A reformulated asset pricing model based on contrarian strategies
Purpose – Researchers have proposed characteristics-based pricing models as an alternative to risk-based pricing models. While supported empirically, these characteristic-based models lack theoretical support. This paper seeks to reformulate an asset-pricing model (RAPM) to demonstrate why firm characteristics help to explain stock returns. Design/methodology/approach – The RAPM is grounded in an economic setting where two groups of agents hold different beliefs about firm fundamental values, and the more sophisticated group (rationals) adopts contrarian strategies against the naïve group (quasis). The model is derived in a static equilibrium within the consumption-investment framework with heterogeneous agents. Findings – The key theoretical result is a parsimonious equation of cross-sectional expected returns that not only are specified by the traditional risk-return relation, but also are determined by contrarian adjustments at both market-wide and firm-specific levels. When the model is taken to empirical specifications, it leads to consistent explanations for the behaviors of growth and value stocks, and for size and book-to-market effects. Research limitations/implications – The RAPM is a one-period model that assumes that “rationals” have perfect knowledge about “quasis” sentiment parameter and their relative market weights. In future research, it is planned to extend this static model to multiple periods to incorporate a learning process by which “rationals” learn these parameters over time. Practical implications – The RAPM clearly identifies four criteria for implementing arbitrage opportunities in investments. These criteria formalize the common practices in the mutual/hedge fund industry. Originality/value – The paper develops an original framework that formally supports the characteristics-based models. It offers insights for researchers in behavioral finance and guidelines for investment practitioners.
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Volume (Year): 23 (2006)
Issue (Month): 3 (August)
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