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Liquidity, Style Premia, And Dynamic Risk Adjustment: Evidence FromAsset Pricing Models And GMM-Based Micro Panel Inference
Issue Vol. 2 No. 02 (2025): Volume 02 Issue 02 --- Section Articles
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Abstract
Asset pricing research has long been preoccupied with the identification, interpretation, and robustness of return premia associated with firm characteristics and trading strategies, such as size, value, momentum, leverage, and liquidity exposure. While a vast empirical literature documents the persistence of these effects across markets and periods, substantial debate remains regarding their theoretical foundations, econometric identification, and sensitivity to methodological choices. In parallel, advances in dynamic panel econometrics—particularly the development of generalized method of moments (GMM) estimators—have provided researchers with powerful tools to address endogeneity, unobserved heterogeneity, and dynamic persistence in financial data. This article integrates these two streams of scholarship by examining how style-based asset pricing regularities can be more rigorously evaluated within dynamic micro panel frameworks using modern GMM inference techniques.
The study develops a comprehensive conceptual framework linking liquidity risk, firm fundamentals, and dynamic return behavior, emphasizing that asset pricing anomalies cannot be fully understood without careful consideration of time dependence and feedback effects. Drawing on foundational contributions in size and value effects (Banz, 1981; Basu, 1983), momentum strategies (Asness, 1997; Asness et al., 2013), and liquidity-adjusted asset pricing (Acharya and Pedersen, 2005), the article situates style premia within a broader risk-based and behavioral debate. Particular attention is given to emerging market contexts and non-U.S. evidence, which have been shown to amplify or challenge canonical findings (Barry et al., 2002; Agarwalla et al., 2017).
Methodologically, the article provides an extensive discussion of dynamic panel modeling in asset pricing, focusing on the role of lagged dependent variables, firm-level heterogeneity, and endogenous regressors. The accuracy and efficiency of alternative GMM inference techniques are critically assessed in light of the detailed simulation and analytical results reported by Kiviet et al. (2017), whose work demonstrates that estimator choice and finite-sample corrections materially affect inference in panels with characteristics typical of financial datasets. Building on this foundation, the article offers a purely text-based interpretation of empirical patterns that would be expected under different model specifications, highlighting how improper inference may exaggerate or attenuate perceived anomalies.
The results are interpreted as supporting a nuanced view of asset pricing premia: while size, value, and momentum effects remain economically meaningful, their statistical significance and economic interpretation depend heavily on dynamic specification, liquidity conditioning, and the choice of inference technique. The discussion situates these findings within ongoing theoretical debates, contrasts rational risk-based explanations with behavioral accounts, and outlines methodological limitations and avenues for future research. Overall, the article contributes to the literature by demonstrating that advances in econometric methodology are not merely technical refinements but central to resolving long-standing controversies in empirical asset pricing.
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References
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