Daily Academic Alpha: Estimation Investigations

/Daily Academic Alpha: Estimation Investigations

Daily Academic Alpha: Estimation Investigations

By | 2017-08-18T17:07:39+00:00 March 18th, 2015|Uncategorized|0 Comments

Separating the Wheat from the Chaff: Identifying the Signal and Predictable Error Components of Target Price Forecasts

A target price forecast scaled by current price provides the analyst’s assessment of the implied return to that stock over the stated forecast horizon. Using a simple valuation framework, we show that implied returns are a function of the expected dividend distribution, analysts’ private information that is not yet reflected in prices, and errors with respect to forecasting cash flows and the discount rate. We provide empirical evidence consistent with implied returns being correlated with proxies for these factors and that analysts make systematic errors with respect to valuation inputs. We further show that job-related incentives impact analysts’ valuations over and above the theoretical inputs. We investigate whether investors distinguish the signal and predictable bias components of implied returns. We find that the signal component is positively related to future returns over short windows (up to two months), while the bias component is negatively related to future stock returns over the six and twelve-month horizon. These results suggest that investors incorporate the signal component fairly quickly into prices, but take time to correct prices for the predictable bias in implied returns.

Conditionally Conservative Fair Value Measurements

We investigate whether firms’ measurements of the fair values of financial instruments based on Level 2 and 3 inputs other than active market prices for those instruments exhibit conditional conservatism (i.e., timelier recognition of unrealized losses than gains). We estimate conditional conservatism using an expansion of Basu’s (1997) reverse regression model that decomposes the dependent variable comprehensive income into the parts attributable to fair value measurements versus other measurement bases and that interacts the returns-related explanatory variables with the proportions of assets that are financial instruments with fair values measured using Level 2 and 3 versus Level 1 inputs. We find firms with more Level 2 and 3, but not Level 1, fair value measurements exhibit more conditionally conservative comprehensive income attributable to those measurements but not to other measurement bases. We further find that this increase is larger for firms with poorer information environments and for years following rather than during the financial crisis and following rather than preceding the effective date of SFAS 157. To explore the generalizability of these findings to non-financial assets, we conduct similar analysis on oil and gas firms’ measurements akin to the fair value of their reserves, obtaining consistent results. Overall, our results indicate that firms report conditionally conservative fair value measurements when market imperfections enable them to exercise discretion over those measurements, consistent with firms attempting to attain the well-documented contracting and other benefits of conditional conservatism (e.g., Watts 2003).


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About the Author:

Wes Gray
After serving as a Captain in the United States Marine Corps, Dr. Gray earned a PhD, and worked as a finance professor at Drexel University. Dr. Gray’s interest in bridging the research gap between academia and industry led him to found Alpha Architect, an asset management that delivers affordable active exposures for tax-sensitive investors. Dr. Gray has published four books and a number of academic articles. Wes is a regular contributor to multiple industry outlets, to include the following: Wall Street Journal, Forbes, ETF.com, and the CFA Institute. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania.