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John R. M. Hand

Researcher at University of North Carolina at Chapel Hill

Publications -  83
Citations -  5088

John R. M. Hand is an academic researcher from University of North Carolina at Chapel Hill. The author has contributed to research in topics: Earnings & Equity (finance). The author has an hindex of 29, co-authored 83 publications receiving 4744 citations. Previous affiliations of John R. M. Hand include University of Chicago.

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The Effect of Bond Rating Agency Announcements on Bond and Stock Prices

TL;DR: In this paper, the authors examined daily excess bond returns associated with announcements of additions to Standard and Poor's Credit Watch List, and to rating changes by Moody's and S&P.
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The Characteristics that Provide Independent Information about Average U.S. Monthly Stock Returns

TL;DR: The authors take up the 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.
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Accruals, Cash Flows, and Equity Values

TL;DR: The authors found that the differential ability of accrual and cash flow components of earnings to forecast future abnormal earnings and the persistence of the components result in the components having different valuation implications.
Journal ArticleDOI

Accruals, Cash Flow and Equity Values

TL;DR: This paper found that the differential ability of accrual and cash flow components of earnings to forecast future abnormal earnings and the persistence of the components results in the components having different valuation implications.
Journal ArticleDOI

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

TL;DR: Karolyi et al. as discussed by the authors take up the 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.