System Shift Risk, Publication Effects, and the Decay of Stock Return Predictability: An Exploratory Empirical Framework - Raymond Rubianto Tjandrawinata (Return : Study of Management, Economic and Bussines, 2026)
The decay of stock return predictability after publication has been well documented, yet the underlying mechanisms are often explained as either statistical overfitting or market efficiency. This study introduces the System Shift Finance framework as a diagnostic lens to interpret why return predictability decays when a signal moves from private discovery into a public, crowded, and adaptive market environment. The framework codes seven system variables (System Condition, Domain Lock, Actor Complexity, Chokepoint Severity, Position Quality, Strategy Quality, and Feedback Maturity) and computes a System Shift Risk Score as the sum of pressure-side minus adaptive-side variables. Using three aggregate empirical phases drawn from the McLean and Pontiff-style return decay structure (in-sample discovery, out-of-sample pre-publication, and post-publication), this proof-of-concept validation employs descriptive statistics, correlation analysis, linear regression, logistic classification, feature importance, and cluster interpretation. Results indicate that a higher System Shift Risk Score is associated with higher total return decay, with Chokepoint Severity showing a particularly strong relationship with both total and post-publication decay. Position Quality and Strategy Quality behave as protective variables, while Feedback Maturity requires conceptual refinement as it appears to capture external market learning rather than internal adaptive capability. The study concludes that return predictability decay should be interpreted not only as a statistical or publication phenomenon but also as a system transition in which visibility, imitation, crowding, arbitrage pressure, and adaptive feedback jointly transform the profitability of trading strategies. The contribution lies in formalizing a systems-based explanatory architecture that can be scaled to anomaly-level datasets.