Market evidence on the opaqueness of banking firms' assets.

We assess the market microstructure properties of U.S. banking firms' equity, to determine whether they exhibit more or less evidence of asset opaqueness than similar-sized nonbanking firms. The evidence strongly indicates that large banks (traded on NASDAQ) trade much less frequently despite microstructure characteristics. Problem (noncurrent) loans tend to raise the frequency with which the bank's equity trades, as well as the equity's return volatility. The implications for regulatory policy and future market microstructure research are discussed.Bank stocks ; Bank assets

Components of short-horizon individual security returns

In this paper, we present a simple model which relates security returns to three components: an expected return, a bid-ask error, and white noise. The relative importance of the various components is empirically assessed, and the model's ability to explain the various time-series properties of individual security and portfolio returns is tested. Time-varying expected returns and bid-ask errors are found to explain substantial proportions (up to 24%) of the variance of security returns. We also reconcile the typically negative autocorrelation in security returns with the strong positive autocorrelation in portfolio returns.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/29106/1/0000144.pd

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

Price reversals : Bid-ask errors or market overreaction?

We show that bid-ask errors in transaction prices are the predominant source of apparent price reversals in the short run for NASDAQ firms. Once we extract measurement errors in prices caused by the bid-ask spread, we find little evidence of market overreaction. On the contrary, we find that security returns are positively, and not negatively, autocorrelated. We also show that bid-ask errors lead to substantial spurious volatility in transaction returns; about half of measured daily return variances can be induced by the bid-ask effect.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/28320/1/0000076.pd