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Asset Pricing: New Theories and Empirical Approaches

Paper Session

Saturday, Jan. 6, 2018 8:00 AM - 10:00 AM

Loews Philadelphia, Washington A
Hosted By: American Finance Association
  • Chair: Stavros Panageas, University of California-Los Angeles

A Dynamic Model of Characteristic-based Return Predictability

Aydoğan Alti
,
University of Texas-Austin
Sheridan Titman
,
University of Texas-Austin

Abstract

We present a model in which characteristic-based investment strategies generate abnormal returns in finite samples but not in the long run. In the model, the “climate of disruptive innovation,” which affects the arrival rate of new projects and the exit rate of existing businesses, is a source of systematic risk that influences the returns of portfolios sorted on value, profitability, and asset growth. If investors are overconfident about their abilities to evaluate the disruption climate, these characteristic-sorted portfolios exhibit persistent expected returns, which increase the likelihood of extreme return realizations in finite samples. Through simulations we analyze the model’s implications for the precision of empirical estimates and assess the likelihood of historical evidence repeating in the future.

What Information Drives Asset Prices?

George Constantinides
,
University of Chicago
Anisha Ghosh
,
Carnegie Mellon University

Abstract

The 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

Adrian Buss
,
INSEAD
Raman Uppal
,
EDHEC Business School
Grigory Vilkov
,
Frankfurt School of Finance & Management

Abstract

Our 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

Valentin Haddad
,
University of California-Los Angeles
Serhiy Kozak
,
University of Michigan
Shrihari Santosh
,
University of Maryland

Abstract

Across 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.
Discussant(s)
Nicolae Gârleanu
,
University of California-Berkeley
Lars Lochstoer
,
University of California-Los Angeles
Valentin Haddad
,
University of California-Los Angeles
Stefano Giglio
,
University of Chicago
JEL Classifications
  • G1 - General Financial Markets