[I present evidence of predictability in a sample constructed to minimize concerns about time-varying risk premia and market-microstructure effects. I use filter rules on lagged return and lagged volume information to uncover weekly over-reaction profits on large-capitalization NYSE and AMEX securities. I find that decreasing-volume stocks experience greater reversals. Increasing-volume stocks exhibit weaker reversals and positive autocorrelation. A real-time simulation of the filter strategies suggests that an investor who pursues the filter strategy with relatively low transaction costs will strongly outperform an investor who follows a buy-and-hold strategy.]

Journal Article Filter Rules Based on Price and Volume in Individual Security Overreaction Get access Michael Cooper Michael Cooper Krannert School of Management Address correspondence to Michael Cooper, Krannert School of Management, 1310 Krannert Building, West Lafayette, IN 47907, or email: [email protected]. Search for other works by this author on: Oxford Academic Google Scholar The Review of Financial Studies, Volume 12, Issue 4, 2 July 1999, Pages 901–935, https://doi.org/10.1093/rfs/12.4.901 Published: 01 June 2015

We show that the performance of the new factor models of Hou et al. (2015) and Fama and French (2015) depends crucially on how their investment factor is constructed. Both models use growth in total assets to measure investment. Their ability to price the cross-section of returns decreases significantly when the investment factor is constructed using traditional investment measures, or measures that also account for investment in intangibles. In contrast, we find that factors based on growth in inventory and accounts receivable contain the bulk of the pricing information in the asset growth factor. We show evidence that the superior performance of the asset growth factor seems to be attributable to its ability to capture aggregate shocks to equity financing costs.

Previous work shows large differences in fees for S&P 500 index funds and other funds and suggests that investors suffer wealth losses investing in high-fee funds when similar low-fee funds are available. In contrast, the neoclassical model of mutual funds (Berk and van Binsbergen, 2015, J. Financ. Econ., 118, 1–20) argues that percentage fees are irrelevant, as fund size will adjust in equilibrium such that net alphas are equal to zero. We show that fees matter from an investor perspective. We document (i) a strong negative association between net-of-fee fund performance and fees in a sample of all US and international equity funds, (ii) economically large, robust, persistent, and pervasive fee dispersion in the mutual fund industry, and (iii) important economic effects for investors. During the sample period, the mutual fund industry has generated a total value lost (i.e., a negative net value added) of 125 billion USD, coming predominantly from high-fee funds.

The percentage of U.S. equity mutual funds that outperform the SPY ETF over the last 30 years decreases substantially as the horizon over which returns are measured is increased. Further, some funds with positive monthly alpha estimates have negative long-horizon abnormal returns. These results reflect positive skewness in the distribution of fund returns that increases with horizon, and highlight the limitations of conditional arithmetic means of short-horizon returns (e.g., alpha) for long-horizon investors. We tabulate an aggregate wealth loss of $1.02 trillion to mutual fund investors over our 30-year sample, when opportunity costs are based on beta-adjusted SPY returns.

In this paper, we examine the predictability of the cross-section of bank stock returns by taking advantage of the unique set of industry characteristics that prevail in the financial services sector. We examine predictability in the cross-section of bank stock returns using information contained in individual bank fundamental variables such as income from derivative usage, previous loan commitments, loan-loss reserves, earnings, and leverage. We find that variables related to non-interest income, loan-loss reserves, earnings, leverage, and standby letters of credit are all univariately important in forecasting the cross-section of bank stock returns. Surprisingly, neither book-to-market nor firm size is important in our sample. We examine whether this cross-sectional predictability is due to increased risk, or another explanation, such as investor under or overreaction. Our results suggest that this predictability is not due to increased risk, but rather is consistent with investor underreaction to changes in banks’ fundamental variables. Furthermore, out-of-sample testing demonstrates this underreaction appears to be exploitable using simple cross-sectional trading strategies.

We introduce and test a firm-level innovation-efficiency measure new to the finance literature. The measure, termed the research quotient (RQ), defined as the firm-specific output elasticity of research and development (R&D), was first developed in the management literature. RQ has a low correlation with existing innovation input, output, and efficiency measures. We test RQ in a number of innovation tests common to the finance literature and find that RQ is robust in all tests of firm value, even after controlling for previous innovation measures. The results suggest that RQ may serve as a relevant complementary measure of a company’s innovation.

We propose that fitted values from market-wide regressions of firm returns on lagged firm characteristics provide useful benchmarks for assessing whether average returns to certain stocks are abnormal. To illustrate, we study eight documented events with abnormal returns, including credit rating and analyst recommendation downgrades, initial and seasoned public equity offerings, mergers and acquisitions, dividend initiations, share repurchases, and stock splits. We show that the apparently abnormal returns in the months after these events are substantially reduced or eliminated when compared to characteristic-based benchmarks. Characteristic-based benchmarks perform better in explaining post-event returns than do recent four- and five-factor models. Received September 19, 2016; editorial decision February 16, 2018 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web Site next to the link to the final published paper online.

The fragility of the CAPM has led to a resurgence of research that frequently uses trading strategies based on sorting procedures to uncover relations between firm characteristics (such as “value” or “glamour”) and equity returns. We examine the propensity of these strategies to generate statistically and economically significant profits due to our familiarity with the data. Under plausible assumptions, data snooping can account for up to 50 percent of the in‐sample relations between firm characteristics and returns uncovered using single (one‐way) sorts. The biases can be much larger if we simultaneously condition returns on two (or more) characteristics.

We investigate stock price reactions to Internet-related name changes in a market downturn. In contrast to the Internet boom period, during which there was a surge of dot.com additions, in the bust period, there is a dramatic reduction in the pace of dot.com additions accompanied by a rapid increase in dot.com name deletions. Following the Internet “crash” of mid-2000, investors react positively to name changes for firms that remove dot.com from their name. This dot.com deletion effect produces cumulative abnormal returns on the order of 64% for the 60 days surrounding the announcement day. Our results add support to a growing body of literature that documents that investors are potentially influenced by cosmetic effects and that managers rationally time corporate actions to take advantage of these biases.