Quantitative finance has traditionally approached alpha as a static predictive phenomenon derived from persistent statistical relationships within financial data. Existing research predominantly focuses on the identification, optimization, and validation of trading signals capable of generating excess returns. While these efforts have significantly advanced systematic investing, comparatively limited attention has been devoted to understanding how alpha opportunities emerge, evolve, compete, and persist within adaptive financial ecosystems. In practice, financial markets consist of heterogeneous participants operating under varying objectives, informational constraints, and technological capabilities. The interactions among these participants continuously reshape the environments within which quantitative strategies function, thereby influencing the life cycles of alpha-generating mechanisms. This preprint introduces a systems-oriented framework for examining quantitative alpha as an emergent property of evolving market ecosystems rather than as a collection of isolated predictive signals. Drawing upon concepts from adaptive market theory, systems thinking, market microstructure, evolutionary finance, and organizational learning, the study proposes that alpha opportunities should be interpreted as dynamic entities whose persistence depends upon interactions among market participants, structural conditions, technological innovation, and competitive pressures. The framework advances the notion that the sustainability of alpha is determined not solely by predictive accuracy but also by the capacity of strategies to adapt within changing financial environments.
Anshuman Sinha (Mon,) studied this question.