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.

/pdf/the-characteristics-that-provide-independent-information-h8btdbplrn.pdf

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 from 1980 to 2014 as a whole, return predictability sharply fell in 2003 such that just two characteristics have been independent determinants since then. Outside of microcaps, the hedge returns to exploiting characteristics-based predictability also have been insignificantly different from zero since 2003. Received January 28, 2015; editorial decision November 28, 2016 by Editor Andrew Karolyi.

We propose a model selection method to systematically evaluate the contribution to asset pricing of any new factor, above and beyond what a high‐dimensional set of existing factors explains. Our methodology accounts for model selection mistakes that produce a bias due to omitted variables, unlike standard approaches that assume perfect variable selection. We apply our procedure to a set of factors recently discovered in the literature. While most of these new factors are shown to be redundant relative to the existing factors, a few have statistically significant explanatory power beyond the hundreds of factors proposed in the past.

/pdf/taming-the-factor-zoo-a-test-of-new-factors-51jae5ju78.pdf

Taming the Factor Zoo: A Test of New Factors

We propose a nonparametric method to test which characteristics provide independent information for the cross section of expected returns. We use the adaptive group LASSO to select characteristics and to estimate how they affect expected returns nonparametrically. Our method can handle a large number of characteristics, allows for a exible functional form, and is insensitive to outliers. Many of the previously identified return predictors do not provide incremental information for expected returns, and nonlinearities are important. Our proposed method has higher out-of-sample explanatory power compared to linear panel regressions, and increases Sharpe ratios by 50%.

Dissecting Characteristics Nonparametrically

This study speaks to investment academics and practitioners by describing and analyzing the population of return predictive signals (RPS) publicly identified during the period 1970-2010. Our supraview brings to light a number of new facts about the population of RPS, including that more than 330 signals have been discovered and reported; the properties of newly discovered RPS remain stable over time; and RPS with higher mean returns have not only larger standard deviations of returns, but higher Sharpe ratios too. Using a sample of RPS, we estimate the average signed (absolute) cross-correlation of returns in the population of RPS to be just 0.05 (0.25). Abstracting from implementation costs, we show that this low of an average signed cross-correlation in RPS returns means that in theory an optimal portfolio of all RPS can have an equally-weighted (value-weighted) annualized Sharpe ratio as large as 3.0 (4.5). We also show that the probability that a given RPS has a positive alpha after being orthogonalized against five (25) other randomly chosen RPS is 62% (32%). Our study suggests that practitioners can expect to create value for their clients by hunting down new sources of alpha, and that academics testing for the existence of a new RPS do not need to orthogonalize the returns of that RPS against all pre-existing RPS. However, our findings also pose a challenge to academic theorists, since they imply that either U.S. stock markets are pervasively inefficient, or there exist a much larger number of rationally priced sources of risk in equity returns than ever previously thought.

The Supraview of Return Predictive Signals

Assuming that firms hold cash to generate rents (NPVs plus growth options), we define the “productivity of cash” as the rents that the firm generates per dollar of cash holdings. We investigate the empirical relationship between the productivity of cash and expected stock returns, and find that the productivity of cash is a highly significant and negative predictor of stock returns. We rationalize this finding as an implication of the inverse relation between the productivity of cash measure and the firm's financial risk. Our evidence suggests that the productivity of cash is a (new) and economically rich factor explaining the cross-section of expected stock returns-one that subsumes details of the firm's financial risk, discount rates, and the composition of the assets generating cash flows. Satyajit Chandrashekar Advanced Research Center State Street Global Advisors 1 Lincoln Street Boston, MA 02111 617-664-9603 satyajit_chandrashekar@ssga.com Ramesh K. S. Rao* McDermott Professor of Finance McCombs School of Business University of Texas at Austin Austin, TX 78712 512-475-8756 ramesh.rao@mccombs.utexas.edu November 1, 2006 Revised March 22, 2007 This version: January 26, 2009 * Corresponding author. The views expressed in this paper are those of the authors and not necessarily those of the institutions they are affiliated with. We are grateful to comments from Michael Clement, Bing Han, Mark Hooker, Hao Jiang, Woochan Kim, Alok Kumar, Ralitsa Petkova, Ehud Ronn, Laura Starks, Sheridan Titman, and the Brownbag participants at McCombs School of Business and participants at the 2007 European Financial Management Association Meetings.

The Productivity of Corporate Cash Holdings and the Cross-Section of Expected Stock Returns

This paper investigates the efficacy of the most simple and commonly used technical trading strategies seen across firms of varying market capitalization. The results show that the success of trading strategies declines sharply with an increase in firm size, supporting the hypothesis that technical analysis is most appropriate for smaller stocks. Technical strategies applied to smaller stocks earn excess average monthly returns of 1.7%, even after adjusting for aggregate risk factors such as market, size, book-to-market, momentum, and liquidity. In contrast, when applied to large stocks, such strategies do not earn excess returns over a buy and hold strategy. The results are robust when adjusted for time varying expected returns, transactions costs, and nonsynchronous trading.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID687177_code418042.pdf?abstractid=687177&mirid=1&type=2

Satyajit Chandrashekar

Papers

The Productivity of Corporate Cash Holdings and the Cross-Section of Expected Stock Returns

Simple Technical Trading Strategies: Returns, Risk and Size