Individual investors and broker types

© 2014 Michael G. Foster School of Business, University of Washington. We study the informativeness of trades via discount and full-service retail brokers. We find that trades via full-service retail brokers are statistically and economically more informative than are trades via discount retail brokers. This finding holds in every year over the 12-year sample period and in various subsamples. We also find that past returns, volatility, and news announcements positively relate to the net volume of discount retail brokers, but these variables are unrelated to the net volume of full-service retail brokers. Our results suggest that broker type selection bias is an important consideration in studying individual investors' trades

How has the Relevance of Institutional Brokerage Changed?

© 2016 International Review of Finance Ltd. 2016 Institutional brokerage rates have been in decline. We investigate whether this reduction has coincided with a fall in benefits provided by brokers to institutional asset managers. We use trade packages from both active and passive equity funds from 1995 to 2001 and active equity funds from 2002 to 2010. We find that later period active funds recoup a combined 1.75 basis point benefit (from price impact cost recovery and short-term alpha) per basis point of brokerage cost. Later period active investors saw improved trade price impact and shorter-term alpha net benefits, relative to earlier period active investors. These results are robust after controlling for trade characteristics and cross-sectional variation over time. Our findings suggest that brokers innovate to provide valuable services in the subsequent, lower brokerage environment

Non-standard 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: 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

Non-standard 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: 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