2011

PROJECTS FUNDED IN 2011

Eliciting Market Expectations in Data-Rich Environments

Link to PDF File: Eliciting Market Expectations in Data-Rich Environments
Bryan Kelly, University of Chicago
Seth Pruitt, Federal Reserve Board

Returns and dividend growth for the aggregate US stock market are highly and robustly predictable. Using information extracted from the cross section of price-dividend ratios, we find an in-sample and out-of-sample forecasting R2 as high as 15% for both series at the annual frequency. We present a general economic framework linking aggregate market expectations to disaggregated valuation ratios in a dynamic latent factor system. To derive our forecasts we use a regression-based filter to extract factors driving aggregate expected returns and dividend growth from the cross section of price-dividend ratios. Our findings shed new light on the dynamic processes of market discount rates and growth expectations.

Expected returns and the related information about appropriate discount rates are extremely important to sponsors, investment managers, and financial analysts who need to made decisions about asset allocations, payout ratios, and valuations. This research attempts to extract cross-sectional information from price-dividend ratios using a three-pass regression filter that the authors developed in previous research. The method promises to obtain better econometric identification of factors that forecast aggregate returns and dividend growth.

The World Price of Credit Risk

Link to PDF File: The World Price of Credit Risk
Doron Avramov, Hebrew University of Jerusalem
Tarun Chordia, Emory University
Gergana Jostova, George Washington University
Alexander Philipov, George Mason University

Global asset-pricing models have failed to capture the cross section of country equity returns. Emerging markets display strikingly large positive pricing errors. Country-level characteristics play a significant role in pricing international equities, suggesting that financial markets may not be fully integrated. This paper offers a risk-based explanation that resolves these deviations from global asset pricing. A world credit risk factor fully explains the positive pricing errors in emerging markets and the explanatory power of country-level characteristics. Over the 1989-2009 period, the risk premium for systematic credit risk exposure is 83 basis points per month and its importance has dramatically increased in recent years.

Understanding how risk is priced is of prime interest to all investors, and most especially to quantitative investors who often are particularly concerned with correlations across markets. The proposed research addresses well identified problems in global asset pricing models by identifying a world credit risk factor. This factor helps explain the variation in returns across countries, and appears to be strongly priced.

Financial Market Dislocations

Link to PDF File: Financial Market Dislocations
Paolo Pasquariello, University of Michigan

Dislocations occur when financial markets, operating under stressful conditions, experience large, widespread asset mispricings. This study documents systematic financial market dislocations in world capital markets and the importance of their fluctuations for expected asset returns. Our novel, model-free measure of these dislocations is a monthly average of six hundred abnormal absolute violations of three textbook arbitrage parities in stock, foreign exchange, and money markets. We find that investors demand economically and statistically significant risk premiums to hold financial assets performing poorly during market dislocations.

Security mispricing interests everyone involved in investment management- from sponsors to speculators. Knowing when mispricing occurs is valuable to managers implementing risk management programs and also speculative programs. This research provides a quantitative time-series measure of dislocation based upon violations of well-known arbitrage relations. Preliminary results suggest that the measure is priced, and thus of significant potential interest to asset managers.

How Does Information Impact Volatility?

Link to PDF File: How Does Information Impact Volatility?
Mike Aguilar, University of North Carolina
Matthew C. Ringgenberg, Washington University

Using a component model of volatility, we investigate the relation between information arrival and the volatility of returns. While recent empirical research has found that information arrival is positively correlated with volatility, relatively little is known about the nature of this correlation. Using company specific news releases at high frequencies, we investigate how, why, and when information is related to volatility. Our results provide new insight into the information processing capabilities of investors and we find evidence that the stylized properties of volatility result from both heterogeneous information processing and the time series properties of information arrival. Thus, our evidence helps reconcile two competing theories on the role of information: the Heterogeneous Market Hypothesis of Muller et al. (1997) and the Mixture of Distribution Hypothesis of Clark (1973), Epps and Epps (1976), and Andersen (1996).

Although most people understand that information flows affect volatility, the relation is not well understood at the micro level of price formation. As investors increasingly use electronic data sources to condition their intraday trading, understanding better how information affects prices is becoming increasingly important. The proposed research examines intraday corporate announcements in an attempt to characterize the microdynamics of price formation and associated volatilities.

The Observer Effect: Does Publication Destroy Return Predictability?

R. David McLean, University of Alberta
Jeffrey Pontiff, Boston College
Link to PDF File: The Observer Effect: Does Publication Destroy Return Predictability?

Does cross-sectional return predictability persist post-publication? The answer to this question is of fundamental interest to both practitioners and academics. Return predictability may decay or disappear entirely for two reasons. First, the original result may have been the outcome of data snooping. Second, return predictability may represent mispricing and therefore cause sophisticated investors who have access to academic research to trade on this information. Using a large sample of published financial research, we propose to estimate the decay that is attributed to these two effects.

Many quantitative investment managers pursue investment strategies that attempt to exploit various cross-sectional return anomalies that practitioner and academic researchers have identified. The success of these strategies depends critically on whether the anomalies are indeed anomalous (Were they falsely identified?) and how long they will persist as the conditions that gave rise to them change and as speculators try to exploit them. Understanding the extent to which these issues explain the decay in performance is critical to decisions about how much money and research should be developed to these strategies. The proposed research will revisit many anomalies to assemble and analyze data suitable for answering these questions.


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