Optical properties of random alloys : Application to Cu_{50}Au_{50} and Ni_{50}Pt_{50}

In an earlier paper [K. K. Saha and A. Mookerjee, Phys. Rev. B 70 (2004) (in press) or, cond-mat/0403456] we had presented a formulation for the calculation of the configuration-averaged optical conductivity in random alloys. Our formulation is based on the augmented-space theorem introduced by one of us [A. Mookerjee, J. Phys. C: Solid State Phys. 6, 1340 (1973)]. In this communication we shall combine our formulation with the tight-binding linear muffin-tin orbitals (TB-LMTO) technique to study the optical conductivities of two alloys Cu_{50}Au_{50} and Ni_{50}Pt_{50}.Comment: 5 pages, 7 figure

Organizing Equity Exchanges

In the last years equity exchanges have diversified their operations into business areas such as derivatives trading, posttrading services, and software sales. Securities trading and post-trading are subject to economies of scale and scope. The integration of these functions into one institution ensures efficiency by economizing on transactions costs. Using balanced panel data from major equity exchanges over the period 2005-2007, we examine empirically the presence of economies of scale in securities trading. Moreover, we analyze the impact of vertical integration of trading, clearing, and settlement, the impact of the size of an exchange, and the impact of diversification on the profitability of exchanges. The evidence confirms that a large number of transactions leads to low costs per trade. The evidence shows that the profitability of equity exchanges is highest for vertically integrated exchanges and that diversification and size have a negative impact on their profitability

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