The exponential growth and variety of studies on returns highlights the lack of comprehensive asset pricing theory for explicitly explaining the empirical data. The paper addresses this challenge by proposing a hierarchical state-based asset pricing model based on two interconnected solutions: the state space of assets and explanatory gain decomposition approach. As a result, the states of assets extend the conventional state of nature for bringing fundamental and macroeconomic characteristics into existing asset pricing models. Then, the decomposition approach tackles the complexity and heterogeneity of asset pricing by advancing all-in analyses with hierarchical piecewise finer-grained regressions. The direction is demonstrated with a multi-step analysis subsequently boosting the explanatory power of regressions between price-to-fundamental ratios and asset quality characteristics and resolving the weak correlation between the HML and RMW factors. Furthermore, the proposed model establishes a direct link between theory and empirics encompassing multi-dimensional data and growing stack of data science techniques.