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Stock (geology)

About: Stock (geology) is a research topic. Over the lifetime, 31009 publications have been published within this topic receiving 783542 citations.


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Journal ArticleDOI
TL;DR: In this paper, the authors show that firms and industries with lower market model R 2 statistics exhibit higher association between current returns and future earnings, indicating more information about future earnings in current stock returns.
Abstract: Roll [1988] observes low R 2 statistics for common asset pricing models due to vigorous firm-specific return variation not associated with public information. He concludes that this implies “either private information or else occasional frenzy unrelated to concrete information” [p. 56]. We show that firms and industries with lower market model R 2 statistics exhibit higher association between current returns and future earnings, indicating more information about future earnings in current stock returns. This supports Roll’s first interpretation: higher firm-specific return variation as a fraction of total variation signals more information-laden stock prices and, therefore, more efficient stock markets.

611 citations

Journal ArticleDOI
TL;DR: In this paper, a five-factor model that adds profitability (RMW) and investment (CMA) factors to the three factor model of Fama and French (1993) suggests a shared story for several average-return anomalies.
Abstract: A five-factor model that adds profitability (RMW) and investment (CMA) factors to the three-factor model of Fama and French (1993) suggests a shared story for several average-return anomalies. Specifically, positive exposures to RMW and CMA (returns that behave like those of the stocks of profitable firms that invest conservatively) capture the high average returns associated with low market β, share repurchases, and low stock return volatility. Conversely, negative RMW and CMA slopes (like those of relatively unprofitable firms that invest aggressively) help explain the low average stock returns associated with high β, large share issues, and highly volatile returns.

605 citations

Journal ArticleDOI
TL;DR: In this paper, a risk-averse Bayesian investor is given the results of estimating linear time-series regressions of stock returns on one or more predictive variables from a statistical perspective.
Abstract: Sample evidence about the predictability of monthly stock returns is considered from the perspective of a risk-averse Bayesian investor who must allocate funds between stocks and cash. The investor uses the sample evidence to update prior beliefs about the parameters in a regression of stock returns on a set of predictive variables. The regression relation can seem weak when described by usual statistical measures, but the current values of the predictive variables can exert a substantial influence on the investor's portfolio decision, even when the investor's prior beliefs are weighted against predictability. INVESTORS IN THE STOCK market are interested in predicting future stock returns, and the academic literature offers numerous empirical investigations of stockreturn predictability. Many of these investigations report the results of estimating linear time-series regressions of stock returns on one or more predictive variables, and considerable effort has been devoted to assessing the strength and reliability of this regression evidence from a statistical perspective. Given that the regression coefficients are estimated with error, confronting the investor with what is commonly termed "estimation risk," to what extent might the regression evidence influence a rational, risk-averse investor's portfolio decision? Consider an investor who, on December 31, 1993, must allocate funds between the value-weighted portfolio of the New York Stock Exchange (NYSE) and one-month Treasury bills. The investor is given the results of estimating the following regression using monthly data from January 1927 through December 1993,

605 citations

01 Jan 2007
Abstract: Today, more than 70 per cent of organizations have adopted some kind of empowerment initiative for at least part of their workforce (Lawler et al., 2001). To be successful in today’s global business environment, companies need the knowledge, ideas, energy, and creativity of every employee, from front line workers to the top level managers in the executive suite. The best organizations accomplish this by empowering their employees to take initiative without prodding, to serve the collective interests of the company without being micro-managed, and to act like owners of the business (O’Toole and Lawler, 2006). So what do we know about empowerment in work organizations? In this chapter, I will conduct an in-depth review of the literature on empowerment at work. I start by framing the two classic approaches to empowerment – social-structural and psychological – before outlining the current state of the literature. I then close the chapter by discussing key debates in the field and emergent directions for future research.

601 citations

Posted Content
TL;DR: In this paper, the authors present evidence which indicates that stock prices, on average, react positively to stock dividend and stock split announcements that are uncontaminated by other contemporaneous firm-specific announcements.
Abstract: This study presents evidence which indicates that stock prices, on average, react positively to stock dividend and stock split announcements that are uncontaminated by other contemporaneous firm-specific announcements. In addition, it documents significantly positive excess returns on and around the ex-dates of stock dividends and splits. Both announcement and ex-date returns were found to be larger for stock dividends than for stock splits. While the announcement returns cannot be explained by forecasts of imminent increases in cash dividends, the paper offers several signaling based explanations for them. These are consistent with a cross-sectional analysis of the announcement period returns.

598 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202237
20211,825
20201,882
20191,697
20181,539
20171,706