X. Frank Zhang

Information Uncertainty and Analyst Forecast Behavior

Prior literature observes that information uncertainty exacerbates investor underreaction behavior. In this paper, I investigate whether, as professional investment intermediaries, sellside analysts suffer more behavioral biases in cases of greater information uncertainty. I show that greater information uncertainty predicts more positive (negative) forecast errors and subsequent forecast revisions following good (bad) news, which corroborates previous findings on the post-analyst-revision drift. The opposite effects of information uncertainty on forecast errors and subsequent forecast revisions following good versus bad news support the analyst underreaction hypothesis and are inconsistent with analyst forecast rationality or optimism suggested in prior literature.

The Characteristics that Provide Independent Information about Average U.S. Monthly Stock Returns

We take up Cochrane’s (2011) challenge to identify the firm characteristics that provide independent information about average U.S. monthly stock returns by simultaneously including 94 characteristics in Fama-MacBeth regressions that avoid overweighting microcaps and adjust for data snooping bias. We find that while 12 characteristics are reliably independent determinants in non-microcap stocks during 1980-2014 as a whole, return predictability fell sharply in 2003 such that just two characteristics have been independent determinants since then. Outside of microcaps, the hedge returns to exploiting characteristics-based predictability have also been insignificantly different from zero since 2003.20+ years after Fama & French (1992), we re-measure the dimensionality of the cross-section of expected U.S. monthly stock returns in light of the large number of return predictive signals (RPS) that have been identified by business academics over the past 40 years. Using 100 readily programmed RPS, we find that a remarkable 24 are multidimensionally priced as defined by their mean coefficients having an absolute t-statistic  3.0 in Fama-MacBeth regressions where all RPS are simultaneously projected onto 1-month ahead returns during 1980-2012. We confirm the high degree of dimensionality in returns using factor analysis of RPS, factor analysis of long/short RPS hedge returns, LASSO regression, regressions of portfolio returns on RPS factor returns, and out-ofsample RPS hedge portfolio returns. We put forward a new empirically determined 10-RPS model of expected returns for consideration by researchers and practitioners. We also discuss other implications of our findings, chief of which is the need for research that explains why stock returns are so multidimensional and why the most empirically important RPS are priced the way they are. This version: April 2, 2014 * Corresponding author. Our paper has greatly benefitted from the comments of Jeff Abarbanell, Sanjeev Bhojraj, Matt Bloomfield, John Cochrane, Oleg Grudin, Bruce Jacobs, Bryan Kelly, Juhani Linnainmaa, Ed Maydew, Scott Richardson, Jacob Sagi, Eric Yeung, and workshop participants at the University of Chicago, Cornell University, UNC Chapel Hill, the Fall 2013 Conference of the Society of Quantitative Analysts, and the Fall 2013 Chicago Quantitative Alliance Conference. The SAS programs we use to create our RPS data and execute most of our statistical analyses will be made publicly available on 7/1/14.

Analyst Interest as an Early Indicator of Firm Fundamental Changes and Stock Returns

We posit that a change in analyst interest in a firm is an early indicator of the firm’s future fundamentals, capital market activities, and stock returns. We measure increases in analyst interest by observing analysts who do not cover a firm but participate in that firm’s earnings conference call, and we measure decreases in analyst interest by observing analysts who cover a firm yet are absent from that firm’s call. We find that increases in analyst interest are positively associated with future changes in firm fundamentals and capital market activities, while decreases in analyst interest are negatively associated with capital market activities. We also find that increases (decreases) in analyst interest are positively (negatively) correlated with future stock returns over the next three months and that a hedge portfolio yields a significant abnormal return. Overall, our study shows that analyst interest is a novel and early indicator of future firm fundamentals and capital market consequences.

Understanding the Accrual Anomaly

Interpreting accruals as working capital investment, we hypothesize that firms rationally adjust their investment to respond to discount rate changes. Consistent with the optimal investment hypothesis, we document that (i) the predictive power of accruals for future stock returns increases with the covariations of accruals with past and current stock returns, and (ii) adding investment- based factors into standard factor regressions substantially reduces the magnitude of the accrual anomaly. High accrual firms also have similar corporate governance and entrenchment indexes as low accrual firms. This evidence suggests that the accrual anomaly is more likely to be driven by optimal investment than by investor overreaction to excessive growth or over-investment.

CEO Optimism and Analyst Forecast Bias

We examine analysts’ forecast behavior in a setting in which CEOs are optimistic and analysts react rationally to CEO optimism. We document that the bias in analysts’ consensus forecasts is negatively related to the level of CEO optimism. The negative relation is stronger for small firms, firms with low analyst followings, and firms with high uncertainty. Analysts revise downward their forecasts for next year’s earnings less relative to their revision for current year’s earnings for firms with more optimistic CEOs, a result consistent with optimistic CEOs are subject to self-attribution bias. The stock price reactions to downward forecast revisions and missing analysts’ forecasts are less negative for firms with optimistic CEOs, indicating that investors understand the implications of CEO optimism for analysts’ forecast bias and subsequent revisions.