Introducing and Testing Firm's Life Cycle as a New Factor in Developing Multifactor Asset Pricing Models using Spanning Regression Approach

• Main Authors: Mehdi Mirzaie, Mahmoud Botshekan, Abdullah Khani
• Format: Article
• Language: fas
• Published: University of Isfahan 2020-09-01
• Series: Journal of Asset Management and Financing
• Subjects: firm's life cycle; multifactor asset pricing models; spanning regression approach
• Online Access: https://amf.ui.ac.ir/article_24402_d3f2d58f9264756d6a04230d32021be6.pdf

Objective: The effect of a firm's life cycle, as a characteristic, on stock return has been documented in the literature. The purpose of this research is to introduce and test "firm life cycle" as a new systematic factor for developing multifactor asset pricing models. Method: Using data of 345 companies listed in the Tehran Stock Exchange (TSE) and Iran Farabourse market during 2004 to 2018, we first show there is a significant difference among returns of companies in different firm cycle stages and that the pattern observed cannot be explained by notable multifactor asset pricing models. Regarding two competitive approaches to explain differences between stocks with different characteristics, namely risk or mispricing, empirical evidence on returns of stocks in different life cycle stages shows that return of mature firms are higher compared to that of firms in other life cycle stages and the pattern is consistent with mispricing of mature firms. Considering these results, five structures for the life cycle factor are proposed, based on the difference in monthly average returns of firms in the maturity stage and firms in other stages. Results:The results of spanning regressions show that none of the notable asset pricing models, namely the Fama and French three-factor model, the Carhart four-factor model, the Fama and French five-factor model, and the Fama and French six-factor model, can explain  life cycle factor return. Therefore, the life cycle factor with proposed structures can be used as an additional factor to improve the performance of these multi-factor models in explaining the variation in (expected) stock returns.