Jeremiah Green

Going, Going, Gone? The Apparent Demise of the Accruals Anomaly

Consistent with public statements made by sophisticated practitioners, we document that the hedge returns to Sloans (Sloan, R. G. 1996. Do stock prices fully reflect information in accruals and cash flows about future earnings? Accounting Rev.71(3) 289--315) accruals anomaly appear to have decayed in U.S. stock markets to the point that they are, on average, no longer reliably positive. We explore some potential reasons why this has happened. Our empirical analyses suggest that the anomalys demise stems in part from an increase in the amount of capital invested by hedge funds into exploiting it, as measured by hedge fund assets under management and trading volume in extreme accrual firms. A decline in the size of the accrual mispricing signal, as measured by the magnitude of extreme decile accruals and the relative persistence of cash flows and accruals, may also play a (weaker) role. This paper was accepted by Stefan Reichelstein, accounting.

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.

The Importance of Accounting Information in Portfolio Optimization

We study the economic importance of accounting information as defined by the value that a sophisticated investor can extract from publicly available financial statements when optimizing a portfolio of U.S. equities. Our approach applies the elegant new parametric portfolio policy method of Brandt, Santa-Clara, and Valkanov (2009) to three simple and firm-specific annual accounting characteristics-accruals, change in earnings, and asset growth. We find that the set of optimal portfolio weights generated by accounting characteristics yield an out-of-sample, pre-transact ions-costs annual information ratio of 1.9 as compared to 1.5 for the standard price-based characteristics of firm size, book-to-market, and momentum. We also find that the delevered hedge portion of the accounting-based optimal portfolio was especially valuable during the severe bear market of 2008 because unlike many hedge finds it delivered a hedged return in 2008 of 12 percent versus only 3 percent for price-based strategies and −38 percent for the value-weighted market.

Do Hedge Funds Prefer Safe Stocks? Revisiting Hedge Fund Preferences for Stock Characteristics

We reexamine hedge fund preferences for stock characteristics using aggregate quarterly hedge fund holdings and a large set of firm characteristics. We find that the expanded set of firm characteristics is important in explaining variation in quarterly hedge fund holdings. Consistent with prior research, we find little evidence that hedge funds prefer stock characteristics that predict higher returns except among characteristics that capture economic and accounting fundamentals. In contrast, we find that hedge fund holdings reveal a strong preference for safe stocks, i.e. stock characteristics that predict low return volatility, that is strongest during bad times (recessions).