Too Big to Fail: Large Samples and the P-Value Problem

The Effects of Too-Big-to-fail Banks on Depositors' Behavior

In the wake of recent financial crisis, large banks have been considered as important factors in financial markets in the world, since these bankschr(chr('39')39chr('39')) failure could affect the whole economy by extending systemic risk. With regard to this issue, when large banks face insolvency or bankruptcy, larger part of economy would be affected, and with the interconnectedness between b...

Research Commentary - Too Big to Fail: Large Samples and the p-Value Problem

Too Big to Fail: Large Samples and the p-Value Problem

T Internet has provided IS researchers with the opportunity to conduct studies with extremely large samples, frequently well over 10,000 observations. There are many advantages to large samples, but researchers using statistical inference must be aware of the p-value problem associated with them. In very large samples, p-values go quickly to zero, and solely relying on p-values can lead the res...

CONSTRAINED BIG BANG-BIG CRUNCH ALGORITHM FOR OPTIMAL SOLUTION OF LARGE SCALE RESERVOIR OPERATION PROBLEM

A constrained version of the Big Bang-Big Crunch algorithm for the efficient solution of the optimal reservoir operation problems is proposed in this paper. Big Bang-Big Crunch (BB-BC) algorithm is a new meta-heuristic population-based algorithm that relies on one of the theories of the evolution of universe namely, the Big Bang and Big Crunch theory. An improved formulation of the algorithm na...

Nber Working Paper Series How Big a Problem Is Too Big to Fail?

This review essay examines whether too-big-to-fail is as serious a problem as Gary Stern and Ron Feldman contend. This essay argues that Stern and Feldman overstate the importance of the too-bigto-fail problem and do not give enough credit to the FDICIA legislation of 1991 for improving bank regulation and supervision. However, this criticism of the Stern and Feldman book does not detract from ...