The allocation of ambiguity among heterogeneous investors
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Sujoy Mukerji, Han N. Ozsoylev and Jean-Marc Tallon
The financial economics literature largely assumes that investors know the distribution of asset returns. In most real-world situations, however, decision-makers are uncertain about the data-generating process, a situation referred to as “ambiguity”. This can have important implications for portfolio choice, because investors may prefer portfolio allocations that are robust across the set of return distributions believed possible.
Mukerji, Ozsoylev and Tallon show that this so-called ambiguity of returns potentially explains several puzzling empirical regularities in financial markets. The authors’ findings rely on two important heterogeneities. First, financial assets differ in how well known are their return distributions. For example, return ambiguity will be high for stocks of new-technology companies or companies exploring new markets whose risks are not yet understood. Second, in addition to their risk aversion, investors differ in their tolerance for ambiguity. Together, these heterogeneities give rise to an extension of the well-known mean-variance portfolio choice paradigm in which the investor considers only the mean return and the variance of the returns when making her decision: investors choose their portfolios as a three-way trade-off between expected return, variance, and ambiguity. For example, more ambiguity-averse investors aim to avoid ambiguity and invest a larger fraction of their risky portfolio in financial assets with better known distributions. This finding accords with common financial planning advice that encourages conservative investors to hold more bonds than stocks, and potentially explains Canner et al’s (1997) asset allocation puzzle.
Since ambiguity affects investor portfolio choices it is also reflected in equilibrium asset prices. As in the standard capital asset pricing model (CAPM*), a single factor – excess return on the market portfolio – prices the cross-section of asset returns. In contrast to standard theory, however, factor loadings (CAPM-beta) are adjusted by the extent to which an asset’s ambiguity correlates with the ambiguity of the market portfolio. Two uncertainty premia explain the cross-section of expected returns: a risk premium and an ambiguity premium. The latter has the potential to explain the size and value premia documented by Fama and French (1992, 1993). For example, high book-to-market firms, which tend to be in financial distress, and small cap firms, over-reliant on external financing, are both likely to carry a high ambiguity premium.
The model’s dynamic predictions are also consistent with empirical regularities. For example, announcements about public earnings or aggregate uncertainty shocks affect the ambiguity of financial assets and change investors’ return-risk-ambiguity trade-offs. Since investors differ in their ambiguity aversion, trade occurs after such signals in equilibrium, leading to very small price movements, which is consistent with the empirical literature, a phenomenon difficult to explain within the traditional risk framework.
Original title of the article: Trading Ambiguity: A Tale of Two Heterogeneities
Published in: tbc
Disponible sur: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3290605
*CAPM : The Capital Asset Pricing Model is a model that describes the relationship between risk and expected return, that effectively is pricing formula of securities