Asset Pricing: New Theories and Empirical Approaches
Saturday, Jan. 6, 2018 8:00 AM - 10:00 AM
- Chair: Stavros Panageas, University of California-Los Angeles
What Information Drives Asset Prices?
AbstractThe market price-dividend ratio is highly correlated with inflation and labor market variables but not with aggregate consumption and GDP. We build a model with learning from inflation, or earnings, or a combination thereof. The estimated model rationalizes the moments of consumption and dividend growth, market return, price-dividend ratio, and real and nominal term structures and the low predictive power of the price-dividend ratio for consumption and dividend growth while a nested model with learning from consumption history alone does not. The intuition is that the beliefs process has high persistence and low variance because beliefs depend on the signal.
Financial Innovation and Asset Prices
AbstractOur objective is to understand the effects of financial innovation on asset prices. To account for the feedback effect from prices on the asset-allocation decisions of investors in an internally-consistent fashion, we develop a dynamic general-equilibrium framework in which asset-allocation decisions and the resulting asset returns are determined endogenously. Our model has three assets---a risk-free bond, a traditional risky asset, and a new ``alternative'' asset---and two types of investors---experienced and inexperienced. The new asset is illiquid and inexperienced investors are uncertain about its expected dividend growth, but rationally learn about it. We use this model to study how the the flow of capital into the new asset affects the prices of traditional assets, the returns of the new asset, and the comovement with the returns of traditional assets, how shocks to the cash flows of the new asset get transmitted to traditional assets, and how the dynamics of the return moments of the new and traditional assets and the asset-allocation decisions of the experienced and inexperienced investors change as investors learn about the cash flows of the new asset. The model yields several interesting predictions that are supported empirically.
Predicting Relative Returns
AbstractAcross a variety of asset classes, we show that relative returns are highly predictable in the time-series in and out of sample, much more so than aggregate returns. Dominant principal components of equity anomalies, a portfolio of Treasuries sorted by maturity, and a currency carry portfolio are more predictable than the index return in their respective asset classes. We show that the common practice of predicting each individual asset separately obscures predictability of relative returns and is often equivalent to predicting only the index. Our approach to predictability uncovers multiple statistically robust and economically relevant sources of discount-rate variation.
- G1 - General Financial Markets