Conditional Multifactor Assets Pricing Model: An Empirical Analysis of Anomalies in the State Space Framework

Factor pricing models are commonly used to assess portfolio risk and predict returns. Factor pricing models are commonly used to assess portfolio risk and predict returns. These models establish a connection between portfolio risk and a set of common factors, which may each have multiple dimensions. The effectiveness of a factor model depends on the selection of risk factors and their perceived sensitivity. In this paper, a Kalman filter-based conditional multifactor price model is employed to examine the influence of fundamentals and macroeconomics on industry portfolios. The approach taken in this study differs from the existing literature in the sense that the time-varying sensitivity of each factor is treated as a series of random processes. In a cross-sectional setting, a sector-based factor model can be used to reduce the possibility of measurement error caused by uncontrolled variables, in particular factor sensitivities. The empirical analysis demonstrates that, with the exception of the travel and leisure industry, the market factor has a substantial impact on the returns of most industries. On the other hand, fundamental components showed statistical significance at 1%, 5%, and 10% levels in explaining sector returns across all industries. The results underline the everlasting significance of fundamental variables across all sectors and the central role of the market factor in generating profits for most enterprises.