(t)$), and an observational layer that accounts for the delayed and heterogeneous market perception of value. This framework provides a novel lens for understanding market behavior, suggesting that profitability or alpha may be derived from observing how efficiently the market learns rather than from merely asserting market efficiency. RIMC does not provide a direct trading strategy but serves as a conceptual tool for researchers and quant analysts to better understand the intricacies of information flow and learning dynamics in financial markets.