The crowd-sourced paper "Non-Standard Errors" of the top Journal of Finance, which Mr. Zhenxing Wu, a researcher of the base, participated in, will be published soon. The study involved 342 collaborators, 164 teams and 34 peer evaluators from a variety of financial backgrounds.

Abstract: 

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: Non-standard 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 better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

Teacher profile

Zhenxing Wu, Associate Professor of annual salary, School of Finance, Zhongnan University of Economics and Law, Researcher of Digital Technology and Modern Finance Innovation and Intellectual Introduction Base. Research areas include the application of market microstructure theory to international finance, asset pricing and corporate finance. The research results have been published in the Journal of International Money & Finance, Journal of Corporate Finance and other international journals.