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We consider a multi-period rational expectations model in which risk-averse investors differ in their information on past transaction prices (the ticker). Some investors (insiders) observe prices in real-time whereas other investors (outsiders) observe prices with a delay. As prices are informative about the asset payoff, insiders get a strictly larger expected utility than outsiders. Yet, information acquisition by one investor exerts a negative externality on other investors. Thus, investors’ average welfare is maximal when access to price information is rationed. We show that a market for price information can implement the fraction of insiders that maximizes investors’ average welfare. This market features a high price to curb excessive acquisition of ticker information. We also show that informational efficiency is greater when the dissemination of ticker information is broader and more timely.
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample 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. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
