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Annual Report of the Society for Financial Studies for 2018–2019
The Society for Financial Studies (SFS) is a global, nonprofit academic society in finance. It owns and runs three academic journals: (1) the Review of Asset Pricing Studies, (2) the Review of Corporate Finance Studies, and (3) the Review of Financial Studies. It also organizes two annual academic conferences: (1) the SFS Cavalcade Asia-Pacific and (2) the SFS Cavalcade North America. It also runs several smaller, specialized conferences and financially supports and co-sponsors other independent conferences. Its governing board is the SFS Council. SFS holds an annual membership meeting in May every year. At that meeting the SFS President, Executive Editors, Cavalcade Chair, and Secretary-Treasurer report on SFS activities. This year, for the first time, we have written up those reports and integrated them into this annual report of the Society for Financial Studies, which will be published in all three SFS journals. The reasons for doing this are to share this information more broadly with SFS members and friends and to create a permanent record for the long run (that is, institutional memory).
The Annual Report of the Society for Financial Studies for 2019–2020
The Society for Financial Society (SFS) is a global, nonprofit academic society in finance. It owns and runs three academic journals: (1) the Review of Asset Pricing Studies, (2) the Review of Corporate Finance Studies, and (3) the Review of Financial Studies. It also organizes two annual academic conferences: (1) the SFS Cavalcade Asia-Pacific and (2) the SFS Cavalcade North America. It also runs several smaller, specialized conferences and financially supports and co-sponsors other independent conferences. Its governing board is the SFS Council. This annual report provides an overview of SFS activities during 2019-2020, including all three journals, both Cavalcade conferences, and the SFS financial and policy report. The purpose of this annual report is to share this information more broadly with SFS members and friends and to create a permanent record for the long-run (i.e., institutional memory).
Nonstandard Errors
In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.